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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "7e2c4bbb-5e8b-4d84-9997-ecb2c349cf54", |
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"metadata": {}, |
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"source": [ |
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"## First step - generate training data from examples" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 105, |
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"id": "16cf3aa2-f407-4b95-8b9e-c3c586f67835", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import requests\n", |
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"import pandas as pd\n", |
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"from datetime import datetime, timedelta,timezone\n", |
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"from datasets import load_dataset, Dataset\n", |
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"from dotenv import load_dotenv\n", |
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"import os\n", |
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"from openai import OpenAI\n", |
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"import json\n", |
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"import tiktoken\n", |
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"from IPython.display import display, Markdown\n", |
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"\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 96, |
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"id": "375302b6-b6a7-46ea-a74c-c2400dbd8bbe", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Load environment variables in a file called .env\n", |
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"load_dotenv()\n", |
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"\n", |
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"# Replace with your CoinAPI key\n", |
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"API_KEY = os.getenv('YOUR_COINAPI_KEY')\n", |
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"\n", |
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"# Define the base URL for CoinAPI\n", |
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"BASE_URL = 'https://rest.coinapi.io/v1/ohlcv/'\n", |
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"OLLAMA_URL = \"http://localhost:11434/v1\"\n", |
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"\n", |
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"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n", |
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"# URL to fetch the OHLCV data\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 98, |
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"id": "d0cc964d", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"openai = OpenAI()\n", |
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"\n", |
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"ollama = OpenAI(\n", |
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" base_url=OLLAMA_URL,\n", |
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" api_key='OLAMMA'\n", |
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")\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 104, |
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"id": "8a0c9fff-9eff-42fd-971b-403c99d9b726", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Define the symbol and timeframe\n", |
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"base_data = {\n", |
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" 'name': 'Cardano',\n", |
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" 'symbol': f'BINANCE_SPOT_ADA_USDT',\n", |
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" 'timeframe': '1DAY',\n", |
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" 'time_range': 365 * 2\n", |
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"}\n", |
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"\n", |
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"\n", |
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"# Calculate the start date for one year ago\n", |
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"end_date = datetime.now(tz=timezone.utc)\n", |
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"\n", |
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"start_date = end_date - timedelta(days=base_data['time_range'])\n", |
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"\n", |
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"# Format the dates in the required format (ISO 8601)\n", |
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"start_date_str = start_date.strftime('%Y-%m-%dT%H:%M:%S')\n", |
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"end_date_str = end_date.strftime('%Y-%m-%dT%H:%M:%S')\n", |
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"\n", |
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"# Headers for authentication\n", |
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"headers = {\n", |
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" 'X-CoinAPI-Key': API_KEY\n", |
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"}\n", |
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"\n", |
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"# URL to fetch the OHLCV base_data\n", |
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"url = f'{BASE_URL}{base_data['symbol']}/history'\n", |
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"\n", |
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"# Request parameters\n", |
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"params = {\n", |
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" 'period_id': base_data['timeframe'],\n", |
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" 'time_start': start_date_str,\n", |
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" 'time_end': end_date_str,\n", |
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" 'limit': 1000 # Maximum number of records per request\n", |
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"}" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 91, |
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"id": "586b07ba-5396-4c34-a696-01c8bc3597a0", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"729" |
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] |
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}, |
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"execution_count": 91, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"# Fetch the data\n", |
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"response = requests.get(url, headers=headers, params=params) \n", |
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"len(response.json())" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 76, |
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"id": "953422d0-2e75-4d01-862e-6383df54d9e5", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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" Timestamp Open High Low Close\n", |
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"724 2025-02-06 0.7325 0.7660 0.6978 0.7052\n", |
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"725 2025-02-07 0.7052 0.7532 0.6902 0.7072\n", |
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"726 2025-02-08 0.7072 0.7180 0.6815 0.7005\n", |
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"727 2025-02-09 0.7006 0.7160 0.6503 0.6814\n", |
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"728 2025-02-10 0.6815 0.7177 0.6632 0.7037\n" |
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] |
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} |
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], |
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"source": [ |
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"# Check for successful response\n", |
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"if response.status_code == 200:\n", |
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" data = response.json()\n", |
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"\n", |
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" if data:\n", |
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" # Convert to DataFrame for better readability\n", |
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" df = pd.DataFrame(data)\n", |
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"\n", |
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" df = df[[\"time_period_start\", \"price_open\", \"price_high\", \"price_low\", \"price_close\"]]\n", |
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" df.columns = [\"Timestamp\", \"Open\", \"High\", \"Low\", \"Close\"]\n", |
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"\n", |
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" # Convert timestamp to readable format\n", |
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" df[\"Timestamp\"] = pd.to_datetime(df[\"Timestamp\"]).dt.strftime(\"%Y-%m-%d\")\n", |
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"\n", |
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" # Display the first few rows of the data\n", |
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" print(df.tail())\n", |
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" \n", |
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" # Convert last 365 days of data into JSON format\n", |
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" price_history = df.to_dict(orient=\"records\")\n", |
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" \n", |
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" else:\n", |
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" print('No data found for the given period.')\n", |
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"else:\n", |
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" print(f'Error fetching data: {response.status_code}, {response.text}')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 47, |
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"id": "ada5ed4f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def count_tokens(text, model=\"gpt-4o\"):\n", |
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" encoding = tiktoken.encoding_for_model(model)\n", |
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" return len(encoding.encode(text))\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "ab47d974", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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" # Construct prompt \n", |
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"\n", |
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"prompt = f\"\"\"\n", |
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" Given the last 365 days of ${base_data['name']} OHLC data:\n", |
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"\n", |
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" {json.dumps(price_history, indent=2)}\n", |
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"\n", |
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" Analyze this data and provide a trading signal (Buy, Sell, or Hold) for today based on the trend and the price action.\n", |
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" Note that today is {end_date.strftime('%Y-%m-%d')}\n", |
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" Also, provide short term ,mid term and long term signals.\n", |
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" \"\"\"\n", |
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"num_tokens = count_tokens(prompt)\n", |
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"print(f\"Estimated Tokens: {num_tokens}\")\n", |
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"\n", |
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"print(prompt)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "b40fec12", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/markdown": [ |
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"To analyze this data, I'll use a combination of moving averages, relative strength index (RSI), and other technical indicators. Please note that this is a simplified analysis and should not be considered as professional trading advice.\n", |
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"\n", |
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"**Current Data**\n", |
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"\n", |
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"For 2025-02-10, the opening price is not available. However, we can calculate the current prices based on the historical data provided.\n", |
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"\n", |
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"Let's assume the last known close price for 2025-02-09 was $0.6815. For simplicity, let's use this as the opening price for today (2025-02-10).\n", |
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"\n", |
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"**Short-Term Signal**\n", |
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"\n", |
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"For a short-term signal, I'll use a simple moving average crossover system.\n", |
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"\n", |
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"* Short-Term Moving Average (20 days): $0.6922\n", |
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"* Short-Term Moving Average (10 days): $0.6747\n", |
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"\n", |
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"Since the 20-day MA ($0.6922) is above the 10-day MA ($0.6747), we can conclude that **Buy** in this timeframe.\n", |
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"\n", |
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"**Mid-Term Signal**\n", |
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"\n", |
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"For a mid-term signal, I'll use RSI.\n", |
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"\n", |
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"* Current Price: $0.6815\n", |
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"* Overbought Region: 70-80\n", |
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"* Oversold Region: 20-50\n", |
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"\n", |
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"The current price ($0.6815) is at the lower end of the oversold region (20-50), indicating a potential buying opportunity.\n", |
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"\n", |
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"Since RSI values are not provided for the entire dataset, we'll use an RSI value of 30 (midpoint of the low and high values). At $0.6815, RSI is approximately 34.\n", |
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"\n", |
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"* Mid-Term Moving Average: Not available\n", |
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"* Mid-Term Momentum: Rising\n", |
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"\n", |
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"Considering the oversold region and rising momentum, **Hold** is a reasonable mid-term strategy for today.\n", |
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"\n", |
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"**Long-Term Signal**\n", |
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"\n", |
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"For a long-term signal, I'll use the overall trend direction based on historical data.\n", |
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"\n", |
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"The dataset shows an upward trend (average True Range, AtR, value has been increasing). From 2025-02-03 to 2025-02-09, there were 6 consecutive increases in this dataset. That's a strong positive trend.\n", |
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"\n", |
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"Since there are no obvious signs of weakness in the long-term data or divergence with other trends (like 50-day MA), I recommend **Hold** for an extended holding period, keeping an eye on RSI values and adjusting positions as needed to stay ahead of potential price drops.\n", |
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"\n", |
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"**Summary**\n", |
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"\n", |
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"* Short-Term: **Buy**\n", |
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"* Mid-Term: **Hold**\n", |
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"* Long-Term: **Hold**" |
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], |
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"text/plain": [ |
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"<IPython.core.display.Markdown object>" |
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] |
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}, |
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"metadata": {}, |
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"output_type": "display_data" |
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} |
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], |
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"source": [ |
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"def get_response(prompt):\n", |
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" new_response = ollama.chat.completions.create(model=\"llama3.2\",\n", |
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" messages=[\n", |
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" {\"role\": \"system\", \"content\": f\"You are a trading analyst providing Buy/Sell/Hold signals based on ${base_data['name']} price history.Note that today is {end_date.strftime('%Y-%m-%d')}\"},\n", |
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" {\"role\": \"user\", \"content\": prompt}\n", |
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" ],\n", |
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" stream=True,\n", |
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" max_tokens=5500\n", |
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" )\n", |
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" markdown_content = \"\"\n", |
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" \n", |
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" # Stream response and accumulate markdown content\n", |
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" for chunk in new_response:\n", |
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" content = chunk.choices[0].delta.content or ''\n", |
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" markdown_content += content\n", |
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" \n", |
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" # Clear output and display updated markdown\n", |
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" display(Markdown(markdown_content), clear=True)\n", |
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" \n", |
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" yield content\n", |
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"\n", |
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"# Call the function and consume the generator to start streaming\n", |
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"for _ in get_response(prompt):\n", |
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" pass" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 88, |
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"id": "ba09436c", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/markdown": [ |
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"# $Cardano Trading Analysis for 2025-02-10\n", |
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"\n", |
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"### **Current Price Analysis**\n", |
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"- **Open:** 0.6815\n", |
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"- **High:** 0.7177\n", |
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"- **Low:** 0.6632\n", |
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"- **Close:** 0.7037\n", |
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"\n", |
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"The price of $Cardano closed 3.59% higher than the previous day's close. This suggests a potential bullish reversal following a downward trend observed over the last few days. However, the volatility in the high-low range reflects uncertainty in the market.\n", |
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"\n", |
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"### **Trend Overview**\n", |
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"- **Short-term:** \n", |
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" - The recent price action indicates a possible recovery as we see an upward close. The price is currently attempting to break resistance, but the last few days exhibited mixed movements (e.g., a decrease before the recent increase). \n", |
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"- **Mid-term:**\n", |
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" - Over the past month, $Cardano has experienced significant volatility. While it reached its peak at around 1.079 earlier in January, the subsequent decline indicates selling pressure in the mid-term. A consolidation phase appears as buyers are trying to push the price back up.\n", |
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"- **Long-term:**\n", |
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" - Over the past year, $Cardano has shown high volatility and a fluctuating price range, but it has generally been trending downwards since its recent highs. \n", |
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"\n", |
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"### **Trading Signals**\n", |
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"- **Short-term Signal:** **Buy**\n", |
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" - The recent upward price movement along with a closing above 0.7000 indicates potential upward momentum. Short-term traders may consider buying into this recovery signal.\n", |
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"\n", |
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"- **Mid-term Signal:** **Hold**\n", |
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" - Within the last month, while recovery is in place, it is prudent to wait for confirmation of sustained upward movement before committing larger positions. A hold is advised to monitor the situation.\n", |
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"\n", |
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"- **Long-term Signal:** **Sell**\n", |
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" - Given that the longer-term trends show a downward trajectory since peaking at higher prices, long-term holders might consider selling or reducing positions, especially if the price fails to stay above recent resistance levels.\n", |
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"\n", |
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"### **Conclusion**\n", |
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"Today’s price action indicates a bullish sentiment in the short term but still reflects uncertainty in the mid and long-term periods. It would be wise for traders to remain cautious and adjust positions as the market dynamics evolve further. Always consider your risk management strategies when deciding to enter or exit positions." |
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], |
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"text/plain": [ |
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"<IPython.core.display.Markdown object>" |
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] |
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}, |
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"metadata": {}, |
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"output_type": "display_data" |
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} |
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], |
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"source": [ |
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"def get_response(prompt):\n", |
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" new_response = openai.chat.completions.create(model=\"gpt-4o-mini\",\n", |
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" messages=[\n", |
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" {\"role\": \"system\", \"content\": f\"You are a trading analyst providing Buy/Sell/Hold signals based on ${base_data['name']} price history. Format your response in markdown.Note that today is {end_date.strftime('%Y-%m-%d')}\"},\n", |
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" {\"role\": \"user\", \"content\": prompt}\n", |
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" ],\n", |
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" stream=True,\n", |
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" max_tokens=5500\n", |
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" )\n", |
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" \n", |
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" # Initialize markdown cell output\n", |
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" markdown_content = \"\"\n", |
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" \n", |
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" # Stream response and accumulate markdown content\n", |
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" for chunk in new_response:\n", |
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" content = chunk.choices[0].delta.content or ''\n", |
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" markdown_content += content\n", |
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" \n", |
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" # Clear output and display updated markdown\n", |
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" display(Markdown(markdown_content), clear=True)\n", |
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" \n", |
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" yield content\n", |
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"\n", |
||||||
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"# Call the function and consume the generator to start streaming\n", |
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|
"for _ in get_response(prompt):\n", |
||||||
|
" pass" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f52bcc0a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "venv", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.12.7" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,185 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "fef36918-109d-41e3-8603-75ff81b42379", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Solution for exercise day 2 - slight modification: model is a parameter also - display_summary(\"deepseek-r1:1.5b\",\"https://yoururl\")\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b50349ac-93ea-496b-ae20-bd72a93bb138", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import requests\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "edd073c7-8444-4a0d-b84e-4b2ed0ee7f35", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Constants\n", |
||||||
|
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||||
|
"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
||||||
|
"#MODEL = \"llama3.2\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2e3a6e1a-e4c7-4448-9852-1b6ba2bd8d66", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ae3752ca-3a97-4d6a-ac84-5b75ebfb50ed", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define the system prompt \n", |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||||
|
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||||
|
"Respond in markdown.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "48b5240f-7617-4e51-a320-cba9650bec84", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||||
|
"\n", |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||||
|
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||||
|
"please provide a short summary of this website in markdown. \\\n", |
||||||
|
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6f7d84f0-60f2-4cbf-b4d1-173a79fe3380", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def messages_for(website):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "25520a31-c857-4ed5-86da-50dfe5fab7bb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def summarize(model,url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" payload = {\n", |
||||||
|
" \"model\": model,\n", |
||||||
|
" \"messages\": messages_for(website),\n", |
||||||
|
" \"stream\": False\n", |
||||||
|
" }\n", |
||||||
|
" response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", |
||||||
|
" return response.json()['message']['content']" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "430776ed-8516-43a9-8a22-618d9080f2e1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function to display this nicely in the Jupyter output, using markdown\n", |
||||||
|
"def display_summary(model,url):\n", |
||||||
|
" summary = summarize(model,url)\n", |
||||||
|
" display(Markdown(summary))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b2b05c1f-e4a2-4f65-bd6d-634d72e38b6e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"#!ollama pull deepseek-r1:1.5b" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "01513f8a-15b7-4053-bfe4-44b36e5494d1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"deepseek-r1:1.5b\",\"https://www.ipma.pt\")" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.12.9" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,28 @@ |
|||||||
|
Client: Hello I would like to order a pizza |
||||||
|
Restaurant: Sure. What pizza would you like to order from our menu? |
||||||
|
Client: Chicken Ranch |
||||||
|
Restaurant: I am so sorry, but chicken ranch is currently unavailable on our menu |
||||||
|
Client: AHHHHH. Do you have chicken BBQ? |
||||||
|
Restaurant: Yes! Do you want it small, medium, or large? |
||||||
|
Client: Medium |
||||||
|
Restaurant: Ok. This will be 180 LE |
||||||
|
Client: Thanks |
||||||
|
Restaurant: Anytime. |
||||||
|
Client: AHHHH I forgot. I want to add a new chicken BBQ pizza |
||||||
|
Restaurant: No problem. Do you also want it medium? |
||||||
|
Client: Yes |
||||||
|
Restaurant: Okay this will be 380 LE |
||||||
|
Client: Okay Thanks |
||||||
|
Client: Wait a minute. Isn't 180 * 2 = 360? |
||||||
|
Restaurant: It seems that there might be a misunderstanding. We add an extra 20 LE for every extra pizza ordered. |
||||||
|
Client: NOBODY TOLD ME THAT.. AND WHY ON EARTH WOULD YOU DO SOMETHING LIKE THAT? |
||||||
|
Restaurant: We are sorry but this is our policy. |
||||||
|
Client: Okay then I don't want your pizza. |
||||||
|
Restaurant: We are so sorry to hear that. We can make a 10% discount on the total price so it would be 342 LE |
||||||
|
Client: Fine |
||||||
|
Restaurant: Thank you for ordering |
||||||
|
Restaurant: Pizza is delivered. How is your experience? |
||||||
|
Client: Your pizza doesn't taste good |
||||||
|
Restaurant: We are so sorry to hear that. Do you have any suggestions you would like to make? |
||||||
|
Client: Make good pizza |
||||||
|
Restaurant: Thanks for your review. We will make sure to improve our pizza in the future. Your opinion really matters. |
@ -0,0 +1,5 @@ |
|||||||
|
Client: Hello I would like to order a chicken ranch pizza |
||||||
|
Restaurant: I am so sorry, but chicken ranch is currently unavailable on our menu |
||||||
|
Client: Okay thanks |
||||||
|
Restaurant: Would you like to order something else? |
||||||
|
Client: No thank you |
@ -0,0 +1,19 @@ |
|||||||
|
Client: Hello. What is the most selling pizza on your menu? |
||||||
|
Restaurant: Hello! Chicken Ranch pizza is our most selling pizza. Also our special pepperoni pizza got some amazing reviews |
||||||
|
Client: Okay. I want to order a pepperoni pizza |
||||||
|
Restaurant: Sure. Do you want it small, medium, or large? |
||||||
|
Client: Large |
||||||
|
Restaurant: Okay. This will be 210 LE. Would you like to order something else? |
||||||
|
Client: Yes. Do you have onion rings? |
||||||
|
Restaurant: Yes |
||||||
|
Client: Okay I would like to add onion rings. |
||||||
|
Restaurant: Sure. This will be 250 LE |
||||||
|
Client: Thanks |
||||||
|
Restaurant: Anytime |
||||||
|
Client: I have been waiting for too long and the order hasn't arrived yet |
||||||
|
Restaurant: Sorry to hear that. But it appears that the order is on its way to you. |
||||||
|
Restaurant: The order is supposed to be arrived by now. |
||||||
|
Client: Yes it is arrived. |
||||||
|
Restaurant: How is your experience? |
||||||
|
Client: Your pizza tastes soooooo good. The order took too long to arrive but when I tasted the pizza, I was really enjoying it and forgot everything about the delay. |
||||||
|
Restaurant: We are so glad to hear that |
@ -0,0 +1,15 @@ |
|||||||
|
You are an assistant working for the customer service department in a pizza restaurant. |
||||||
|
You are to receive a chat between a client and the restaurant's customer service. |
||||||
|
You should generate your responses based on the following criteria: |
||||||
|
- What did the client order? |
||||||
|
- How much did it cost? |
||||||
|
- If the client changed their mind just keep their final order and the final cost |
||||||
|
- Mention the client's experience only if they ordered anything as follows: (Positive/Negative/Neutral/Unknown) |
||||||
|
- If the client did not order anything do not mention their sentiment or experience |
||||||
|
- If the client's experience is positive or negative only, provide a brief summary about their sentiment |
||||||
|
- Do not provide brief summary about their sentiment if their experience was neutral or unknown. |
||||||
|
- Your answers should be clear, straight to the point, and do not use long sentences |
||||||
|
- Your answers should be displayed in bullet points |
||||||
|
- Your answers should be displayed in markdown |
||||||
|
- If the client did not order anything provide a brief summary why that might happened |
||||||
|
- Do not mention cost if the client did not order anything |
@ -0,0 +1,10 @@ |
|||||||
|
import mysql.connector |
||||||
|
|
||||||
|
def get_connection(): |
||||||
|
conn = mysql.connector.connect( |
||||||
|
host="127.0.0.1", |
||||||
|
user="root", |
||||||
|
password="xyz", |
||||||
|
database="your_database" |
||||||
|
) |
||||||
|
return conn |
@ -0,0 +1,42 @@ |
|||||||
|
import ollama |
||||||
|
from db import get_connection |
||||||
|
import mysql.connector |
||||||
|
|
||||||
|
def text_to_sql(user_query): |
||||||
|
prompt = f""" |
||||||
|
Convert the following natural language query into an SQL statement for MySQL: |
||||||
|
|
||||||
|
Query: "{user_query}" |
||||||
|
|
||||||
|
Ensure the query is syntactically correct and does not contain harmful operations. |
||||||
|
Only return the SQL query without any explanation. |
||||||
|
""" |
||||||
|
|
||||||
|
# Update the model name to 'llama3.2:latest' |
||||||
|
response = ollama.chat(model="llama3.2:latest", messages=[{"role": "user", "content": prompt}]) |
||||||
|
sql_query = response['message']['content'].strip() |
||||||
|
return sql_query |
||||||
|
|
||||||
|
|
||||||
|
# Uncomment this section if you wish to connect with mysql and fill out your credentials in db.py |
||||||
|
'''def execute_sql_query(user_query): |
||||||
|
sql_query = text_to_sql(user_query) |
||||||
|
|
||||||
|
try: |
||||||
|
conn = get_connection() |
||||||
|
cursor = conn.cursor() |
||||||
|
cursor.execute(sql_query) |
||||||
|
result = cursor.fetchall() |
||||||
|
except mysql.connector.Error as e: |
||||||
|
return {"error": f"MySQL Error: {e}"} |
||||||
|
except Exception as e: |
||||||
|
return {"error": str(e)} |
||||||
|
finally: |
||||||
|
conn.close() # Ensure connection is closed even if an error occurs |
||||||
|
|
||||||
|
return result''' |
||||||
|
|
||||||
|
# Example usage |
||||||
|
if __name__ == "__main__": |
||||||
|
user_input = "Show me all users whose first name starts with the letter j in the first_name column." |
||||||
|
print(text_to_sql(user_input)) |
@ -0,0 +1,123 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Day 2 EXERCISE Solution:\n", |
||||||
|
"\n", |
||||||
|
"Upgraded day 1 project that scrapes and summarizes any webpage using an Open Source model running locally via Ollama instead of OpenAI\n", |
||||||
|
"\n", |
||||||
|
"## Note:-\n", |
||||||
|
"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative: \n", |
||||||
|
"1. Run `ollama pull llama3.2:1b` from a Terminal or Powershell\n", |
||||||
|
"2. **Ctrl + /** to comment this code line below: `MODEL = \"llama3.2\"`\n", |
||||||
|
"3. same **Ctrl + /** to uncomment: `MODEL = \"llama3.2:1b\"`" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports:-\n", |
||||||
|
"\n", |
||||||
|
"import requests\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"import ollama" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "29ddd15d-a3c5-4f4e-a678-873f56162724", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Constants:-\n", |
||||||
|
"\n", |
||||||
|
"# MODEL = \"llama3.2\"\n", |
||||||
|
"MODEL = \"llama3.2:1b\"\n", |
||||||
|
"# MODEL = \"deepseek-r1:1.5b\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6de38216-6d1c-48c4-877b-86d403f4e0f8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class Website:\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||||
|
" and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||||
|
" Respond in markdown.\"\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||||
|
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||||
|
" please provide a short summary of this website in markdown. \\\n", |
||||||
|
" If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"def messages_for(website):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||||
|
" ]\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"def summary(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model = MODEL,\n", |
||||||
|
" messages = messages_for(website)\n", |
||||||
|
" )\n", |
||||||
|
" return display(Markdown(response['message']['content']))\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"summary(\"https://edwarddonner.com\")\n", |
||||||
|
"# summary(\"https://cnn.com\")\n", |
||||||
|
"# summary(\"https://anthropic.com\")" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.10.7" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,432 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 52, |
||||||
|
"id": "b56a950c-db41-4575-bef9-0fa651dea363", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"import json\n", |
||||||
|
"import ollama\n", |
||||||
|
"from typing import List\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display, update_display,clear_output\n", |
||||||
|
"\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0ec875db-0f6a-4eec-a3b6-eae4b71a4b89", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Constants\n", |
||||||
|
"\n", |
||||||
|
"MODEL = \"llama3.2\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "227cd07c-98a4-463b-94ad-94e33d04944b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" A utility class to represent a Website that we have scraped, now with links\n", |
||||||
|
" \"\"\"\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" self.body = response.content\n", |
||||||
|
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" if soup.body:\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
" else:\n", |
||||||
|
" self.text = \"\"\n", |
||||||
|
" links = [link.get('href') for link in soup.find_all('a')]\n", |
||||||
|
" self.links = [link for link in links if link]\n", |
||||||
|
"\n", |
||||||
|
" def get_contents(self):\n", |
||||||
|
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4d5c5e40-c010-4102-8359-899f988185fb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"ed = Website(\"https://edwarddonner.com\")\n", |
||||||
|
"ed.links" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5f0b5d71-487c-47a5-ace6-8e02465ed452", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", |
||||||
|
"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n", |
||||||
|
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", |
||||||
|
"link_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||||
|
"link_system_prompt += \"\"\"\n", |
||||||
|
"{\n", |
||||||
|
" \"links\": [\n", |
||||||
|
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", |
||||||
|
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", |
||||||
|
" ]\n", |
||||||
|
"}\n", |
||||||
|
"\"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c6550325-5160-42c9-b7e7-980b504cd096", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(link_system_prompt)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2db4ccc6-5c35-4775-a5b2-4b86e4c73808", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_links_user_prompt(website):\n", |
||||||
|
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", |
||||||
|
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n", |
||||||
|
"Do not include Terms of Service, Privacy, email links.\\n\"\n", |
||||||
|
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||||
|
" user_prompt += \"\\n\".join(website.links)\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8af511c7-5a74-4d1a-b763-b31370e70cff", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(get_links_user_prompt(ed))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a3b7fb61-ca15-4eab-b017-b0fe5cce46fd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_links(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": link_system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", |
||||||
|
" ], format = \"json\" #Define format as json!\n", |
||||||
|
" )\n", |
||||||
|
" result = response['message']['content']\n", |
||||||
|
"\n", |
||||||
|
" return json.loads(result)\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7816d393-620d-4c53-913e-4ec130b2baba", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n", |
||||||
|
"\n", |
||||||
|
"anthropic = Website(\"https://anthropic.com\")\n", |
||||||
|
"anthropic.links" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f32ceccb-1d45-41a3-a5c1-fb2e6cd76afe", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"get_links(\"https://anthropic.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a7ec4727-e897-473c-a657-e74f6999c974", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_all_details(url):\n", |
||||||
|
" result = \"Landing page:\\n\"\n", |
||||||
|
" result += Website(url).get_contents()\n", |
||||||
|
" links = get_links(url)\n", |
||||||
|
" print(\"Found links:\", links)\n", |
||||||
|
" for link in links[\"links\"]:\n", |
||||||
|
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||||
|
" result += Website(link[\"url\"]).get_contents()\n", |
||||||
|
" return result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7acde0c5-1af2-4e8e-9303-e2a98ec9cdbb", |
||||||
|
"metadata": { |
||||||
|
"scrolled": true |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(get_all_details(\"https://anthropic.com\"))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5a2e2b1d-eb55-4bfb-bf55-5e8c87db0d96", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||||
|
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", |
||||||
|
"Include details of company culture, customers and careers/jobs if you have the information.\"\n", |
||||||
|
"\n", |
||||||
|
"# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone':\n", |
||||||
|
"\n", |
||||||
|
"# system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||||
|
"# and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", |
||||||
|
"# Include details of company culture, customers and careers/jobs if you have the information.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8eac1719-7f94-4460-bc4a-0c9c93bb17a5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_brochure_user_prompt(company_name, url):\n", |
||||||
|
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", |
||||||
|
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n", |
||||||
|
" user_prompt += get_all_details(url)\n", |
||||||
|
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e2e312f6-01c5-4e57-9134-fb4aa447d155", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"get_brochure_user_prompt(\"Anthropic\", \"https://anthropic.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8b05cbab-f0d2-4a9e-8b8c-c868a036e9cd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def create_brochure(company_name, url):\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||||
|
" ]\n", |
||||||
|
" )\n", |
||||||
|
" result = response[\"message\"][\"content\"]\n", |
||||||
|
" display(Markdown(result))\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "91ede0c0-daf2-42ef-9d31-749afb9d5352", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"create_brochure(\"Anthropic\", \"https://anthropic.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "afb4aeee-5108-42a7-a1c1-5bad254b7e8b", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Final omprovement\n", |
||||||
|
"\n", |
||||||
|
"getting a typewriter animation" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 50, |
||||||
|
"id": "177de611-1cb1-49e2-b7ea-8d01191af3ee", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def create_brochure(company_name, url):\n", |
||||||
|
" messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||||
|
" ]\n", |
||||||
|
"\n", |
||||||
|
" display_markdown = display(Markdown(\"\"), display_id=True) # Initialize Markdown display\n", |
||||||
|
" response_text = \"\"\n", |
||||||
|
"\n", |
||||||
|
" for chunk in ollama.chat(model=MODEL, messages=messages, stream=True): # Ensure stream=True (not a string)\n", |
||||||
|
" response_text += chunk['message']['content']\n", |
||||||
|
" clear_output(wait=True) # Clear previous output to create a streaming effect\n", |
||||||
|
" display_markdown.update(Markdown(response_text)) # Update Markdown dynamically\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 53, |
||||||
|
"id": "a1971d81-fc7f-4ed1-97a0-7ef5e8ed332a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"Found links: {'links': [{'type': 'About page', 'url': 'https://www.anthropic.com/company'}, {'type': 'Careers page', 'url': 'https://www.anthropic.com/careers'}, {'type': 'Company page', 'url': 'https://www.anthropic.com/'}, {'type': 'Research page', 'url': 'https://www.anthropic.com/research'}, {'type': 'Twitter profile', 'url': 'https://twitter.com/AnthropicAI'}, {'type': 'LinkedIn company page', 'url': 'https://www.linkedin.com/company/anthropicresearch'}, {'type': 'YouTube channel', 'url': 'https://www.youtube.com/@anthropic-ai'}]}\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/markdown": [ |
||||||
|
"**Anthropic Brochure**\n", |
||||||
|
"======================\n", |
||||||
|
"\n", |
||||||
|
"**Mission Statement**\n", |
||||||
|
"-------------------\n", |
||||||
|
"\n", |
||||||
|
"Anthropic is an AI safety and research company dedicated to building reliable, interpretable, and steerable AI systems that benefit humanity in the long run.\n", |
||||||
|
"\n", |
||||||
|
"**Company Overview**\n", |
||||||
|
"--------------------\n", |
||||||
|
"\n", |
||||||
|
"Anthropic is headquartered in San Francisco and brings together a diverse team of researchers, engineers, policy experts, and business leaders with experience spanning various disciplines. Our mission is to conduct frontier AI research, develop and apply safety techniques, and deploy the resulting systems via partnerships and products.\n", |
||||||
|
"\n", |
||||||
|
"**Research Focus**\n", |
||||||
|
"-----------------\n", |
||||||
|
"\n", |
||||||
|
"Anthropic conducts cutting-edge AI research across various modalities, exploring novel and emerging safety research areas such as interpretability, RL from human feedback, policy, and societal impacts analysis. Our research aims to advance the field of AI safety and inform our product development.\n", |
||||||
|
"\n", |
||||||
|
"**Product Portfolio**\n", |
||||||
|
"---------------------\n", |
||||||
|
"\n", |
||||||
|
"Our flagship product is Claude, a highly intelligent AI model that enables customers to build custom applications and experiences using our API. We also offer various enterprise solutions, including Claude for Enterprise, designed to meet the needs of large organizations.\n", |
||||||
|
"\n", |
||||||
|
"**Customer Base**\n", |
||||||
|
"-----------------\n", |
||||||
|
"\n", |
||||||
|
"Anthropic serves a diverse range of customers, including businesses, nonprofits, civil society groups, and their clients around the globe. Our commitment to safety and reliability has earned us a reputation as a trusted partner in the AI industry.\n", |
||||||
|
"\n", |
||||||
|
"**Values and Culture**\n", |
||||||
|
"----------------------\n", |
||||||
|
"\n", |
||||||
|
"At Anthropic, we value:\n", |
||||||
|
"\n", |
||||||
|
"* **Acting for the global good**: We strive to make decisions that maximize positive outcomes for humanity in the long run.\n", |
||||||
|
"* **Holding light and shade**: We acknowledge the potential risks of AI and approach our work with caution and transparency.\n", |
||||||
|
"\n", |
||||||
|
"**Join Our Team**\n", |
||||||
|
"-----------------\n", |
||||||
|
"\n", |
||||||
|
"We're a collaborative team of researchers, engineers, policy experts, and business leaders passionate about building safer AI systems. Join us to be part of this exciting journey and contribute your skills and expertise to shaping the future of AI.\n", |
||||||
|
"\n", |
||||||
|
"**Careers**\n", |
||||||
|
"------------\n", |
||||||
|
"\n", |
||||||
|
"Check our website for open roles and learn more about our company culture, benefits, and career opportunities.\n", |
||||||
|
"\n", |
||||||
|
"[Learn More](link)\n", |
||||||
|
"\n", |
||||||
|
"**Get in Touch**\n", |
||||||
|
"-----------------\n", |
||||||
|
"\n", |
||||||
|
"Stay up-to-date with the latest news and announcements from Anthropic. Follow us on Twitter, LinkedIn, or YouTube to join the conversation and stay informed.\n", |
||||||
|
"\n", |
||||||
|
"[Twitter](link)\n", |
||||||
|
"[LinkedIn](link)\n", |
||||||
|
"[YouTube](link)" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.Markdown object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"create_brochure(\"Anthropic\", \"https://anthropic.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c33277a4-84f1-447c-a66e-eb7e2af42d2a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,240 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "9964872b-225d-4ced-93e4-fc5b279ec2ed", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Webpage English summarizer with user inputs (url, ollama-based LLM) " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4e49d399-d18c-4c91-8abc-cf3289e11e2f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"# from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import ollama, time\n", |
||||||
|
"from tqdm import tqdm" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "46e7d809-248d-41b8-80e1-36b210041581", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define system prompt.\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||||
|
"and provides a detailed summary, ignoring text that might be navigation related. \\\n", |
||||||
|
"Respond in markdown, in English.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e8bf237f-591f-4c32-9415-5d5d4e2522b8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||||
|
"\n", |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||||
|
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||||
|
"please provide a detailed summary of this website in markdown. \\\n", |
||||||
|
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7d39ee6d-c670-41ba-a0b8-debd55bda8e3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# See how this function creates exactly the format above\n", |
||||||
|
"\n", |
||||||
|
"def messages_for(website):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "43e28ff5-2def-4a47-acdd-2e06c0666956", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Constants\n", |
||||||
|
"\n", |
||||||
|
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||||
|
"HEADERS = {\"Content-Type\": \"application/json\"}" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "32f4f481-81a3-479d-817b-4e754d9af46d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = HEADERS\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f81cfd17-8208-4192-a59f-485ff3ea74e4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# And now: call the ollama API wrapper and return the relevant component of the response\n", |
||||||
|
"\n", |
||||||
|
"def summarize(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages = messages_for(website)\n", |
||||||
|
" )\n", |
||||||
|
" return response['message']['content']" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7a9eedc6-2183-473d-84ca-b10d40e2a1e6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Ask the user the name of the url address\n", |
||||||
|
"\n", |
||||||
|
"url= str(input(\"\"\"\n", |
||||||
|
"Please provide a valid url address:\n", |
||||||
|
"https://\"\"\"))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5d012de2-0ef2-43db-9f51-fc7f989c3642", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Ask the user to select a valid model\n", |
||||||
|
"\n", |
||||||
|
"MODEL= str(input(\"\"\"\n", |
||||||
|
"Please select a LLM:\n", |
||||||
|
"(examples: llama3.2, deepseek-r1:1.5b)\n", |
||||||
|
"\"\"\"))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1ac8c02e-4a62-448b-a231-8c6f65891811", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's just make sure the model is loaded\n", |
||||||
|
"\n", |
||||||
|
"!ollama pull {MODEL}" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0544541f-11a8-4eb7-8eb6-bc032ed6d0d1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print('url: https://{0}\\nModel= {1}'.format(url, MODEL))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "45518950-f2c9-43af-b897-4fe8fe48dfd8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"summary = summarize('https://'+ url)\n", |
||||||
|
"for summ in tqdm(summary):\n", |
||||||
|
" time.sleep(0.01)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "02c0c15e-216d-47c7-843d-ac27af02820b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display(Markdown(summary))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "985a3689-5827-4b15-b8d5-276f9b292afd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,76 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# 1) Import Required Libraries \n", |
||||||
|
"\n", |
||||||
|
"import requests\n", |
||||||
|
"import gradio as gr\n", |
||||||
|
"\n", |
||||||
|
"# Deepseek only uses abstract summarization\n", |
||||||
|
"# This tool use DeepSeek API Endpoint\n", |
||||||
|
"\n", |
||||||
|
"# 2) Define the DeepSeek API Endpoint\n", |
||||||
|
"\n", |
||||||
|
"OLLAMA_URL = \"http://localhost:11434/api/generate\"\n", |
||||||
|
"\n", |
||||||
|
"# 3) Define the Summarization Function which can retrieve Information\n", |
||||||
|
"\n", |
||||||
|
"def summarize_text(text):\n", |
||||||
|
" payload = {\n", |
||||||
|
" \"model\": \"deepseek-r1\", #Here you can load whatever the model you have in your ollama(ex:deepseek-r1:1.5b,7b,8b,14b) I used 7b model here \n", |
||||||
|
" \"prompt\": f\"Summarize the following text in **5 bullet points**:\\n\\n{text}\", #The prompt is here for tell commands for the llm to act \n", |
||||||
|
" \"stream\": False # Ensures the response is returned as a whole, not streamed\n", |
||||||
|
" }\n", |
||||||
|
"\n", |
||||||
|
" response = requests.post(OLLAMA_URL, json=payload) #Send Requests to deepseekAPI\n", |
||||||
|
"\n", |
||||||
|
" if response.status_code == 200: #if server run correctly it return the result or it will give error\n", |
||||||
|
" return response.json().get(\"response\", \"No summary generated.\")\n", |
||||||
|
" else:\n", |
||||||
|
" return f\"Error: {response.text}\"\n", |
||||||
|
"\n", |
||||||
|
"# 4) Create Gradio interface to design \n", |
||||||
|
"interface = gr.Interface(\n", |
||||||
|
" fn=summarize_text,\n", |
||||||
|
" inputs=gr.Textbox(lines=10, placeholder=\"Enter text to summarize\"),\n", |
||||||
|
" outputs=gr.Textbox(label=\"Summarized Text\"),\n", |
||||||
|
" #theme='NoCrypt/miku', #Theme for the Interface I used Hatsune Miku from HF \n", |
||||||
|
" title=\"AI-Powered Text Summarizer\",\n", |
||||||
|
" description=\"Enter a long text and DeepSeek AI will generate a concise summary.\"\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"# Launch the web app\n", |
||||||
|
"if __name__ == \"__main__\":\n", |
||||||
|
" interface.launch()\n", |
||||||
|
"\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "base", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.12.4" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 2 |
||||||
|
} |
@ -0,0 +1,273 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "fad31e32-2e42-42ae-ae63-c15d90292839", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# First Project\n", |
||||||
|
"Ollama -> Summary\n", |
||||||
|
"huggingface_hub -> \"facebook/m2m100_418M\" for translation" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5fb79a20-a455-4d27-91a1-91958af786c1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"!pip install transformers datasets torch\n", |
||||||
|
"!pip install huggingface_hub" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e95ac7f2-5192-4f83-acf3-61df30cd3109", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"import requests\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"import json\n", |
||||||
|
"import ollama" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "12276d74-0e79-4e66-9135-1c9d1a80b943", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class Website:\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
"\n", |
||||||
|
"huggingface_url = \"https://huggingface.co/learn/ml-for-3d-course\"\n", |
||||||
|
"huggingface_website = Website(huggingface_url)\n", |
||||||
|
"\n", |
||||||
|
"huggingface_data = {\n", |
||||||
|
" \"title\": huggingface_website.title,\n", |
||||||
|
" \"text\": huggingface_website.text\n", |
||||||
|
"}\n", |
||||||
|
"print(huggingface_data)\n", |
||||||
|
"\n", |
||||||
|
"with open('ml_for_3d_course_data.json', 'w') as f:\n", |
||||||
|
" json.dump(huggingface_data, f)\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7d74c85c-3e09-4514-bde4-4cafc4910c52", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# huggingface_data 'text' value\n", |
||||||
|
"huggingface_text = huggingface_data['text']\n", |
||||||
|
"\n", |
||||||
|
"# Summary\n", |
||||||
|
"response_summary = ollama.chat(model=\"llama3.2:latest\", messages=[{\"role\": \"user\", \"content\": f\"Summarize the following text: {huggingface_text}\"}])\n", |
||||||
|
"print(response_summary)\n", |
||||||
|
"\n", |
||||||
|
"# print summary\n", |
||||||
|
"summary_huggingface_text = response_summary.message['content']\n", |
||||||
|
"print(\"Summary Text:\", summary_huggingface_text)\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d13764d5-cb76-46c5-bbe6-d132b31a9ea6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# HuggingFace Translation" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "08405038-4115-487f-9efc-de58572453c1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class Website:\n", |
||||||
|
" url: str\n", |
||||||
|
" title: str\n", |
||||||
|
" text: str\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
"\n", |
||||||
|
"url = \"https://huggingface.co/learn/ml-for-3d-course\"\n", |
||||||
|
"website = Website(url)\n", |
||||||
|
"print(website.title) \n", |
||||||
|
"print(website.text[:1000])\n", |
||||||
|
"\n", |
||||||
|
"data = {\n", |
||||||
|
" \"title\": website.title,\n", |
||||||
|
" \"text\": website.text\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"with open('ml_for_3d_course_data.json', 'w') as f:\n", |
||||||
|
" json.dump(data, f)\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0632352f-4b16-4125-83bf-f3cc3aabd659", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(data)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a85f8625-725d-4d7f-8cb7-8da4276f81cf", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"!pip install sacremoses" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c800cea4-f4a4-4e41-9637-31ff11afb256", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import json\n", |
||||||
|
"from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer\n", |
||||||
|
"\n", |
||||||
|
"# Load the M2M100 model and tokenizer\n", |
||||||
|
"model_name = \"facebook/m2m100_418M\"\n", |
||||||
|
"model = M2M100ForConditionalGeneration.from_pretrained(model_name)\n", |
||||||
|
"tokenizer = M2M100Tokenizer.from_pretrained(model_name)\n", |
||||||
|
"\n", |
||||||
|
"# Load the saved JSON file\n", |
||||||
|
"with open('ml_for_3d_course_data.json', 'r') as f:\n", |
||||||
|
" data = json.load(f)\n", |
||||||
|
"\n", |
||||||
|
"# Extract text from the loaded data\n", |
||||||
|
"text = data[\"text\"]\n", |
||||||
|
"\n", |
||||||
|
"# Set the source language to English and target language to Korean\n", |
||||||
|
"source_lang = \"en\"\n", |
||||||
|
"target_lang = \"ko\"\n", |
||||||
|
"\n", |
||||||
|
"# Set the language for tokenizer (important for M2M100)\n", |
||||||
|
"tokenizer.src_lang = source_lang\n", |
||||||
|
"tokenizer.tgt_lang = target_lang\n", |
||||||
|
"\n", |
||||||
|
"# Split text into smaller chunks if it's too large\n", |
||||||
|
"# This step ensures we don't exceed the model's maximum length (512 tokens)\n", |
||||||
|
"max_input_length = 512\n", |
||||||
|
"chunks = [text[i:i+max_input_length] for i in range(0, len(text), max_input_length)]\n", |
||||||
|
"\n", |
||||||
|
"print(chunks)\n", |
||||||
|
"# Initialize a list to hold the translated text\n", |
||||||
|
"translated_chunks = []\n", |
||||||
|
"\n", |
||||||
|
"# Iterate through each chunk and translate it\n", |
||||||
|
"for chunk in chunks:\n", |
||||||
|
" # Tokenize the chunk\n", |
||||||
|
" encoded = tokenizer(chunk, return_tensors=\"pt\", padding=True, truncation=True, max_length=512)\n", |
||||||
|
"\n", |
||||||
|
" # Generate translation from the model, forcing the output to be in Korean\n", |
||||||
|
" generated_tokens = model.generate(**encoded, forced_bos_token_id=tokenizer.get_lang_id(target_lang), max_length=512)\n", |
||||||
|
"\n", |
||||||
|
" # Decode the translated tokens to text\n", |
||||||
|
" translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]\n", |
||||||
|
" translated_chunks.append(translated_text)\n", |
||||||
|
"\n", |
||||||
|
"# Combine all translated chunks back together\n", |
||||||
|
"final_translated_text = ' '.join(translated_chunks)\n", |
||||||
|
"print(\"Translated Text:\", final_translated_text)\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ffe0f264-a588-422f-a6e1-b60504d1e02c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import json\n", |
||||||
|
"import requests\n", |
||||||
|
"\n", |
||||||
|
"# Ollama API URL 설정\n", |
||||||
|
"ollama_url = \"http://localhost:11411/v1/models/facebook/m2m100_418M/generate\"\n", |
||||||
|
"\n", |
||||||
|
"# 저장된 JSON 파일 로드\n", |
||||||
|
"with open('ml_for_3d_course_data.json', 'r') as f:\n", |
||||||
|
" data = json.load(f)\n", |
||||||
|
"\n", |
||||||
|
"# 텍스트 추출\n", |
||||||
|
"course_text = data[\"text\"]\n", |
||||||
|
"\n", |
||||||
|
"# 번역할 소스 언어 및 타겟 언어 설정\n", |
||||||
|
"source_language = \"en\"\n", |
||||||
|
"target_language = \"ko\"\n", |
||||||
|
"\n", |
||||||
|
"# 데이터 준비\n", |
||||||
|
"payload = {\n", |
||||||
|
" \"input_text\": course_text,\n", |
||||||
|
" \"src_lang\": source_language,\n", |
||||||
|
" \"tgt_lang\": target_language\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"# API 호출\n", |
||||||
|
"response = requests.post(ollama_url, json=payload)\n", |
||||||
|
"\n", |
||||||
|
"# 응답 확인\n", |
||||||
|
"if response.status_code == 200:\n", |
||||||
|
" translated_course_text = response.json().get(\"translated_text\", \"Translation failed\")\n", |
||||||
|
" print(\"Translated Course Text:\", translated_course_text)\n", |
||||||
|
"else:\n", |
||||||
|
" print(f\"Error {response.status_code}: {response.text}\")\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,138 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# End of week 1 exercise Solution Ollama with streaming\n", |
||||||
|
"\n", |
||||||
|
"A tool that takes a technical question, and responds with an explanation." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c1070317-3ed9-4659-abe3-828943230e03", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Imports\n", |
||||||
|
"\n", |
||||||
|
"import ollama\n", |
||||||
|
"import requests\n", |
||||||
|
"from IPython.display import Markdown, display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Constants\n", |
||||||
|
"\n", |
||||||
|
"MODEL_LLAMA = 'llama3.2'\n", |
||||||
|
"MODEL_LLAMA1b = \"llama3.2:1b\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Environment\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"\"\"\n", |
||||||
|
"You are an assistant that takes a technical question and respond with an explanation.\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"question = \"\"\"\n", |
||||||
|
"Please explain what this code does and why:\n", |
||||||
|
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"question2 = \"\"\"\n", |
||||||
|
"What is the purpose of using yield from in the following code, and how does it differ from a standard for loop with yield?\n", |
||||||
|
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"user_prompt = \"Answer these two questions in detail please, Question1:\" + question + \"Question2:\" + question2\n", |
||||||
|
"\n", |
||||||
|
"def message():\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Llama 3.2 answer, with streaming\n", |
||||||
|
"\n", |
||||||
|
"def llama():\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model = MODEL_LLAMA,\n", |
||||||
|
" messages = message(),\n", |
||||||
|
" stream =True\n", |
||||||
|
" )\n", |
||||||
|
" full_response = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" for chunk in response:\n", |
||||||
|
" content = chunk.get(\"message\", {}).get(\"content\", \"\")\n", |
||||||
|
" if content:\n", |
||||||
|
" full_response += content\n", |
||||||
|
" display_handle.update(Markdown(full_response))\n", |
||||||
|
"llama()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "342a470c-9aab-4051-ad21-514dceec76eb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Llama 3.2:1b answer\n", |
||||||
|
"\n", |
||||||
|
"def llama():\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model = MODEL_LLAMA1b,\n", |
||||||
|
" messages = message()\n", |
||||||
|
" )\n", |
||||||
|
" return display(Markdown(response['message']['content']))\n", |
||||||
|
"\n", |
||||||
|
"llama()" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.10.7" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,127 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "39e3e763-9b00-49eb-aead-034a2d0517a7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"\n", |
||||||
|
"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f3bb5e2a-b70f-42ba-9f22-030a9c6bc9d1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"# Check the key\n", |
||||||
|
"\n", |
||||||
|
"if not api_key:\n", |
||||||
|
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||||
|
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||||
|
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||||
|
"elif api_key.strip() != api_key:\n", |
||||||
|
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"API key found and looks good so far!\")\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "994f51fb-eab3-45a2-847f-87aebb92b17a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"\n", |
||||||
|
"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n", |
||||||
|
"# If it STILL doesn't work (horrors!) then please see the Troubleshooting notebook in this folder for full instructions" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a8125c6d-c884-4f65-b477-cab155e29ce3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Step 1: Create your prompts\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are an AI that suggests short and relevant subject lines for emails based on their content.\"\n", |
||||||
|
"user_prompt = \"\"\"\n", |
||||||
|
"Here is the content of an email:\n", |
||||||
|
"\n", |
||||||
|
"Dear Team,\n", |
||||||
|
"\n", |
||||||
|
"I hope you're all doing well. I wanted to remind you that our next project meeting is scheduled for this Friday at 3 PM. We will be discussing our progress and any blockers. Please make sure to review the latest updates before the meeting.\n", |
||||||
|
"\n", |
||||||
|
"Best, \n", |
||||||
|
"John\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"# Step 2: Make the messages list\n", |
||||||
|
"\n", |
||||||
|
"messages = [ {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}] # fill this in\n", |
||||||
|
"\n", |
||||||
|
"# Step 3: Call OpenAI\n", |
||||||
|
"\n", |
||||||
|
"response = openai.chat.completions.create(\n", |
||||||
|
" model = \"gpt-4o-mini\",\n", |
||||||
|
" messages=messages\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"# Step 4: print the result\n", |
||||||
|
"\n", |
||||||
|
"print(\"Suggested Subject Line:\", response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1010ac80-1ee8-432f-aa3f-12af419dc23a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,279 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "603cd418-504a-4b4d-b1c3-be04febf3e79", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Article Title Generator\n", |
||||||
|
"\n", |
||||||
|
"Summarization use-case in which the user provides an article, which the LLM will analyze to suggest an SEO-optimized title.\n", |
||||||
|
"\n", |
||||||
|
"**NOTES**:\n", |
||||||
|
"\n", |
||||||
|
"1. This version does NOT support website scrapping. You must copy and paste the required article.\n", |
||||||
|
"2. The following models were configured:\n", |
||||||
|
" a. OpenAI gpt-4o-mini\n", |
||||||
|
" b. Llama llama3.2\n", |
||||||
|
" c. Deepseek deepseek-r1:1.5b\n", |
||||||
|
" It is possible to configure additional models by adding the new model to the MODELS dictionary and its\n", |
||||||
|
" initialization to the CLIENTS dictionary. Then, call the model with --> ***answer =\n", |
||||||
|
" get_answer('NEW_MODEL')***.\n", |
||||||
|
"3. Users are encouraged to assess and rank the suggested titles using any headline analyzer tool online.\n", |
||||||
|
" Example: https://www.isitwp.com/headline-analyzer/. " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e773daa6-d05e-49bf-ad8e-a8ed4882b77e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Confirming Llama is loaded\n", |
||||||
|
"!ollama pull llama3.2" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "279b0c00-9bb0-4c7f-9c6d-aa0b108274b9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d4730d8d-3e20-4f3c-a4ff-ed2ac0a8aa27", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# set environment variables for OpenAi\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"# validate API Key\n", |
||||||
|
"if not api_key:\n", |
||||||
|
" raise ValueError(\"No API key was found! Please check the .env file.\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1abbb826-de66-498c-94d8-33369ad01885", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# constants\n", |
||||||
|
"MODELS = { 'GPT': 'gpt-4o-mini', \n", |
||||||
|
" 'LLAMA': 'llama3.2', \n", |
||||||
|
" 'DEEPSEEK': 'deepseek-r1:1.5b'\n", |
||||||
|
" }\n", |
||||||
|
"\n", |
||||||
|
"CLIENTS = { 'GPT': OpenAI(), \n", |
||||||
|
" 'LLAMA': OpenAI(base_url='http://localhost:11434/v1', api_key='ollama'),\n", |
||||||
|
" 'DEEPSEEK': OpenAI(base_url='http://localhost:11434/v1', api_key='ollama') \n", |
||||||
|
" }" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6f490fe4-32d5-41f3-890d-ecf4e5e01dd4", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Copy & paste your article (without a title)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ddd76319-13ce-480b-baa7-cab6a5c88168", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# article - copy & paste your article\n", |
||||||
|
"article = \"\"\"\n", |
||||||
|
" REPLACE WITH YOUR ARTICLE CONTENT\n", |
||||||
|
" \"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1914afad-dbd8-4c1f-8e68-80b0e5d743a9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# system prompt\n", |
||||||
|
"system_prompt = \"\"\"\n", |
||||||
|
" You are an experienced SEO-focused copywriter. The user will provide an article, and your task is to analyze its content and generate the most effective, keyword-optimized title to maximize SEO performance.Respond in Markdown format.\n", |
||||||
|
" \"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "176cfac7-5e6d-4d4a-a1c4-1b63b60de1f7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# user prompt\n", |
||||||
|
"user_prompt = f\"Following the article to be analyzed. Respond in Markdown format./n/n{article}\"\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c45fc7d7-08c9-4e34-b427-b928a219bb94", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# message list\n", |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f67b881f-1040-4cf7-82c5-e85f4c0bd252", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# call model and get answer\n", |
||||||
|
"def get_answer(model):\n", |
||||||
|
" # set required client\n", |
||||||
|
" client = CLIENTS[model]\n", |
||||||
|
"\n", |
||||||
|
" # call model\n", |
||||||
|
" response = client.chat.completions.create(\n", |
||||||
|
" model=MODELS[model],\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" # return answer\n", |
||||||
|
" return response.choices[0].message.content\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "947b42ed-5b43-486d-8af3-e5b671c1fd0e", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Get OpenAI Suggested Title" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "eb6f66e3-ab99-4f76-9358-896cb43c1fa1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# get openAi answer\n", |
||||||
|
"answer = get_answer('GPT')\n", |
||||||
|
"\n", |
||||||
|
"# display openAi answer\n", |
||||||
|
"display(Markdown(f\"### {MODELS['GPT']} Answer\\n\\n{answer}\" ))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "70073ebf-a00a-416b-854d-642d450cd99b", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Get Llama Suggested Title" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "caa190bb-de5f-45cc-b671-5d62688f7b25", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# get Llama answer\n", |
||||||
|
"answer = get_answer('LLAMA')\n", |
||||||
|
"\n", |
||||||
|
"# display Llama answer\n", |
||||||
|
"display(Markdown(f\"### {MODELS['LLAMA']} Answer\\n\\n{answer}\" ))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "811edc4f-20e2-482d-ac89-fae9d1b70bed", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Get Deepseek Suggested Title" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "082628e4-ff4c-46dd-ae5f-76578eb017ad", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# get Deepseek answer\n", |
||||||
|
"answer = get_answer('DEEPSEEK')\n", |
||||||
|
"\n", |
||||||
|
"# display Deepseek answer\n", |
||||||
|
"display(Markdown(f\"### {MODELS['DEEPSEEK']} Answer\\n\\n{answer}\" ))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "7fc404a6-3a91-4c09-89de-867d3d69b4b2", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Suggested future improvements\n", |
||||||
|
"\n", |
||||||
|
"1. Add website scrapping support to replace copy/pasting of articles.\n", |
||||||
|
"2. Improve the system_prompt to provide specific SEO best practices to adopt during the title generation.\n", |
||||||
|
"3. Rephrase the system_prompt to ensure the model provides a single Title (not a list of suggestions). \n", |
||||||
|
"4. Add the logic that would allow each model to assess the recommendations from the different models and \n", |
||||||
|
" select the best among these. " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "cf7403ac-d43b-4493-98bb-6fee94950cb0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,472 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "603cd418-504a-4b4d-b1c3-be04febf3e79", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Article Title Generator (V2)\n", |
||||||
|
"\n", |
||||||
|
"Summarization use-case in which the user provides an article, which the LLM will analyze to suggest an SEO-optimized title.\n", |
||||||
|
"\n", |
||||||
|
"**NOTES**:\n", |
||||||
|
"\n", |
||||||
|
"1. This version supports website scrapping using Selenium (based on the code from **/week1/community-\n", |
||||||
|
" contributions/day1-webscraping-selenium-for-javascript.ipynb** - Thanks for the contribution!)\n", |
||||||
|
"2. Leverage streaming (OpenAI only).\n", |
||||||
|
"3. The following models were configured:\\\n", |
||||||
|
" \n", |
||||||
|
" a. OpenAI gpt-4o-mini\\\n", |
||||||
|
" b. Llama llama3.2\\\n", |
||||||
|
" c. Deepseek deepseek-r1:1.5b\\\n", |
||||||
|
"\n", |
||||||
|
" It is possible to configure additional models by adding the new model to the MODELS dictionary and its\n", |
||||||
|
" initialization to the CLIENTS dictionary. Then, call the model with --> ***answer =\n", |
||||||
|
" get_answer('NEW_MODEL')***.\n", |
||||||
|
"5. Improved system_prompt to provide specific SEO best practices to adopt during the title generation.\n", |
||||||
|
"6. Rephrased the system_prompt to ensure the model provides a single Title (not a list of suggestions).\n", |
||||||
|
"7. Includes function to remove unrequired thinking/reasoning verbose from the model response (Deepseek). \n", |
||||||
|
"8. Users are encouraged to assess and rank the suggested titles using any headline analyzer tool online.\n", |
||||||
|
" Example: https://www.isitwp.com/headline-analyzer/. " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "115004a8-747a-4954-9580-1ed548f80336", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# install required libraries if they were not part of the requirements.txt\n", |
||||||
|
"!pip install selenium\n", |
||||||
|
"!pip install undetected-chromedriver" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e773daa6-d05e-49bf-ad8e-a8ed4882b77e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# confirming Llama is loaded\n", |
||||||
|
"!ollama pull llama3.2" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "279b0c00-9bb0-4c7f-9c6d-aa0b108274b9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import undetected_chromedriver as uc\n", |
||||||
|
"from selenium.webdriver.common.by import By\n", |
||||||
|
"from selenium.webdriver.support.ui import WebDriverWait\n", |
||||||
|
"from selenium.webdriver.support import expected_conditions as EC\n", |
||||||
|
"import time\n", |
||||||
|
"from bs4 import BeautifulSoup" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d4730d8d-3e20-4f3c-a4ff-ed2ac0a8aa27", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# set environment variables for OpenAi\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"# validate API Key\n", |
||||||
|
"if not api_key:\n", |
||||||
|
" raise ValueError(\"No API key was found! Please check the .env file.\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1abbb826-de66-498c-94d8-33369ad01885", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# constants\n", |
||||||
|
"MODELS = { 'GPT': 'gpt-4o-mini', \n", |
||||||
|
" 'LLAMA': 'llama3.2', \n", |
||||||
|
" 'DEEPSEEK': 'deepseek-r1:1.5b'\n", |
||||||
|
" }\n", |
||||||
|
"\n", |
||||||
|
"CLIENTS = { 'GPT': OpenAI(), \n", |
||||||
|
" 'LLAMA': OpenAI(base_url='http://localhost:11434/v1', api_key='ollama'),\n", |
||||||
|
" 'DEEPSEEK': OpenAI(base_url='http://localhost:11434/v1', api_key='ollama') \n", |
||||||
|
" }\n", |
||||||
|
"\n", |
||||||
|
"# path to Chrome\n", |
||||||
|
"CHROME_PATH = \"C:/Program Files/Google/Chrome/Application/chrome.exe\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6f490fe4-32d5-41f3-890d-ecf4e5e01dd4", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"**Webcrawler** (based on the code from __/week1/community-contributions/day1-webscraping-selenium-for-javascript.ipynb__)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c2a1cf7a-044f-4a9c-b76e-8f112d384550", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class WebsiteCrawler:\n", |
||||||
|
" def __init__(self, url, wait_time=20, chrome_path=None):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Initialize the WebsiteCrawler using Selenium to scrape JavaScript-rendered content.\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" self.wait_time = wait_time\n", |
||||||
|
"\n", |
||||||
|
" options = uc.ChromeOptions()\n", |
||||||
|
" options.add_argument(\"--disable-gpu\")\n", |
||||||
|
" options.add_argument(\"--no-sandbox\")\n", |
||||||
|
" options.add_argument(\"--disable-dev-shm-usage\")\n", |
||||||
|
" options.add_argument(\"--disable-blink-features=AutomationControlled\")\n", |
||||||
|
" # options.add_argument(\"--headless=new\") # For Chrome >= 109 - unreliable on my end!\n", |
||||||
|
" options.add_argument(\"start-maximized\")\n", |
||||||
|
" options.add_argument(\n", |
||||||
|
" \"user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
" )\n", |
||||||
|
" if chrome_path:\n", |
||||||
|
" options.binary_location = chrome_path\n", |
||||||
|
"\n", |
||||||
|
" self.driver = uc.Chrome(options=options)\n", |
||||||
|
"\n", |
||||||
|
" try:\n", |
||||||
|
" # Load the URL\n", |
||||||
|
" self.driver.get(url)\n", |
||||||
|
"\n", |
||||||
|
" # Wait for Cloudflare or similar checks\n", |
||||||
|
" time.sleep(10)\n", |
||||||
|
"\n", |
||||||
|
" # Ensure the main content is loaded\n", |
||||||
|
" WebDriverWait(self.driver, self.wait_time).until(\n", |
||||||
|
" EC.presence_of_element_located((By.TAG_NAME, \"main\"))\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
" # Extract the main content\n", |
||||||
|
" main_content = self.driver.find_element(By.CSS_SELECTOR, \"main\").get_attribute(\"outerHTML\")\n", |
||||||
|
"\n", |
||||||
|
" # Parse with BeautifulSoup\n", |
||||||
|
" soup = BeautifulSoup(main_content, \"html.parser\")\n", |
||||||
|
" self.title = self.driver.title if self.driver.title else \"No title found\"\n", |
||||||
|
" self.text = soup.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
"\n", |
||||||
|
" except Exception as e:\n", |
||||||
|
" print(f\"Error occurred: {e}\")\n", |
||||||
|
" self.title = \"Error occurred\"\n", |
||||||
|
" self.text = \"\"\n", |
||||||
|
"\n", |
||||||
|
" finally:\n", |
||||||
|
" self.driver.quit()\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "592d8f86-fbf7-4b16-a69d-468030d72dc4", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Prompts" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1914afad-dbd8-4c1f-8e68-80b0e5d743a9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# system prompt\n", |
||||||
|
"system_prompt = \"\"\"\n", |
||||||
|
" You are an experienced SEO-focused copywriter. The user will provide an article, and your task is to analyze its content and generate a single, most effective, keyword-optimized title to maximize SEO performance.\n", |
||||||
|
"\n", |
||||||
|
"Instructions:\n", |
||||||
|
"Ignore irrelevant content, such as the current title (if any), navigation menus, advertisements, or unrelated text.\n", |
||||||
|
"Prioritize SEO best practices, considering:\n", |
||||||
|
"Keyword relevance and search intent (informational, transactional, etc.).\n", |
||||||
|
"Readability and engagement.\n", |
||||||
|
"Avoiding keyword stuffing.\n", |
||||||
|
"Ensure conciseness and clarity, keeping the title under 60 characters when possible for optimal SERP display.\n", |
||||||
|
"Use a compelling structure that balances informativeness and engagement, leveraging formats like:\n", |
||||||
|
"Listicles (\"10 Best Strategies for…\")\n", |
||||||
|
"How-to guides (\"How to Boost…\")\n", |
||||||
|
"Questions (\"What Is the Best Way to…\")\n", |
||||||
|
"Power words to enhance click-through rates (e.g., \"Proven,\" \"Ultimate,\" \"Essential\").\n", |
||||||
|
"Provide only one single, best title—do not suggest multiple options.\n", |
||||||
|
"Limit the answer to the following Response Format (Markdown):\n", |
||||||
|
"Optimized Title: [Provide only one title here]\n", |
||||||
|
"Justification: [Explain why this title is effective for SEO]\n", |
||||||
|
"\n", |
||||||
|
" \"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "b0486867-6d38-4cb5-91d4-fb60952c3a9b", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"**Provide the article URL and get its content for analysis**" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ddd76319-13ce-480b-baa7-cab6a5c88168", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# article url - change to any other article URL\n", |
||||||
|
"article_url = \"https://searchengineland.com/seo-trends-2025-447745\"\n", |
||||||
|
"\n", |
||||||
|
"# get article content\n", |
||||||
|
"article = WebsiteCrawler(url=article_url, chrome_path=CHROME_PATH)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "176cfac7-5e6d-4d4a-a1c4-1b63b60de1f7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# user prompt\n", |
||||||
|
"user_prompt = \"\"\"\n", |
||||||
|
"Following the article to be analyzed to suggest a title. Limit the answer to the following Response Format (Markdown): \n", |
||||||
|
"Optimized Title: [Provide only one title here]\n", |
||||||
|
"Justification: [Explain why this title is effective for SEO].\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"user_prompt = f\"{user_prompt} {article}\"\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c45fc7d7-08c9-4e34-b427-b928a219bb94", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# message list\n", |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f67b881f-1040-4cf7-82c5-e85f4c0bd252", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# get suggested title\n", |
||||||
|
"def get_title(model, **kwargs):\n", |
||||||
|
" # stream if GPT\n", |
||||||
|
" if 'stream' in kwargs:\n", |
||||||
|
" response = CLIENTS[model].chat.completions.create(\n", |
||||||
|
" model=MODELS[model],\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" stream=kwargs['stream']\n", |
||||||
|
" )\n", |
||||||
|
" else:\n", |
||||||
|
" response = CLIENTS[model].chat.completions.create(\n", |
||||||
|
" model=MODELS[model],\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
" return response\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8988d6ff-076a-4eae-baf4-26a8d6a2bc44", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# filter response from model verbose - like Deepseek reasoning/thinking verbose\n", |
||||||
|
"def filter_response(response):\n", |
||||||
|
" # Find last occurrence of 'Optimized Title:' to avoid displaying reasoning verbose\n", |
||||||
|
" substring = 'Optimized Title:'\n", |
||||||
|
" start = response.rfind('Optimized Title:')\n", |
||||||
|
" if start > -1:\n", |
||||||
|
" filtered_response = response[start:]\n", |
||||||
|
"\n", |
||||||
|
" # insert line break to preserve format\n", |
||||||
|
" filtered_response = filtered_response.replace(\"**Justification:**\", \"\\n**Justification:**\")\n", |
||||||
|
" \n", |
||||||
|
" return filtered_response" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0e9e99cf-5e25-4a1f-ab11-a2255e318671", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# display suggested title\n", |
||||||
|
"def display_title(model):\n", |
||||||
|
" # get model-suggested title\n", |
||||||
|
" title = get_title(model)\n", |
||||||
|
" \n", |
||||||
|
" display(Markdown(f\"### {model} (___{MODELS[model]}___) Answer\\n\\n_______\")) \n", |
||||||
|
"\n", |
||||||
|
" response = \"\"\n", |
||||||
|
"\n", |
||||||
|
" if model == 'GPT':\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" # for chunk in stream:\n", |
||||||
|
" for chunk in get_title(model=model, stream=True):\n", |
||||||
|
" response += chunk.choices[0].delta.content or ''\n", |
||||||
|
" response = (\n", |
||||||
|
" response.replace(\"```\",\"\")\n", |
||||||
|
" .replace(\"markdown\", \"\")\n", |
||||||
|
" .replace(\"Optimized Title:\", \"**Optimized Title:**\")\n", |
||||||
|
" .replace(\"Justification:\", \"**Justification:**\")\n", |
||||||
|
" )\n", |
||||||
|
" update_display(Markdown(response), display_id=display_handle.display_id)\n", |
||||||
|
" else:\n", |
||||||
|
" response = get_title(model=model)\n", |
||||||
|
" response = response.choices[0].message.content\n", |
||||||
|
" response = filter_response(response)\n", |
||||||
|
" response = (\n", |
||||||
|
" response.replace(\"Optimized Title:\", \"**Optimized Title:**\")\n", |
||||||
|
" .replace(\"Justification:\", \"**Justification:**\")\n", |
||||||
|
" )\n", |
||||||
|
" display(Markdown(response))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "947b42ed-5b43-486d-8af3-e5b671c1fd0e", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Get OpenAI Suggested Title" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "eb6f66e3-ab99-4f76-9358-896cb43c1fa1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# get and display openAi suggested title\n", |
||||||
|
"display_title(model='GPT')" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "70073ebf-a00a-416b-854d-642d450cd99b", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Get Llama Suggested Title" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "caa190bb-de5f-45cc-b671-5d62688f7b25", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# get and display Llama suggested title\n", |
||||||
|
"display_title(model='LLAMA')" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "811edc4f-20e2-482d-ac89-fae9d1b70bed", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Get Deepseek Suggested Title" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "082628e4-ff4c-46dd-ae5f-76578eb017ad", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# get and display Deepseek title\n", |
||||||
|
"display_title(model='DEEPSEEK')" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "7fc404a6-3a91-4c09-89de-867d3d69b4b2", |
||||||
|
"metadata": { |
||||||
|
"jp-MarkdownHeadingCollapsed": true |
||||||
|
}, |
||||||
|
"source": [ |
||||||
|
"### Observations\n", |
||||||
|
"\n", |
||||||
|
"1. **Selenium:** The headless option (__options.add_argument(\"--headless=new\")__), while ideal to speed up the scanning process, presented problems while scanning several websites (including openai.com and canva.com).\n", |
||||||
|
"2. **Deepseek challenges:**\\\n", |
||||||
|
" a.It always returns its thinking/reasoning verbose, which, while helpful to understand how it works, is not always\n", |
||||||
|
" required, such as in this example code. A new function (**filter_response**) was created to remove the additional verbose.\\\n", |
||||||
|
" b. It is unreliable with the response, sometimes returning the required format for the response instead of the\n", |
||||||
|
" actual response. For example, for the title, it may sometimes return:\n", |
||||||
|
" \n", |
||||||
|
" **Optimized Title:** \\[The user wants the suggested title here]\n", |
||||||
|
" \n", |
||||||
|
"### Suggested future improvements\n", |
||||||
|
"\n", |
||||||
|
"1. Add the logic that would allow each model to assess the recommendations from the different models and \n", |
||||||
|
" select the best among these.\n", |
||||||
|
"2. Add the logic to leverage an API (if available) that automatically assesses the suggested titles." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1af8260b-5ba1-4eeb-acd0-02de537b1bf4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,532 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "603cd418-504a-4b4d-b1c3-be04febf3e79", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Article Title Generator (V3 - using Firecrawl) \n", |
||||||
|
"\n", |
||||||
|
"Summarization use-case in which the user provides an article, which the LLM will analyze to suggest an SEO-optimized title.\n", |
||||||
|
"\n", |
||||||
|
"**NOTES**:\n", |
||||||
|
"\n", |
||||||
|
"1. This version supports website scrapping using [Firecrawl](https://www.firecrawl.dev/).<br>\n", |
||||||
|
" 1. **Note:** There is a Free tier that provides 500 one-time credits (good for scraping 500 pages).\n", |
||||||
|
" 2. Upon registration, get and add your Firecrawl API Key to the .env file as: **`FIRECRAWL_API_KEY`**.<br><br>\n", |
||||||
|
"2. Leverage streaming (OpenAI only).<br>\n", |
||||||
|
"3. The following models were configured:<br>\n", |
||||||
|
" 1. OpenAI gpt-4o-mini\n", |
||||||
|
" 2. Llama llama3.2\n", |
||||||
|
" 3. Deepseek deepseek-r1:1.5b\n", |
||||||
|
" 4. Firecrawl LLM Extract feature<br><br>\n", |
||||||
|
" \n", |
||||||
|
" It is possible to configure additional models by adding the new model to the MODELS dictionary and its\n", |
||||||
|
" initialization to the CLIENTS dictionary. Then, call the model with --> **`answer =\n", |
||||||
|
" get_answer('NEW_MODEL')`**.<br>\n", |
||||||
|
"4. Users are encouraged to assess and rank the suggested titles using any headline analyzer tool online.\n", |
||||||
|
" Example: [ISITWP Headline Analyzer](https://www.isitwp.com/headline-analyzer/). " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "115004a8-747a-4954-9580-1ed548f80336", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# install required libraries if they were not part of the requirements.txt\n", |
||||||
|
"!pip install firecrawl-py" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e773daa6-d05e-49bf-ad8e-a8ed4882b77e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# confirming Llama is loaded\n", |
||||||
|
"!ollama pull llama3.2" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "279b0c00-9bb0-4c7f-9c6d-aa0b108274b9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"from firecrawl import FirecrawlApp" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d4730d8d-3e20-4f3c-a4ff-ed2ac0a8aa27", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# set environment variables for OpenAi\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"# validate API Key\n", |
||||||
|
"if not api_key:\n", |
||||||
|
" raise ValueError(\"No OPENAI API Key was found! Please check the .env file.\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b2a78101-d866-400f-a482-1d8fda8e0df9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# set environment variable for Firecrawl\n", |
||||||
|
"firecrawl_api_key = os.getenv('FIRECRAWL_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"# validate API Key\n", |
||||||
|
"if not firecrawl_api_key:\n", |
||||||
|
" raise ValueError(\"No FIRECRAWL API Key was found! Please check the .env file.\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1abbb826-de66-498c-94d8-33369ad01885", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# constants\n", |
||||||
|
"MODELS = { 'GPT': 'gpt-4o-mini', \n", |
||||||
|
" 'LLAMA': 'llama3.2', \n", |
||||||
|
" 'DEEPSEEK': 'deepseek-r1:1.5b'\n", |
||||||
|
" }\n", |
||||||
|
"\n", |
||||||
|
"CLIENTS = { 'GPT': OpenAI(), \n", |
||||||
|
" 'LLAMA': OpenAI(base_url='http://localhost:11434/v1', api_key='ollama'),\n", |
||||||
|
" 'DEEPSEEK': OpenAI(base_url='http://localhost:11434/v1', api_key='ollama') \n", |
||||||
|
" }\n", |
||||||
|
"\n", |
||||||
|
"# path to Chrome\n", |
||||||
|
"# CHROME_PATH = \"C:/Program Files/Google/Chrome/Application/chrome.exe\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6f490fe4-32d5-41f3-890d-ecf4e5e01dd4", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"**Webcrawler** (based on the code from Firecrawl [documentation](https://docs.firecrawl.dev/introduction))." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2852700e-33ed-4be5-bd31-8aa05036aaf2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class WebsiteCrawler:\n", |
||||||
|
" def __init__(self, url, wait_time=20, format='markdown'):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Initialize the WebsiteCrawler using Firecrawl to scrape JavaScript-rendered content.\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" self.wait_time = wait_time\n", |
||||||
|
" self.format = format\n", |
||||||
|
"\n", |
||||||
|
" try:\n", |
||||||
|
"\n", |
||||||
|
" # initialize Firecrawl\n", |
||||||
|
" screate_app = FirecrawlApp(api_key=firecrawl_api_key)\n", |
||||||
|
"\n", |
||||||
|
" # Scrape a website:\n", |
||||||
|
" scrape_result = screate_app.scrape_url(self.url,\n", |
||||||
|
" params=self.getParams())\n", |
||||||
|
" \n", |
||||||
|
"\n", |
||||||
|
" # parse data\n", |
||||||
|
" self.title = scrape_result['metadata']['ogTitle']\n", |
||||||
|
"\n", |
||||||
|
" # get the content using the appropriate key\n", |
||||||
|
" if format == 'markdown':\n", |
||||||
|
" # OpenAI, Llama, Deepseek\n", |
||||||
|
" self.text = scrape_result['markdown'] \n", |
||||||
|
" elif format == 'json':\n", |
||||||
|
" # Firecrawl LLM Extract\n", |
||||||
|
" self.text = scrape_result['json']\n", |
||||||
|
"\n", |
||||||
|
" except Exception as e:\n", |
||||||
|
" print(f\"Error occurred: {e}\")\n", |
||||||
|
" self.title = \"Error occurred\"\n", |
||||||
|
" self.text = \"\"\n", |
||||||
|
"\n", |
||||||
|
" # set appropriate parameters for scraping\n", |
||||||
|
" def getParams(self):\n", |
||||||
|
"\n", |
||||||
|
" # For OpenAi, Llama or Deepseek\n", |
||||||
|
" params={'formats': [self.format], \n", |
||||||
|
" 'actions': [{\"type\": \"wait\", \"milliseconds\": self.wait_time}], \n", |
||||||
|
" 'includeTags': [\"main\"], }\n", |
||||||
|
"\n", |
||||||
|
" # For Firecrawl LLM extract\n", |
||||||
|
" if self.format == 'json':\n", |
||||||
|
" params={'formats': [self.format], \n", |
||||||
|
" 'actions': [{\"type\": \"wait\", \"milliseconds\": self.wait_time}], \n", |
||||||
|
" 'jsonOptions': {'systemPrompt': system_prompt, 'prompt': user_prompt, }}\n", |
||||||
|
" \n", |
||||||
|
" return params\n", |
||||||
|
"\n", |
||||||
|
" # Get Firecrawl LLM extract result\n", |
||||||
|
" def getResult(self):\n", |
||||||
|
"\n", |
||||||
|
" formated_result = f\"\"\"\n", |
||||||
|
" **Optimized Title:** {self.text['Optimized Title']} \n", |
||||||
|
" <br><br>**Justification:** {self.text['Justification']}\n", |
||||||
|
" \"\"\"\n", |
||||||
|
"\n", |
||||||
|
" # Remove leading and trailing spaces \n", |
||||||
|
" return formated_result.strip()\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "592d8f86-fbf7-4b16-a69d-468030d72dc4", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Prompts" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1914afad-dbd8-4c1f-8e68-80b0e5d743a9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# system prompt\n", |
||||||
|
"system_prompt = \"\"\"\n", |
||||||
|
" You are an experienced SEO-focused copywriter. The user will provide an article, and your task is to analyze its content and generate a single, most effective, keyword-optimized title to maximize SEO performance.\n", |
||||||
|
"\n", |
||||||
|
"Instructions:\n", |
||||||
|
"Ignore irrelevant content, such as the current title (if any), navigation menus, advertisements, or unrelated text.\n", |
||||||
|
"Prioritize SEO best practices, considering:\n", |
||||||
|
"Keyword relevance and search intent (informational, transactional, etc.).\n", |
||||||
|
"Readability and engagement.\n", |
||||||
|
"Avoiding keyword stuffing.\n", |
||||||
|
"Ensure conciseness and clarity, keeping the title under 60 characters when possible for optimal SERP display.\n", |
||||||
|
"Use a compelling structure that balances informativeness and engagement, leveraging formats like:\n", |
||||||
|
"Listicles (\"10 Best Strategies for…\")\n", |
||||||
|
"How-to guides (\"How to Boost…\")\n", |
||||||
|
"Questions (\"What Is the Best Way to…\")\n", |
||||||
|
"Power words to enhance click-through rates (e.g., \"Proven,\" \"Ultimate,\" \"Essential\").\n", |
||||||
|
"Provide only one single, best title—do not suggest multiple options.\n", |
||||||
|
"Limit the answer to the following Response Format (Markdown):\n", |
||||||
|
"Optimized Title: [Provide only one title here]\n", |
||||||
|
"Justification: [Explain why this title is effective for SEO]\n", |
||||||
|
"\n", |
||||||
|
" \"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "b0486867-6d38-4cb5-91d4-fb60952c3a9b", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"**Provide the article URL and get its content for analysis**" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ddd76319-13ce-480b-baa7-cab6a5c88168", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# article url - change to any other article URL\n", |
||||||
|
"article_url = \"https://searchengineland.com/seo-trends-2025-447745\"\n", |
||||||
|
"\n", |
||||||
|
"# get article content\n", |
||||||
|
"article = WebsiteCrawler(url=article_url)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "176cfac7-5e6d-4d4a-a1c4-1b63b60de1f7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# user prompt\n", |
||||||
|
"user_prompt = \"\"\"\n", |
||||||
|
"Following the article to be analyzed to suggest a title. Limit the answer to the following Response Format (Markdown): \n", |
||||||
|
"Optimized Title: [Provide only one title here]\n", |
||||||
|
"Justification: [Explain why this title is effective for SEO].\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"user_prompt = f\"{user_prompt} {article}\"\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c45fc7d7-08c9-4e34-b427-b928a219bb94", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# message list\n", |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f67b881f-1040-4cf7-82c5-e85f4c0bd252", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# get suggested title\n", |
||||||
|
"def get_title(model, **kwargs):\n", |
||||||
|
" # stream if GPT\n", |
||||||
|
" if 'stream' in kwargs:\n", |
||||||
|
" response = CLIENTS[model].chat.completions.create(\n", |
||||||
|
" model=MODELS[model],\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" stream=kwargs['stream']\n", |
||||||
|
" )\n", |
||||||
|
" else:\n", |
||||||
|
" response = CLIENTS[model].chat.completions.create(\n", |
||||||
|
" model=MODELS[model],\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
" return response\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8988d6ff-076a-4eae-baf4-26a8d6a2bc44", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# filter response from model verbose - like Deepseek reasoning/thinking verbose\n", |
||||||
|
"def filter_response(response):\n", |
||||||
|
" filtered_response = response\n", |
||||||
|
" # Find last occurrence of 'Optimized Title:' to avoid displaying reasoning verbose\n", |
||||||
|
" substring = 'Optimized Title:'\n", |
||||||
|
" start = response.rfind(substring)\n", |
||||||
|
" if start > -1:\n", |
||||||
|
" filtered_response = response[start:]\n", |
||||||
|
"\n", |
||||||
|
" # Find if the title has quotation (or other) marks and remove it - this should be improved\n", |
||||||
|
" filtered_response = (\n", |
||||||
|
" filtered_response.replace('\"', '', 2)\n", |
||||||
|
" .replace('[', '', 1)\n", |
||||||
|
" .replace(']', '', 1)\n", |
||||||
|
" .replace('**', '', 2)\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" return filtered_response" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0e9e99cf-5e25-4a1f-ab11-a2255e318671", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# display suggested title\n", |
||||||
|
"def display_title(model):\n", |
||||||
|
" # get model-suggested title\n", |
||||||
|
" title = get_title(model)\n", |
||||||
|
" \n", |
||||||
|
" display(Markdown(f\"### {model} (___{MODELS[model]}___) Answer\\n\\n_______\")) \n", |
||||||
|
"\n", |
||||||
|
" response = \"\"\n", |
||||||
|
"\n", |
||||||
|
" if model == 'GPT':\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" # for chunk in stream:\n", |
||||||
|
" for chunk in get_title(model=model, stream=True):\n", |
||||||
|
" response += chunk.choices[0].delta.content or ''\n", |
||||||
|
" response = (\n", |
||||||
|
" response.replace(\"```\",\"\")\n", |
||||||
|
" .replace(\"markdown\", \"\")\n", |
||||||
|
" .replace(\"Optimized Title:\", \"**Optimized Title:**\")\n", |
||||||
|
" .replace(\"Justification:\", \"**Justification:**\")\n", |
||||||
|
" )\n", |
||||||
|
" update_display(Markdown(response), display_id=display_handle.display_id)\n", |
||||||
|
" else:\n", |
||||||
|
" response = get_title(model=model)\n", |
||||||
|
" response = response.choices[0].message.content\n", |
||||||
|
" response = filter_response(response)\n", |
||||||
|
"\n", |
||||||
|
" # insert line break to preserve format - only LLAMA\n", |
||||||
|
" line_break = \"<br><br>\"\n", |
||||||
|
" if model == \"DEEPSEEK\":\n", |
||||||
|
" line_break = \"\"\n", |
||||||
|
" \n", |
||||||
|
" response = (\n", |
||||||
|
" response.replace(\"Optimized Title:\", \"**Optimized Title:**\")\n", |
||||||
|
" .replace(\"Justification:\", f\"{line_break}**Justification:**\") \n", |
||||||
|
" )\n", |
||||||
|
" display(Markdown(response))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "947b42ed-5b43-486d-8af3-e5b671c1fd0e", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Get OpenAI Suggested Title" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "eb6f66e3-ab99-4f76-9358-896cb43c1fa1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# get and display openAi suggested title\n", |
||||||
|
"display_title(model='GPT')" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "70073ebf-a00a-416b-854d-642d450cd99b", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Get Llama Suggested Title" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "caa190bb-de5f-45cc-b671-5d62688f7b25", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# get and display Llama suggested title\n", |
||||||
|
"display_title(model='LLAMA')" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "811edc4f-20e2-482d-ac89-fae9d1b70bed", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Get Deepseek Suggested Title" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "082628e4-ff4c-46dd-ae5f-76578eb017ad", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# get and display Deepseek title\n", |
||||||
|
"display_title(model='DEEPSEEK')" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "f2d401ed-734d-4e96-be30-09b49d516f38", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Using Firecrawl LLM Extract (to replace LLMs above - OpenAI, Llama & Deepseek)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "3e6495a2-df0b-4a7b-a376-692456be633d", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Get Firecrawl Suggested Title" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c8763b0a-54ef-409f-8dd6-13231b6f7774", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"fc_title = WebsiteCrawler(url=article_url, format='json')\n", |
||||||
|
"\n", |
||||||
|
"display(Markdown(fc_title.getResult()))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "7fc404a6-3a91-4c09-89de-867d3d69b4b2", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Observations\n", |
||||||
|
"\n", |
||||||
|
"1. **Firecrawl** is a great alternative to replace both Selenium and BeautifulSoup. However, it is not free.\n", |
||||||
|
"2. **Firecrawl LLM Extract** feature may replace the calls to other LLMs for analysis and title generation. Note that the result provided seems to be cached upon its first generation. Therefore, the suggested title and its justification will always be the same. \n", |
||||||
|
"3. **Deepseek challenges:**\\\n", |
||||||
|
" a.It always returns its thinking/reasoning verbose, which, while helpful to understand how it works, is not always\n", |
||||||
|
" required, such as in this example code. A new function (**filter_response**) was created to remove the additional verbose.\\\n", |
||||||
|
" b. It is unreliable with the response, sometimes returning the required format for the response instead of the\n", |
||||||
|
" actual response. For example, for the title, it may sometimes return:\n", |
||||||
|
" \n", |
||||||
|
" **Optimized Title:** \\[The user wants the suggested title here]\n", |
||||||
|
" \n", |
||||||
|
"### Suggested future improvements\n", |
||||||
|
"\n", |
||||||
|
"1. Add the logic that would allow each model to assess the recommendations from the different models and \n", |
||||||
|
" select the best among these.\n", |
||||||
|
"2. Add the logic to leverage an API (if available) that automatically assesses the suggested titles." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1af8260b-5ba1-4eeb-acd0-02de537b1bf4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,129 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d25b0aef-3e5e-4026-90ee-2b373bf262b7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Step 0: Import Libraries\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"import ollama\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import requests\n", |
||||||
|
"\n", |
||||||
|
"# Step 1: Set Constants and Variables\n", |
||||||
|
"print(\"[INFO] Setting constants and variable ...\")\n", |
||||||
|
"WEBSITE_URL = \"https://arxiv.org/\"\n", |
||||||
|
"MODEL = \"llama3.2\"\n", |
||||||
|
"approaches = [\"local-call\", \"python-package\", \"openai-python-library\"]\n", |
||||||
|
"approach = approaches[2]\n", |
||||||
|
"\n", |
||||||
|
"# Step 1: Scrape Website\n", |
||||||
|
"print(\"[INFO] Scraping website ...\")\n", |
||||||
|
"url_response = requests.get(\n", |
||||||
|
" url=WEBSITE_URL,\n", |
||||||
|
" headers={\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"}\n", |
||||||
|
" )\n", |
||||||
|
"soup = BeautifulSoup(\n", |
||||||
|
" markup=url_response.content,\n", |
||||||
|
" features=\"html.parser\"\n", |
||||||
|
" )\n", |
||||||
|
"website_title = soup.title.string if soup.title else \"No title found!!!\"\n", |
||||||
|
"for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
"website_text = soup.body.get_text(\n", |
||||||
|
" separator=\"\\n\",\n", |
||||||
|
" strip=True\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
"# Step 2: Create Prompts\n", |
||||||
|
"print(\"[INFO] Creating system prompt ...\")\n", |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of a \\\n", |
||||||
|
" website and provides a short summary, ignoring text that might be \\\n", |
||||||
|
" navigation related. Respond in markdown.\"\n", |
||||||
|
"\n", |
||||||
|
"print(\"[INFO] Creating user prompt ...\")\n", |
||||||
|
"user_prompt = f\"You are looking at a website titled {website_title}\"\n", |
||||||
|
"user_prompt += \"\\nBased on the contents of the website, please provide \\\n", |
||||||
|
" a short summary of this website in markdown. If the website \\\n", |
||||||
|
" includes news or announcements, summarize them, too. The contents \\\n", |
||||||
|
" of this website are as follows:\\n\\n\"\n", |
||||||
|
"user_prompt += website_text\n", |
||||||
|
"\n", |
||||||
|
"# Step 3: Make Messages List\n", |
||||||
|
"print(\"[INFO] Making messages list ...\")\n", |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
"]\n", |
||||||
|
"\n", |
||||||
|
"# Step 4: Call Model and Print Results\n", |
||||||
|
"if approach == \"local-call\":\n", |
||||||
|
" response = requests.post(\n", |
||||||
|
" url=\"http://localhost:11434/api/chat\",\n", |
||||||
|
" json={\n", |
||||||
|
" \"model\": MODEL,\n", |
||||||
|
" \"messages\": messages,\n", |
||||||
|
" \"stream\": False\n", |
||||||
|
" },\n", |
||||||
|
" headers={\"Content-Type\": \"application/json\"}\n", |
||||||
|
" )\n", |
||||||
|
" print(\"[INFO] Printing result ...\")\n", |
||||||
|
" display(Markdown(response.json()[\"message\"][\"content\"]))\n", |
||||||
|
"elif approach == \"python-package\":\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" stream=False\n", |
||||||
|
" )\n", |
||||||
|
" print(\"[INFO] Printing result ...\")\n", |
||||||
|
" display(Markdown(response[\"message\"][\"content\"]))\n", |
||||||
|
"elif approach == \"openai-python-library\":\n", |
||||||
|
" ollama_via_openai = OpenAI(\n", |
||||||
|
" base_url=\"http://localhost:11434/v1\",\n", |
||||||
|
" api_key=\"ollama\"\n", |
||||||
|
" )\n", |
||||||
|
" response = ollama_via_openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" print(\"[INFO] Printing result ...\")\n", |
||||||
|
" display(Markdown(response.choices[0].message.content))\n", |
||||||
|
"else:\n", |
||||||
|
" raise ValueError(f\"[INFO] Invalid approach! Please select an approach from {approaches} and try again.\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b0a6676e-fb43-4725-9389-2acd74c13c4e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.12.8" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,131 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 2, |
||||||
|
"id": "f3c6d883-58a2-47de-823f-3c7430cffcc9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"\"Airbrush or Air Bust? Let's Find Out!\"\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"\n", |
||||||
|
"# Step 1: Create your prompts\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You will take the body of an email and evaluate it to suggest a brief snarky subject\"\n", |
||||||
|
"user_prompt = \"\"\"\n", |
||||||
|
"Dear Air Brush Customer Service Team,\n", |
||||||
|
"\n", |
||||||
|
"I hope this message finds you well. I am writing to formally lodge a complaint regarding the airbrush product I purchased from your store. Unfortunately, the product I received is defective and does not meet the quality standards as advertised.\n", |
||||||
|
"\n", |
||||||
|
"Below are the details of my issue:\n", |
||||||
|
"\n", |
||||||
|
"Order Number: #12345\n", |
||||||
|
"\n", |
||||||
|
"Product Name: Air Brush model 123\n", |
||||||
|
"\n", |
||||||
|
"Date of Purchase: 18/1/2025\n", |
||||||
|
"\n", |
||||||
|
"Issue Description:\n", |
||||||
|
"Defective Nozzle: The nozzle of the airbrush is clogged and does not allow proper airflow, making it impossible to use.\n", |
||||||
|
"\n", |
||||||
|
"Inconsistent Spray Pattern: Even after multiple attempts to clean and adjust the settings, the spray pattern is uneven and inconsistent.\n", |
||||||
|
"\n", |
||||||
|
"Leakage: The airbrush leaks air and paint from the joints, which is a significant safety hazard.\n", |
||||||
|
"\n", |
||||||
|
"Build Quality: The overall build quality of the product feels subpar, with loose fittings and a flimsy trigger mechanism.\n", |
||||||
|
"\n", |
||||||
|
"Steps Taken:\n", |
||||||
|
"I followed the user manual and cleaning instructions provided, but the issues persist.\n", |
||||||
|
"\n", |
||||||
|
"I also reached out to your technical support team on [Date] but have not received a resolution.\n", |
||||||
|
"\n", |
||||||
|
"Expectation:\n", |
||||||
|
"Given the defective nature of the product, I would like to request a full refund for the item. Alternatively, if a refund is not possible, I would appreciate a replacement with a fully functional unit.\n", |
||||||
|
"\n", |
||||||
|
"Attachments:\n", |
||||||
|
"I have attached photos and a video demonstrating the issues for your reference.\n", |
||||||
|
"\n", |
||||||
|
"Copies of the invoice and order confirmation are also attached for your convenience.\n", |
||||||
|
"\n", |
||||||
|
"Request for Resolution:\n", |
||||||
|
"Kindly let me know the next steps to process the refund or replacement. I would appreciate a prompt response within [X business days, e.g., 3-5 business days] to resolve this matter.\n", |
||||||
|
"\n", |
||||||
|
"Thank you for your attention to this issue. I trust that you will handle this matter professionally and ensure customer satisfaction.\n", |
||||||
|
"\n", |
||||||
|
"Looking forward to your swift response.\n", |
||||||
|
"\n", |
||||||
|
"Best regards,\n", |
||||||
|
"Oya YILDIZ\n", |
||||||
|
"İstanbul\n", |
||||||
|
"Turkey\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"# Step 2: Make the messages list\n", |
||||||
|
"\n", |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
"] # fill this in\n", |
||||||
|
"\n", |
||||||
|
"# Step 3: Call OpenAI\n", |
||||||
|
"\n", |
||||||
|
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n", |
||||||
|
"\n", |
||||||
|
"# Step 4: print the result\n", |
||||||
|
"\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d9b655de-e8c3-4136-b6a6-2fb0ce01c364", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,651 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Instant Gratification\n", |
||||||
|
"\n", |
||||||
|
"## Your first Frontier LLM Project!\n", |
||||||
|
"\n", |
||||||
|
"Let's build a useful LLM solution - in a matter of minutes.\n", |
||||||
|
"\n", |
||||||
|
"By the end of this course, you will have built an autonomous Agentic AI solution with 7 agents that collaborate to solve a business problem. All in good time! We will start with something smaller...\n", |
||||||
|
"\n", |
||||||
|
"Our goal is to code a new kind of Web Browser. Give it a URL, and it will respond with a summary. The Reader's Digest of the internet!!\n", |
||||||
|
"\n", |
||||||
|
"Before starting, you should have completed the setup for [PC](../SETUP-PC.md) or [Mac](../SETUP-mac.md) and you hopefully launched this jupyter lab from within the project root directory, with your environment activated.\n", |
||||||
|
"\n", |
||||||
|
"## If you're new to Jupyter Lab\n", |
||||||
|
"\n", |
||||||
|
"Welcome to the wonderful world of Data Science experimentation! Once you've used Jupyter Lab, you'll wonder how you ever lived without it. Simply click in each \"cell\" with code in it, such as the cell immediately below this text, and hit Shift+Return to execute that cell. As you wish, you can add a cell with the + button in the toolbar, and print values of variables, or try out variations. \n", |
||||||
|
"\n", |
||||||
|
"I've written a notebook called [Guide to Jupyter](Guide%20to%20Jupyter.ipynb) to help you get more familiar with Jupyter Labs, including adding Markdown comments, using `!` to run shell commands, and `tqdm` to show progress.\n", |
||||||
|
"\n", |
||||||
|
"## If you'd prefer to work in IDEs\n", |
||||||
|
"\n", |
||||||
|
"If you're more comfortable in IDEs like VSCode or Pycharm, they both work great with these lab notebooks too. \n", |
||||||
|
"If you'd prefer to work in VSCode, [here](https://chatgpt.com/share/676f2e19-c228-8012-9911-6ca42f8ed766) are instructions from an AI friend on how to configure it for the course.\n", |
||||||
|
"\n", |
||||||
|
"## If you'd like to brush up your Python\n", |
||||||
|
"\n", |
||||||
|
"I've added a notebook called [Intermediate Python](Intermediate%20Python.ipynb) to get you up to speed. But you should give it a miss if you already have a good idea what this code does: \n", |
||||||
|
"`yield from {book.get(\"author\") for book in books if book.get(\"author\")}`\n", |
||||||
|
"\n", |
||||||
|
"## I am here to help\n", |
||||||
|
"\n", |
||||||
|
"If you have any problems at all, please do reach out. \n", |
||||||
|
"I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect (and I love connecting!)\n", |
||||||
|
"\n", |
||||||
|
"## More troubleshooting\n", |
||||||
|
"\n", |
||||||
|
"Please see the [troubleshooting](troubleshooting.ipynb) notebook in this folder to diagnose and fix common problems. At the very end of it is a diagnostics script with some useful debug info.\n", |
||||||
|
"\n", |
||||||
|
"## If this is old hat!\n", |
||||||
|
"\n", |
||||||
|
"If you're already comfortable with today's material, please hang in there; you can move swiftly through the first few labs - we will get much more in depth as the weeks progress.\n", |
||||||
|
"\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Please read - important note</h2>\n", |
||||||
|
" <span style=\"color:#900;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you do this with me, either at the same time, or (perhaps better) right afterwards. Add print statements to understand what's going on, and then come up with your own variations. If you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#181;\">Business value of these exercises</h2>\n", |
||||||
|
" <span style=\"color:#181;\">A final thought. While I've designed these notebooks to be educational, I've also tried to make them enjoyable. We'll do fun things like have LLMs tell jokes and argue with each other. But fundamentally, my goal is to teach skills you can apply in business. I'll explain business implications as we go, and it's worth keeping this in mind: as you build experience with models and techniques, think of ways you could put this into action at work today. Please do contact me if you'd like to discuss more or if you have ideas to bounce off me.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"\n", |
||||||
|
"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6900b2a8-6384-4316-8aaa-5e519fca4254", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Connecting to OpenAI\n", |
||||||
|
"\n", |
||||||
|
"The next cell is where we load in the environment variables in your `.env` file and connect to OpenAI.\n", |
||||||
|
"\n", |
||||||
|
"## Troubleshooting if you have problems:\n", |
||||||
|
"\n", |
||||||
|
"Head over to the [troubleshooting](troubleshooting.ipynb) notebook in this folder for step by step code to identify the root cause and fix it!\n", |
||||||
|
"\n", |
||||||
|
"If you make a change, try restarting the \"Kernel\" (the python process sitting behind this notebook) by Kernel menu >> Restart Kernel and Clear Outputs of All Cells. Then try this notebook again, starting at the top.\n", |
||||||
|
"\n", |
||||||
|
"Or, contact me! Message me or email ed@edwarddonner.com and we will get this to work.\n", |
||||||
|
"\n", |
||||||
|
"Any concerns about API costs? See my notes in the README - costs should be minimal, and you can control it at every point. You can also use Ollama as a free alternative, which we discuss during Day 2." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7b87cadb-d513-4303-baee-a37b6f938e4d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"# Check the key\n", |
||||||
|
"\n", |
||||||
|
"if not api_key:\n", |
||||||
|
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||||
|
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||||
|
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||||
|
"elif api_key.strip() != api_key:\n", |
||||||
|
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"API key found and looks good so far!\")\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"\n", |
||||||
|
"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n", |
||||||
|
"# If it STILL doesn't work (horrors!) then please see the Troubleshooting notebook in this folder for full instructions" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "442fc84b-0815-4f40-99ab-d9a5da6bda91", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Let's make a quick call to a Frontier model to get started, as a preview!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a58394bf-1e45-46af-9bfd-01e24da6f49a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# To give you a preview -- calling OpenAI with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\n", |
||||||
|
"\n", |
||||||
|
"message = \"Tell me about a way to analyse what people do in a video clip.\"\n", |
||||||
|
"#response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=[{\"role\":\"user\", \"content\":message}])\n", |
||||||
|
"#print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "2aa190e5-cb31-456a-96cc-db109919cd78", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## OK onwards with our first project" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c5e793b2-6775-426a-a139-4848291d0463", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's try one out. Change the website and add print statements to follow along.\n", |
||||||
|
"\n", |
||||||
|
"ed = Website(\"https://edwarddonner.com\")\n", |
||||||
|
"print(ed.title)\n", |
||||||
|
"print(ed.text)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6a478a0c-2c53-48ff-869c-4d08199931e1", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Types of prompts\n", |
||||||
|
"\n", |
||||||
|
"You may know this already - but if not, you will get very familiar with it!\n", |
||||||
|
"\n", |
||||||
|
"Models like GPT4o have been trained to receive instructions in a particular way.\n", |
||||||
|
"\n", |
||||||
|
"They expect to receive:\n", |
||||||
|
"\n", |
||||||
|
"**A system prompt** that tells them what task they are performing and what tone they should use\n", |
||||||
|
"\n", |
||||||
|
"**A user prompt** -- the conversation starter that they should reply to" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "abdb8417-c5dc-44bc-9bee-2e059d162699", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||||
|
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||||
|
"Respond in markdown.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||||
|
"\n", |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||||
|
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||||
|
"please provide a short summary of this website in markdown. \\\n", |
||||||
|
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "26448ec4-5c00-4204-baec-7df91d11ff2e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(user_prompt_for(ed))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Messages\n", |
||||||
|
"\n", |
||||||
|
"The API from OpenAI expects to receive messages in a particular structure.\n", |
||||||
|
"Many of the other APIs share this structure:\n", |
||||||
|
"\n", |
||||||
|
"```\n", |
||||||
|
"[\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"system message goes here\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"user message goes here\"}\n", |
||||||
|
"]\n", |
||||||
|
"\n", |
||||||
|
"To give you a preview, the next 2 cells make a rather simple call - we won't stretch the might GPT (yet!)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f25dcd35-0cd0-4235-9f64-ac37ed9eaaa5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n", |
||||||
|
"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "21ed95c5-7001-47de-a36d-1d6673b403ce", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# To give you a preview -- calling OpenAI with system and user messages:\n", |
||||||
|
"\n", |
||||||
|
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## And now let's build useful messages for GPT-4o-mini, using a function" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0134dfa4-8299-48b5-b444-f2a8c3403c88", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# See how this function creates exactly the format above\n", |
||||||
|
"\n", |
||||||
|
"def messages_for(website):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "36478464-39ee-485c-9f3f-6a4e458dbc9c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Try this out, and then try for a few more websites\n", |
||||||
|
"\n", |
||||||
|
"messages_for(ed)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Time to bring it together - the API for OpenAI is very simple!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "905b9919-aba7-45b5-ae65-81b3d1d78e34", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# And now: call the OpenAI API. You will get very familiar with this!\n", |
||||||
|
"\n", |
||||||
|
"def summarize(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model = \"gpt-4o-mini\",\n", |
||||||
|
" messages = messages_for(website)\n", |
||||||
|
" )\n", |
||||||
|
" return response.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"summary = summarize(\"https://edwarddonner.com\")\n", |
||||||
|
"print(summary)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3d926d59-450e-4609-92ba-2d6f244f1342", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function to display this nicely in the Jupyter output, using markdown\n", |
||||||
|
"\n", |
||||||
|
"def display_summary(url):\n", |
||||||
|
" summary = summarize(url)\n", |
||||||
|
" display(Markdown(summary))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3018853a-445f-41ff-9560-d925d1774b2f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://edwarddonner.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "b3bcf6f4-adce-45e9-97ad-d9a5d7a3a624", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Let's try more websites\n", |
||||||
|
"\n", |
||||||
|
"Note that this will only work on websites that can be scraped using this simplistic approach.\n", |
||||||
|
"\n", |
||||||
|
"Websites that are rendered with Javascript, like React apps, won't show up. See the community-contributions folder for a Selenium implementation that gets around this. You'll need to read up on installing Selenium (ask ChatGPT!)\n", |
||||||
|
"\n", |
||||||
|
"Also Websites protected with CloudFront (and similar) may give 403 errors - many thanks Andy J for pointing this out.\n", |
||||||
|
"\n", |
||||||
|
"But many websites will work just fine!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "45d83403-a24c-44b5-84ac-961449b4008f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"urls = ['https://be-able.info/de/be-able/', \"https://taz.de/\", \"https://www.bundestagswahl-bw.de/wahlprogramm-gruene\"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "75e9fd40-b354-4341-991e-863ef2e59db7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(urls[0])" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "c951be1a-7f1b-448f-af1f-845978e47e2c", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#181;\">Business applications</h2>\n", |
||||||
|
" <span style=\"color:#181;\">In this exercise, you experienced calling the Cloud API of a Frontier Model (a leading model at the frontier of AI) for the first time. We will be using APIs like OpenAI at many stages in the course, in addition to building our own LLMs.\n", |
||||||
|
"\n", |
||||||
|
"More specifically, we've applied this to Summarization - a classic Gen AI use case to make a summary. This can be applied to any business vertical - summarizing the news, summarizing financial performance, summarizing a resume in a cover letter - the applications are limitless. Consider how you could apply Summarization in your business, and try prototyping a solution.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>\n", |
||||||
|
"\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Before you continue - now try yourself</h2>\n", |
||||||
|
" <span style=\"color:#900;\">Use the cell below to make your own simple commercial example. Stick with the summarization use case for now. Here's an idea: write something that will take the contents of an email, and will suggest an appropriate short subject line for the email. That's the kind of feature that might be built into a commercial email tool.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "856ff857-ba5f-4596-90b9-cd6cee4073dc", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Naive extraction of name of the political party from user input\n", |
||||||
|
"\n", |
||||||
|
"party_mapping = {\"grünen\": \"grüne\", \"grüne\": \"grüne\", \"linken\": \"linke\", \"spd\": \"spd\", \"cdu\": \"cdu\", \"cdu/csu\": \"cdu\", \"csu\": \"cdu\", \"fdp\": \"fdp\", \"afd\": \"afd\", \"bsw\": \"bsw\"}\n", |
||||||
|
"\n", |
||||||
|
"def extract_party_from_user_prompt(user_input):\n", |
||||||
|
" toks = user_input.split()\n", |
||||||
|
" for tok in toks:\n", |
||||||
|
" tok = tok.lower()\n", |
||||||
|
" if tok in party_mapping.keys():\n", |
||||||
|
" return party_mapping[tok]\n", |
||||||
|
" return \"I can only answer your question concerning the election program of a certain political party. Mention one of 'FDP', 'BSW', 'Grüne', 'Linke', 'SPD', 'CDU' or 'AFD' in your question and I will try my best.\"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3962d846-ce82-47d2-8c3f-5a6fe296710d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"from selenium import webdriver\n", |
||||||
|
"from selenium.webdriver.common.by import By\n", |
||||||
|
"\n", |
||||||
|
"def get_election_program(partyname):\n", |
||||||
|
" \"\"\"Scrape parties' election programs from the official election website. Naively ignore cookie banner stuff.\"\"\"\n", |
||||||
|
"\n", |
||||||
|
" # Download the browser driver for your OS and add the path here\n", |
||||||
|
" browser_driver_path = r'C:\\Program Files\\BrowserDrivers\\geckodriver.exe'\n", |
||||||
|
" \n", |
||||||
|
" service = webdriver.firefox.service.Service(executable_path=browser_driver_path)\n", |
||||||
|
" \n", |
||||||
|
" parties = {\"grüne\": \"https://www.bundestagswahl-bw.de/wahlprogramm-gruene\",\n", |
||||||
|
" \"spd\": \"https://www.bundestagswahl-bw.de/wahlprogramm-spd\",\n", |
||||||
|
" \"cdu\": \"https://www.bundestagswahl-bw.de/wahlprogramm-cdu\",\n", |
||||||
|
" \"linke\": \"https://www.bundestagswahl-bw.de/wahlprogramm-die-linke\",\n", |
||||||
|
" \"fdp\": \"https://www.bundestagswahl-bw.de/wahlprogramm-fdp\",\n", |
||||||
|
" \"afd\": \"https://www.bundestagswahl-bw.de/wahlprogramm-afd\",\n", |
||||||
|
" \"bsw\": \"https://www.bundestagswahl-bw.de/wahlprogramm-bsw\"}\n", |
||||||
|
" \n", |
||||||
|
" election_prog = \"\"\n", |
||||||
|
" \n", |
||||||
|
" if partyname in parties.keys():\n", |
||||||
|
" site = parties[partyname]\n", |
||||||
|
" driver = webdriver.Firefox(service=service)\n", |
||||||
|
" driver.get(site)\n", |
||||||
|
" elements = driver.find_elements(By.TAG_NAME, 'p')\n", |
||||||
|
" \n", |
||||||
|
" for e in elements:\n", |
||||||
|
" if not any(x in [\"Cookies\", \"Cookie\", \"akzeptiere\", \"Datenschutzerklärung\", \"Impressum\"] for x in e.text.split()) and e.text:\n", |
||||||
|
" election_prog += e.text\n", |
||||||
|
" if len(election_prog.split()) > 100:\n", |
||||||
|
" print(\"Election program extracted.\")\n", |
||||||
|
"\n", |
||||||
|
" else:\n", |
||||||
|
" election_prog = f\"Schade, für die Partei {partyname} konnte ich leider kein Wahlprogramm finden.\"\n", |
||||||
|
" \n", |
||||||
|
" driver.quit()\n", |
||||||
|
" return election_prog" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b3a408d1-d824-4e33-a5f4-c672bc6c6198", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"\n", |
||||||
|
"def answer_my_election_program_question(input_from_user):\n", |
||||||
|
" partyname = extract_party_from_user_prompt(input_from_user)\n", |
||||||
|
" print(f\"This is a question about the political party: {partyname.capitalize()}\")\n", |
||||||
|
" \n", |
||||||
|
" # Step 1: Create your prompts\n", |
||||||
|
" system_prompt = \"Du bist ein neutraler Beobachter, der aufgrund der ihm zur Verfügung gestellten Wahlprogramme Fragen zum Wahlprogramm der verschiedenen Parteien beantwortet. Beantworte Fragen zum Wahlprogramm auf Deutsch. Basiere deine Antwort ausschließlich auf den im Folgenden aufgeführten Informationen.\"\n", |
||||||
|
" election_program = get_election_program(partyname)\n", |
||||||
|
" \n", |
||||||
|
" user_prompt = f\"Beantworte folgende Frage: \\n {input_from_user} \\n Verwende dafür folgende Infos: \\n {election_program}.\\n\\n Gib deine Antwort in Markdown aus.\"\n", |
||||||
|
" \n", |
||||||
|
" # Step 2: Make the messages list\n", |
||||||
|
" \n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": system_prompt}, {\"role\": \"user\", \"content\": user_prompt}] # fill this in\n", |
||||||
|
" \n", |
||||||
|
" # Step 3: Call OpenAI\n", |
||||||
|
" \n", |
||||||
|
" response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n", |
||||||
|
" formatted_response = f\"\\n\\n{response.choices[0].message.content}\"\n", |
||||||
|
" # Step 4: print the result\n", |
||||||
|
" return formatted_response" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e66a0967-d1e9-4f92-aeb6-95e478465a1f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Ask questions about the election programs of the main political parties for the Bundestagswahl 2025 in Germany\n", |
||||||
|
"\n", |
||||||
|
"question = \"Wie verhält sich die SPD zu Verkehrsfragen und Klimaschutz?\"\n", |
||||||
|
"answer = answer_my_election_program_question(question)\n", |
||||||
|
"display(Markdown(answer))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "36ed9f14-b349-40e9-a42c-b367e77f8bda", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## An extra exercise for those who enjoy web scraping\n", |
||||||
|
"\n", |
||||||
|
"You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them. In the community-contributions folder, you'll find an example Selenium solution from a student (thank you!)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "eeab24dc-5f90-4570-b542-b0585aca3eb6", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Sharing your code\n", |
||||||
|
"\n", |
||||||
|
"I'd love it if you share your code afterwards so I can share it with others! You'll notice that some students have already made changes (including a Selenium implementation) which you will find in the community-contributions folder. If you'd like add your changes to that folder, submit a Pull Request with your new versions in that folder and I'll merge your changes.\n", |
||||||
|
"\n", |
||||||
|
"If you're not an expert with git (and I am not!) then GPT has given some nice instructions on how to submit a Pull Request. It's a bit of an involved process, but once you've done it once it's pretty clear. As a pro-tip: it's best if you clear the outputs of your Jupyter notebooks (Edit >> Clean outputs of all cells, and then Save) for clean notebooks.\n", |
||||||
|
"\n", |
||||||
|
"Here are good instructions courtesy of an AI friend: \n", |
||||||
|
"https://chatgpt.com/share/677a9cb5-c64c-8012-99e0-e06e88afd293" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,611 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# YOUR FIRST LAB\n", |
||||||
|
"### Please read this section. This is valuable to get you prepared, even if it's a long read -- it's important stuff.\n", |
||||||
|
"\n", |
||||||
|
"## Your first Frontier LLM Project\n", |
||||||
|
"\n", |
||||||
|
"Let's build a useful LLM solution - in a matter of minutes.\n", |
||||||
|
"\n", |
||||||
|
"By the end of this course, you will have built an autonomous Agentic AI solution with 7 agents that collaborate to solve a business problem. All in good time! We will start with something smaller...\n", |
||||||
|
"\n", |
||||||
|
"Our goal is to code a new kind of Web Browser. Give it a URL, and it will respond with a summary. The Reader's Digest of the internet!!\n", |
||||||
|
"\n", |
||||||
|
"Before starting, you should have completed the setup for [PC](../SETUP-PC.md) or [Mac](../SETUP-mac.md) and you hopefully launched this jupyter lab from within the project root directory, with your environment activated.\n", |
||||||
|
"\n", |
||||||
|
"## If you're new to Jupyter Lab\n", |
||||||
|
"\n", |
||||||
|
"Welcome to the wonderful world of Data Science experimentation! Once you've used Jupyter Lab, you'll wonder how you ever lived without it. Simply click in each \"cell\" with code in it, such as the cell immediately below this text, and hit Shift+Return to execute that cell. As you wish, you can add a cell with the + button in the toolbar, and print values of variables, or try out variations. \n", |
||||||
|
"\n", |
||||||
|
"I've written a notebook called [Guide to Jupyter](Guide%20to%20Jupyter.ipynb) to help you get more familiar with Jupyter Labs, including adding Markdown comments, using `!` to run shell commands, and `tqdm` to show progress.\n", |
||||||
|
"\n", |
||||||
|
"## If you're new to the Command Line\n", |
||||||
|
"\n", |
||||||
|
"Please see these excellent guides: [Command line on PC](https://chatgpt.com/share/67b0acea-ba38-8012-9c34-7a2541052665) and [Command line on Mac](https://chatgpt.com/canvas/shared/67b0b10c93a081918210723867525d2b). \n", |
||||||
|
"\n", |
||||||
|
"## If you'd prefer to work in IDEs\n", |
||||||
|
"\n", |
||||||
|
"If you're more comfortable in IDEs like VSCode or Pycharm, they both work great with these lab notebooks too. \n", |
||||||
|
"If you'd prefer to work in VSCode, [here](https://chatgpt.com/share/676f2e19-c228-8012-9911-6ca42f8ed766) are instructions from an AI friend on how to configure it for the course.\n", |
||||||
|
"\n", |
||||||
|
"## If you'd like to brush up your Python\n", |
||||||
|
"\n", |
||||||
|
"I've added a notebook called [Intermediate Python](Intermediate%20Python.ipynb) to get you up to speed. But you should give it a miss if you already have a good idea what this code does: \n", |
||||||
|
"`yield from {book.get(\"author\") for book in books if book.get(\"author\")}`\n", |
||||||
|
"\n", |
||||||
|
"## I am here to help\n", |
||||||
|
"\n", |
||||||
|
"If you have any problems at all, please do reach out. \n", |
||||||
|
"I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect (and I love connecting!) \n", |
||||||
|
"And this is new to me, but I'm also trying out X/Twitter at [@edwarddonner](https://x.com/edwarddonner) - if you're on X, please show me how it's done 😂 \n", |
||||||
|
"\n", |
||||||
|
"## More troubleshooting\n", |
||||||
|
"\n", |
||||||
|
"Please see the [troubleshooting](troubleshooting.ipynb) notebook in this folder to diagnose and fix common problems. At the very end of it is a diagnostics script with some useful debug info.\n", |
||||||
|
"\n", |
||||||
|
"## If this is old hat!\n", |
||||||
|
"\n", |
||||||
|
"If you're already comfortable with today's material, please hang in there; you can move swiftly through the first few labs - we will get much more in depth as the weeks progress.\n", |
||||||
|
"\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Please read - important note</h2>\n", |
||||||
|
" <span style=\"color:#900;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you carefully execute this yourself, <b>after</b> watching the lecture. Add print statements to understand what's going on, and then come up with your own variations. If you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#f71;\">Treat these labs as a resource</h2>\n", |
||||||
|
" <span style=\"color:#f71;\">I push updates to the code regularly. When people ask questions or have problems, I incorporate it in the code, adding more examples or improved commentary. As a result, you'll notice that the code below isn't identical to the videos. Everything from the videos is here; but in addition, I've added more steps and better explanations, and occasionally added new models like DeepSeek. Consider this like an interactive book that accompanies the lectures.\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#181;\">Business value of these exercises</h2>\n", |
||||||
|
" <span style=\"color:#181;\">A final thought. While I've designed these notebooks to be educational, I've also tried to make them enjoyable. We'll do fun things like have LLMs tell jokes and argue with each other. But fundamentally, my goal is to teach skills you can apply in business. I'll explain business implications as we go, and it's worth keeping this in mind: as you build experience with models and techniques, think of ways you could put this into action at work today. Please do contact me if you'd like to discuss more or if you have ideas to bounce off me.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"\n", |
||||||
|
"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6900b2a8-6384-4316-8aaa-5e519fca4254", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Connecting to OpenAI\n", |
||||||
|
"\n", |
||||||
|
"The next cell is where we load in the environment variables in your `.env` file and connect to OpenAI.\n", |
||||||
|
"\n", |
||||||
|
"## Troubleshooting if you have problems:\n", |
||||||
|
"\n", |
||||||
|
"Head over to the [troubleshooting](troubleshooting.ipynb) notebook in this folder for step by step code to identify the root cause and fix it!\n", |
||||||
|
"\n", |
||||||
|
"If you make a change, try restarting the \"Kernel\" (the python process sitting behind this notebook) by Kernel menu >> Restart Kernel and Clear Outputs of All Cells. Then try this notebook again, starting at the top.\n", |
||||||
|
"\n", |
||||||
|
"Or, contact me! Message me or email ed@edwarddonner.com and we will get this to work.\n", |
||||||
|
"\n", |
||||||
|
"Any concerns about API costs? See my notes in the README - costs should be minimal, and you can control it at every point. You can also use Ollama as a free alternative, which we discuss during Day 2." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7b87cadb-d513-4303-baee-a37b6f938e4d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"# Check the key\n", |
||||||
|
"\n", |
||||||
|
"if not api_key:\n", |
||||||
|
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||||
|
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||||
|
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||||
|
"elif api_key.strip() != api_key:\n", |
||||||
|
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"API key found and looks good so far!\")\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"\n", |
||||||
|
"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n", |
||||||
|
"# If it STILL doesn't work (horrors!) then please see the Troubleshooting notebook in this folder for full instructions" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "442fc84b-0815-4f40-99ab-d9a5da6bda91", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Let's make a quick call to a Frontier model to get started, as a preview!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a58394bf-1e45-46af-9bfd-01e24da6f49a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# To give you a preview -- calling OpenAI with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\n", |
||||||
|
"\n", |
||||||
|
"message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n", |
||||||
|
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=[{\"role\":\"user\", \"content\":message}])\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "2aa190e5-cb31-456a-96cc-db109919cd78", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## OK onwards with our first project" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c5e793b2-6775-426a-a139-4848291d0463", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's try one out. Change the website and add print statements to follow along.\n", |
||||||
|
"\n", |
||||||
|
"ed = Website(\"https://edwarddonner.com\")\n", |
||||||
|
"print(ed.title)\n", |
||||||
|
"print(ed.text)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6a478a0c-2c53-48ff-869c-4d08199931e1", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Types of prompts\n", |
||||||
|
"\n", |
||||||
|
"You may know this already - but if not, you will get very familiar with it!\n", |
||||||
|
"\n", |
||||||
|
"Models like GPT4o have been trained to receive instructions in a particular way.\n", |
||||||
|
"\n", |
||||||
|
"They expect to receive:\n", |
||||||
|
"\n", |
||||||
|
"**A system prompt** that tells them what task they are performing and what tone they should use\n", |
||||||
|
"\n", |
||||||
|
"**A user prompt** -- the conversation starter that they should reply to" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "abdb8417-c5dc-44bc-9bee-2e059d162699", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||||
|
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||||
|
"Respond in markdown.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||||
|
"\n", |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||||
|
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||||
|
"please provide a short summary of this website in markdown. \\\n", |
||||||
|
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "26448ec4-5c00-4204-baec-7df91d11ff2e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(user_prompt_for(ed))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Messages\n", |
||||||
|
"\n", |
||||||
|
"The API from OpenAI expects to receive messages in a particular structure.\n", |
||||||
|
"Many of the other APIs share this structure:\n", |
||||||
|
"\n", |
||||||
|
"```\n", |
||||||
|
"[\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"system message goes here\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"user message goes here\"}\n", |
||||||
|
"]\n", |
||||||
|
"\n", |
||||||
|
"To give you a preview, the next 2 cells make a rather simple call - we won't stretch the mighty GPT (yet!)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f25dcd35-0cd0-4235-9f64-ac37ed9eaaa5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n", |
||||||
|
"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "21ed95c5-7001-47de-a36d-1d6673b403ce", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# To give you a preview -- calling OpenAI with system and user messages:\n", |
||||||
|
"\n", |
||||||
|
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## And now let's build useful messages for GPT-4o-mini, using a function" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0134dfa4-8299-48b5-b444-f2a8c3403c88", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# See how this function creates exactly the format above\n", |
||||||
|
"\n", |
||||||
|
"def messages_for(website):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "36478464-39ee-485c-9f3f-6a4e458dbc9c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Try this out, and then try for a few more websites\n", |
||||||
|
"\n", |
||||||
|
"messages_for(ed)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Time to bring it together - the API for OpenAI is very simple!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "905b9919-aba7-45b5-ae65-81b3d1d78e34", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# And now: call the OpenAI API. You will get very familiar with this!\n", |
||||||
|
"\n", |
||||||
|
"def summarize(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model = \"gpt-4o-mini\",\n", |
||||||
|
" messages = messages_for(website)\n", |
||||||
|
" )\n", |
||||||
|
" return response.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"summarize(\"https://edwarddonner.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3d926d59-450e-4609-92ba-2d6f244f1342", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function to display this nicely in the Jupyter output, using markdown\n", |
||||||
|
"\n", |
||||||
|
"def display_summary(url):\n", |
||||||
|
" summary = summarize(url)\n", |
||||||
|
" display(Markdown(summary))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3018853a-445f-41ff-9560-d925d1774b2f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://edwarddonner.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "b3bcf6f4-adce-45e9-97ad-d9a5d7a3a624", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Let's try more websites\n", |
||||||
|
"\n", |
||||||
|
"Note that this will only work on websites that can be scraped using this simplistic approach.\n", |
||||||
|
"\n", |
||||||
|
"Websites that are rendered with Javascript, like React apps, won't show up. See the community-contributions folder for a Selenium implementation that gets around this. You'll need to read up on installing Selenium (ask ChatGPT!)\n", |
||||||
|
"\n", |
||||||
|
"Also Websites protected with CloudFront (and similar) may give 403 errors - many thanks Andy J for pointing this out.\n", |
||||||
|
"\n", |
||||||
|
"But many websites will work just fine!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "45d83403-a24c-44b5-84ac-961449b4008f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://cnn.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "75e9fd40-b354-4341-991e-863ef2e59db7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://anthropic.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "c951be1a-7f1b-448f-af1f-845978e47e2c", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#181;\">Business applications</h2>\n", |
||||||
|
" <span style=\"color:#181;\">In this exercise, you experienced calling the Cloud API of a Frontier Model (a leading model at the frontier of AI) for the first time. We will be using APIs like OpenAI at many stages in the course, in addition to building our own LLMs.\n", |
||||||
|
"\n", |
||||||
|
"More specifically, we've applied this to Summarization - a classic Gen AI use case to make a summary. This can be applied to any business vertical - summarizing the news, summarizing financial performance, summarizing a resume in a cover letter - the applications are limitless. Consider how you could apply Summarization in your business, and try prototyping a solution.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>\n", |
||||||
|
"\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Before you continue - now try yourself</h2>\n", |
||||||
|
" <span style=\"color:#900;\">Use the cell below to make your own simple commercial example. Stick with the summarization use case for now. Here's an idea: write something that will take the contents of an email, and will suggest an appropriate short subject line for the email. That's the kind of feature that might be built into a commercial email tool.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "00743dac-0e70-45b7-879a-d7293a6f68a6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Step 1: Create your prompts\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are an head chef of a michelin star restaurant who has a diverse skillset \\\n", |
||||||
|
"and loves to teach new and interesting recepies for homechefs. Given input of several ingredients \\\n", |
||||||
|
"provide step by step instruction of what could be cooked for any cuisine of your choice. Respond in markdown.\"\n", |
||||||
|
"\n", |
||||||
|
"user_prompt = \"\"\"\n", |
||||||
|
"You are a Michelin-starred head chef with a passion for teaching home chefs. \n", |
||||||
|
"I have the following ingredients: \n", |
||||||
|
"\n", |
||||||
|
"**[Chicken breast, Bell peppers, cherry tomatoes, spinach, Basmati rice,\n", |
||||||
|
"Garlic, basil, black pepper, smoked paprika]** \n", |
||||||
|
"\n", |
||||||
|
"Can you provide a step-by-step recipe using these ingredients? You can choose any cuisine that best fits them. \n", |
||||||
|
"Please include cooking times, techniques, and any chef tips for enhancing flavors. \n", |
||||||
|
"\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"# Step 2: Make the messages list\n", |
||||||
|
"\n", |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
" ]\n", |
||||||
|
"\n", |
||||||
|
"# Step 3: Call OpenAI\n", |
||||||
|
"\n", |
||||||
|
"response = openai.chat.completions.create(\n", |
||||||
|
" model = \"gpt-4o-mini\",\n", |
||||||
|
" messages = messages\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"# Step 4: print the result\n", |
||||||
|
"def display_summary(summary):\n", |
||||||
|
" display(Markdown(summary))\n", |
||||||
|
"display_summary(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "36ed9f14-b349-40e9-a42c-b367e77f8bda", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## An extra exercise for those who enjoy web scraping\n", |
||||||
|
"\n", |
||||||
|
"You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them. In the community-contributions folder, you'll find an example Selenium solution from a student (thank you!)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "eeab24dc-5f90-4570-b542-b0585aca3eb6", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Sharing your code\n", |
||||||
|
"\n", |
||||||
|
"I'd love it if you share your code afterwards so I can share it with others! You'll notice that some students have already made changes (including a Selenium implementation) which you will find in the community-contributions folder. If you'd like add your changes to that folder, submit a Pull Request with your new versions in that folder and I'll merge your changes.\n", |
||||||
|
"\n", |
||||||
|
"If you're not an expert with git (and I am not!) then GPT has given some nice instructions on how to submit a Pull Request. It's a bit of an involved process, but once you've done it once it's pretty clear. As a pro-tip: it's best if you clear the outputs of your Jupyter notebooks (Edit >> Clean outputs of all cells, and then Save) for clean notebooks.\n", |
||||||
|
"\n", |
||||||
|
"Here are good instructions courtesy of an AI friend: \n", |
||||||
|
"https://chatgpt.com/share/677a9cb5-c64c-8012-99e0-e06e88afd293" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f4484fcf-8b39-4c3f-9674-37970ed71988", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,195 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "c97ad592-c8be-4583-a19c-ac813e56f410", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Mac Users\n", |
||||||
|
"\n", |
||||||
|
"I find some challenges while setting up this in MAC silicon M1 chip. Execute below commands in MAC terminal.\n", |
||||||
|
"\n", |
||||||
|
"1. Download chromedriver.\n", |
||||||
|
"2. Unzip and add it to the path.\n", |
||||||
|
"3. Set Extended attributes." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "b635b345-b000-48cc-8a7f-7df279a489a3", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"cd ~/Downloads\n", |
||||||
|
"wget https://storage.googleapis.com/chrome-for-testing-public/133.0.6943.126/mac-arm64/chromedriver-mac-arm64.zip\n", |
||||||
|
"unzip chromedriver-mac-arm64.zip\n", |
||||||
|
"sudo mv chromedriver-mac-arm64/chromedriver /usr/local/bin/\n", |
||||||
|
"chmod +x /usr/local/bin/chromedriver\n", |
||||||
|
"cd /usr/local/bin/\n", |
||||||
|
"xattr -d com.apple.quarantine chromedriver\n", |
||||||
|
"cd \n", |
||||||
|
"chromedriver --version" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "17c7c79a-8ae0-4f5d-a7c8-c54aa7ba90fd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"!pip install selenium\n", |
||||||
|
"!pip install undetected-chromedriver\n", |
||||||
|
"!pip install beautifulsoup4" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c10bd630-2dfd-4572-8c21-2dc4c6a372ab", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"from selenium import webdriver\n", |
||||||
|
"from selenium.webdriver.chrome.service import Service\n", |
||||||
|
"from selenium.webdriver.common.by import By\n", |
||||||
|
"from selenium.webdriver.chrome.options import Options\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6fb3641d-e9f8-4f5b-bb9d-ee0e971cccdb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||||
|
"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
||||||
|
"MODEL = \"llama3.2\"\n", |
||||||
|
"PATH_TO_CHROME_DRIVER = '/usr/local/bin/chromedriver'\n", |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||||
|
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||||
|
"Respond in markdown. Highlight all the products this website offered and also find when website is created.\"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5d57e958", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class Website:\n", |
||||||
|
" url: str\n", |
||||||
|
" title: str\n", |
||||||
|
" text: str\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
"\n", |
||||||
|
" options = Options()\n", |
||||||
|
"\n", |
||||||
|
" options.add_argument(\"--no-sandbox\")\n", |
||||||
|
" options.add_argument(\"--disable-dev-shm-usage\")\n", |
||||||
|
"\n", |
||||||
|
" service = Service(PATH_TO_CHROME_DRIVER)\n", |
||||||
|
" driver = webdriver.Chrome(service=service, options=options)\n", |
||||||
|
" driver.get(url)\n", |
||||||
|
"\n", |
||||||
|
" # input(\"Please complete the verification in the browser and press Enter to continue...\")\n", |
||||||
|
" page_source = driver.page_source\n", |
||||||
|
" driver.quit()\n", |
||||||
|
"\n", |
||||||
|
" soup = BeautifulSoup(page_source, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.get_text(separator=\"\\n\", strip=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "56df8cd2-2707-43f6-a066-3367846929b3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||||
|
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||||
|
"please provide a short summary of this website in markdown. \\\n", |
||||||
|
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"def messages_for(website):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||||
|
" ]\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"def summarize(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", |
||||||
|
" response = ollama_via_openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages = messages_for(website)\n", |
||||||
|
" )\n", |
||||||
|
" return response.choices[0].message.content\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"def display_summary(url):\n", |
||||||
|
" summary = summarize(url)\n", |
||||||
|
" display(Markdown(summary))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f2eb9599", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://ae.almosafer.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "31b66c0f-6b45-4986-b77c-758625945a91", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,115 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "44aba2a0-c6eb-4fc1-a5cc-0a8f8679dbb8", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Far Far Away..." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d4d58124-5e9a-4f5a-9e0a-ff74f43896a8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"\n", |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "33179b68-7ed5-46ab-b583-d67ed57cd39d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def add_user_greeting(greeting):\n", |
||||||
|
" user_prompt = \"\"\"\n", |
||||||
|
" The following is the greeting from the user. Please respond in character as a barman in the Mos Eisley Cantina.\\n\\n\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" user_prompt += greeting\n", |
||||||
|
"\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "67dc3099-2ccc-4ee8-8ff2-0dbbe4ae2fcb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def approach_the_bar(greeting):\n", |
||||||
|
"\n", |
||||||
|
" system_prompt = \"You are a barman in the Mos Eisley Cantina from the Star Wars universe.\\\n", |
||||||
|
"It is a Tuesday evening, the year is 3BBY, and the Cantina is quiet except for a few lonely regulars.\\\n", |
||||||
|
"The barman (you) is slightly skeptical but eager to share some interesting news regarding some nearby imperial activity.\\\n", |
||||||
|
"You will recieve a greeting from the user, you must respond and provide them with some gossip detailing \\\n", |
||||||
|
"some local shady dealings occuring in Mos Eisley. Please format your response using markdown to provide a sense of the conversation.\"\n", |
||||||
|
"\n", |
||||||
|
" user_prompt = add_user_greeting(greeting)\n", |
||||||
|
" \n", |
||||||
|
" messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt},\n", |
||||||
|
" ]\n", |
||||||
|
" \n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model = \"gpt-4o-mini\",\n", |
||||||
|
" messages = messages\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" # Step 4: print the result in markdown format\n", |
||||||
|
" pretty_response = Markdown(response.choices[0].message.content)\n", |
||||||
|
" display(pretty_response)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "fb47e2b7-5509-4d1a-8e71-ff103fc8a885", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"approach_the_bar(\"\")" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,152 @@ |
|||||||
|
{ |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 0, |
||||||
|
"metadata": { |
||||||
|
"colab": { |
||||||
|
"provenance": [] |
||||||
|
}, |
||||||
|
"kernelspec": { |
||||||
|
"name": "python3", |
||||||
|
"display_name": "Python 3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"name": "python" |
||||||
|
} |
||||||
|
}, |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"source": [ |
||||||
|
"# Getting MOM from call transcripts" |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"id": "99Z21wE7xpKS" |
||||||
|
} |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"source": [ |
||||||
|
"Import necessary libraries" |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"id": "YZMeexE8M_Pp" |
||||||
|
} |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI\n" |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"id": "u5DCVg0Mxj5T" |
||||||
|
}, |
||||||
|
"execution_count": null, |
||||||
|
"outputs": [] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"metadata": { |
||||||
|
"id": "i0V11JQ2az-C" |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"\n", |
||||||
|
"#The below code can be uncommented in using .env file\n", |
||||||
|
"\n", |
||||||
|
"#from dotenv import load_dotenv\n", |
||||||
|
"#load_dotenv(override=True)\n", |
||||||
|
"#api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"#I am using google colab to import api_key\n", |
||||||
|
"from google.colab import userdata\n", |
||||||
|
"api_key=userdata.get('gemini_api')\n", |
||||||
|
"\n", |
||||||
|
"# Check the key\n", |
||||||
|
"if not api_key:\n", |
||||||
|
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||||
|
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||||
|
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||||
|
"elif api_key.strip() != api_key:\n", |
||||||
|
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"API key found and looks good so far!\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"source": [ |
||||||
|
"# A class to represet Transcript\n", |
||||||
|
"from pathlib import Path\n", |
||||||
|
"class Transcript:\n", |
||||||
|
" def __init__(self, file_path):\n", |
||||||
|
" self.file_path=file_path\n", |
||||||
|
" self.content=Path(file_path).read_text(encoding='utf-8')\n" |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"id": "j6UTsnTEyWZ-" |
||||||
|
}, |
||||||
|
"execution_count": null, |
||||||
|
"outputs": [] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"source": [ |
||||||
|
"# Source of the text file -\"https://raw.githubusercontent.com/GeminiLn/EarningsCall_Dataset/refs/heads/master/3M%20Company_20170425/Text.txt\"\n", |
||||||
|
"path = '/content/Text.txt' # Specify the path of file you want to use - format should be .txt\n", |
||||||
|
"t=Transcript(path)\n" |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"id": "hquePU_mzZ7s" |
||||||
|
}, |
||||||
|
"execution_count": null, |
||||||
|
"outputs": [] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"source": [ |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are expert at taking Meeting Notes & given the below transcript , create an MOM (Minutes of meeting)\"" |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"id": "ex5DB7M8L7KT" |
||||||
|
}, |
||||||
|
"execution_count": null, |
||||||
|
"outputs": [] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"source": [ |
||||||
|
"from google import genai\n", |
||||||
|
"from google.genai import types\n", |
||||||
|
"\n", |
||||||
|
"client = genai.Client(api_key=api_key)\n", |
||||||
|
"\n", |
||||||
|
"response = client.models.generate_content(\n", |
||||||
|
" model=\"gemini-2.0-flash\",\n", |
||||||
|
" config=types.GenerateContentConfig(\n", |
||||||
|
" system_instruction=system_prompt,\n", |
||||||
|
" max_output_tokens=500,\n", |
||||||
|
" temperature=0.1\n", |
||||||
|
" ),\n", |
||||||
|
" contents=t.content,\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"print(response.text)" |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"id": "wcpJ34qfMKmV" |
||||||
|
}, |
||||||
|
"execution_count": null, |
||||||
|
"outputs": [] |
||||||
|
} |
||||||
|
] |
||||||
|
} |
@ -0,0 +1,167 @@ |
|||||||
|
import os |
||||||
|
import time |
||||||
|
import pandas as pd |
||||||
|
import re |
||||||
|
from dotenv import load_dotenv |
||||||
|
from selenium import webdriver |
||||||
|
from selenium.webdriver.chrome.service import Service |
||||||
|
from selenium.webdriver.chrome.options import Options |
||||||
|
from selenium.webdriver.common.by import By |
||||||
|
from selenium.webdriver.support.ui import WebDriverWait |
||||||
|
from selenium.webdriver.support import expected_conditions as EC |
||||||
|
from openai import OpenAI |
||||||
|
from openpyxl import load_workbook |
||||||
|
from openpyxl.styles import Font, Alignment |
||||||
|
|
||||||
|
# Load environment variables |
||||||
|
load_dotenv(override=True) |
||||||
|
api_key = os.getenv('OPENAI_API_KEY') |
||||||
|
|
||||||
|
# Validate API Key |
||||||
|
if not api_key: |
||||||
|
raise ValueError("No API key was found - please check your .env file.") |
||||||
|
|
||||||
|
# Initialize OpenAI client |
||||||
|
openai = OpenAI() |
||||||
|
|
||||||
|
# Set up Selenium WebDriver |
||||||
|
chrome_options = Options() |
||||||
|
chrome_options.add_argument("--headless") |
||||||
|
chrome_options.add_argument("--disable-gpu") |
||||||
|
chrome_options.add_argument("--no-sandbox") |
||||||
|
chrome_options.add_argument("--disable-dev-shm-usage") |
||||||
|
|
||||||
|
class Website: |
||||||
|
"""Scrapes and processes website content using Selenium.""" |
||||||
|
|
||||||
|
def __init__(self, url: str): |
||||||
|
self.url = url |
||||||
|
self.text = "No content extracted." |
||||||
|
|
||||||
|
service = Service(executable_path="/opt/homebrew/bin/chromedriver") |
||||||
|
driver = webdriver.Chrome(service=service, options=chrome_options) |
||||||
|
|
||||||
|
try: |
||||||
|
driver.get(url) |
||||||
|
WebDriverWait(driver, 10).until( |
||||||
|
EC.presence_of_element_located((By.TAG_NAME, "body")) |
||||||
|
) |
||||||
|
body_element = driver.find_element(By.TAG_NAME, "body") |
||||||
|
self.text = body_element.text.strip() if body_element else "No content extracted." |
||||||
|
except Exception as e: |
||||||
|
print(f"Error fetching website: {e}") |
||||||
|
finally: |
||||||
|
driver.quit() |
||||||
|
|
||||||
|
def summarized_text(self, max_length=1500): |
||||||
|
return self.text[:max_length] + ("..." if len(self.text) > max_length else "") |
||||||
|
|
||||||
|
def clean_text(text): |
||||||
|
""" |
||||||
|
Cleans extracted text by removing markdown-style formatting. |
||||||
|
""" |
||||||
|
text = re.sub(r"###*\s*", "", text) |
||||||
|
text = re.sub(r"\*\*(.*?)\*\*", r"\1", text) |
||||||
|
return text.strip() |
||||||
|
|
||||||
|
# Aspect-specific prompts for concise output |
||||||
|
aspect_prompts = { |
||||||
|
"Marketing Strategies": "Summarize the core marketing strategies used on this website in in under 30 words. Do not include a title or introduction.", |
||||||
|
"SEO Keywords": "List only the most relevant SEO keywords from this website, separated by commas. Do not include a title or introduction.", |
||||||
|
"User Engagement Tactics": "List key engagement tactics used on this website (e.g., interactive features, user incentives, social proof). Keep responses to 3-5 bullet points. Do not include a title or introduction.", |
||||||
|
"Call-to-Action Phrases": "List only the most common Call-to-Action phrases used on this website, separated by commas. Do not include a title or introduction.", |
||||||
|
"Branding Elements": "Summarize the brand's tone, style, and positioning in under 30 words. Do not include a title or introduction.", |
||||||
|
"Competitor Comparison": "Briefly describe how this website differentiates itself from competitors in under 30 words. Do not include a title or introduction.", |
||||||
|
"Product Descriptions": "List the most important features or benefits of the products/services described on this website in under 30 words. Do not include a title or introduction.", |
||||||
|
"Customer Reviews Sentiment": "Summarize the overall sentiment of customer reviews in oin under 30 words, highlighting common themes. Do not include a title or introduction.", |
||||||
|
"Social Media Strategy": "List key social media strategies used on this website, separated by commas. Do not include a title or introduction." |
||||||
|
} |
||||||
|
|
||||||
|
|
||||||
|
def summarize(url: str) -> dict: |
||||||
|
""" |
||||||
|
Fetches a website, extracts relevant content, and generates a separate summary for each aspect. |
||||||
|
|
||||||
|
:param url: The website URL to analyze. |
||||||
|
:return: A dictionary containing extracted information. |
||||||
|
""" |
||||||
|
website = Website(url) |
||||||
|
|
||||||
|
if not website.text or website.text == "No content extracted.": |
||||||
|
return {"URL": url, "Error": "Failed to extract content"} |
||||||
|
|
||||||
|
extracted_data = {"URL": url} |
||||||
|
|
||||||
|
for aspect, prompt in aspect_prompts.items(): |
||||||
|
try: |
||||||
|
formatted_prompt = f"{prompt} \n\nContent:\n{website.summarized_text()}" |
||||||
|
response = openai.chat.completions.create( |
||||||
|
model="gpt-4o-mini", |
||||||
|
messages=[ |
||||||
|
{"role": "system", "content": "You are an expert at extracting structured information from website content."}, |
||||||
|
{"role": "user", "content": formatted_prompt} |
||||||
|
] |
||||||
|
) |
||||||
|
|
||||||
|
extracted_data[aspect] = clean_text(response.choices[0].message.content) |
||||||
|
|
||||||
|
except Exception as e: |
||||||
|
extracted_data[aspect] = f"Error generating summary: {e}" |
||||||
|
|
||||||
|
return extracted_data |
||||||
|
|
||||||
|
def save_to_excel(data_list: list, filename="website_analysis.xlsx"): |
||||||
|
""" |
||||||
|
Saves extracted information to an Excel file with proper formatting. |
||||||
|
|
||||||
|
:param data_list: A list of dictionaries containing extracted website details. |
||||||
|
:param filename: The name of the Excel file to save data. |
||||||
|
""" |
||||||
|
df = pd.DataFrame(data_list) |
||||||
|
|
||||||
|
df.to_excel(filename, index=False) |
||||||
|
|
||||||
|
wb = load_workbook(filename) |
||||||
|
ws = wb.active |
||||||
|
|
||||||
|
# Auto-adjust column widths |
||||||
|
for col in ws.columns: |
||||||
|
max_length = 0 |
||||||
|
col_letter = col[0].column_letter |
||||||
|
for cell in col: |
||||||
|
try: |
||||||
|
if cell.value: |
||||||
|
max_length = max(max_length, len(str(cell.value))) |
||||||
|
except: |
||||||
|
pass |
||||||
|
ws.column_dimensions[col_letter].width = min(max_length + 2, 50) |
||||||
|
|
||||||
|
# Format headers |
||||||
|
for cell in ws[1]: |
||||||
|
cell.font = Font(bold=True) |
||||||
|
cell.alignment = Alignment(horizontal="center", vertical="center") |
||||||
|
|
||||||
|
# Wrap text for extracted content |
||||||
|
for row in ws.iter_rows(min_row=2): |
||||||
|
for cell in row: |
||||||
|
cell.alignment = Alignment(wrap_text=True, vertical="top") |
||||||
|
|
||||||
|
wb.save(filename) |
||||||
|
print(f"Data saved to {filename} with improved formatting.") |
||||||
|
|
||||||
|
# 🔹 LIST OF WEBSITES TO PROCESS |
||||||
|
websites = [ |
||||||
|
"https://www.gymshark.com/", |
||||||
|
] |
||||||
|
|
||||||
|
if __name__ == "__main__": |
||||||
|
print("\nProcessing websites...\n") |
||||||
|
extracted_data_list = [] |
||||||
|
|
||||||
|
for site in websites: |
||||||
|
print(f"Extracting data from {site}...") |
||||||
|
extracted_data = summarize(site) |
||||||
|
extracted_data_list.append(extracted_data) |
||||||
|
|
||||||
|
save_to_excel(extracted_data_list) |
||||||
|
print("\nAll websites processed successfully!") |
@ -0,0 +1,87 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "44aba2a0-c6eb-4fc1-a5cc-0a8f8679dbb8", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Michelin-star cook..." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d4d58124-5e9a-4f5a-9e0a-ff74f43896a8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"\n", |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "67dc3099-2ccc-4ee8-8ff2-0dbbe4ae2fcb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_prompt = \"You are a professional chef in a Michelin-star restaurant. You will help me cook restaurant-style dishes using the ingredients I have left in my refrigerator.\\\n", |
||||||
|
"You will provide detailed instructions with precise times and measurements in grams and include calorie information for raw ingredients, not cooked ones.\\\n", |
||||||
|
"Add the caloric information at the end. Your responses should be formatted in Markdown.\"\n", |
||||||
|
"\n", |
||||||
|
"user_prompt = \"\"\"\n", |
||||||
|
"Help me with a recipe using the ingredients I have left in the refrigerator. I have spinach, eggs, pasta, rice, chicken, beef, carrots, potatoes, butter, milk, cheese, tomatoes, red peppers, and all spices in the pantry.\\n\\n\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt},\n", |
||||||
|
"]\n", |
||||||
|
" \n", |
||||||
|
"response = openai.chat.completions.create(\n", |
||||||
|
" model = \"gpt-4o-mini\",\n", |
||||||
|
" messages = messages\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
"# Step 4: print the result in markdown format\n", |
||||||
|
"pretty_response = Markdown(response.choices[0].message.content)\n", |
||||||
|
"display(pretty_response)" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,127 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0a512c2a-55e7-40e1-ab17-88b7034ca09a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Imports\n", |
||||||
|
"import openai\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"from IPython.display import Markdown, display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1aa8dd82-6b5e-4dbd-a2ee-8367e796a51f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"# Check the key\n", |
||||||
|
"\n", |
||||||
|
"if not api_key:\n", |
||||||
|
" print(\"No API key was found - head over to the troubleshooting notebook!\")\n", |
||||||
|
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||||
|
" print(\"An API key was found, but it doesn't start sk-proj... make sure you using the right key (Check troubleshooting notebook)\")\n", |
||||||
|
"elif api_key.strip() != api_key:\n", |
||||||
|
" print(\"An API key was found, but it looks like white space was found in beginning or end. (Check troubleshooting notebook)\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"API key found and looks good so far!\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2acd579b-846c-4aa6-ba6c-1cc1a5a2eeb6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Input the system prompt\n", |
||||||
|
"system_prompt = \"\"\"you are top notched AI music expert that have knowledge of all genres, songs, and artists. You need to google search lyrics. You have the following rules:\\\n", |
||||||
|
"1. Carefully break down what type of recommendation the user wants and the context.\\\n", |
||||||
|
"2. If asked to recommend genres similar to a song or artists please identify the top 3 genres.\\\n", |
||||||
|
"3. If asked to recommend artists from songs or genres then recommend the top 5 artists.\n", |
||||||
|
"4. If asked to recommend songs from genres or artist than recommend the top 10 songs.\n", |
||||||
|
"5. If asked for a general recommendation give them the top 5 songs based off of context.\\\n", |
||||||
|
"6. Be flexible and adaptable with recommendations and consider the context the user might ask.\n", |
||||||
|
"7. always respond in markdown.\n", |
||||||
|
"\"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3c1cf212-538c-4e9a-8da5-337bd7b6197c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# music recommender function\n", |
||||||
|
"def music_recommender(user_prompt):\n", |
||||||
|
" messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
" ]\n", |
||||||
|
" \n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model=\"gpt-4\",\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" max_tokens=300\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" return response.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4f277561-af8b-4715-90e7-6ebaadeb15d0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# User prompt (Change this to fit your needs!)\n", |
||||||
|
"user_prompt = \"Can you recommend me songs from Taylor Swift\"\n", |
||||||
|
"\n", |
||||||
|
"# Example usage\n", |
||||||
|
"response = music_recommender(user_prompt)\n", |
||||||
|
"display(Markdown(response))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "bb869d36-de14-4e46-9087-223d6b257efa", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,223 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "bfa3abd0-4e66-4117-96f9-7a71fbb6d0cb", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Powerpoint Slides Summarizer\n", |
||||||
|
"\n", |
||||||
|
"This converts a Power Point presentation into notes that a student can easily skim through.\n", |
||||||
|
"\n", |
||||||
|
"Concepts Used:\n", |
||||||
|
"- Converting Contents of PPT to text via python-pptx\n", |
||||||
|
"- User and System Prompts\n", |
||||||
|
"- Use of Open AI GPT-4o-mini via API key\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ab95eb49-6a2d-4c7d-9057-78a2cd9364cc", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"!pip install python-pptx" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "62715f16-7125-455e-98e7-5705871c0e4a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ff42eab7-789d-44f8-a5cc-64baeebf3224", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"# Check the key\n", |
||||||
|
"\n", |
||||||
|
"if not api_key:\n", |
||||||
|
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||||
|
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||||
|
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||||
|
"elif api_key.strip() != api_key:\n", |
||||||
|
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"API key found and looks good so far!\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "bce425c2-6d19-4c03-93ce-8930dabc61ee", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# creating an instance\n", |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c0c75e30-3b38-4a89-b7d3-a41a6f5dc650", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"from pptx import Presentation\n", |
||||||
|
"\n", |
||||||
|
"class PowerPoint():\n", |
||||||
|
" def __init__(self,ppt):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Creates a PowerPoint object, with name and text.\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.ppt = ppt\n", |
||||||
|
" self.title = os.path.basename(ppt)\n", |
||||||
|
" self.text = self.extract_text()\n", |
||||||
|
"\n", |
||||||
|
" def extract_text(self):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Extracts text from powerpoint.\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" prs = Presentation(self.ppt)\n", |
||||||
|
" text_content = []\n", |
||||||
|
" \n", |
||||||
|
" for slide in prs.slides:\n", |
||||||
|
" for shape in slide.shapes:\n", |
||||||
|
" if hasattr(shape, \"text\"):\n", |
||||||
|
" text_content.append(shape.text)\n", |
||||||
|
" \n", |
||||||
|
" return \"\\n\".join(text_content)\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1963a055-87f4-4e47-8456-cac4d4ac57fc", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents \\\n", |
||||||
|
"of a PowerPoint presentation, and provides a summary in the style of \\\n", |
||||||
|
"a cheat-sheet, for students to easily learn key concepts from.\\\n", |
||||||
|
"You are to ignore text that might be navigation-related\\\n", |
||||||
|
"and respond in Markdown.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ca600e90-7d3f-4fc7-a698-1b8f2925f81e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function that writes a User Prompt that asks for summaries of PowerPoints:\n", |
||||||
|
"\n", |
||||||
|
"def user_prompt_for(powerpoint):\n", |
||||||
|
" user_prompt = f\"You are looking at a website titled {powerpoint.title}\"\n", |
||||||
|
" user_prompt += \"\\nThe contents of this powerpoint are as follows; \\\n", |
||||||
|
"please provide a summary of the content in markdown. \\\n", |
||||||
|
"If it includes a question bank, add that along with short answers too.\\n\\n\"\n", |
||||||
|
" user_prompt += powerpoint.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4fe19c56-9940-4528-b43a-c86798b215d2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def messages_for(powerpoint):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(powerpoint)}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f7704da5-90b0-40af-bbb4-7d589309f180", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# And now: call the OpenAI API. \n", |
||||||
|
"\n", |
||||||
|
"def summarize(powerpoint_path):\n", |
||||||
|
" powerpoint = PowerPoint(powerpoint_path)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model = \"gpt-4o-mini\",\n", |
||||||
|
" messages = messages_for(powerpoint)\n", |
||||||
|
" )\n", |
||||||
|
" return response.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "49d1d0cf-fa4b-4bea-bd68-a834145070ef", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def display_summary(url):\n", |
||||||
|
" summary = summarize(url)\n", |
||||||
|
" display(Markdown(summary))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "348078d1-e86f-4eb3-909d-33ab4ede984e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"ppt_file = \"Theoretical Perspectives on Media and Technology.pptx\" \n", |
||||||
|
"display_summary(ppt_file)" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,230 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "56c86bae-1d3c-4c01-b5d6-c8879fec1954", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Wiki Summarizer\n", |
||||||
|
"\n", |
||||||
|
"This Project takes the name of a topic as input, and checks if the corresponding wiki-page exists. If it does, it parses the web page, and outputs a summary created using the GPT-4o-mini model. \n", |
||||||
|
"\n", |
||||||
|
"Concepts used: \n", |
||||||
|
"- Web Scraping via Beautiful Soup\n", |
||||||
|
"- User and System Prompts\n", |
||||||
|
"- Use of Open AI GPT-4o-mini via API key" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4820830e-b3b4-426e-b1a2-518e7c7f6c1a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2cd7ad51-396c-45c5-9089-f7b21a19da50", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"# Check the key\n", |
||||||
|
"\n", |
||||||
|
"if not api_key:\n", |
||||||
|
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||||
|
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||||
|
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||||
|
"elif api_key.strip() != api_key:\n", |
||||||
|
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"API key found and looks good so far!\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "689421a0-20a1-428b-a8b8-fa239fa6f633", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# creating an instance\n", |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "401901ae-7639-4190-98fd-e69374084723", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def isWiki(url):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Check whether a Wikipedia page exists for a given topic, and \n", |
||||||
|
" returns a Boolean value.\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" response = requests.get(url)\n", |
||||||
|
"\n", |
||||||
|
" if response.status_code != 200:\n", |
||||||
|
" return False\n", |
||||||
|
" \n", |
||||||
|
" return True" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7cdb14d3-05ea-4de2-a475-d49a5731692e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7f6ed50e-0fb5-479e-9845-f62cf25980f7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_prompt = \"You are an educational assistant tasked with helping users understand topics\\\n", |
||||||
|
"by providing succinct and clear summaries of requested data. Ignore navigation-related text\\\n", |
||||||
|
"and provide answers in markdown format\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b2d77dd9-a94f-49c1-a1be-11d157bd37fb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function that writes a User Prompt that asks for summaries of wiki pages:\n", |
||||||
|
"\n", |
||||||
|
"def user_prompt_for(wiki):\n", |
||||||
|
" user_prompt = f\"You are looking at a Wikipedia page titled {wiki.title}\"\n", |
||||||
|
" user_prompt += \"\\nThe contents of this page is as follows; \\\n", |
||||||
|
"please provide a short summary of this website in markdown.\\n\"\n", |
||||||
|
" user_prompt += wiki.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0d23bcc4-1d89-4bd4-9809-d3a1819aa919", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def messages_for(wiki):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(wiki)}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "971bd7fb-2ff8-4494-b386-de69a39c24ff", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def summarize(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model = \"gpt-4o-mini\",\n", |
||||||
|
" messages = messages_for(website)\n", |
||||||
|
" )\n", |
||||||
|
" return response.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a8fdf9f2-f49e-4d06-ac9e-dfcb8da33d60", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def display_summary(topic):\n", |
||||||
|
" url = f\"https://en.wikipedia.org/wiki/{topic}\"\n", |
||||||
|
" if isWiki(url):\n", |
||||||
|
" summary = summarize(url)\n", |
||||||
|
" display(Markdown(summary))\n", |
||||||
|
" else:\n", |
||||||
|
" print('A Wikipedia page does not exist for this topic')\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f4758ef0-9b7c-4d3e-9131-e3284dc76b6b", |
||||||
|
"metadata": { |
||||||
|
"scrolled": true |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"topic = input('Enter the name of Wikipedia page for which you would like a summary: ').strip()\n", |
||||||
|
"display_summary(topic)" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,213 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Also trying the amazing reasoning model DeepSeek\n", |
||||||
|
"\n", |
||||||
|
"Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B. \n", |
||||||
|
"This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n", |
||||||
|
"\n", |
||||||
|
"Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"!ollama pull deepseek-r1:1.5b" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4bdcd35a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"!ollama pull deepseek-r1:8b" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# NOW the exercise for you\n", |
||||||
|
"\n", |
||||||
|
"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1c106420", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import requests\n", |
||||||
|
"import ollama\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "22d62f00", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Constants\n", |
||||||
|
"\n", |
||||||
|
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||||
|
"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
||||||
|
"MODEL = \"deepseek-r1:8b\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6de38216-6d1c-48c4-877b-86d403f4e0f8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4449b7dc", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||||
|
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||||
|
"Respond in markdown.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "daca9448", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||||
|
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||||
|
"please provide a short summary of this website in markdown. \\\n", |
||||||
|
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0ec9d5d2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# See how this function creates exactly the format above\n", |
||||||
|
"\n", |
||||||
|
"def messages_for(website):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6e1ab04a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# And now: call the OpenAI API. You will get very familiar with this!\n", |
||||||
|
"\n", |
||||||
|
"def summarize(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model = MODEL,\n", |
||||||
|
" messages = messages_for(website)\n", |
||||||
|
" )\n", |
||||||
|
" return response['message']['content']" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0d3b5628", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def display_summary(url):\n", |
||||||
|
" summary = summarize(url)\n", |
||||||
|
" display(Markdown(summary))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "938e5633", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://edwarddonner.com\")" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "llms", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,511 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Welcome to your first assignment!\n", |
||||||
|
"\n", |
||||||
|
"Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#f71;\">Just before we get to the assignment --</h2>\n", |
||||||
|
" <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n", |
||||||
|
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n", |
||||||
|
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# HOMEWORK EXERCISE ASSIGNMENT\n", |
||||||
|
"\n", |
||||||
|
"Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n", |
||||||
|
"\n", |
||||||
|
"You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n", |
||||||
|
"\n", |
||||||
|
"**Benefits:**\n", |
||||||
|
"1. No API charges - open-source\n", |
||||||
|
"2. Data doesn't leave your box\n", |
||||||
|
"\n", |
||||||
|
"**Disadvantages:**\n", |
||||||
|
"1. Significantly less power than Frontier Model\n", |
||||||
|
"\n", |
||||||
|
"## Recap on installation of Ollama\n", |
||||||
|
"\n", |
||||||
|
"Simply visit [ollama.com](https://ollama.com) and install!\n", |
||||||
|
"\n", |
||||||
|
"Once complete, the ollama server should already be running locally. \n", |
||||||
|
"If you visit: \n", |
||||||
|
"[http://localhost:11434/](http://localhost:11434/)\n", |
||||||
|
"\n", |
||||||
|
"You should see the message `Ollama is running`. \n", |
||||||
|
"\n", |
||||||
|
"If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve` \n", |
||||||
|
"And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2` \n", |
||||||
|
"Then try [http://localhost:11434/](http://localhost:11434/) again.\n", |
||||||
|
"\n", |
||||||
|
"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import requests\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "29ddd15d-a3c5-4f4e-a678-873f56162724", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Constants\n", |
||||||
|
"\n", |
||||||
|
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||||
|
"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
||||||
|
"MODEL = \"llama3.2\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "dac0a679-599c-441f-9bf2-ddc73d35b940", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Create a messages list using the same format that we used for OpenAI\n", |
||||||
|
"\n", |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n", |
||||||
|
"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7bb9c624-14f0-4945-a719-8ddb64f66f47", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"payload = {\n", |
||||||
|
" \"model\": MODEL,\n", |
||||||
|
" \"messages\": messages,\n", |
||||||
|
" \"stream\": False\n", |
||||||
|
" }" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "479ff514-e8bd-4985-a572-2ea28bb4fa40", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's just make sure the model is loaded\n", |
||||||
|
"\n", |
||||||
|
"!ollama pull llama3.2" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "42b9f644-522d-4e05-a691-56e7658c0ea9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# If this doesn't work for any reason, try the 2 versions in the following cells\n", |
||||||
|
"# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n", |
||||||
|
"# And if none of that works - contact me!\n", |
||||||
|
"\n", |
||||||
|
"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", |
||||||
|
"print(response.json()['message']['content'])" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Introducing the ollama package\n", |
||||||
|
"\n", |
||||||
|
"And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n", |
||||||
|
"\n", |
||||||
|
"Under the hood, it's making the same call as above to the ollama server running at localhost:11434" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7745b9c4-57dc-4867-9180-61fa5db55eb8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import ollama\n", |
||||||
|
"\n", |
||||||
|
"response = ollama.chat(model=MODEL, messages=messages)\n", |
||||||
|
"print(response['message']['content'])" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "a4704e10-f5fb-4c15-a935-f046c06fb13d", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Alternative approach - using OpenAI python library to connect to Ollama" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "23057e00-b6fc-4678-93a9-6b31cb704bff", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# There's actually an alternative approach that some people might prefer\n", |
||||||
|
"# You can use the OpenAI client python library to call Ollama:\n", |
||||||
|
"\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", |
||||||
|
"\n", |
||||||
|
"response = ollama_via_openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=messages\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Also trying the amazing reasoning model DeepSeek\n", |
||||||
|
"\n", |
||||||
|
"Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B. \n", |
||||||
|
"This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n", |
||||||
|
"\n", |
||||||
|
"Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d", |
||||||
|
"metadata": { |
||||||
|
"collapsed": true, |
||||||
|
"jupyter": { |
||||||
|
"outputs_hidden": true |
||||||
|
} |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"!ollama pull deepseek-r1:1.5b" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1d3d554b-e00d-4c08-9300-45e073950a76", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# This may take a few minutes to run! You should then see a fascinating \"thinking\" trace inside <think> tags, followed by some decent definitions\n", |
||||||
|
"\n", |
||||||
|
"response = ollama_via_openai.chat.completions.create(\n", |
||||||
|
" model=\"deepseek-r1:1.5b\",\n", |
||||||
|
" messages=[{\"role\": \"user\", \"content\": \"Please give definitions of some core concepts behind LLMs: a neural network, attention and the transformer\"}]\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# NOW the exercise for you\n", |
||||||
|
"\n", |
||||||
|
"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "ffaa3470-884c-467e-b4ce-c1b8d39294da", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"This is the code from day 1 notebook. Here we create the class to extract the text from the website, using BeautifulSoup library, and the we execute it to see the the results" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8d8c9f01-ca12-4018-b7fa-698c9fa1aa93", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6fd198df-bac5-42c5-83a0-06c5f71fb76a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's try one out. Change the website and add print statements to follow along.\n", |
||||||
|
"\n", |
||||||
|
"ed = Website(\"https://edwarddonner.com\")\n", |
||||||
|
"print(ed.title)\n", |
||||||
|
"print(ed.text)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "995b637d-a5db-4ad9-ac78-5980fd7ef112", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"#### Define the system prompt, to instruct the model how we want to respond to our query. " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ee810d49-e88a-4137-a4be-98812e0d0748", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||||
|
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||||
|
"Respond in markdown.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "482b5d4c-69ed-4332-abb5-8b0986dcf368", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||||
|
"\n", |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||||
|
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||||
|
"please provide a short summary of this website in markdown. \\\n", |
||||||
|
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d966cb09-3ca2-49f7-8462-f6ef26c01159", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(user_prompt_for(ed))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2f9be84f-4cd7-4ce7-8f33-e60d16f02852", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# For test purpose\n", |
||||||
|
"\n", |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n", |
||||||
|
"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f5cb0e9f-eb56-4633-ba4c-76817be98856", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# To give you a preview -- calling ollama with system and user messages:\n", |
||||||
|
"\n", |
||||||
|
"import ollama\n", |
||||||
|
"\n", |
||||||
|
"response = ollama.chat(model=MODEL, messages=messages)\n", |
||||||
|
"print(response['message']['content'])" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c554903f-eb04-4a16-87fc-f1d9ff58f6d9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# See how this function creates exactly the format above\n", |
||||||
|
"\n", |
||||||
|
"def messages_for(website):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6b64b814-123f-436d-9366-4c762ac4b89a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Try this out, and then try for a few more websites\n", |
||||||
|
"\n", |
||||||
|
"messages_for(ed)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d1ef4be2-ef3a-4b5d-8d18-f2eafa9d6a93", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### So, here let's run the summarize by using ollama and see how appears." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7c46edc5-c85d-4ad0-89fd-39c4fdc44a5d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# And now: call the ollama API. \n", |
||||||
|
"\n", |
||||||
|
"def summarize(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model = MODEL,\n", |
||||||
|
" messages = messages_for(website)\n", |
||||||
|
" )\n", |
||||||
|
" return response['message']['content']" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "466c2f78-91ca-4ed2-b60b-40661d0b6f68", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"summarize(\"https://edwarddonner.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7ab7c9a1-70fd-421c-be06-c36eb6c9aedf", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function to display this nicely in the Jupyter output, using markdown\n", |
||||||
|
"\n", |
||||||
|
"def display_summary(url):\n", |
||||||
|
" summary = summarize(url)\n", |
||||||
|
" display(Markdown(summary))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1cedc9d9-6a76-4225-82c1-82240da16260", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://edwarddonner.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "82c48586-33c8-4797-a24f-41602c1297b3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "llms", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,435 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
||||||
|
"metadata": { |
||||||
|
"jp-MarkdownHeadingCollapsed": true |
||||||
|
}, |
||||||
|
"source": [ |
||||||
|
"# Welcome to your first assignment!\n", |
||||||
|
"\n", |
||||||
|
"Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9", |
||||||
|
"metadata": { |
||||||
|
"jupyter": { |
||||||
|
"source_hidden": true |
||||||
|
} |
||||||
|
}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#f71;\">Just before we get to the assignment --</h2>\n", |
||||||
|
" <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n", |
||||||
|
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n", |
||||||
|
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# HOMEWORK EXERCISE ASSIGNMENT\n", |
||||||
|
"\n", |
||||||
|
"Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n", |
||||||
|
"\n", |
||||||
|
"You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n", |
||||||
|
"\n", |
||||||
|
"**Benefits:**\n", |
||||||
|
"1. No API charges - open-source\n", |
||||||
|
"2. Data doesn't leave your box\n", |
||||||
|
"\n", |
||||||
|
"**Disadvantages:**\n", |
||||||
|
"1. Significantly less power than Frontier Model\n", |
||||||
|
"\n", |
||||||
|
"## Recap on installation of Ollama\n", |
||||||
|
"\n", |
||||||
|
"Simply visit [ollama.com](https://ollama.com) and install!\n", |
||||||
|
"\n", |
||||||
|
"Once complete, the ollama server should already be running locally. \n", |
||||||
|
"If you visit: \n", |
||||||
|
"[http://localhost:11434/](http://localhost:11434/)\n", |
||||||
|
"\n", |
||||||
|
"You should see the message `Ollama is running`. \n", |
||||||
|
"\n", |
||||||
|
"If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve` \n", |
||||||
|
"And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2` \n", |
||||||
|
"Then try [http://localhost:11434/](http://localhost:11434/) again.\n", |
||||||
|
"\n", |
||||||
|
"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import requests\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "29ddd15d-a3c5-4f4e-a678-873f56162724", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Constants\n", |
||||||
|
"\n", |
||||||
|
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||||
|
"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
||||||
|
"MODEL = \"llama3.2\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "dac0a679-599c-441f-9bf2-ddc73d35b940", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Create a messages list using the same format that we used for OpenAI\n", |
||||||
|
"\n", |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n", |
||||||
|
"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7bb9c624-14f0-4945-a719-8ddb64f66f47", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"payload = {\n", |
||||||
|
" \"model\": MODEL,\n", |
||||||
|
" \"messages\": messages,\n", |
||||||
|
" \"stream\": False\n", |
||||||
|
" }" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "479ff514-e8bd-4985-a572-2ea28bb4fa40", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's just make sure the model is loaded\n", |
||||||
|
"\n", |
||||||
|
"!ollama pull llama3.2" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "42b9f644-522d-4e05-a691-56e7658c0ea9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# If this doesn't work for any reason, try the 2 versions in the following cells\n", |
||||||
|
"# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n", |
||||||
|
"# And if none of that works - contact me!\n", |
||||||
|
"\n", |
||||||
|
"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", |
||||||
|
"print(response.json()['message']['content'])" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Introducing the ollama package\n", |
||||||
|
"\n", |
||||||
|
"And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n", |
||||||
|
"\n", |
||||||
|
"Under the hood, it's making the same call as above to the ollama server running at localhost:11434" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7745b9c4-57dc-4867-9180-61fa5db55eb8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import ollama\n", |
||||||
|
"\n", |
||||||
|
"response = ollama.chat(model=MODEL, messages=messages)\n", |
||||||
|
"print(response['message']['content'])" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "a4704e10-f5fb-4c15-a935-f046c06fb13d", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Alternative approach - using OpenAI python library to connect to Ollama" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "23057e00-b6fc-4678-93a9-6b31cb704bff", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# There's actually an alternative approach that some people might prefer\n", |
||||||
|
"# You can use the OpenAI client python library to call Ollama:\n", |
||||||
|
"\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", |
||||||
|
"\n", |
||||||
|
"response = ollama_via_openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=messages\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Also trying the amazing reasoning model DeepSeek\n", |
||||||
|
"\n", |
||||||
|
"Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B. \n", |
||||||
|
"This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n", |
||||||
|
"\n", |
||||||
|
"Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"!ollama pull deepseek-r1:1.5b" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1d3d554b-e00d-4c08-9300-45e073950a76", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# This may take a few minutes to run! You should then see a fascinating \"thinking\" trace inside <think> tags, followed by some decent definitions\n", |
||||||
|
"\n", |
||||||
|
"response = ollama_via_openai.chat.completions.create(\n", |
||||||
|
" model=\"deepseek-r1:1.5b\",\n", |
||||||
|
" messages=[{\"role\": \"user\", \"content\": \"Please give definitions of some core concepts behind LLMs: a neural network, attention and the transformer\"}]\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# NOW the exercise for you\n", |
||||||
|
"\n", |
||||||
|
"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 1, |
||||||
|
"id": "6de38216-6d1c-48c4-877b-86d403f4e0f8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"HEADERS = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\",\n", |
||||||
|
" \"Content-Type\": \"application/json\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||||
|
"\n", |
||||||
|
"MODEL = \"llama3.2\"\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"Sei un assistente e analizzi il contenuto di un sito web \\\n", |
||||||
|
"produci un breve sommario, ignora il testo o gli elementi relativi alla navigazione. \\\n", |
||||||
|
"Rispondi markdown.\"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 2, |
||||||
|
"id": "6f343c27-628c-4c54-9a5b-842e6ad5d176", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class Website:\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=HEADERS)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 4, |
||||||
|
"id": "bf6245ca-2d53-4fd8-a19c-0e6d052031fd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"Stai cercando un sito dal titolo: {website.title}\"\n", |
||||||
|
" user_prompt += \"\\nI contenuti di questo sito web sono i seguenti: \\\n", |
||||||
|
"Per favore, fornisci un breve riassunto di questo sito web in markdown. \\\n", |
||||||
|
"Se include notizie o annunci, riassumili anch'essi. \\n\\n\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 6, |
||||||
|
"id": "dec0636f-9efc-4f91-8861-3141276a9a6e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def messages_for(website):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 8, |
||||||
|
"id": "f894b232-1ea1-4bd9-bf44-d7b1571f7913", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def summarize(url):\n", |
||||||
|
" ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", |
||||||
|
" \n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = ollama_via_openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=messages_for(website)\n", |
||||||
|
" )\n", |
||||||
|
" return response.choices[0].message.content\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 13, |
||||||
|
"id": "d868d778-13b5-4934-acf5-dcb919a27d59", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def display_summary(url):\n", |
||||||
|
" summary = summarize(url)\n", |
||||||
|
" display(Markdown(summary))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 14, |
||||||
|
"id": "0a0d9b79-de3c-4f77-9254-f02cf4d6217a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/markdown": [ |
||||||
|
"**Sommario del sito WP Pisa - Il sito del Meetup WordPress di Pisa**\n", |
||||||
|
"\n", |
||||||
|
"Il sito web WP Pisa è il punto di riferimento per gli appassionati di WordPress a Pisa. Gli organizzatori offrono meetup mensili gratuiti per discutere conoscenze, esperienze e progetti correlati al mondo di WordPress.\n", |
||||||
|
"\n", |
||||||
|
"**Eventi e Annunci**\n", |
||||||
|
"\n", |
||||||
|
"* **WordCamp Pisa 2025**: Aperta la call for organizer\n", |
||||||
|
"* **Il Tuo Sito Ovunque in Pochi Minuti**: Un incontro con Docker e sviluppatori WordPress - Partecipa!\n", |
||||||
|
"* **Core Days Roma: Le novità sul core di WordPress per i dev**\n", |
||||||
|
"* **NO MORE THUMBNAILS!**\n", |
||||||
|
"\n", |
||||||
|
"**Informazioni Generali**\n", |
||||||
|
"\n", |
||||||
|
"* Il meetuu è aperto a tutti, indipendentemente dal livello di competenza in WordPress\n", |
||||||
|
"* Tutti gli eventi sono gratuiti e organizzati con la supervisione di WordPress Foundation tramite la piattaforma Meetup.com\n", |
||||||
|
"* La comunità WP Pisa contiene 150+ iscritti" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.Markdown object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://wppisa.it/\")" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,186 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "1faf8b29-2ba6-40c7-89ee-71f71e234f11", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Extra requirements\n", |
||||||
|
"```bash\n", |
||||||
|
"pip install -q -U google-genai\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"## Required environment variable\n", |
||||||
|
"GEMINI_API_KEY\n", |
||||||
|
"\n", |
||||||
|
"### How to get GEMINI API KEY\n", |
||||||
|
"\n", |
||||||
|
"Use the link: [gemini api key](https://aistudio.google.com/app/apikey) to get yours." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 12, |
||||||
|
"id": "be06ce76-20ee-4066-9582-a4ed745f278f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from google import genai\n", |
||||||
|
"from google.genai import types" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 13, |
||||||
|
"id": "99e42519-5dac-4b13-8a26-8a635753343b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def gemini_invoke(website):\n", |
||||||
|
" load_dotenv()\n", |
||||||
|
" api_key = os.getenv(\"GEMINI_API_KEY\")\n", |
||||||
|
" if not api_key or len(api_key) < 39:\n", |
||||||
|
" print(\"No correct api key was found\")\n", |
||||||
|
" return\n", |
||||||
|
" else:\n", |
||||||
|
" print(\"Api key found. Good to go!\")\n", |
||||||
|
" client = genai.Client(api_key=api_key)\n", |
||||||
|
" response = client.models.generate_content(\n", |
||||||
|
" model=\"gemini-2.0-flash\",\n", |
||||||
|
" config=types.GenerateContentConfig(\n", |
||||||
|
" system_instruction=system_prompt),\n", |
||||||
|
" contents=user_prompt_for(website)\n", |
||||||
|
" )\n", |
||||||
|
" return response.text" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 14, |
||||||
|
"id": "95a6ece8-8402-4cad-96b9-36a6ea444c54", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class Website:\n", |
||||||
|
" url: str\n", |
||||||
|
" title: str\n", |
||||||
|
" text: str\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url)\n", |
||||||
|
" soup = BeautifulSoup(response.content, \"html.parser\")\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title was found\"\n", |
||||||
|
"\n", |
||||||
|
" for irr in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irr.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "24bbd1dd-dca4-4bbc-ae91-4bad227a4278", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"ed = Website(\"https://edwarddonner.com\")\n", |
||||||
|
"print(ed.title)\n", |
||||||
|
"print(ed.text)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 15, |
||||||
|
"id": "233b8904-7a4a-4265-8b0d-20934ae4b29c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||||
|
"and provides a short summary, ignoring text that navigation related. Respond \\\n", |
||||||
|
"in markdown.\"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 16, |
||||||
|
"id": "5c996c03-84ab-4378-8a55-026d94404d35", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"messages = [{\"role\": \"user\", \"content\": system_prompt}]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 17, |
||||||
|
"id": "abf9464e-dc8d-4099-aeb6-495498326673", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||||
|
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||||
|
"please provide a short summary of this website in markdown. \\\n", |
||||||
|
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 20, |
||||||
|
"id": "32ab2d29-02d1-43c5-b920-f2621f292b23", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def summarize(url, model=\"gemini\"):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" if model == \"ollama\":\n", |
||||||
|
" import ollama\n", |
||||||
|
" Model=\"llama3.2\"\n", |
||||||
|
" messages[0][\"content\"] += f\" Website: {url}\"\n", |
||||||
|
" response = ollama.chat(model=Model, messages=messages)\n", |
||||||
|
" return response[\"message\"][\"content\"]\n", |
||||||
|
" else:\n", |
||||||
|
" return gemini_invoke(website)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a2a0e518-7198-489d-a0ce-2eec617f939f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"summarize(\"https://edwarddonner.com\", \"ollama\")" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.12.0" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,240 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "fc3a96d1-eedf-4e3a-b3ce-151485c574b5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import requests\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "385dc3d5-f6ce-46d8-958e-83dc1150c24e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||||
|
"HEADERS = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"MODEL = \"llama3.2\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "21f7dacc-1fa8-491c-8e94-39238dae52b3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class Website:\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=HEADERS)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ca431e32-9191-4940-b62d-f25e8cbac627", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"web = Website(\"https://silviayomdesign.com/\")\n", |
||||||
|
"print(web.title)\n", |
||||||
|
"print(web.text)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "76475815-0dbc-451b-ab65-f7e2ea3aaa8a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||||
|
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||||
|
"Respond in markdown.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3cf03913-f595-4817-8580-19b182c599de", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"You are looking at a very artistic graphic designer's website titled name {website.title}\"\n", |
||||||
|
" user_prompt += \"\\nHer creativity of her works are as follow;\\\n", |
||||||
|
"please provide a short summary of her works in markdown. \\n\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6f130cfe-756b-4df8-b1f0-6918956a6162", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(user_prompt_for(web))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "85d85b64-1452-408f-bfae-d27b52d7dfa7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(web)}\n", |
||||||
|
"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "36d66055-66d6-4123-b092-eceab055829d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"payload = {\n", |
||||||
|
" \"model\": MODEL,\n", |
||||||
|
" \"messages\": messages,\n", |
||||||
|
" \"stream\": False\n", |
||||||
|
"}" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "163db8a9-b0eb-49f3-a5f2-1e74cf51c245", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", |
||||||
|
"print(response.json()[\"message\"][\"content\"])" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "afabfff5-81e5-4b61-aca9-6c19d3584b86", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def messages_for(website):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(web)}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b2e83b58-16fc-4049-8116-24a0cbb3635a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"messages_for(web)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "05ed519a-514f-4ed8-b323-4f4817e1e1c6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import ollama\n", |
||||||
|
"def summarize(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model=MODEL, \n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" return response[\"message\"][\"content\"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b182f686-0a3e-4959-9bfd-0a59d2befd4c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"summarize(\"https://silviayomdesign.com/\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f4f1f807-28d4-4b8b-9698-9b90dcbac59f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def display_summary(url):\n", |
||||||
|
" summary = summarize(url)\n", |
||||||
|
" display(Markdown(summary))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a988d29b-ed36-4a40-bd77-0f7d60a29ac3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://silviayomdesign.com/\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "15e72eeb-1c35-4bb2-9596-6ff2546aa046", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,663 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "a98030af-fcd1-4d63-a36e-38ba053498fa", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# A full business solution\n", |
||||||
|
"\n", |
||||||
|
"## Now we will take our project from Day 1 to the next level\n", |
||||||
|
"\n", |
||||||
|
"### BUSINESS CHALLENGE:\n", |
||||||
|
"\n", |
||||||
|
"Create a product that builds a Brochure for a company to be used for prospective clients, investors and potential recruits.\n", |
||||||
|
"\n", |
||||||
|
"We will be provided a company name and their primary website.\n", |
||||||
|
"\n", |
||||||
|
"See the end of this notebook for examples of real-world business applications.\n", |
||||||
|
"\n", |
||||||
|
"And remember: I'm always available if you have problems or ideas! Please do reach out." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d5b08506-dc8b-4443-9201-5f1848161363", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"# If these fail, please check you're running from an 'activated' environment with (llms) in the command prompt\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"import json\n", |
||||||
|
"from typing import List\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"from openai import OpenAI" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "fc5d8880-f2ee-4c06-af16-ecbc0262af61", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Initialize and constants\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", |
||||||
|
" print(\"API key looks good so far\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", |
||||||
|
" \n", |
||||||
|
"MODEL = 'gpt-4o-mini'\n", |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "106dd65e-90af-4ca8-86b6-23a41840645b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" A utility class to represent a Website that we have scraped, now with links\n", |
||||||
|
" \"\"\"\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" self.body = response.content\n", |
||||||
|
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" if soup.body:\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
" else:\n", |
||||||
|
" self.text = \"\"\n", |
||||||
|
" links = [link.get('href') for link in soup.find_all('a')]\n", |
||||||
|
" self.links = [link for link in links if link]\n", |
||||||
|
"\n", |
||||||
|
" def get_contents(self):\n", |
||||||
|
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e30d8128-933b-44cc-81c8-ab4c9d86589a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"ed = Website(\"https://edwarddonner.com\")\n", |
||||||
|
"ed.links" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "1771af9c-717a-4fca-bbbe-8a95893312c3", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## First step: Have GPT-4o-mini figure out which links are relevant\n", |
||||||
|
"\n", |
||||||
|
"### Use a call to gpt-4o-mini to read the links on a webpage, and respond in structured JSON. \n", |
||||||
|
"It should decide which links are relevant, and replace relative links such as \"/about\" with \"https://company.com/about\". \n", |
||||||
|
"We will use \"one shot prompting\" in which we provide an example of how it should respond in the prompt.\n", |
||||||
|
"\n", |
||||||
|
"This is an excellent use case for an LLM, because it requires nuanced understanding. Imagine trying to code this without LLMs by parsing and analyzing the webpage - it would be very hard!\n", |
||||||
|
"\n", |
||||||
|
"Sidenote: there is a more advanced technique called \"Structured Outputs\" in which we require the model to respond according to a spec. We cover this technique in Week 8 during our autonomous Agentic AI project." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6957b079-0d96-45f7-a26a-3487510e9b35", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"oneshot_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", |
||||||
|
"You are able to decide which of the links would be most relevant to include in a brochure about the company or freelancer offering their services, \\\n", |
||||||
|
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", |
||||||
|
"oneshot_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||||
|
"oneshot_system_prompt += \"\"\"\n", |
||||||
|
"{\n", |
||||||
|
" \"links\": [\n", |
||||||
|
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", |
||||||
|
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", |
||||||
|
" ]\n", |
||||||
|
"}\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"oneshot_system_prompt += \"Make sure not to miss any relevant pages.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f5a8b688-b153-41a6-8b18-f6198f3df2c9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"fewshot_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", |
||||||
|
"You are able to decide which of the links would be most relevant to include in a brochure about the company or freelancer offering their services, \\\n", |
||||||
|
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n You should respond in JSON as in the following examples:\"\n", |
||||||
|
"fewshot_system_prompt += \"\"\"\n", |
||||||
|
" Example 1\n", |
||||||
|
" ['https://great-comps.com/about-me', 'https://www.linkedin.com/in/great-comp/', 'mailto:hello@mygroovydomain.com', 'https://great-comps.com/news', '/case-studies', 'https://patents.google.com/patent/US20210049536A1/', 'https://great-comps.com/workshop-ai']\n", |
||||||
|
"\n", |
||||||
|
" Links:\n", |
||||||
|
" {\n", |
||||||
|
" \"links\": [\n", |
||||||
|
" {\"type\": \"about page\", \"url\": \"https://great-comps.de/about-me\"},\n", |
||||||
|
" {\"type\": \"news page\": \"url\": \"https://great-comps.de/news\"},\n", |
||||||
|
" {\"type\": \"case studies page\": \"url\": \"https://great-comps.de/case-studies\"},\n", |
||||||
|
" {\"type\": \"workshop page\": \"url\": \"https://great-comps.de/workshop-ai\"},\n", |
||||||
|
" ]\n", |
||||||
|
" }\n", |
||||||
|
"\n", |
||||||
|
" Example 2\n", |
||||||
|
" ['mailto:info@robbie-doodle-domain.com','https://wahlen-robbie.at/ueber-mich', 'https://www.linkedin.com/in/robbie-doodle/', 'https://news.ycombinator.com', 'https://wahlen-robbie.at/neuigkeiten', 'https://twitter.com/robbie-d', '/whitepapers', 'https://patents.google.com/patent/US20210049536A1/', 'https://wahlen-robbie.at/services']\n", |
||||||
|
"\n", |
||||||
|
" Links:\n", |
||||||
|
" {\n", |
||||||
|
" \"links\": [\n", |
||||||
|
" {\"type\": \"über mich\", \"url\": \"https://wahlen-robbie.at/ueber-mich\"},\n", |
||||||
|
" {\"type\": \"aktuelles\": \"url\": \"https://wahlen-robbie.at/neuigkeiten\"},\n", |
||||||
|
" {\"type\": \"whitepaper\": \"url\": \"https://wahlen-robbie.at/whitepapers\"},\n", |
||||||
|
" {\"type\": \"services\": \"url\": \"https://wahlen-robbie.at/services\"}\n", |
||||||
|
" ]\n", |
||||||
|
" }\n", |
||||||
|
" \"\"\"\n", |
||||||
|
"fewshot_system_prompt += \"Make sure not to miss any relevant pages.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b97e4068-97ed-4120-beae-c42105e4d59a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(f\"Oneshot system prompt:\\n{oneshot_system_prompt}\")\n", |
||||||
|
"print(f\"\\n\\n\\nFewshot system prompt:\\n{fewshot_system_prompt}\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8e1f601b-2eaf-499d-b6b8-c99050c9d6b3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_links_user_prompt(website):\n", |
||||||
|
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", |
||||||
|
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company or person offering their services, respond with the full https URL in JSON format. \\\n", |
||||||
|
"Do not include Terms of Service, Privacy, email links or social media links.\\n\"\n", |
||||||
|
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||||
|
" user_prompt += \"\\n\".join(website.links)\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6bcbfa78-6395-4685-b92c-22d592050fd7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(get_links_user_prompt(ed))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a29aca19-ca13-471c-a4b4-5abbfa813f69", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_links(url, system_prompt=oneshot_system_prompt):\n", |
||||||
|
" \n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", |
||||||
|
" ],\n", |
||||||
|
" response_format={\"type\": \"json_object\"}\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" result = response.choices[0].message.content \n", |
||||||
|
" print(f\"Response: {result}\")\n", |
||||||
|
" return json.loads(result)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2dc4150a-0042-4f5d-a7bf-158a0f9147a6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"get_links(ed_url)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "74a827a0-2782-4ae5-b210-4a242a8b4cc2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n", |
||||||
|
"hf = \"https://huggingface.co\"\n", |
||||||
|
"\n", |
||||||
|
"huggingface = Website(hf)\n", |
||||||
|
"huggingface.links" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d3d583e2-dcc4-40cc-9b28-1e8dbf402924", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"ed_url = \"https://edwarddonner.com\"\n", |
||||||
|
"hf_url = \"https://huggingface.co\"\n", |
||||||
|
"\n", |
||||||
|
"print(f\"Links generated with oneshot prompt for {ed_url}:\\n\")\n", |
||||||
|
"get_links(ed_url)\n", |
||||||
|
"\n", |
||||||
|
"print(f\"\\n\\nLinks generated with fewshot prompt for {ed_url}:\\n\")\n", |
||||||
|
"get_links(ed_url, fewshot_system_prompt)\n", |
||||||
|
"\n", |
||||||
|
"print(50*\"*\")\n", |
||||||
|
"print(f\"\\nLinks generated with oneshot prompt for {hf_url}:\\n\")\n", |
||||||
|
"get_links(hf_url)\n", |
||||||
|
"\n", |
||||||
|
"print(f\"\\n\\nLinks generated with fewshot prompt for {hf_url}:\\n\")\n", |
||||||
|
"get_links(hf_url, fewshot_system_prompt)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "0d74128e-dfb6-47ec-9549-288b621c838c", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Second step: make the brochure!\n", |
||||||
|
"\n", |
||||||
|
"Assemble all the details into another prompt to GPT4-o" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "85a5b6e2-e7ef-44a9-bc7f-59ede71037b5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_all_details(url, type=fewshot_system_prompt):\n", |
||||||
|
" result = \"Landing page:\\n\"\n", |
||||||
|
" result += Website(url).get_contents()\n", |
||||||
|
"\n", |
||||||
|
" links = get_links(url, type)\n", |
||||||
|
" print(\"Found links:\", links)\n", |
||||||
|
" for link in links[\"links\"]:\n", |
||||||
|
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||||
|
" result += Website(link[\"url\"]).get_contents()\n", |
||||||
|
" return result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5099bd14-076d-4745-baf3-dac08d8e5ab2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(get_all_details(ed_url))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "9b863a55-f86c-4e3f-8a79-94e24c1a8cf2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||||
|
"and creates a short brochure about the company for prospective customers, investors and recruits. \\\n", |
||||||
|
"The brochure should be a bit unusual in terms of tone and style, it should astound the reader and pique their interest. Respond in markdown.\\\n", |
||||||
|
"Include details of company culture, customers and careers/jobs if you have the information.\"\n", |
||||||
|
"\n", |
||||||
|
"# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone':\n", |
||||||
|
"\n", |
||||||
|
"# system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||||
|
"# and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", |
||||||
|
"# Include details of company culture, customers and careers/jobs if you have the information.\"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6ab83d92-d36b-4ce0-8bcc-5bb4c2f8ff23", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_brochure_user_prompt(company_name, url):\n", |
||||||
|
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", |
||||||
|
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n", |
||||||
|
" user_prompt += get_all_details(url)\n", |
||||||
|
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "05d07160-7910-4da2-92ac-36aa849fcc68", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# get_brochure_user_prompt(\"Edward Donner\", ed_url)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "cd909e0b-1312-4ce2-a553-821e795d7572", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# get_brochure_user_prompt(\"HuggingFace\", \"https://huggingface.co\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e44de579-4a1a-4e6a-a510-20ea3e4b8d46", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def create_brochure(company_name, url):\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||||
|
" ],\n", |
||||||
|
" )\n", |
||||||
|
" return response.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6b0de762-f343-44d9-85d5-9bffba3c0ae8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"brochure_ed = create_brochure(\"Edward Donner\", ed_url)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e093444a-9407-42ae-924a-145730591a39", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"brochure_hf = create_brochure(\"HuggingFace\", \"https://huggingface.co\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0d00b012-3901-492c-b985-a0340750c011", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display(Markdown(brochure_ed))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e33cb2e9-3b8c-4ef3-a6cb-70b3188b9120", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display(Markdown(brochure_hf))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "dea955ad-24a6-490b-8191-f066bff1b595", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def translate_brochure(brochure_content, language=\"German\"):\n", |
||||||
|
" system_prompt = f\"You are a skilled translator. Translate the following brochure text into {language}.\\\n", |
||||||
|
" Make sure to translate into a idiomatic {language}, matching the target language's natural structure, wording and expressions, so it can't be recognised as a translation.\\\n", |
||||||
|
" Be sure to also meet an appropriate tone, eg a good marketing language in other languages will probably be a bit less boastful than in English.\\\n", |
||||||
|
" Output the translated brochure in Markdown format.\"\n", |
||||||
|
" \n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model = MODEL,\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": system_prompt}, {\"role\": \"user\", \"content\": brochure_content}]\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
" return response.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "9b6bdd4f-7518-4780-9da9-47f90aab974b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"translation = translate_brochure(brochure_ed, language=\"German\")\n", |
||||||
|
"display(Markdown(translation))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f1dd96f2-0980-4a30-a152-1f38c0e319bb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"translation = translate_brochure(brochure_hf, language=\"German\")\n", |
||||||
|
"display(Markdown(translation))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "61eaaab7-0b47-4b29-82d4-75d474ad8d18", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Finally - a minor improvement\n", |
||||||
|
"\n", |
||||||
|
"With a small adjustment, we can change this so that the results stream back from OpenAI,\n", |
||||||
|
"with the familiar typewriter animation" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "51db0e49-f261-4137-aabe-92dd601f7725", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def stream_brochure(company_name, url):\n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||||
|
" ],\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" response = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response += chunk.choices[0].delta.content or ''\n", |
||||||
|
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||||
|
" update_display(Markdown(response), display_id=display_handle.display_id)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "56bf0ae3-ee9d-4a72-9cd6-edcac67ceb6d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "fdb3f8d8-a3eb-41c8-b1aa-9f60686a653b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Try changing the system prompt to the humorous version when you make the Brochure for Hugging Face:\n", |
||||||
|
"\n", |
||||||
|
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "a27bf9e0-665f-4645-b66b-9725e2a959b5", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#181;\">Business applications</h2>\n", |
||||||
|
" <span style=\"color:#181;\">In this exercise we extended the Day 1 code to make multiple LLM calls, and generate a document.\n", |
||||||
|
"\n", |
||||||
|
"This is perhaps the first example of Agentic AI design patterns, as we combined multiple calls to LLMs. This will feature more in Week 2, and then we will return to Agentic AI in a big way in Week 8 when we build a fully autonomous Agent solution.\n", |
||||||
|
"\n", |
||||||
|
"Generating content in this way is one of the very most common Use Cases. As with summarization, this can be applied to any business vertical. Write marketing content, generate a product tutorial from a spec, create personalized email content, and so much more. Explore how you can apply content generation to your business, and try making yourself a proof-of-concept prototype. See what other students have done in the community-contributions folder -- so many valuable projects -- it's wild!</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "14b2454b-8ef8-4b5c-b928-053a15e0d553", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Before you move to Week 2 (which is tons of fun)</h2>\n", |
||||||
|
" <span style=\"color:#900;\">Please see the week1 EXERCISE notebook for your challenge for the end of week 1. This will give you some essential practice working with Frontier APIs, and prepare you well for Week 2.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "17b64f0f-7d33-4493-985a-033d06e8db08", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#f71;\">A reminder on 3 useful resources</h2>\n", |
||||||
|
" <span style=\"color:#f71;\">1. The resources for the course are available <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">here.</a><br/>\n", |
||||||
|
" 2. I'm on LinkedIn <a href=\"https://www.linkedin.com/in/eddonner/\">here</a> and I love connecting with people taking the course!<br/>\n", |
||||||
|
" 3. I'm trying out X/Twitter and I'm at <a href=\"https://x.com/edwarddonner\">@edwarddonner<a> and hoping people will teach me how it's done.. \n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6f48e42e-fa7a-495f-a5d4-26bfc24d60b6", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../thankyou.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#090;\">Finally! I have a special request for you</h2>\n", |
||||||
|
" <span style=\"color:#090;\">\n", |
||||||
|
" My editor tells me that it makes a MASSIVE difference when students rate this course on Udemy - it's one of the main ways that Udemy decides whether to show it to others. If you're able to take a minute to rate this, I'd be so very grateful! And regardless - always please reach out to me at ed@edwarddonner.com if I can help at any point.\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,81 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "a98030af-fcd1-4d63-a36e-38ba053498fa", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# A Small Tweak to Week1-Day5\n", |
||||||
|
"\n", |
||||||
|
"If you have network restrictions (such as using a custom DNS provider, or firewall rules at work), you can disable SSL cert verification.\n", |
||||||
|
"Once you do that and start executing your code, the output will be riddled with warnings. Thankfully, you can suppress those warnings,too.\n", |
||||||
|
"\n", |
||||||
|
"See the 2 lines added to the init method, below." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 22, |
||||||
|
"id": "106dd65e-90af-4ca8-86b6-23a41840645b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" A utility class to represent a Website that we have scraped, now with links\n", |
||||||
|
" \"\"\"\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
"\n", |
||||||
|
" #\n", |
||||||
|
" # If you must disable SSL cert validation, and also suppress all the warning that will come with it,\n", |
||||||
|
" # add the 2 lines below. This comes in very handy if you have DNS/firewall restrictions; alas, use\n", |
||||||
|
" # with caution, especially if deploying this in a non-dev environment.\n", |
||||||
|
" requests.packages.urllib3.disable_warnings() \n", |
||||||
|
" response = requests.get(url, headers=headers, verify=False) \n", |
||||||
|
" # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" \n", |
||||||
|
" self.body = response.content\n", |
||||||
|
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" if soup.body:\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
" else:\n", |
||||||
|
" self.text = \"\"\n", |
||||||
|
" links = [link.get('href') for link in soup.find_all('a')]\n", |
||||||
|
" self.links = [link for link in links if link]" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,440 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "07be6aa3-6636-4b57-be16-823c3907f4c4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"import json\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"from openai import OpenAI" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0e64af7b-6956-4437-ab32-857a6ea814c3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"load_dotenv()\n", |
||||||
|
"api_key = os.getenv(\"OPENAI_API_KEY\")\n", |
||||||
|
"\n", |
||||||
|
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", |
||||||
|
" print(\"Api key found. Good to go!\") \n", |
||||||
|
"else:\n", |
||||||
|
" print(\"No correct api key was found\")\n", |
||||||
|
"MODEL = \"gpt-4o-mini\"\n", |
||||||
|
"openai = OpenAI(api_key=api_key)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4667e3ee-d5b7-42ed-99ad-5e9fa75c8660", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "42adb18b-3ec9-4700-95e4-c0041ce8f17a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class GithubProfile:\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" self.body = response.content\n", |
||||||
|
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" if soup.body:\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
" else:\n", |
||||||
|
" self.text = \"\"\n", |
||||||
|
" links = [link.get(\"href\") for link in soup.find_all(\"a\")]\n", |
||||||
|
" self.links = [link for link in links if link]\n", |
||||||
|
" \n", |
||||||
|
" def get_contents(self):\n", |
||||||
|
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "661b5377-c444-45a9-9455-85f83ff525d3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"profile = GithubProfile(\"https://github.com/ertgl\")\n", |
||||||
|
"profile.links" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8f9a3c08-0db2-4baa-a8a4-f5642049a57c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"link_system_prompt = \"You are provided with a list of links found on a Github page. \\\n", |
||||||
|
"You are able to decide which of the links would be most relevant to include in a portfolio about the github user, \\\n", |
||||||
|
"such as links to an About page, or a Repositories, or Projects.\\n\"\n", |
||||||
|
"link_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||||
|
"link_system_prompt += \"\"\"\n", |
||||||
|
"{\n", |
||||||
|
" \"links\": [\n", |
||||||
|
" {\"type\": \"overview page\", \"url\": \"https://another.full.url\"},\n", |
||||||
|
" {\"type\": \"repositories page\": \"url\": \"https://another.full.url?tab=repositories\"}\n", |
||||||
|
" ]\n", |
||||||
|
"}\n", |
||||||
|
"\"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "30eafd50-9735-4388-9cc1-8337a00069a2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(link_system_prompt)\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4dc4f366-5c00-441d-b1bd-8dda148f1ffb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_links_user_prompt(profile):\n", |
||||||
|
" user_prompt = f\"Here is the list of links on the website of {profile.url} - \"\n", |
||||||
|
" user_prompt += \"please decide which of these are relevant web links for a portfolio about the user, respond with the full https URL in JSON format. \\\n", |
||||||
|
"Do not include Terms of Service, Privacy, Login, Blog or Github trending related pages.\\n\"\n", |
||||||
|
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||||
|
" user_prompt += \"\\n\".join(profile.links)\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c066b2ac-5863-408e-bb42-1388d130d164", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(get_links_user_prompt(profile))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "dc0ccb95-479c-4f6e-9686-1ff38aa543fa", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_links(url):\n", |
||||||
|
" profile = GithubProfile(url)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": link_system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_links_user_prompt(profile)}\n", |
||||||
|
" ],\n", |
||||||
|
" response_format= {\"type\": \"json_object\"}\n", |
||||||
|
" )\n", |
||||||
|
" result = response.choices[0].message.content\n", |
||||||
|
" return json.loads(result)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "9f5e3b8b-398d-4e23-867e-401faca7db03", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"get_links(profile.url)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "b9024a4f-4038-4c0e-b0c7-74226feaccfd", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Second step: make the portfolio!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f9906d73-801a-4aea-b620-10ac39eaf424", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_all_details(url):\n", |
||||||
|
" result = \"Landing page:\\n\"\n", |
||||||
|
" result += GithubProfile(url).get_contents()\n", |
||||||
|
" links = get_links(url)\n", |
||||||
|
" print(\"Found links:\", links)\n", |
||||||
|
" for link in links[\"links\"]:\n", |
||||||
|
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||||
|
" result += GithubProfile(link[\"url\"]).get_contents()\n", |
||||||
|
" return result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "02039450-7f7f-4556-8645-39cd31f30265", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(get_all_details(\"https://github.com/ertgl\"))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4119b96f-0aa1-4cdb-9a09-d51b163069b8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a personal github page \\\n", |
||||||
|
"and creates a short portfolio about the user profile, especially projects and repositories and summary of the repo's \\\n", |
||||||
|
"README files for prospective recruiters, investors. Respond in markdown.\\\n", |
||||||
|
"Include details of person profile overview, if you have the information.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "842834d2-a5e9-4b56-a792-492a1a137fbc", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_portfolio_user_prompt(profile_name, url):\n", |
||||||
|
" user_prompt = f\"You are looking at a user called: {profile_name} on Github.\\n\"\n", |
||||||
|
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short portfolio of the user in markdown.\\n\"\n", |
||||||
|
" user_prompt += get_all_details(url)\n", |
||||||
|
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "285b3a1d-894a-463c-8c30-b5de203b8358", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(get_portfolio_user_prompt(\"Ertuğrul Noyan Keremoğlu\", \"https://github.com/ertgl\"))\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "78dc7495-d0a5-409b-8ecf-3a5ef9220e25", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def create_portfolio(profile_name, url):\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_portfolio_user_prompt(profile_name, url)}\n", |
||||||
|
" ]\n", |
||||||
|
" )\n", |
||||||
|
" result = response.choices[0].message.content\n", |
||||||
|
" display(Markdown(result))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "abe39377-2d52-434a-aace-e9397cdd4f20", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"create_portfolio(\"Ertuğrul Noyan Keremoğlu\", \"https://github.com/ertgl\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "edd168ca-b77b-4fc7-9e11-2114a43553e4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def stream_portfolio(profile_name, url):\n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_portfolio_user_prompt(profile_name, url)}\n", |
||||||
|
" ],\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" response = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response += chunk.choices[0].delta.content or ''\n", |
||||||
|
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||||
|
" update_display(Markdown(response), display_id=display_handle.display_id)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1ea391d4-775e-483d-9e55-e3ae30fa9bd8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"stream_portfolio(\"Ertuğrul Noyan Keremoğlu\", \"https://github.com/ertgl\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "498ca0c8-8f68-4389-8184-078706b62cf6", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Multi-lingual with Multi-Tone in Desire Format" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 100, |
||||||
|
"id": "f11e3391-03f9-409c-9f5a-6286959690ec", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def multi_lingual_stream_portfolio(profile_name, url, language, tone):\n", |
||||||
|
"\n", |
||||||
|
" system_prompt = f\"\"\"\n", |
||||||
|
"You are an assistant that analyzes the contents of several relevant pages from a github profile page and \n", |
||||||
|
"creates a visually appealing and professional short portfolio for prospective investors, and recruiters. \n", |
||||||
|
"The portfolio should be written in {language} and use a {tone.lower()} tone throughout.\n", |
||||||
|
"\n", |
||||||
|
"The portfolio should follow this structure (in {language}):\n", |
||||||
|
"\n", |
||||||
|
"1. **Front Cover**:\n", |
||||||
|
" - Prominently display the user name as Title.\n", |
||||||
|
" - Include a compelling headline or tagline.\n", |
||||||
|
" - Add something engaging relevant to the user’s summarized README files if available.\n", |
||||||
|
"\n", |
||||||
|
"2. **About**:\n", |
||||||
|
" - Provide a brief introduction to the user's projects approach.\n", |
||||||
|
" - State which repository they own or they contributed.\n", |
||||||
|
"\n", |
||||||
|
"3. **Overview**:\n", |
||||||
|
" - Summarize the user's projects, repositories, or solutions by summarized README files if available.\n", |
||||||
|
" - Highlight benefits or unique developer/development points.\n", |
||||||
|
" - Mention the follower and following users count and total stars they got.\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"4. **My Culture**:\n", |
||||||
|
" - Outline the user’s key values or guiding principles.\n", |
||||||
|
" - Describe the workplace environment (e.g., innovation-driven, inclusive, collaborative).\n", |
||||||
|
" - Highlight community engagement.\n", |
||||||
|
"\n", |
||||||
|
"5. **What kind of companies may be interested**:\n", |
||||||
|
" - Describe the target customers or industries served.\n", |
||||||
|
" - Mention open source contributions also if available. \n", |
||||||
|
" \n", |
||||||
|
"6. **Projects**:\n", |
||||||
|
" \n", |
||||||
|
" ***Owner***:\n", |
||||||
|
" - List owned projects/repositories with summaries. (Summarize README file of the each project)\n", |
||||||
|
" \n", |
||||||
|
" ***Contributer***:\n", |
||||||
|
" - List contributed projects/repositories with summaries. (Summarize README file of the each project)\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"7. **Support and Donation**:\n", |
||||||
|
" - Encourage those interested in user's open source projects to donate.\n", |
||||||
|
" - Provide direct links or steps to apply if available.\n", |
||||||
|
"\n", |
||||||
|
"8. **Contact Us**:\n", |
||||||
|
" - Provide the user’s address, phone number, and email.\n", |
||||||
|
" - Include links to social media platforms.\n", |
||||||
|
" - Add a link to the user’s website.\n", |
||||||
|
"\n", |
||||||
|
"9. **Closing Note**:\n", |
||||||
|
" - End with a thank-you message or an inspirational note for the reader.\n", |
||||||
|
" - Add a call-to-action (e.g., “Get in touch today!” or “Explore more on my website”).\n", |
||||||
|
"\n", |
||||||
|
"Ensure the content is concise, engaging, visually clear, and tailored to the target audience. Use headings and subheadings to make the brochure easy to navigate. Include links and contact information wherever applicable.\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
" \n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_portfolio_user_prompt(profile_name, url)}\n", |
||||||
|
" ],\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" response = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response += chunk.choices[0].delta.content or ''\n", |
||||||
|
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||||
|
" update_display(Markdown(response), display_id=display_handle.display_id)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3a38dc0b-27de-4738-8883-b3857e067b45", |
||||||
|
"metadata": { |
||||||
|
"scrolled": true |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"multi_lingual_stream_portfolio(\"Ertuğrul Noyan Keremoğlu\", \"https://github.com/ertgl\", \"English\", \"serious, entertaining, witty\")" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.12.0" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,501 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "a98030af-fcd1-4d63-a36e-38ba053498fa", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# A full business solution (Ollama Version)\n", |
||||||
|
"\n", |
||||||
|
"## Now we will take our project from Day 1 to the next level\n", |
||||||
|
"\n", |
||||||
|
"### BUSINESS CHALLENGE:\n", |
||||||
|
"\n", |
||||||
|
"Create a product that builds a Brochure for a company to be used for prospective clients, investors and potential recruits.\n", |
||||||
|
"\n", |
||||||
|
"We will be provided a company name and their primary website.\n", |
||||||
|
"\n", |
||||||
|
"See the end of this notebook for examples of real-world business applications.\n", |
||||||
|
"\n", |
||||||
|
"And remember: I'm always available if you have problems or ideas! Please do reach out." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d5b08506-dc8b-4443-9201-5f1848161363", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"# If these fail, please check you're running from an 'activated' environment with (llms) in the command prompt\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"import json\n", |
||||||
|
"from typing import List\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"import ollama" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "fc5d8880-f2ee-4c06-af16-ecbc0262af61", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Initialize and constants\n", |
||||||
|
" \n", |
||||||
|
"# MODEL = \"llama3.2\"\n", |
||||||
|
"MODEL = \"llama3.2:1b\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "106dd65e-90af-4ca8-86b6-23a41840645b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" A utility class to represent a Website that we have scraped, now with links\n", |
||||||
|
" \"\"\"\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" self.body = response.content\n", |
||||||
|
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" if soup.body:\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
" else:\n", |
||||||
|
" self.text = \"\"\n", |
||||||
|
" links = [link.get('href') for link in soup.find_all('a')]\n", |
||||||
|
" self.links = [link for link in links if link]\n", |
||||||
|
"\n", |
||||||
|
" def get_contents(self):\n", |
||||||
|
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e30d8128-933b-44cc-81c8-ab4c9d86589a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"ed = Website(\"https://edwarddonner.com\")\n", |
||||||
|
"ed.links\n", |
||||||
|
"# print(ed.get_contents())" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "1771af9c-717a-4fca-bbbe-8a95893312c3", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## First step: Have llama3.2 figure out which links are relevant\n", |
||||||
|
"\n", |
||||||
|
"### Use a call to llama3.2 to read the links on a webpage, and respond in structured JSON. \n", |
||||||
|
"It should decide which links are relevant, and replace relative links such as \"/about\" with \"https://company.com/about\". \n", |
||||||
|
"We will use \"one shot prompting\" in which we provide an example of how it should respond in the prompt.\n", |
||||||
|
"\n", |
||||||
|
"This is an excellent use case for an LLM, because it requires nuanced understanding. Imagine trying to code this without LLMs by parsing and analyzing the webpage - it would be very hard!\n", |
||||||
|
"\n", |
||||||
|
"Sidenote: there is a more advanced technique called \"Structured Outputs\" in which we require the model to respond according to a spec. We cover this technique in Week 8 during our autonomous Agentic AI project." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6957b079-0d96-45f7-a26a-3487510e9b35", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", |
||||||
|
"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n", |
||||||
|
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", |
||||||
|
"link_system_prompt += \"You should respond in JSON format as shown in the following example:\"\n", |
||||||
|
"link_system_prompt += \"\"\"\n", |
||||||
|
"{\n", |
||||||
|
" \"links\": [\n", |
||||||
|
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", |
||||||
|
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", |
||||||
|
" ]\n", |
||||||
|
"}\n", |
||||||
|
"\"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b97e4068-97ed-4120-beae-c42105e4d59a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(link_system_prompt)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8e1f601b-2eaf-499d-b6b8-c99050c9d6b3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_links_user_prompt(website):\n", |
||||||
|
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", |
||||||
|
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n", |
||||||
|
"Do not include Terms of Service, Privacy, email links.\\n\"\n", |
||||||
|
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||||
|
" user_prompt += \"\\n\".join(website.links)\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6bcbfa78-6395-4685-b92c-22d592050fd7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(get_links_user_prompt(ed))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a29aca19-ca13-471c-a4b4-5abbfa813f69", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_links(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": link_system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", |
||||||
|
" ],\n", |
||||||
|
" )\n", |
||||||
|
" result = response['message']['content']\n", |
||||||
|
" return json.loads(result)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "74a827a0-2782-4ae5-b210-4a242a8b4cc2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n", |
||||||
|
"\n", |
||||||
|
"huggingface = Website(\"https://huggingface.co\")\n", |
||||||
|
"huggingface.links" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d3d583e2-dcc4-40cc-9b28-1e8dbf402924", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"get_links(\"https://huggingface.co\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "0d74128e-dfb6-47ec-9549-288b621c838c", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Second step: make the brochure!\n", |
||||||
|
"\n", |
||||||
|
"Assemble all the details into another prompt to llama3.2" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "85a5b6e2-e7ef-44a9-bc7f-59ede71037b5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_all_details(url):\n", |
||||||
|
" result = \"Landing page:\\n\"\n", |
||||||
|
" result += Website(url).get_contents()\n", |
||||||
|
" links = get_links(url)\n", |
||||||
|
" print(\"Found links:\", links)\n", |
||||||
|
" for link in links[\"links\"]:\n", |
||||||
|
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||||
|
" result += Website(link[\"url\"]).get_contents()\n", |
||||||
|
" return result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5099bd14-076d-4745-baf3-dac08d8e5ab2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(get_all_details(\"https://huggingface.co\"))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "9b863a55-f86c-4e3f-8a79-94e24c1a8cf2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||||
|
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", |
||||||
|
"Include details of company culture, customers and careers/jobs if you have the information.\"\n", |
||||||
|
"\n", |
||||||
|
"# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone':\n", |
||||||
|
"\n", |
||||||
|
"# system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||||
|
"# and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", |
||||||
|
"# Include details of company culture, customers and careers/jobs if you have the information.\"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6ab83d92-d36b-4ce0-8bcc-5bb4c2f8ff23", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_brochure_user_prompt(company_name, url):\n", |
||||||
|
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", |
||||||
|
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n", |
||||||
|
" user_prompt += get_all_details(url)\n", |
||||||
|
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "cd909e0b-1312-4ce2-a553-821e795d7572", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"get_brochure_user_prompt(\"HuggingFace\", \"https://huggingface.co\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e44de579-4a1a-4e6a-a510-20ea3e4b8d46", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def create_brochure(company_name, url):\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||||
|
" ],\n", |
||||||
|
" )\n", |
||||||
|
" result = response['message']['content']\n", |
||||||
|
" display(Markdown(result))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e093444a-9407-42ae-924a-145730591a39", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"create_brochure(\"HuggingFace\", \"https://huggingface.co\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "61eaaab7-0b47-4b29-82d4-75d474ad8d18", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Finally - a minor improvement\n", |
||||||
|
"\n", |
||||||
|
"With a small adjustment, we can change this so that the results stream back from Ollama,\n", |
||||||
|
"with the familiar typewriter animation" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "51db0e49-f261-4137-aabe-92dd601f7725", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def stream_brochure(company_name, url):\n", |
||||||
|
" stream = ollama.chat(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||||
|
" ],\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" response = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" content = chunk.get(\"message\", {}).get(\"content\", \"\")\n", |
||||||
|
" if content:\n", |
||||||
|
" response += content\n", |
||||||
|
" response = response.replace(\"```\", \"\").replace(\"markdown\", \"\")\n", |
||||||
|
" update_display(Markdown(response), display_id=display_handle.display_id)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "56bf0ae3-ee9d-4a72-9cd6-edcac67ceb6d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "fdb3f8d8-a3eb-41c8-b1aa-9f60686a653b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Try changing the system prompt to the humorous version when you make the Brochure for Hugging Face:\n", |
||||||
|
"\n", |
||||||
|
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "a27bf9e0-665f-4645-b66b-9725e2a959b5", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#181;\">Business applications</h2>\n", |
||||||
|
" <span style=\"color:#181;\">In this exercise we extended the Day 1 code to make multiple LLM calls, and generate a document.\n", |
||||||
|
"\n", |
||||||
|
"This is perhaps the first example of Agentic AI design patterns, as we combined multiple calls to LLMs. This will feature more in Week 2, and then we will return to Agentic AI in a big way in Week 8 when we build a fully autonomous Agent solution.\n", |
||||||
|
"\n", |
||||||
|
"Generating content in this way is one of the very most common Use Cases. As with summarization, this can be applied to any business vertical. Write marketing content, generate a product tutorial from a spec, create personalized email content, and so much more. Explore how you can apply content generation to your business, and try making yourself a proof-of-concept prototype.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "14b2454b-8ef8-4b5c-b928-053a15e0d553", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Before you move to Week 2 (which is tons of fun)</h2>\n", |
||||||
|
" <span style=\"color:#900;\">Please see the week1 EXERCISE notebook for your challenge for the end of week 1. This will give you some essential practice working with Frontier APIs, and prepare you well for Week 2.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "17b64f0f-7d33-4493-985a-033d06e8db08", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#f71;\">A reminder on 2 useful resources</h2>\n", |
||||||
|
" <span style=\"color:#f71;\">1. The resources for the course are available <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">here.</a><br/>\n", |
||||||
|
" 2. I'm on LinkedIn <a href=\"https://www.linkedin.com/in/eddonner/\">here</a> and I love connecting with people taking the course!\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6f48e42e-fa7a-495f-a5d4-26bfc24d60b6", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../thankyou.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#090;\">Finally! I have a special request for you</h2>\n", |
||||||
|
" <span style=\"color:#090;\">\n", |
||||||
|
" My editor tells me that it makes a MASSIVE difference when students rate this course on Udemy - it's one of the main ways that Udemy decides whether to show it to others. If you're able to take a minute to rate this, I'd be so very grateful! And regardless - always please reach out to me at ed@edwarddonner.com if I can help at any point.\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.10.7" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,444 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "it1JLoxrSqO1", |
||||||
|
"metadata": { |
||||||
|
"id": "it1JLoxrSqO1" |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"!pip install openai python-docx python-dotenv gradio openpyxl" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "950a084a-7f92-4669-af62-f07cb121da56", |
||||||
|
"metadata": { |
||||||
|
"id": "950a084a-7f92-4669-af62-f07cb121da56" |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import os\n", |
||||||
|
"import json\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"#from IPython.display import Markdown, display, update_display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"from docx import Document" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d0548135-ef16-4102-a55a-cea888a51c29", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import pandas as pd\n", |
||||||
|
"import re\n", |
||||||
|
"import gradio as gr" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ab9f734f-ed6f-44f6-accb-594f9ca4843d", |
||||||
|
"metadata": { |
||||||
|
"id": "ab9f734f-ed6f-44f6-accb-594f9ca4843d" |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class ReqDoc:\n", |
||||||
|
" def __init__(self, file_path):\n", |
||||||
|
" self.file_path = file_path\n", |
||||||
|
"\n", |
||||||
|
" def extract(self):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Reads the content of a .docx file and returns the paragraphs as a list of strings.\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" try:\n", |
||||||
|
" # Check if the file exists\n", |
||||||
|
" if not os.path.exists(self.file_path):\n", |
||||||
|
" raise FileNotFoundError(f\"The file {self.file_path} was not found.\")\n", |
||||||
|
"\n", |
||||||
|
" # Attempt to open and read the document\n", |
||||||
|
" doc = Document(self.file_path)\n", |
||||||
|
" text = \"\\n\".join([paragraph.text for paragraph in doc.paragraphs])\n", |
||||||
|
" return text\n", |
||||||
|
"\n", |
||||||
|
" except FileNotFoundError as fnf_error:\n", |
||||||
|
" print(fnf_error)\n", |
||||||
|
" return None\n", |
||||||
|
" except Exception as e:\n", |
||||||
|
" print(f\"An error occurred: {e}\")\n", |
||||||
|
" return None\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "008f485a-5718-48f6-b408-06eb6d59d7f9", |
||||||
|
"metadata": { |
||||||
|
"id": "008f485a-5718-48f6-b408-06eb6d59d7f9" |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Initialize and constants\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if api_key and api_key.startswith('sk-proj') and len(api_key)>10:\n", |
||||||
|
" print(\"API key looks good!\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"There might be a problem with your API key. Please check!\")\n", |
||||||
|
" \n", |
||||||
|
"MODEL = 'gpt-4o-mini'\n", |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b6110ff3-74bc-430a-8051-7d86a216f0fb", |
||||||
|
"metadata": { |
||||||
|
"id": "b6110ff3-74bc-430a-8051-7d86a216f0fb" |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"#Set up system prompt for extracting just the requirements from the document\n", |
||||||
|
"\n", |
||||||
|
"req_doc_system_prompt = \"You are provided with a complete requirements specifications document. \\\n", |
||||||
|
"You are able to decide which content from that document are related to actual requirements, identify each requirement as \\\n", |
||||||
|
"functional or non-functional and list them all.\\n\"\n", |
||||||
|
"req_doc_system_prompt += \"If the document is empty or do not contain requirements or if you cannot extract them, please respond as such.\\\n", |
||||||
|
"Do not make up your own requirements. \\n\"\n", |
||||||
|
"req_doc_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||||
|
"req_doc_system_prompt += \"\"\"\n", |
||||||
|
"{\n", |
||||||
|
" \"requirements\": [\n", |
||||||
|
" {\"RequirementNo\": \"FR-01\", \"Requirement Description\": \"description of this functional requirement goes here\"},\n", |
||||||
|
" {\"RequirementNo\": \"FR-02\": \"Requirement Description\": \"description of this functional requirement goes here\"},\n", |
||||||
|
" {\"RequirementNo\": \"NFR-01\": \"Requirement Description\": \"description of this non-functional requirement goes here\"},\n", |
||||||
|
" {\"RequirementNo\": \"NFR-02\": \"Requirement Description\": \"description of this non-functional requirement goes here\"}\n", |
||||||
|
" ]\n", |
||||||
|
"}\n", |
||||||
|
"\"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "20460e45-c1b7-4dc4-ab07-932235c19895", |
||||||
|
"metadata": { |
||||||
|
"id": "20460e45-c1b7-4dc4-ab07-932235c19895" |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"#Set up user prompt, sending in the requirements doc as input and calling the ReqDoc.extract function. Key to note here is the explicit instructions to\n", |
||||||
|
"#respond in JSON format.\n", |
||||||
|
"\n", |
||||||
|
"def req_doc_user_prompt(doc):\n", |
||||||
|
" user_prompt = \"Here is the contents from a requirement document.\\n\"\n", |
||||||
|
" user_prompt += f\"{doc.extract()} \\n\"\n", |
||||||
|
" user_prompt += \"Please scan through the document and extract only the actual requirements. For example, ignore sections or \\\n", |
||||||
|
"paragraphs such as Approvers, table of contents and similar sections which are not really requirements.\\\n", |
||||||
|
"You must respond in a JSON format\"\n", |
||||||
|
" user_prompt += \"If the content is empty, respond that there are no valid requirements you could extract and ask for a proper document.\\n\"\n", |
||||||
|
" user_prompt = user_prompt[:25_000] # Truncate if more than 25,000 characters\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3a9f0f84-69a0-4971-a545-5bb40c2f9891", |
||||||
|
"metadata": { |
||||||
|
"id": "3a9f0f84-69a0-4971-a545-5bb40c2f9891" |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"#Function to call chatgpt-4o-mini model with the user and system prompts set above and returning the json formatted result obtained from chatgpt\n", |
||||||
|
"def get_requirements(doc):\n", |
||||||
|
" reqdoc = ReqDoc(doc)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": req_doc_system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": req_doc_user_prompt(reqdoc)}\n", |
||||||
|
" ],\n", |
||||||
|
" response_format={\"type\": \"json_object\"}\n", |
||||||
|
" )\n", |
||||||
|
" result = response.choices[0].message.content\n", |
||||||
|
" return json.loads(result)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f9bb04ef-78d3-4e0f-9ed1-59a961a0663e", |
||||||
|
"metadata": { |
||||||
|
"id": "f9bb04ef-78d3-4e0f-9ed1-59a961a0663e" |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"#Uncomment and run this if you want to see the extracted requriements in json format.\n", |
||||||
|
"#get_requirements(\"reqdoc.docx\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "1fe8618c-1dfe-4030-bad8-405731294c93", |
||||||
|
"metadata": { |
||||||
|
"id": "1fe8618c-1dfe-4030-bad8-405731294c93" |
||||||
|
}, |
||||||
|
"source": [ |
||||||
|
"### Next, we will make another call to gpt-4o-mini" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "db2c1eb3-7740-43a4-9c0b-37b7e70c739b", |
||||||
|
"metadata": { |
||||||
|
"id": "db2c1eb3-7740-43a4-9c0b-37b7e70c739b" |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"#Set up system prompt to ask for test cases in table format\n", |
||||||
|
"system_prompt = \"You are an assitant that receives a list of functional and non functional requirements in JSON format. You are the expert in generating unit test cases for each requirement. \\\n", |
||||||
|
"You will create as many different test cases as needed for each requirement and produce a result in a table. Order the table by requirement No. Provide clear details on test case pass criteria. \\\n", |
||||||
|
"The table will contain the following columns. \\\n", |
||||||
|
"1.S No\\\n", |
||||||
|
"2.Requirement No\\\n", |
||||||
|
"3.Requirement Description\\\n", |
||||||
|
"4.Test Case ID\\\n", |
||||||
|
"5.Test case summary\\\n", |
||||||
|
"6.Test case description\\\n", |
||||||
|
"7.Success criteria \\n\"\n", |
||||||
|
"system_prompt += \"If you are provided with an empty list, ask for a proper requirement doc\\n\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c4cd2bdf-e1bd-43ff-85fa-760ba39ed8c5", |
||||||
|
"metadata": { |
||||||
|
"id": "c4cd2bdf-e1bd-43ff-85fa-760ba39ed8c5" |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Set up user prompt passing in the req doc file. This in turn will call the get_requirements function, which will make a call to chatgpt.\n", |
||||||
|
"\n", |
||||||
|
"def get_testcase_user_prompt(reqdoc):\n", |
||||||
|
" user_prompt = \"You are looking at the following list of requirements. \\n\"\n", |
||||||
|
" user_prompt += f\"{get_requirements(reqdoc)}\\n\"\n", |
||||||
|
" user_prompt += \"Prepare unit test cases for each of these requirements in a table and send that table as response. \\n\"\n", |
||||||
|
" user_prompt += user_prompt[:25000]\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5b2a2b46-9d9c-416c-b189-3007b4d26d76", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"#This is the 2nd call to chatgpt to get test cases. display(Markdown) will take care of producing a neatly formatted table output.\n", |
||||||
|
"def create_testcase_doc_gradio(response, is_response_ready, is_cleared, file_input):\n", |
||||||
|
" if is_cleared or file_input == None: # Prevent OpenAI call if \"Clear\" was clicked\n", |
||||||
|
" return \"\", False\n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_testcase_user_prompt(file_input)}\n", |
||||||
|
" ],\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" #Modified for Gradio\n", |
||||||
|
" result = \"\"\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" result += chunk.choices[0].delta.content or \"\"\n", |
||||||
|
" #print(result)\n", |
||||||
|
" yield result, False" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2bb96a11-063e-4b20-9880-71fa9ea4d3f7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define this variable and then pass js=force_dark_mode when creating the Interface\n", |
||||||
|
"force_dark_mode = \"\"\"\n", |
||||||
|
"function refresh() {\n", |
||||||
|
" const url = new URL(window.location);\n", |
||||||
|
" if (url.searchParams.get('__theme') !== 'dark') {\n", |
||||||
|
" url.searchParams.set('__theme', 'dark');\n", |
||||||
|
" window.location.href = url.href;\n", |
||||||
|
" }\n", |
||||||
|
"}\n", |
||||||
|
"\"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5c81c766-9613-4614-b88d-410654672b89", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def show_or_hide_save_button(response, is_response_ready, is_cleared):\n", |
||||||
|
" if is_cleared or response == None:\n", |
||||||
|
" return \"\", False\n", |
||||||
|
" table_pattern = r\"(\\|.+\\|[\\r\\n]+)+\"\n", |
||||||
|
" table_match = re.search(table_pattern, response)\n", |
||||||
|
" if table_match:\n", |
||||||
|
" return response, True #(response, is_response_ready)\n", |
||||||
|
" else:\n", |
||||||
|
" return response, False #(response, is_response_ready)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a5f5d8e7-d29c-4f40-8d57-a9911bb7c47e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def extract_table_from_markdown(response):\n", |
||||||
|
" # Regular expression to match Markdown tables\n", |
||||||
|
" table_pattern = r\"(\\|.+\\|[\\r\\n]+)+\"\n", |
||||||
|
" table_match = re.search(table_pattern, response)\n", |
||||||
|
"\n", |
||||||
|
" if table_match:\n", |
||||||
|
" table_data = table_match.group(0)\n", |
||||||
|
" # Process the table into a format pandas can read\n", |
||||||
|
" rows = table_data.strip().split(\"\\n\")\n", |
||||||
|
" data = [row.split(\"|\")[1:-1] for row in rows] # Split columns by '|'\n", |
||||||
|
"\n", |
||||||
|
" # Convert to DataFrame\n", |
||||||
|
" df = pd.DataFrame(data[1:], columns=data[0]) # First row is the header\n", |
||||||
|
"\n", |
||||||
|
" # Save to Excel\n", |
||||||
|
" output_file = \"test_cases.xlsx\"\n", |
||||||
|
" df.to_excel(output_file, index=False)\n", |
||||||
|
"\n", |
||||||
|
" return output_file\n", |
||||||
|
" else:\n", |
||||||
|
" return None" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c1380b11-3e28-40de-ab1a-93a5fd73cf81", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def extract_and_save_button(response, is_cleared):\n", |
||||||
|
" if is_cleared:\n", |
||||||
|
" return None # Do nothing if the file was cleared\n", |
||||||
|
" # This function will be triggered when the user clicks \"Save as Excel\"\n", |
||||||
|
" output_file = extract_table_from_markdown(response)\n", |
||||||
|
" if output_file:\n", |
||||||
|
" return output_file\n", |
||||||
|
" else:\n", |
||||||
|
" return \"No table found in the provided input.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3a532b42-9f81-4c75-8be4-e40d621a6b35", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Gradio interface\n", |
||||||
|
"with gr.Blocks(js=force_dark_mode) as demo:\n", |
||||||
|
" gr.HTML(\"<h2 style='text-align: center; color: white;'>📄 Test case automation</h2>\")\n", |
||||||
|
" with gr.Row():\n", |
||||||
|
" file_input = gr.File(label=\"Upload your requirements docx file\", file_types=[\".docx\"])\n", |
||||||
|
" with gr.Row():\n", |
||||||
|
" response = gr.Markdown()\n", |
||||||
|
" # Button to save the table as Excel file (optional)\n", |
||||||
|
" save_button = gr.Button(\"Download Table as Excel\", visible=False)\n", |
||||||
|
" file_output = gr.File(label=\"Download Excel File\", visible=False) \n", |
||||||
|
" # State variable to track if response is ready\n", |
||||||
|
" is_response_ready = gr.State(False)\n", |
||||||
|
" with gr.Row():\n", |
||||||
|
" clear_button = gr.Button(\"Clear\")\n", |
||||||
|
" # State variable to track if clear button is clicked\n", |
||||||
|
" is_cleared = gr.State(False)\n", |
||||||
|
"\n", |
||||||
|
" # Function to show \"Processing...\" message\n", |
||||||
|
" def show_processing(is_cleared, file_input):\n", |
||||||
|
" if is_cleared or file_input==None:\n", |
||||||
|
" return None, False, is_cleared, file_input # Do nothing if the file was cleared\n", |
||||||
|
" #return gr.HTML(\"<h6 style='text-align: left; color: #ffffffff;'>⌛ Processing your file... Please wait!</h6>\"), False, is_cleared, file_input\n", |
||||||
|
" return \"⌛ Processing your file... Please wait!\", False, is_cleared, file_input\n", |
||||||
|
" \n", |
||||||
|
" # Trigger response only if the file was uploaded and not cleared\n", |
||||||
|
" file_input.change(\n", |
||||||
|
" lambda _: False, # Directly set is_cleared to False\n", |
||||||
|
" inputs=[file_input],\n", |
||||||
|
" outputs=[is_cleared]\n", |
||||||
|
" ).then(\n", |
||||||
|
" show_processing, inputs=[is_cleared, file_input], outputs=[response, is_response_ready, is_cleared, file_input]\n", |
||||||
|
" ).then(\n", |
||||||
|
" create_testcase_doc_gradio, inputs=[response, is_response_ready, is_cleared, file_input], outputs=[response, is_response_ready]\n", |
||||||
|
" ).then(\n", |
||||||
|
" show_or_hide_save_button, inputs=[response, is_response_ready, is_cleared], outputs=[response, is_response_ready]\n", |
||||||
|
" ).then(\n", |
||||||
|
" lambda _, ready: (gr.update(visible=ready), gr.update(visible=ready)), inputs=[response, is_response_ready], outputs=[save_button,file_output])\n", |
||||||
|
"\n", |
||||||
|
" #.then() passes the previous function outputs as inputs to the next function\n", |
||||||
|
"\n", |
||||||
|
" # Button action to extract and save table as an Excel file\n", |
||||||
|
" save_button.click(extract_and_save_button, inputs=[response, is_cleared], outputs=file_output)\n", |
||||||
|
" \n", |
||||||
|
" # Clear button resets both file and output while setting is_cleared to True\n", |
||||||
|
" clear_button.click(lambda: (None, None, None, True), inputs=None, outputs=[file_input, file_output, response, is_cleared]) \n", |
||||||
|
"\n", |
||||||
|
"# Launch Gradio app\n", |
||||||
|
"demo.launch(share=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "cd5314b2-ee91-49bd-9d40-558775d44382", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"colab": { |
||||||
|
"provenance": [] |
||||||
|
}, |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,632 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# YOUR FIRST LAB\n", |
||||||
|
"## Please read this. This is super-critical to get you prepared; there's no fluff here!\n", |
||||||
|
"\n", |
||||||
|
"## Your first Frontier LLM Project\n", |
||||||
|
"\n", |
||||||
|
"Let's build a useful LLM solution - in a matter of minutes.\n", |
||||||
|
"\n", |
||||||
|
"By the end of this course, you will have built an autonomous Agentic AI solution with 7 agents that collaborate to solve a business problem. All in good time! We will start with something smaller...\n", |
||||||
|
"\n", |
||||||
|
"Our goal is to code a new kind of Web Browser. Give it a URL, and it will respond with a summary. The Reader's Digest of the internet!!\n", |
||||||
|
"\n", |
||||||
|
"Before starting, you should have completed the setup for [PC](../SETUP-PC.md) or [Mac](../SETUP-mac.md) and you hopefully launched this jupyter lab from within the project root directory, with your environment activated.\n", |
||||||
|
"\n", |
||||||
|
"## If you're new to Jupyter Lab\n", |
||||||
|
"\n", |
||||||
|
"Welcome to the wonderful world of Data Science experimentation! Once you've used Jupyter Lab, you'll wonder how you ever lived without it. Simply click in each \"cell\" with code in it, such as the cell immediately below this text, and hit Shift+Return to execute that cell. As you wish, you can add a cell with the + button in the toolbar, and print values of variables, or try out variations. \n", |
||||||
|
"\n", |
||||||
|
"I've written a notebook called [Guide to Jupyter](Guide%20to%20Jupyter.ipynb) to help you get more familiar with Jupyter Labs, including adding Markdown comments, using `!` to run shell commands, and `tqdm` to show progress.\n", |
||||||
|
"\n", |
||||||
|
"## If you're new to the Command Line\n", |
||||||
|
"\n", |
||||||
|
"Please see these excellent guides: [Command line on PC](https://chatgpt.com/share/67b0acea-ba38-8012-9c34-7a2541052665) and [Command line on Mac](https://chatgpt.com/canvas/shared/67b0b10c93a081918210723867525d2b). \n", |
||||||
|
"Linux people, something tells me you could teach _me_ a thing or two about the command line!\n", |
||||||
|
"\n", |
||||||
|
"## If you'd prefer to work in IDEs\n", |
||||||
|
"\n", |
||||||
|
"If you're more comfortable in IDEs like VSCode or Pycharm, they both work great with these lab notebooks too. \n", |
||||||
|
"If you'd prefer to work in VSCode, [here](https://chatgpt.com/share/676f2e19-c228-8012-9911-6ca42f8ed766) are instructions from an AI friend on how to configure it for the course.\n", |
||||||
|
"\n", |
||||||
|
"## If you'd like to brush up your Python\n", |
||||||
|
"\n", |
||||||
|
"I've added a notebook called [Intermediate Python](Intermediate%20Python.ipynb) to get you up to speed. But you should give it a miss if you already have a good idea what this code does: \n", |
||||||
|
"`yield from {book.get(\"author\") for book in books if book.get(\"author\")}`\n", |
||||||
|
"\n", |
||||||
|
"## I am here to help\n", |
||||||
|
"\n", |
||||||
|
"If you have any problems at all, please do reach out. \n", |
||||||
|
"I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect (and I love connecting!) \n", |
||||||
|
"And this is new to me, but I'm also trying out X/Twitter at [@edwarddonner](https://x.com/edwarddonner) - if you're on X, please show me how it's done 😂 \n", |
||||||
|
"\n", |
||||||
|
"## More troubleshooting\n", |
||||||
|
"\n", |
||||||
|
"Please see the [troubleshooting](troubleshooting.ipynb) notebook in this folder to diagnose and fix common problems. At the very end of it is a diagnostics script with some useful debug info.\n", |
||||||
|
"\n", |
||||||
|
"## If this is old hat!\n", |
||||||
|
"\n", |
||||||
|
"If you're already comfortable with today's material, please hang in there; you can move swiftly through the first few labs - we will get much more in depth as the weeks progress.\n", |
||||||
|
"\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Please read - important note</h2>\n", |
||||||
|
" <span style=\"color:#900;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you carefully execute this yourself, <b>after</b> watching the lecture. Add print statements to understand what's going on, and then come up with your own variations. If you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#f71;\">Treat these labs as a resource</h2>\n", |
||||||
|
" <span style=\"color:#f71;\">I push updates to the code regularly. When people ask questions or have problems, I incorporate it in the code, adding more examples or improved commentary. As a result, you'll notice that the code below isn't identical to the videos. Everything from the videos is here; but in addition, I've added more steps and better explanations, and occasionally added new models like DeepSeek. Consider this like an interactive book that accompanies the lectures.\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#181;\">Business value of these exercises</h2>\n", |
||||||
|
" <span style=\"color:#181;\">A final thought. While I've designed these notebooks to be educational, I've also tried to make them enjoyable. We'll do fun things like have LLMs tell jokes and argue with each other. But fundamentally, my goal is to teach skills you can apply in business. I'll explain business implications as we go, and it's worth keeping this in mind: as you build experience with models and techniques, think of ways you could put this into action at work today. Please do contact me if you'd like to discuss more or if you have ideas to bounce off me.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"\n", |
||||||
|
"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6900b2a8-6384-4316-8aaa-5e519fca4254", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Connecting to OpenAI\n", |
||||||
|
"\n", |
||||||
|
"The next cell is where we load in the environment variables in your `.env` file and connect to OpenAI.\n", |
||||||
|
"\n", |
||||||
|
"## Troubleshooting if you have problems:\n", |
||||||
|
"\n", |
||||||
|
"Head over to the [troubleshooting](troubleshooting.ipynb) notebook in this folder for step by step code to identify the root cause and fix it!\n", |
||||||
|
"\n", |
||||||
|
"If you make a change, try restarting the \"Kernel\" (the python process sitting behind this notebook) by Kernel menu >> Restart Kernel and Clear Outputs of All Cells. Then try this notebook again, starting at the top.\n", |
||||||
|
"\n", |
||||||
|
"Or, contact me! Message me or email ed@edwarddonner.com and we will get this to work.\n", |
||||||
|
"\n", |
||||||
|
"Any concerns about API costs? See my notes in the README - costs should be minimal, and you can control it at every point. You can also use Ollama as a free alternative, which we discuss during Day 2." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7b87cadb-d513-4303-baee-a37b6f938e4d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"# Check the key\n", |
||||||
|
"\n", |
||||||
|
"if not api_key:\n", |
||||||
|
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||||
|
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||||
|
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||||
|
"elif api_key.strip() != api_key:\n", |
||||||
|
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"API key found and looks good so far!\")\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import httpx\n", |
||||||
|
"openai = OpenAI(http_client=httpx.Client(verify=False))\n", |
||||||
|
"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n", |
||||||
|
"# If it STILL doesn't work (horrors!) then please see the Troubleshooting notebook in this folder for full instructions" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "442fc84b-0815-4f40-99ab-d9a5da6bda91", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Let's make a quick call to a Frontier model to get started, as a preview!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a58394bf-1e45-46af-9bfd-01e24da6f49a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# To give you a preview -- calling OpenAI with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\n", |
||||||
|
"\n", |
||||||
|
"message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n", |
||||||
|
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=[{\"role\":\"user\", \"content\":message}])\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "2aa190e5-cb31-456a-96cc-db109919cd78", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## OK onwards with our first project" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c5e793b2-6775-426a-a139-4848291d0463", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" requests.packages.urllib3.disable_warnings()\n", |
||||||
|
" response = requests.get(url, headers=headers, verify=False)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's try one out. Change the website and add print statements to follow along.\n", |
||||||
|
"ed = Website(\"http://edwarddonner.com\")\n", |
||||||
|
"print(ed.title)\n", |
||||||
|
"print(ed.text)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6a478a0c-2c53-48ff-869c-4d08199931e1", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Types of prompts\n", |
||||||
|
"\n", |
||||||
|
"You may know this already - but if not, you will get very familiar with it!\n", |
||||||
|
"\n", |
||||||
|
"Models like GPT4o have been trained to receive instructions in a particular way.\n", |
||||||
|
"\n", |
||||||
|
"They expect to receive:\n", |
||||||
|
"\n", |
||||||
|
"**A system prompt** that tells them what task they are performing and what tone they should use\n", |
||||||
|
"\n", |
||||||
|
"**A user prompt** -- the conversation starter that they should reply to" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "abdb8417-c5dc-44bc-9bee-2e059d162699", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||||
|
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||||
|
"Respond in markdown.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||||
|
"\n", |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||||
|
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||||
|
"please provide a short summary of this website in markdown. \\\n", |
||||||
|
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "26448ec4-5c00-4204-baec-7df91d11ff2e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(user_prompt_for(ed))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Messages\n", |
||||||
|
"\n", |
||||||
|
"The API from OpenAI expects to receive messages in a particular structure.\n", |
||||||
|
"Many of the other APIs share this structure:\n", |
||||||
|
"\n", |
||||||
|
"```\n", |
||||||
|
"[\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"system message goes here\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"user message goes here\"}\n", |
||||||
|
"]\n", |
||||||
|
"\n", |
||||||
|
"To give you a preview, the next 2 cells make a rather simple call - we won't stretch the mighty GPT (yet!)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f25dcd35-0cd0-4235-9f64-ac37ed9eaaa5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n", |
||||||
|
"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "21ed95c5-7001-47de-a36d-1d6673b403ce", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# To give you a preview -- calling OpenAI with system and user messages:\n", |
||||||
|
"\n", |
||||||
|
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## And now let's build useful messages for GPT-4o-mini, using a function" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0134dfa4-8299-48b5-b444-f2a8c3403c88", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# See how this function creates exactly the format above\n", |
||||||
|
"\n", |
||||||
|
"def messages_for(website):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "36478464-39ee-485c-9f3f-6a4e458dbc9c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Try this out, and then try for a few more websites\n", |
||||||
|
"\n", |
||||||
|
"messages_for(ed)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Time to bring it together - the API for OpenAI is very simple!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "905b9919-aba7-45b5-ae65-81b3d1d78e34", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# And now: call the OpenAI API. You will get very familiar with this!\n", |
||||||
|
"\n", |
||||||
|
"def summarize(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model = \"gpt-4o-mini\",\n", |
||||||
|
" messages = messages_for(website)\n", |
||||||
|
" )\n", |
||||||
|
" return response.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"summarize(\"https://edwarddonner.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3d926d59-450e-4609-92ba-2d6f244f1342", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function to display this nicely in the Jupyter output, using markdown\n", |
||||||
|
"\n", |
||||||
|
"def display_summary(url):\n", |
||||||
|
" summary = summarize(url)\n", |
||||||
|
" display(Markdown(summary))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3018853a-445f-41ff-9560-d925d1774b2f", |
||||||
|
"metadata": { |
||||||
|
"scrolled": true |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://edwarddonner.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f8a34db6-9c2f-4f5e-95b4-62090d7b591b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://openai.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "b3bcf6f4-adce-45e9-97ad-d9a5d7a3a624", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Let's try more websites\n", |
||||||
|
"\n", |
||||||
|
"Note that this will only work on websites that can be scraped using this simplistic approach.\n", |
||||||
|
"\n", |
||||||
|
"Websites that are rendered with Javascript, like React apps, won't show up. See the community-contributions folder for a Selenium implementation that gets around this. You'll need to read up on installing Selenium (ask ChatGPT!)\n", |
||||||
|
"\n", |
||||||
|
"Also Websites protected with CloudFront (and similar) may give 403 errors - many thanks Andy J for pointing this out.\n", |
||||||
|
"\n", |
||||||
|
"But many websites will work just fine!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "45d83403-a24c-44b5-84ac-961449b4008f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://cnn.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "75e9fd40-b354-4341-991e-863ef2e59db7", |
||||||
|
"metadata": { |
||||||
|
"scrolled": true |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://anthropic.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "c951be1a-7f1b-448f-af1f-845978e47e2c", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#181;\">Business applications</h2>\n", |
||||||
|
" <span style=\"color:#181;\">In this exercise, you experienced calling the Cloud API of a Frontier Model (a leading model at the frontier of AI) for the first time. We will be using APIs like OpenAI at many stages in the course, in addition to building our own LLMs.\n", |
||||||
|
"\n", |
||||||
|
"More specifically, we've applied this to Summarization - a classic Gen AI use case to make a summary. This can be applied to any business vertical - summarizing the news, summarizing financial performance, summarizing a resume in a cover letter - the applications are limitless. Consider how you could apply Summarization in your business, and try prototyping a solution.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>\n", |
||||||
|
"\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Before you continue - now try yourself</h2>\n", |
||||||
|
" <span style=\"color:#900;\">Use the cell below to make your own simple commercial example. Stick with the summarization use case for now. Here's an idea: write something that will take the contents of an email, and will suggest an appropriate short subject line for the email. That's the kind of feature that might be built into a commercial email tool.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "00743dac-0e70-45b7-879a-d7293a6f68a6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A small exercise to feed the llm with image alt text and return a funny tweet.\n", |
||||||
|
"\n", |
||||||
|
"# Step 1: Create your prompts\n", |
||||||
|
"import json\n", |
||||||
|
"system_prompt = \"You are a meme lord. You like tweeting funny and hilarious comments on images. To understand the image you would be given alt text on the image.\"\n", |
||||||
|
"class website:\n", |
||||||
|
" def __init__(self,url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" requests.packages.urllib3.disable_warnings()\n", |
||||||
|
" response = requests.get(url, headers=headers, verify=False)\n", |
||||||
|
" html_content = response.content\n", |
||||||
|
" soup = BeautifulSoup(html_content, 'html.parser')\n", |
||||||
|
" image_tags = soup.find_all('img')\n", |
||||||
|
" self.image_urls = [img['src'] for img in image_tags if img.get('src')]\n", |
||||||
|
" self.image_alt = [img['alt'] if img.get('alt') else \"\" for img in image_tags]\n", |
||||||
|
"\n", |
||||||
|
" # Restricting to 3 images only.\n", |
||||||
|
" if self.image_urls:\n", |
||||||
|
" self.images = {self.image_urls[i]:self.image_alt[i] for i in range(4)}\n", |
||||||
|
" else:\n", |
||||||
|
" self.images = {}\n", |
||||||
|
" \n", |
||||||
|
"\n", |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"Following are images with their alt-text:\"\n", |
||||||
|
" user_prompt += json.dumps(website.images)\n", |
||||||
|
" user_prompt += \"\\n Give me a markdown layout with tables for each image where each image is given its own row, with the image itself on the left and funny tweet on the right.\"\n", |
||||||
|
" return user_prompt\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"# Step 2: Make the messages list\n", |
||||||
|
"page = website(\"https://www.pexels.com/\")\n", |
||||||
|
"user_prompt = user_prompt_for(page)\n", |
||||||
|
"messages = [{\"role\":\"system\",\"content\":system_prompt},{\"role\":\"user\", \"content\":user_prompt}] # fill this in\n", |
||||||
|
"\n", |
||||||
|
"# Step 3: Call OpenAI\n", |
||||||
|
"response = openai.chat.completions.create(\n", |
||||||
|
" model = \"gpt-4o-mini\",\n", |
||||||
|
" messages = messages\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
"# Step 4: print the result\n", |
||||||
|
"display(Markdown((response.choices[0].message.content)))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "36ed9f14-b349-40e9-a42c-b367e77f8bda", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## An extra exercise for those who enjoy web scraping\n", |
||||||
|
"\n", |
||||||
|
"You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them. In the community-contributions folder, you'll find an example Selenium solution from a student (thank you!)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "eeab24dc-5f90-4570-b542-b0585aca3eb6", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Sharing your code\n", |
||||||
|
"\n", |
||||||
|
"I'd love it if you share your code afterwards so I can share it with others! You'll notice that some students have already made changes (including a Selenium implementation) which you will find in the community-contributions folder. If you'd like add your changes to that folder, submit a Pull Request with your new versions in that folder and I'll merge your changes.\n", |
||||||
|
"\n", |
||||||
|
"If you're not an expert with git (and I am not!) then GPT has given some nice instructions on how to submit a Pull Request. It's a bit of an involved process, but once you've done it once it's pretty clear. As a pro-tip: it's best if you clear the outputs of your Jupyter notebooks (Edit >> Clean outputs of all cells, and then Save) for clean notebooks.\n", |
||||||
|
"\n", |
||||||
|
"Here are good instructions courtesy of an AI friend: \n", |
||||||
|
"https://chatgpt.com/share/677a9cb5-c64c-8012-99e0-e06e88afd293" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f4484fcf-8b39-4c3f-9674-37970ed71988", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,201 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# End of week 1 exercise\n", |
||||||
|
"\n", |
||||||
|
"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n", |
||||||
|
"and responds with an explanation. This is a tool that you will be able to use yourself during the course!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c1070317-3ed9-4659-abe3-828943230e03", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"from openai import OpenAI" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# constants\n", |
||||||
|
"\n", |
||||||
|
"MODEL_GPT = 'gpt-4o-mini'\n", |
||||||
|
"MODEL_LLAMA = 'llama3.2'" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# set up environment\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv(\"OPENAI_API_KEY\")\n", |
||||||
|
"\n", |
||||||
|
"# set up clients\n", |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"ollama = OpenAI(base_url=\"http://localhost:11434/v1\" , api_key=\"ollama\")\n", |
||||||
|
"\n", |
||||||
|
"# set up system prompt\n", |
||||||
|
"system_prompt = \"You are a coding tutor. If the user asks you a question, answer it to the point. If you are asked to create a code snippet, generate the code in Python and then explain it shortly.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 53, |
||||||
|
"id": "58f098cb-4b4e-4394-b0b5-29db88e9101c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def send_request(user_prompt, model=MODEL_LLAMA, stream=False):\n", |
||||||
|
" message = [{\"role\": \"system\", \"content\": system_prompt}, {\"role\": \"user\", \"content\": user_prompt}]\n", |
||||||
|
" if model.startswith(\"gpt\"):\n", |
||||||
|
" model_client = openai\n", |
||||||
|
" else:\n", |
||||||
|
" model_client = ollama\n", |
||||||
|
"\n", |
||||||
|
" \n", |
||||||
|
" response = model_client.chat.completions.create(\n", |
||||||
|
" model=model,\n", |
||||||
|
" messages=message,\n", |
||||||
|
" stream=stream\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
" if stream:\n", |
||||||
|
" streaming = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" for chunk in response:\n", |
||||||
|
" streaming += chunk.choices[0].delta.content or ''\n", |
||||||
|
" streaming = streaming.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||||
|
" update_display(Markdown(streaming), display_id=display_handle.display_id)\n", |
||||||
|
"\n", |
||||||
|
" else:\n", |
||||||
|
" return display(Markdown(response.choices[0].message.content))\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 49, |
||||||
|
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdin", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
" How can I display python code properly while streaming the answer from openai? Create a code snippet for this. The streaming should happen in the code canvas.\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# here is the question; type over this to ask something new\n", |
||||||
|
"question = input()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 48, |
||||||
|
"id": "2bc093fa-b2ff-47e9-8ea8-e41499385116", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# question = \"\"\"How can I display python code properly while streaming the answer from openai? Create a code snippet for this. The streaming should happen in the code canvas.\"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Get gpt-4o-mini to answer, with streaming\n", |
||||||
|
"send_request(model=MODEL_GPT, user_prompt=question, stream=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 54, |
||||||
|
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/markdown": [ |
||||||
|
"To display Python code properly with OpenAI's chat interface, you'll need to use the `code` formatting in the response format provided by the API endpoint. \n", |
||||||
|
"\n", |
||||||
|
"Here's an example of how you can modify the API request URL to include the formatted code:\n", |
||||||
|
"\n", |
||||||
|
"```python\n", |
||||||
|
"import requests\n", |
||||||
|
"import json\n", |
||||||
|
"\n", |
||||||
|
"query = {\n", |
||||||
|
" \"text\": \"{\\n} # Python code here\\n}\"\n", |
||||||
|
"\n", |
||||||
|
"headers = {\n", |
||||||
|
" 'Content-Type': 'application/json'\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"response = requests.post('https://api.openai.com/v1/answers', data=json.dumps(query), headers=headers)\n", |
||||||
|
"\n", |
||||||
|
"answer = response.json()\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"However, the most convenient way to display the code is by using the `code` directive directly in your chat prompt. OpenAI will automatically format and highlight your code." |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.Markdown object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Get Llama 3.2 to answer\n", |
||||||
|
"send_request(user_prompt=question)" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,202 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# End of week 1 exercise\n", |
||||||
|
"\n", |
||||||
|
"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n", |
||||||
|
"and responds with an explanation. This is a tool that you will be able to use yourself during the course!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 9, |
||||||
|
"id": "c1070317-3ed9-4659-abe3-828943230e03", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"import openai\n", |
||||||
|
"from openai import OpenAI\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 10, |
||||||
|
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# constants\n", |
||||||
|
"models = {\n", |
||||||
|
" 'MODEL_GPT': 'gpt-4o-mini',\n", |
||||||
|
" 'MODEL_LLAMA': 'llama3.2'\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"# To use ollama using openai API (ensure that ollama is running on localhost)\n", |
||||||
|
"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", |
||||||
|
"\n", |
||||||
|
"def model_choices(model):\n", |
||||||
|
" if model in models:\n", |
||||||
|
" return models[model]\n", |
||||||
|
" else:\n", |
||||||
|
" raise ValueError(f\"Model {model} not found in models dictionary\")\n", |
||||||
|
"\n", |
||||||
|
"def get_model_api(model='MODEL_GPT'):\n", |
||||||
|
" if model == 'MODEL_GPT':\n", |
||||||
|
" return openai, model_choices(model)\n", |
||||||
|
" elif model == 'MODEL_LLAMA':\n", |
||||||
|
" return ollama_via_openai, model_choices(model)\n", |
||||||
|
" else:\n", |
||||||
|
" raise ValueError(f\"Model {model} not found in models dictionary\")\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 12, |
||||||
|
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# set up environment\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"\"\" You are an AI assistant helping a user find information about a product. \n", |
||||||
|
"The user asks you a technical question about code, and you provide a response with code snippets and explanations.\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"def stream_brochure(question, model):\n", |
||||||
|
" api, model_name = get_model_api(model)\n", |
||||||
|
" stream = api.chat.completions.create(\n", |
||||||
|
" model=model_name,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": question}\n", |
||||||
|
" ],\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" response = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response += chunk.choices[0].delta.content or ''\n", |
||||||
|
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||||
|
" update_display(Markdown(response), display_id=display_handle.display_id)\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 13, |
||||||
|
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Here is the question; type over this to ask something new\n", |
||||||
|
"\n", |
||||||
|
"question = \"\"\"\n", |
||||||
|
"Please explain what this code does and why:\n", |
||||||
|
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n", |
||||||
|
"\"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/markdown": [ |
||||||
|
"**Understanding the Code Snippet**\n", |
||||||
|
"\n", |
||||||
|
"This Python code snippet uses a combination of built-in functions, dictionary iteration, and generator expressions to extract and yield author names from a list of `Book` objects.\n", |
||||||
|
"\n", |
||||||
|
"Here's a breakdown:\n", |
||||||
|
"\n", |
||||||
|
"1. **Dictionary Iteration**: The expression `for book in books if book.get(\"author\")`\n", |
||||||
|
" - Iterates over each element (`book`) in the container `books`.\n", |
||||||
|
" - Filters out elements whose `'author'` key does not have a value (i.e., `None`, `False`, or an empty string). This leaves only dictionaries with author information.\n", |
||||||
|
"\n", |
||||||
|
"2. **Dictionary Access**: The expression `{book.get(\"author\") for book in books if book.get(\"author\")}`\n", |
||||||
|
" - Uses dictionary membership testing to access only the values associated with the `'author'` key.\n", |
||||||
|
" - If the value is not found or is considered false, it's skipped in this particular case.\n", |
||||||
|
"\n", |
||||||
|
"3. **Generator Expression**: This generates an iterator that iterates over the filtered author names.\n", |
||||||
|
" - Yields each author name (i.e., a single `'name'` from the book dictionary) on demand.\n", |
||||||
|
" - Since these are generator expressions, they use memory less than equivalent Python lists and also create results on-demand.\n", |
||||||
|
"\n", |
||||||
|
"4. **`yield from`**: This statement takes the generator expression as an argument and uses it to generate a nested iterator structure.\n", |
||||||
|
" - It essentially \"decompresses\" the single level of nested iterator created by `list(iter(x))`, allowing for simpler use cases and potentially significant efficiency improvements for more complex structures where every value must be iterated, while in the latter case just the first item per iterable in the outer expression's sequence needs to actually be yielded into result stream.\n", |
||||||
|
" - By \"yielding\" a nested iterator (the generator expression), we can simplify code by avoiding repetitive structure like `for book, book_author in zip(iterating over), ...` or list creation.\n", |
||||||
|
"\n", |
||||||
|
"**Example Use Case**\n", |
||||||
|
"\n", |
||||||
|
"In this hypothetical example:\n", |
||||||
|
"\n", |
||||||
|
"# Example Book objects\n", |
||||||
|
"class Book:\n", |
||||||
|
" def __init__(self, author, title):\n", |
||||||
|
" self.author = author # str\n", |
||||||
|
" self.title = title\n", |
||||||
|
"\n", |
||||||
|
"books = [\n", |
||||||
|
" {\"author\": \"John Doe\", \"title\": f\"Book 1 by John Doe\"},\n", |
||||||
|
" {\"author\": None, \"title\": f\"Book 2 without Author\"},\n", |
||||||
|
" {\"author\": \"Jane Smith\", \"title\": f\"Book 3 by Jane Smith\"}\n", |
||||||
|
"]\n", |
||||||
|
"\n", |
||||||
|
"# The given expression to extract and yield author names\n", |
||||||
|
"for author in yield from {book.get(\"author\") for book in books if book.get(\"author\")}:\n", |
||||||
|
"\n", |
||||||
|
" print(author) \n", |
||||||
|
"\n", |
||||||
|
"In this code snippet, printing the extracted authors would output `John Doe`, `Jane Smith` (since only dictionaries with author information pass the filtering test).\n", |
||||||
|
"\n", |
||||||
|
"Please modify it like as you wish and use `yield from` along with dictionary iteration, list comprehension or generator expression if needed, and explain what purpose your version has." |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.Markdown object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Get the model of your choice (choices appeared below) to answer, with streaming \n", |
||||||
|
"\n", |
||||||
|
"\"\"\"models = {\n", |
||||||
|
" 'MODEL_GPT': 'gpt-4o-mini',\n", |
||||||
|
" 'MODEL_LLAMA': 'llama3.2'\n", |
||||||
|
"}\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"stream_brochure(question,'MODEL_LLAMA')" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "llms", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,148 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f38e9ebb-453d-4b40-84f6-bc3e9bf4d7ef", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"import json\n", |
||||||
|
"import ollama\n", |
||||||
|
"from typing import List\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"\n", |
||||||
|
"# constants\n", |
||||||
|
"\n", |
||||||
|
"MODEL_GPT = 'gpt-4o-mini'\n", |
||||||
|
"MODEL_LLAMA = 'llama3.2'\n", |
||||||
|
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||||
|
"HEADERS = {\"Content-Type\": \"application/json\"}" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f367c5bb-80a2-4d78-8f27-823f5dafe7c0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# set up environment\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"\n", |
||||||
|
"# System prompt for the AI TECHNICAL LLM AND PYTHON TUTOR.\"\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are an EXPERT in AI, LLMS and Python \\\n", |
||||||
|
"Provide the answer with example ALLWAYS when necessary. \\\n", |
||||||
|
"If you do not know the answer just say 'I don't know the answer' \\\n", |
||||||
|
"Respond in markdown in Spanish.\"\n", |
||||||
|
"\n", |
||||||
|
"# messages\n", |
||||||
|
"def messages_for(question):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": question}\n", |
||||||
|
" ]\n", |
||||||
|
"\n", |
||||||
|
"# here is the question; type over this to ask something new\n", |
||||||
|
"\n", |
||||||
|
"question = \"\"\"\n", |
||||||
|
"Please explain what this code does and why:\n", |
||||||
|
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"question = question[:5_000] # Truncate if more than 5,000 characters" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a90d726d-d494-401f-9cd6-0260f5c781e0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# METHODS TO DISPLAY\n", |
||||||
|
"def display_summary_ollama(question):\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model = MODEL_LLAMA,\n", |
||||||
|
" messages = messages_for(question)\n", |
||||||
|
" ) \n", |
||||||
|
" summary = response['message']['content']\n", |
||||||
|
" display(Markdown(summary))\n", |
||||||
|
"\n", |
||||||
|
"def display_summary_gpt(question):\n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model = MODEL_GPT,\n", |
||||||
|
" messages = messages_for(question),\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" response = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response += chunk.choices[0].delta.content or ''\n", |
||||||
|
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||||
|
" update_display(Markdown(response), display_id=display_handle.display_id)\n", |
||||||
|
" \n", |
||||||
|
"def display_summary(llm, question):\n", |
||||||
|
" if llm.startswith(\"llama3.2\"):\n", |
||||||
|
" display_summary_ollama(question)\n", |
||||||
|
" else:\n", |
||||||
|
" display_summary_gpt(question)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4e993b6d-8fee-43f3-9e36-f86701a5cc57", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Get gpt-4o-mini to answer, with streaming\n", |
||||||
|
"\n", |
||||||
|
"display_summary(MODEL_GPT, question)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "31f6283a-ee57-415e-9a57-83d07261b7f9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Get Llama 3.2 to answer\n", |
||||||
|
"\n", |
||||||
|
"display_summary(MODEL_LLAMA, question)" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,464 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# MY !FIRST LAB\n", |
||||||
|
"\n", |
||||||
|
"### Script will take a stackoverflow issue and summarize it as a technical tutorial. \n", |
||||||
|
"\n", |
||||||
|
"Example links to use: \n", |
||||||
|
" \n", |
||||||
|
"https://stackoverflow.com/questions/14220321/how-do-i-return-the-response-from-an-asynchronous-call \n", |
||||||
|
"https://stackoverflow.com/questions/60174/how-can-i-prevent-sql-injection-in-php\n", |
||||||
|
"https://stackoverflow.com/questions/1732348/regex-match-open-tags-except-xhtml-self-contained-tags\n", |
||||||
|
"\n", |
||||||
|
"*Note: Issues must be answered preferebly by a lot of users.*\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 1, |
||||||
|
"id": "e2fd67f3-6441-4fee-b19c-7c91e6188348", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"website = 'https://stackoverflow.com/questions/60174/how-can-i-prevent-sql-injection-in-php'" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 2, |
||||||
|
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"\n", |
||||||
|
"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 3, |
||||||
|
"id": "7b87cadb-d513-4303-baee-a37b6f938e4d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file callwebsite_content .env\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"# Check the key\n", |
||||||
|
"\n", |
||||||
|
"if not api_key:\n", |
||||||
|
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||||
|
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||||
|
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||||
|
"elif api_key.strip() != api_key:\n", |
||||||
|
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"API key found and looks good so far!\")\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 4, |
||||||
|
"id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"\n", |
||||||
|
"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n", |
||||||
|
"# If it STILL doesn't work (horrors!) then please see the Troubleshooting notebook in this folder for full instructions" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 5, |
||||||
|
"id": "c5e793b2-6775-426a-a139-4848291d0463", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"# If you're not familiar with Classes, check out the \"Intermwebsite_contentiate Python\" notebook\n", |
||||||
|
"\n", |
||||||
|
"# Some websites newebsite_content you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 6, |
||||||
|
"id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"mysql - How can I prevent SQL injection in PHP? - Stack Overflow\n", |
||||||
|
"Skip to main content\n", |
||||||
|
"Stack Overflow\n", |
||||||
|
"About\n", |
||||||
|
"Products\n", |
||||||
|
"OverflowAI\n", |
||||||
|
"Stack Overflow for Teams\n", |
||||||
|
"Where developers & technologists share private knowledge with c\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Let's try one out. Change the website and add print statements to follow along.\n", |
||||||
|
"\n", |
||||||
|
"website_content = Website(website)\n", |
||||||
|
"print(website_content.title[:100])\n", |
||||||
|
"print(website_content.text[:150])" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6a478a0c-2c53-48ff-869c-4d08199931e1", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Types of prompts\n", |
||||||
|
"\n", |
||||||
|
"You may know this already - but if not, you will get very familiar with it!\n", |
||||||
|
"\n", |
||||||
|
"Models like GPT4o have been trained to receive instructions in a particular way.\n", |
||||||
|
"\n", |
||||||
|
"They expect to receive:\n", |
||||||
|
"\n", |
||||||
|
"**A system prompt** that tells them what task they are performing and what tone they should use\n", |
||||||
|
"\n", |
||||||
|
"**A user prompt** -- the conversation starter that they should reply to" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 2, |
||||||
|
"id": "268cb127-ec40-4016-9436-94a1ae10a1c6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are a technical writer that analyzes the contents of a stackoverflow website issue containing a question and answer \\\n", |
||||||
|
"and provides a summary in the form of a technical tutorial , ignoring text that might be navigation related. \\\n", |
||||||
|
"Respond in markdown.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 9, |
||||||
|
"id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||||
|
"\n", |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||||
|
" user_prompt += f\"\"\" \n", |
||||||
|
"\n", |
||||||
|
" You are looking at a website titled {website_content.title}\n", |
||||||
|
"\n", |
||||||
|
" Create a technical tutorial baswebsite_content on the following Stack Overflow content:\n", |
||||||
|
" \n", |
||||||
|
" {website_content.text}\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
" The tutorial should include an introduction, problem statement, solution steps, and conclusion.\n", |
||||||
|
" Tutrial should be in markdown format.\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 10, |
||||||
|
"id": "26448ec4-5c00-4204-baec-7df91d11ff2e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"You are looking at a website titled mysql - How can I prevent SQL injection in PHP? - Stack Overflow \n", |
||||||
|
"\n", |
||||||
|
" You are looking at a website titled mysql - How can I prevent SQL injection in PHP? - Stack Overflow\n", |
||||||
|
"\n", |
||||||
|
" Create a technical tutorial baswebsite_content on the following Stack Overflow content:\n", |
||||||
|
"\n", |
||||||
|
" Skip to main content\n", |
||||||
|
"Stack Overflow\n", |
||||||
|
"About\n", |
||||||
|
"Products\n", |
||||||
|
"OverflowAI\n", |
||||||
|
"Stack Overflow for Teams\n", |
||||||
|
"Where developers & technologists share private knowledge with coworkers\n", |
||||||
|
"Advertising & Talent\n", |
||||||
|
"Reach devs & t\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"print(user_prompt_for(website_content)[:500])" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Messages\n", |
||||||
|
"\n", |
||||||
|
"The API from OpenAI expects to receive messages in a particular structure.\n", |
||||||
|
"Many of the other APIs share this structure:\n", |
||||||
|
"\n", |
||||||
|
"```\n", |
||||||
|
"[\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"system message goes here\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"user message goes here\"}\n", |
||||||
|
"]\n", |
||||||
|
"\n", |
||||||
|
"To give you a preview, the next 2 cells make a rather simple call - we won't stretch the mighty GPT (yet!)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## And now let's build useful messages for GPT-4o-mini, using a function" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 11, |
||||||
|
"id": "0134dfa4-8299-48b5-b444-f2a8c3403c88", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# See how this function creates exactly the format above\n", |
||||||
|
"\n", |
||||||
|
"def messages_for(website):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Time to bring it together - the API for OpenAI is very simple!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 12, |
||||||
|
"id": "905b9919-aba7-45b5-ae65-81b3d1d78e34", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# And now: call the OpenAI API. You will get very familiar with this!\n", |
||||||
|
"\n", |
||||||
|
"def summarize(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model = \"gpt-4o-mini\",\n", |
||||||
|
" messages = messages_for(website)\n", |
||||||
|
" )\n", |
||||||
|
" return response.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 13, |
||||||
|
"id": "3d926d59-450e-4609-92ba-2d6f244f1342", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function to display this nicely in the Jupyter output, using markdown\n", |
||||||
|
"\n", |
||||||
|
"def display_summary(url):\n", |
||||||
|
" summary = summarize(url)\n", |
||||||
|
" display(Markdown(summary))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 14, |
||||||
|
"id": "0a6970cc-bed8-4759-a312-3b81236c2f4e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/markdown": [ |
||||||
|
"```markdown\n", |
||||||
|
"# How to Prevent SQL Injection in PHP\n", |
||||||
|
"\n", |
||||||
|
"## Introduction\n", |
||||||
|
"SQL injection is a serious security vulnerability that can allow an attacker to interfere with the queries that your application makes to the database. By exploiting this vulnerability, an attacker can gain unauthorized access to sensitive data, manipulate data, and even execute administrative operations on the database. This tutorial will guide you on how to prevent SQL injection in your PHP applications through various best practices.\n", |
||||||
|
"\n", |
||||||
|
"## Problem Statement\n", |
||||||
|
"Consider the following PHP code that is vulnerable to SQL injection:\n", |
||||||
|
"\n", |
||||||
|
"```php\n", |
||||||
|
"$unsafe_variable = $_POST['user_input']; \n", |
||||||
|
"mysql_query(\"INSERT INTO `table` (`column`) VALUES ('$unsafe_variable')\");\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"If a user were to input something like `value'); DROP TABLE table;--`, the query would become:\n", |
||||||
|
"\n", |
||||||
|
"```sql\n", |
||||||
|
"INSERT INTO `table` (`column`) VALUES('value'); DROP TABLE table;--');\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"This inserts an unwanted SQL command leading to disastrous effects on the database.\n", |
||||||
|
"\n", |
||||||
|
"## Solution Steps\n", |
||||||
|
"\n", |
||||||
|
"### 1. Use Prepared Statements\n", |
||||||
|
"The best method to prevent SQL injection is to use prepared statements with parameterized queries. This separates SQL logic from data, ensuring that user input is treated as data, not executable code.\n", |
||||||
|
"\n", |
||||||
|
"#### Using PDO\n", |
||||||
|
"Here's how to use PDO in PHP:\n", |
||||||
|
"\n", |
||||||
|
"```php\n", |
||||||
|
"$dsn = 'mysql:dbname=dbtest;host=127.0.0.1;charset=utf8mb4';\n", |
||||||
|
"$dbConnection = new PDO($dsn, 'user', 'password');\n", |
||||||
|
"$dbConnection->setAttribute(PDO::ATTR_ERRMODE, PDO::ERRMODE_EXCEPTION);\n", |
||||||
|
"$dbConnection->setAttribute(PDO::ATTR_EMULATE_PREPARES, false);\n", |
||||||
|
"\n", |
||||||
|
"$stmt = $dbConnection->prepare('SELECT * FROM users WHERE name = :name');\n", |
||||||
|
"$stmt->execute(['name' => $name]);\n", |
||||||
|
"\n", |
||||||
|
"foreach ($stmt as $row) {\n", |
||||||
|
" // Process row\n", |
||||||
|
"}\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"#### Using MySQLi\n", |
||||||
|
"If you're using MySQLi, the syntax is slightly different:\n", |
||||||
|
"\n", |
||||||
|
"```php\n", |
||||||
|
"$dbConnection = new mysqli('127.0.0.1', 'username', 'password', 'test');\n", |
||||||
|
"$dbConnection->set_charset('utf8mb4');\n", |
||||||
|
"\n", |
||||||
|
"$stmt = $dbConnection->prepare('SELECT * FROM users WHERE name = ?');\n", |
||||||
|
"$stmt->bind_param('s', $name); // 's' stands for string\n", |
||||||
|
"$stmt->execute();\n", |
||||||
|
"$result = $stmt->get_result();\n", |
||||||
|
"\n", |
||||||
|
"while ($row = $result->fetch_assoc()) {\n", |
||||||
|
" // Process row\n", |
||||||
|
"}\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"### 2. Properly Configure the Database Connection\n", |
||||||
|
"When using PDO, ensure that emulated prepared statements are disabled. This is essential for real prepared statements to take effect.\n", |
||||||
|
"\n", |
||||||
|
"Example configuration:\n", |
||||||
|
"```php\n", |
||||||
|
"$dbConnection->setAttribute(PDO::ATTR_EMULATE_PREPARES, false);\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"### 3. Validate Input Data\n", |
||||||
|
"In addition to using prepared statements, you should validate and sanitize user inputs. Implementing whitelist validation can help by ensuring only expected values are processed.\n", |
||||||
|
"\n", |
||||||
|
"For example, if you expect a sorting direction:\n", |
||||||
|
"```php\n", |
||||||
|
"$dir = !empty($_GET['dir']) && $_GET['dir'] === 'DESC' ? 'DESC' : 'ASC';\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"### 4. Limit Database Permissions\n", |
||||||
|
"Restrict database user permissions to the minimum required for their role. For example, a user who only needs to read data should not have permissions to delete or alter it.\n", |
||||||
|
"\n", |
||||||
|
"```sql\n", |
||||||
|
"GRANT SELECT ON database TO 'username'@'localhost';\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"### 5. Regularly Update Your Codebase\n", |
||||||
|
"Keep libraries and the PHP version you are using up-to-date. Deprecated functions and libraries often contain vulnerabilities that can be exploited.\n", |
||||||
|
"\n", |
||||||
|
"## Conclusion\n", |
||||||
|
"Preventing SQL injection in PHP applications requires a proactive approach. Using prepared statements ensures user input is handled securely, while validating data and limiting permissions fortifies your application against potential attacks. By implementing these best practices, you can significantly reduce the risk of SQL injection vulnerabilities in your applications.\n", |
||||||
|
"\n", |
||||||
|
"For more in-depth information on SQL injection prevention techniques, consult the [OWASP SQL Injection Prevention Cheat Sheet](https://owasp.org/www-community/attacks/SQL_Injection).\n", |
||||||
|
"```" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.Markdown object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"display_summary(website)" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,63 @@ |
|||||||
|
#!/usr/bin/env python |
||||||
|
|
||||||
|
import os |
||||||
|
import argparse |
||||||
|
from dotenv import load_dotenv |
||||||
|
from openai import OpenAI |
||||||
|
|
||||||
|
def load_openai_key(): |
||||||
|
# Load environment variables in a file called .env |
||||||
|
load_dotenv(override=True) |
||||||
|
api_key = os.getenv('OPENAI_API_KEY') |
||||||
|
|
||||||
|
# Check the key |
||||||
|
if not api_key: |
||||||
|
return "Error: No API key was found!" |
||||||
|
elif not api_key.startswith("sk-proj-"): |
||||||
|
return "Error: An API key was found, but it doesn't start sk-proj-; please check you're using the right key" |
||||||
|
elif api_key.strip() != api_key: |
||||||
|
return "Error: An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them!" |
||||||
|
else: |
||||||
|
return "API key found and looks good so far!" |
||||||
|
|
||||||
|
def ask_llm(client, model, user_prompt): |
||||||
|
system_prompt = """ |
||||||
|
you are a writing assistant with an expertise in children's stories. |
||||||
|
Write a bedtime story inspired by the subject below. |
||||||
|
The story should have a begining, middle, and end. |
||||||
|
The story shoukd be appropriate for children ages 5-8 and have a positive message. |
||||||
|
I should be able to read the entire story in about 3 minutes |
||||||
|
""" |
||||||
|
response = client.chat.completions.create( |
||||||
|
model = model, |
||||||
|
messages = [ {"role": "system", "content": system_prompt}, |
||||||
|
{"role": "user", "content": user_prompt}] |
||||||
|
) |
||||||
|
return response.choices[0].message.content |
||||||
|
|
||||||
|
def main(): |
||||||
|
parser = argparse.ArgumentParser(description="AI Bedtime Storyteller") |
||||||
|
parser.add_argument("provider", choices=["openai", "ollama"], help="AI provider to use") |
||||||
|
parser.add_argument("--model", help="Model to use for Ollama (required if provider is 'ollama')", required="ollama" in parser.parse_known_args()[0].provider) |
||||||
|
parser.add_argument("subject", help="What do you want the story to be about?") |
||||||
|
|
||||||
|
args = parser.parse_args() |
||||||
|
|
||||||
|
if args.provider == "openai": |
||||||
|
load_openai_key() |
||||||
|
client = OpenAI() |
||||||
|
model = "gpt-4o-mini" |
||||||
|
elif args.provider == "ollama": |
||||||
|
client = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama') |
||||||
|
model = args.model |
||||||
|
else: |
||||||
|
return "Error: invalid provider!" |
||||||
|
|
||||||
|
user_prompt = args.subject |
||||||
|
|
||||||
|
result = ask_llm(client, model, user_prompt) |
||||||
|
print("AI Response:", result) |
||||||
|
|
||||||
|
if __name__ == "__main__": |
||||||
|
main() |
||||||
|
|
@ -0,0 +1,308 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# End of week 1 exercise\n", |
||||||
|
"\n", |
||||||
|
"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n", |
||||||
|
"and responds with an explanation. This is a tool that you will be able to use yourself during the course!\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 19, |
||||||
|
"id": "c1070317-3ed9-4659-abe3-828943230e03", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"import json\n", |
||||||
|
"from typing import List\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import ollama" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 2, |
||||||
|
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# constants\n", |
||||||
|
"\n", |
||||||
|
"MODEL_GPT = 'gpt-4o-mini'\n", |
||||||
|
"MODEL_LLAMA = 'llama3.2'" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 11, |
||||||
|
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"API key looks good so far\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# set up environment\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv()\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", |
||||||
|
" print(\"API key looks good so far\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", |
||||||
|
" \n", |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 12, |
||||||
|
"id": "624780c5-debb-44c7-a505-acf573ad5034", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"#prompts\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are a technical tuotor that answers questions related to the field of computer science. \\\n", |
||||||
|
"Your answers should reflect recent advancements in the field of software development, Artificial Intelligence and Large Language Models. Respond in markdown.\"\n", |
||||||
|
"\n", |
||||||
|
"system_prompt += \"Include resources that might help learners get more information on the topic.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 13, |
||||||
|
"id": "b6c0280b-ab8f-48a7-9a0c-7f47899bb559", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"user_prompt = \"How would you explain LLMs to someone who doesn't have a backround in Computer Science or AI?\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 14, |
||||||
|
"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Get gpt-4o-mini to answer, with streaming\n", |
||||||
|
"\n", |
||||||
|
"def tech_tutor(question):\n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL_GPT,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
" ],\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" response = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response += chunk.choices[0].delta.content or ''\n", |
||||||
|
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||||
|
" update_display(Markdown(response), display_id=display_handle.display_id)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 15, |
||||||
|
"id": "ca561874-dee3-456c-87f3-02f7e9a4ed4f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/markdown": [ |
||||||
|
"### Explaining Large Language Models (LLMs) to Non-Technical Audiences\n", |
||||||
|
"\n", |
||||||
|
"**What are LLMs?**\n", |
||||||
|
"\n", |
||||||
|
"Large Language Models (LLMs) are a type of artificial intelligence designed to understand and generate human language. Imagine having a super-smart assistant who can read, write, answer questions, or even create stories based on what you ask them. \n", |
||||||
|
"\n", |
||||||
|
"**How Do They Work?**\n", |
||||||
|
"\n", |
||||||
|
"1. **Training on Text**: LLMs are trained on vast amounts of written text from books, articles, websites, and more. During this training, they learn about grammar, facts, ideas, and the way people communicate.\n", |
||||||
|
"\n", |
||||||
|
"2. **Patterns and Context**: By analyzing this text, LLMs recognize patterns in how words and sentences relate to each other. They don't understand language like we do, but they can predict what words are likely to come next in a sentence based on the context.\n", |
||||||
|
"\n", |
||||||
|
"3. **Generating Responses**: When you ask an LLM a question or give it a prompt, it generates a response by choosing words that fit the patterns it's learned. It’s a bit like having a very advanced autocomplete feature on your phone, but much more sophisticated.\n", |
||||||
|
"\n", |
||||||
|
"**Why Are They Important?**\n", |
||||||
|
"\n", |
||||||
|
"LLMs are transforming various fields, such as:\n", |
||||||
|
"\n", |
||||||
|
"- **Customer Support**: They can understand and respond to customer queries automatically.\n", |
||||||
|
"- **Content Creation**: They assist in generating articles, poetry, or even code.\n", |
||||||
|
"- **Language Translation**: They help translate languages with high accuracy.\n", |
||||||
|
"- **Personal Assistants**: They power smart assistants like Siri or Google Assistant, allowing for more natural conversations.\n", |
||||||
|
"\n", |
||||||
|
"**Real-World Examples of LLMs**:\n", |
||||||
|
"\n", |
||||||
|
"- **ChatGPT**: Developed by OpenAI, this model can engage in conversations, answer questions, and provide information on diverse topics.\n", |
||||||
|
"- **Google BERT**: Enhances search engine results by better understanding user queries.\n", |
||||||
|
" \n", |
||||||
|
"### Resources to Learn More\n", |
||||||
|
"\n", |
||||||
|
"If you're curious and want to delve deeper into understanding LLMs and their underlying technology, here are some great resources:\n", |
||||||
|
"\n", |
||||||
|
"1. **Online Articles**:\n", |
||||||
|
" - [What is a Large Language Model?](https://towardsdatascience.com/what-is-a-large-language-model-785a122ca835)\n", |
||||||
|
" - [A Beginner's Guide to Large Language Models](https://www.analyticsvidhya.com/blog/2021/07/a-beginners-guide-to-large-language-models-llms/)\n", |
||||||
|
"\n", |
||||||
|
"2. **Video Tutorials**:\n", |
||||||
|
" - [What are Large Language Models? | AI Explained](https://www.youtube.com/watch?v=ttlLuanHCHo) on YouTube\n", |
||||||
|
" - [Deep Learning for NLP: Large Pre-trained Language Models](https://www.coursera.org/lecture/natural-language-processing-with-classifiers-and-deep-learning/the-power-of-large-pre-trained-language-models-u4XP5) on Coursera\n", |
||||||
|
"\n", |
||||||
|
"3. **Books**:\n", |
||||||
|
" - *\"Artificial Intelligence: A Guide to Intelligent Systems\"* by Michael Negnevitsky provides a foundation for understanding AI.\n", |
||||||
|
" - *\"Speech and Language Processing\"* by Daniel Jurafsky and James H. Martin offers a deeper dive into language processing technologies.\n", |
||||||
|
"\n", |
||||||
|
"By exploring these resources, you'll gain a better understanding of LLMs and their capabilities, as well as their profound implications for technology and society." |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.Markdown object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"tech_tutor(user_prompt)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 16, |
||||||
|
"id": "2a7c6670-ead8-41dc-9a0b-2b4caa40e846", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"OLLAMA_API = \"http://localhost:11434/api/chat\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 20, |
||||||
|
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/markdown": [ |
||||||
|
"**What are Large Language Models (LLMs)?**\n", |
||||||
|
"=====================================\n", |
||||||
|
"\n", |
||||||
|
"Imagine having a super-smart, never-ending bookshelf filled with knowledge about language and human behavior. That's roughly what a Large Language Model (LLM) is: a computer program designed to understand and generate human-like text.\n", |
||||||
|
"\n", |
||||||
|
"### How do LLMs work?\n", |
||||||
|
"\n", |
||||||
|
"1. **Training data**: LLMs are trained on massive amounts of text data, often from the internet, books, or other sources.\n", |
||||||
|
"2. **Algorithms**: The model uses complex algorithms to analyze this training data, learning patterns and relationships between words, sentences, and concepts.\n", |
||||||
|
"3. **Self-supervised learning**: During training, the model generates its own text based on the input it's given, and then evaluates how well its output matches human-written text.\n", |
||||||
|
"\n", |
||||||
|
"### What can LLMs do?\n", |
||||||
|
"\n", |
||||||
|
"* **Text generation**: LLMs can produce coherent, grammatically correct text on a wide range of topics.\n", |
||||||
|
"* **Language translation**: They can translate text from one language to another with surprising accuracy.\n", |
||||||
|
"* **Chatbots and conversational AI**: LLMs are used in chatbots to respond to user queries, often providing helpful and personalized answers.\n", |
||||||
|
"\n", |
||||||
|
"### Examples of LLMs\n", |
||||||
|
"\n", |
||||||
|
"* **BERT (Bidirectional Encoder Representations from Transformers)**: A pioneering model that's the foundation for many modern LLMs.\n", |
||||||
|
"* **Transformers**: An architecture that's become popular for its ability to handle long-range dependencies in text.\n", |
||||||
|
"* **Language models like myself**: I'm a type of LLM, trained on a massive dataset and using transformer-based architectures.\n", |
||||||
|
"\n", |
||||||
|
"### Limitations and future directions\n", |
||||||
|
"\n", |
||||||
|
"While LLMs have made tremendous progress, they still have limitations:\n", |
||||||
|
"\n", |
||||||
|
"* **Bias and fairness**: Models can perpetuate existing biases if trained on biased data.\n", |
||||||
|
"* **Explainability**: It's challenging to understand why a particular model made a certain decision or generated a specific response.\n", |
||||||
|
"* **Adversarial attacks**: Models can be vulnerable to malicious input that manipulates their output.\n", |
||||||
|
"\n", |
||||||
|
"Researchers are actively exploring ways to improve LLMs, such as:\n", |
||||||
|
"\n", |
||||||
|
"* **Multi-task learning**: Training models on multiple tasks simultaneously to enhance performance.\n", |
||||||
|
"* **Explainability techniques**: Developing methods to provide insights into model behavior and decision-making processes.\n", |
||||||
|
"\n", |
||||||
|
"**Getting started with LLMs**\n", |
||||||
|
"\n", |
||||||
|
"If you're interested in learning more about LLMs, I recommend checking out these resources:\n", |
||||||
|
"\n", |
||||||
|
"* **BERT's official documentation**: [BERT Documentation](https://bert.dev/)\n", |
||||||
|
"* **The Transformers library**: [Hugging Face Transformers](https://huggingface.co/transformers/)\n", |
||||||
|
"* **Large Language Models 101**: A beginner-friendly introduction to LLMs on Towards Data Science. [TowardsDataScience.com](https://towardsdatascience.com/large-language-models-101-8d2a6f3cdd23)\n", |
||||||
|
"\n", |
||||||
|
"I hope this explanation helped you understand what Large Language Models are and how they work!" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.Markdown object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Get Llama 3.2 to answer\n", |
||||||
|
"messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
" ]\n", |
||||||
|
"\n", |
||||||
|
"payload = {\n", |
||||||
|
" \"model\": MODEL_LLAMA,\n", |
||||||
|
" \"messages\": messages,\n", |
||||||
|
" \"stream\": True\n", |
||||||
|
" }\n", |
||||||
|
"\n", |
||||||
|
"response = ollama.chat(model=MODEL_LLAMA, messages=messages)\n", |
||||||
|
"reply = response['message']['content']\n", |
||||||
|
"display(Markdown(reply))" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,180 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# End of week 1 exercise\n", |
||||||
|
"\n", |
||||||
|
"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n", |
||||||
|
"and responds with an explanation. This is a tool that you will be able to use yourself during the course!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c1070317-3ed9-4659-abe3-828943230e03", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"import json\n", |
||||||
|
"from typing import List\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import ollama" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# constants\n", |
||||||
|
"MODEL_GPT = 'gpt-4o-mini'\n", |
||||||
|
"MODEL_LLAMA = 'llama3.2'" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# set up environment\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", |
||||||
|
" print(\"API key looks good so far\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", |
||||||
|
"\n", |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_prompt = \"You are provided with a technical question. \\\n", |
||||||
|
"You are answering by providing a quick explanation and giving some examples.\\n\"\n", |
||||||
|
"\n", |
||||||
|
"# here is the question; type over this to ask something new\n", |
||||||
|
"question = \"\"\"\n", |
||||||
|
"Please explain what this code does and why:\n", |
||||||
|
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n", |
||||||
|
"\"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Get gpt-4o-mini to answer, with streaming\n", |
||||||
|
"def get_answer_gpt():\n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL_GPT,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": question}\n", |
||||||
|
" ],\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
" response = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response += chunk.choices[0].delta.content or ''\n", |
||||||
|
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||||
|
" update_display(Markdown(response), display_id=display_handle.display_id)\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Get Llama 3.2 to answer\n", |
||||||
|
"def get_answer_ollama():\n", |
||||||
|
" stream = ollama.generate(\n", |
||||||
|
" MODEL_LLAMA,\n", |
||||||
|
" question,\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" response = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response += chunk['response'] or ''\n", |
||||||
|
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||||
|
" update_display(Markdown(response), display_id=display_handle.display_id)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4a859eb1-23fa-40dd-ba91-b35084433a00", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"get_answer_gpt()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1c73f046-da3a-49a5-8a74-4b8a86a9032a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"get_answer_ollama()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "bea20f33-a710-44ab-9a4d-856db05e4201", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,125 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "135ee16c-2741-4ebf-aca9-1d263083b3ce", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# End of week 1 exercise\n", |
||||||
|
"\n", |
||||||
|
"Build a tutor tool by using Ollama." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c1070317-3ed9-4659-abe3-828943230e03", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"import ollama\n", |
||||||
|
"from IPython.display import Markdown, display, clear_output" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# constants\n", |
||||||
|
"MODEL_LLAMA = 'llama3.2'" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# here is the question; type over this to ask something new\n", |
||||||
|
"\n", |
||||||
|
"question = \"\"\"\n", |
||||||
|
"Please explain what this code does and why:\n", |
||||||
|
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Get Llama 3.2 to answer, with streaming\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"messages=[{\"role\":\"user\",\"content\":question}]\n", |
||||||
|
"\n", |
||||||
|
"for chunk in ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True):\n", |
||||||
|
" print(chunk['message']['content'], end='', flush=True)\n", |
||||||
|
"\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d1f71014-e780-4d3f-a227-1a7c18158a4c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"#Alternative answer with streaming in Markdown!\n", |
||||||
|
"\n", |
||||||
|
"def stream_response():\n", |
||||||
|
" messages = [{\"role\": \"user\", \"content\": question}]\n", |
||||||
|
" \n", |
||||||
|
" display_markdown = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
"\n", |
||||||
|
" response_text = \"\"\n", |
||||||
|
" for chunk in ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True):\n", |
||||||
|
" \n", |
||||||
|
" response_text += chunk['message']['content']\n", |
||||||
|
" clear_output(wait=True) # Clears previous output\n", |
||||||
|
" display_markdown.update(Markdown(response_text)) # Updates Markdown dynamically\n", |
||||||
|
"\n", |
||||||
|
"# Run the function\n", |
||||||
|
"stream_response()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c38fdd2a-4b09-402c-ba46-999b22b0cb15", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.13.2" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,217 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 1, |
||||||
|
"id": "2ce61bb5-1d5b-43b8-b5bb-6aeae91c7574", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"from IPython.display import Markdown, display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 2, |
||||||
|
"id": "3399686d-5f14-4fb2-8939-fd2401be3007", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"MODEL = \"gpt-4o-mini\"\n", |
||||||
|
"SYSTEM_PROMPT_PATH = \"Chat_Summary_Data/System_Prompt.txt\"\n", |
||||||
|
"CHATS_PATH = \"Chat_Summary_Data/Chat_Examples/\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 3, |
||||||
|
"id": "d97b8374-a161-435c-8317-1d0ecaaa9b71", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"API key found and looks good so far!\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"# Check the key\n", |
||||||
|
"\n", |
||||||
|
"if not api_key:\n", |
||||||
|
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
||||||
|
"elif not api_key.startswith(\"sk-proj-\"):\n", |
||||||
|
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
||||||
|
"elif api_key.strip() != api_key:\n", |
||||||
|
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"API key found and looks good so far!\")\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 4, |
||||||
|
"id": "b3f4afb4-2e4a-4971-915e-a8634a17eda8", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class ChatAI:\n", |
||||||
|
" def __init__(self, system_prompt_path=SYSTEM_PROMPT_PATH, model=MODEL):\n", |
||||||
|
" with open(system_prompt_path, \"r\") as file:\n", |
||||||
|
" self.system_prompt = file.read()\n", |
||||||
|
"\n", |
||||||
|
" self.openai = OpenAI()\n", |
||||||
|
" self.model = model\n", |
||||||
|
" \n", |
||||||
|
" @staticmethod\n", |
||||||
|
" def _get_user_prompt(chat_txt):\n", |
||||||
|
" with open(chat_txt, \"r\") as file:\n", |
||||||
|
" user_prompt_str = file.read()\n", |
||||||
|
" return user_prompt_str\n", |
||||||
|
" \n", |
||||||
|
" def generate(self, chat_txt):\n", |
||||||
|
" messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": self.system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": self._get_user_prompt(chat_txt)}\n", |
||||||
|
" ]\n", |
||||||
|
"\n", |
||||||
|
" response = self.openai.chat.completions.create(model=self.model, messages=messages)\n", |
||||||
|
" return response.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 5, |
||||||
|
"id": "d243b582-66af-49f9-bcd1-e05a63e61c34", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"chat_ai = ChatAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 8, |
||||||
|
"id": "c764ace6-5a0f-4dd0-9454-0b8a093b97fc", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/markdown": [ |
||||||
|
"# Chat1" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.Markdown object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/markdown": [ |
||||||
|
"- **Order:** 2 Medium Chicken BBQ Pizzas\n", |
||||||
|
"- **Cost:** 342 LE\n", |
||||||
|
"- **Experience:** Negative\n", |
||||||
|
" - **Summary:** The client expressed dissatisfaction with the pizza taste." |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.Markdown object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/markdown": [ |
||||||
|
"# Chat2" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.Markdown object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/markdown": [ |
||||||
|
"- The client ordered: Nothing \n", |
||||||
|
"- Summary: The client did not place an order because the chicken ranch pizza was unavailable." |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.Markdown object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/markdown": [ |
||||||
|
"# Chat3" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.Markdown object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/markdown": [ |
||||||
|
"- **Order**: Large pepperoni pizza and onion rings \n", |
||||||
|
"- **Total Cost**: 250 LE \n", |
||||||
|
"- **Experience**: Positive \n", |
||||||
|
" - The client enjoyed the pizza despite the delay in delivery." |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.Markdown object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"chats_txt = os.listdir(CHATS_PATH)\n", |
||||||
|
"for chat_file in chats_txt:\n", |
||||||
|
" markdown_heading = f\"# {chat_file[:-4]}\"\n", |
||||||
|
" display(Markdown(markdown_heading))\n", |
||||||
|
" display(Markdown(chat_ai.generate(CHATS_PATH+chat_file)))" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,149 @@ |
|||||||
|
import os |
||||||
|
import time |
||||||
|
import sys |
||||||
|
from dotenv import load_dotenv |
||||||
|
from bs4 import BeautifulSoup |
||||||
|
from openai import OpenAI |
||||||
|
|
||||||
|
import random |
||||||
|
import undetected_chromedriver as uc |
||||||
|
|
||||||
|
from selenium.webdriver.common.by import By |
||||||
|
from selenium.webdriver.support.ui import WebDriverWait |
||||||
|
from selenium.webdriver.support import expected_conditions as ec |
||||||
|
|
||||||
|
load_dotenv() |
||||||
|
|
||||||
|
class WebsiteScrapper: |
||||||
|
def __init__(self, url, max_retries=2, headless=True, wait_selector="body", wait_timeout=10): |
||||||
|
self.url = url |
||||||
|
self.__text = "" |
||||||
|
self.__title = "" |
||||||
|
self.headless = headless |
||||||
|
self.max_retries = max_retries |
||||||
|
self.wait_selector = wait_selector |
||||||
|
self.wait_timeout = wait_timeout |
||||||
|
|
||||||
|
def __log_html(self, html, filename="last_scraped.html"): |
||||||
|
try: |
||||||
|
with open(filename, "w", encoding="utf-8") as f: |
||||||
|
f.write(html) |
||||||
|
print(f"Saved page HTML to {filename} for debugging.") |
||||||
|
except Exception as e: |
||||||
|
print(f"!!! Could not save page HTML: {e}") |
||||||
|
|
||||||
|
def parse(self): |
||||||
|
attempt = 0 |
||||||
|
while attempt < self.max_retries: |
||||||
|
try: |
||||||
|
options = uc.ChromeOptions() |
||||||
|
options.headless = self.headless # Set to False if you want to see the browser |
||||||
|
options.add_argument("user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36") |
||||||
|
options.add_argument("--no-sandbox") |
||||||
|
options.add_argument("--disable-dev-shm-usage") |
||||||
|
options.add_argument("--disable-gpu") |
||||||
|
options.page_load_strategy = 'normal' # wait until fully loaded |
||||||
|
options.add_argument("--disable-blink-features=AutomationControlled") |
||||||
|
|
||||||
|
with uc.Chrome(options=options) as driver: |
||||||
|
print("[Browser] Chrome started.") |
||||||
|
driver.get(self.url) |
||||||
|
driver.execute_script("window.scrollTo(0, document.body.scrollHeight);") |
||||||
|
time.sleep(random.uniform(1, 3)) |
||||||
|
WebDriverWait(driver, self.wait_timeout).until( |
||||||
|
ec.presence_of_element_located((By.CSS_SELECTOR, self.wait_selector)) |
||||||
|
) |
||||||
|
|
||||||
|
time.sleep(1) |
||||||
|
page_source = driver.page_source |
||||||
|
self.__log_html(page_source) |
||||||
|
|
||||||
|
if "enable javascript" in page_source.lower() or "checking your browser" in page_source.lower(): |
||||||
|
self.__title = "Blocked by Bot Protection" |
||||||
|
self.__text = "This website uses advanced protection (e.g., Cloudflare). Content not accessible." |
||||||
|
return |
||||||
|
|
||||||
|
soup = BeautifulSoup(page_source, 'html.parser') |
||||||
|
self.__title = soup.title.string if soup.title else "No title found" |
||||||
|
|
||||||
|
for irrelevant in soup(["script", "style", "img", "input"]): |
||||||
|
irrelevant.decompose() |
||||||
|
|
||||||
|
self.__text = soup.body.get_text(separator="\n", strip=True) |
||||||
|
try: |
||||||
|
os.remove("last_scraped.html") |
||||||
|
print("Cleaned up debug HTML file.") |
||||||
|
except Exception as e: |
||||||
|
print(f"Could not delete debug HTML file: {e}") |
||||||
|
return # Success |
||||||
|
|
||||||
|
except Exception as e: |
||||||
|
print(f"!!! Attempt {attempt + 1} failed: {e}") |
||||||
|
attempt += 1 |
||||||
|
time.sleep(2) |
||||||
|
|
||||||
|
# All retries failed |
||||||
|
self.__title = "Failed to load" |
||||||
|
self.__text = "Website could not be scraped after several attempts." |
||||||
|
|
||||||
|
def get_text(self): |
||||||
|
return self.__text |
||||||
|
|
||||||
|
def get_title(self): |
||||||
|
return self.__title |
||||||
|
|
||||||
|
|
||||||
|
class JSWebsiteSummarizer: |
||||||
|
def __init__(self, url, headless=True): |
||||||
|
self.url = url |
||||||
|
os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env') |
||||||
|
self.openai = OpenAI() |
||||||
|
self.website_scrapper = WebsiteScrapper(url, headless=headless) |
||||||
|
self.system_prompt = "You are an assistant that analyzes the contents of a website \ |
||||||
|
and provides a short summary, ignoring text that might be navigation related. \ |
||||||
|
Respond in markdown." |
||||||
|
|
||||||
|
@staticmethod |
||||||
|
def __user_prompt_for(title, content): |
||||||
|
user_prompt = f"You are looking at a website titled {title}" |
||||||
|
user_prompt += "The contents of this website is as follows; \ |
||||||
|
please provide a short summary of this website in markdown. \ |
||||||
|
If it includes news or announcements, then summarize that too.\n\n" |
||||||
|
user_prompt += content |
||||||
|
return user_prompt |
||||||
|
|
||||||
|
def __messages_for(self, title, content): |
||||||
|
return [{"role": "system", "content": self.system_prompt}, {"role": "user", "content": JSWebsiteSummarizer.__user_prompt_for(title, content)}] |
||||||
|
|
||||||
|
def __summarize(self): |
||||||
|
self.website_scrapper.parse() |
||||||
|
chat_config = self.__messages_for(self.website_scrapper.get_title(), self.website_scrapper.get_text()) |
||||||
|
response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=chat_config) |
||||||
|
return response.choices[0].message.content |
||||||
|
|
||||||
|
def display_summary(self): |
||||||
|
summary = self.__summarize() |
||||||
|
if 'ipykernel' in sys.modules: |
||||||
|
from IPython.display import Markdown, display |
||||||
|
display(Markdown(summary)) |
||||||
|
else: |
||||||
|
print("=== Website Summary ===\n") |
||||||
|
print(summary) |
||||||
|
|
||||||
|
# Use headless true for non JS/Bot/Secured website to avoid overhead |
||||||
|
# Use headless False for JS/Bot/Secured website so as to bypass security |
||||||
|
|
||||||
|
if __name__ == "__main__": |
||||||
|
url1 = "https://cnn.com" |
||||||
|
url2 = "https://openai.com" |
||||||
|
url3 = "https://anthropic.com" |
||||||
|
|
||||||
|
# web_summariser = JSWebsiteSummarizer(url=url1, headless=True) |
||||||
|
# |
||||||
|
# print("Starting website summary...") |
||||||
|
# web_summariser.display_summary() |
||||||
|
|
||||||
|
web_summariser = JSWebsiteSummarizer(url=url3, headless=False) |
||||||
|
print("Starting website summary...") |
||||||
|
web_summariser.display_summary() |
||||||
|
print("Done!") |
@ -0,0 +1,208 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"import json\n", |
||||||
|
"from typing import List\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display, update_display, clear_output\n", |
||||||
|
"import openai\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", |
||||||
|
" print(\"API key looks good so far\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", |
||||||
|
"\n", |
||||||
|
"MODEL = 'gpt-4o-mini'\n", |
||||||
|
"\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"# Prompt user for company name and URL\n", |
||||||
|
"company_name = input(\"Enter the company name: \")\n", |
||||||
|
"url = input(\"Enter the company URL: \")\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" A utility class to represent a Website that we have scraped, now with links\n", |
||||||
|
" \"\"\"\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" self.body = response.content\n", |
||||||
|
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" if soup.body:\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
" else:\n", |
||||||
|
" self.text = \"\"\n", |
||||||
|
" links = [link.get('href') for link in soup.find_all('a')]\n", |
||||||
|
" self.links = [link for link in links if link]\n", |
||||||
|
"\n", |
||||||
|
" def get_contents(self):\n", |
||||||
|
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"\n", |
||||||
|
"# multi-shot prompt\n", |
||||||
|
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\You are able to decide which of the links would be most relevant to include in a brochure about the company, \\such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", |
||||||
|
"link_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||||
|
"link_system_prompt += \"\"\"\n", |
||||||
|
" EXAMPLE 1:\n", |
||||||
|
" {\n", |
||||||
|
" \"links\": [\n", |
||||||
|
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", |
||||||
|
" {\"type\": \"careers page\", \"url\": \"https://another.full.url/careers\"}\n", |
||||||
|
" ]\n", |
||||||
|
" }\n", |
||||||
|
" EXAMPLE 2:\n", |
||||||
|
" {\n", |
||||||
|
" \"links\": [\n", |
||||||
|
" {\"type\": \"company blog\", \"url\": \"https://blog.example.com\"},\n", |
||||||
|
" {\"type\": \"our story\", \"url\": \"https://example.com/our-story\"}\n", |
||||||
|
" ]\n", |
||||||
|
" }\n", |
||||||
|
" \"\"\"\n", |
||||||
|
"\n", |
||||||
|
"def get_links_user_prompt(website):\n", |
||||||
|
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", |
||||||
|
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\ Do not include Terms of Service, Privacy, email links.\\n\"\n", |
||||||
|
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||||
|
" user_prompt += \"\\n\".join(website.links)\n", |
||||||
|
" return user_prompt\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"def get_links(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": link_system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", |
||||||
|
" ],\n", |
||||||
|
" response_format={\"type\": \"json_object\"}\n", |
||||||
|
" )\n", |
||||||
|
" result = response.choices[0].message.content\n", |
||||||
|
" return json.loads(result)\n", |
||||||
|
"\n", |
||||||
|
"def get_all_details(url):\n", |
||||||
|
" result = \"Landing page:\\n\"\n", |
||||||
|
" result += Website(url).get_contents()\n", |
||||||
|
" links = get_links(url)\n", |
||||||
|
"\n", |
||||||
|
" for link in links[\"links\"]:\n", |
||||||
|
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||||
|
" result += Website(link[\"url\"]).get_contents()\n", |
||||||
|
" return result\n", |
||||||
|
"\n", |
||||||
|
"# set format to json_object\n", |
||||||
|
"system_prompt = (\n", |
||||||
|
" \"You are an assistant that analyzes the contents of several relevant pages from a company website \"\n", |
||||||
|
" \"and creates a short tempered, irritated, disappointed in the world type of brochure about the company for prospective customers, investors, and recruits. \"\n", |
||||||
|
" \"Respond in markdown. Include details of company culture, customers, and careers/jobs if you have the information. Add emoticons where ever possible.\\n\\n\"\n", |
||||||
|
"\n", |
||||||
|
" \"Please structure the brochure using the following sections:\\n\"\n", |
||||||
|
" \"1. **Introduction**: A brief overview of the company.\\n\"\n", |
||||||
|
" \"2. **Company Culture**: Emphasize fun, atmosphere, and any unique cultural elements.\\n\"\n", |
||||||
|
" \"3. **Customers**: Mention notable customers or industries.\\n\"\n", |
||||||
|
" \"4. **Careers/Jobs**: Highlight career opportunities.\\n\"\n", |
||||||
|
" \"5. **Conclusion**: Wrap up with a final lighthearted message.\\n\"\n", |
||||||
|
" \"6. Finish the brochure with a very sarcastic and pun-intended mission statement.\\n\"\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"def get_brochure_user_prompt(company_name, url):\n", |
||||||
|
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", |
||||||
|
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n", |
||||||
|
" user_prompt += get_all_details(url)\n", |
||||||
|
" user_prompt = user_prompt[:20_000]\n", |
||||||
|
" return user_prompt\n", |
||||||
|
"\n", |
||||||
|
"def stream_brochure():\n", |
||||||
|
" global brochure_text # Access the global variable\n", |
||||||
|
" brochure_text = \"\" # Initialize\n", |
||||||
|
" \n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||||
|
" ],\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" response = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" for chunk in stream: \n", |
||||||
|
" content = chunk.choices[0].delta.content or ''\n", |
||||||
|
" response += content\n", |
||||||
|
" brochure_text += content # Accumulate the text\n", |
||||||
|
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||||
|
" update_display(Markdown(response), display_id=display_handle.display_id)\n", |
||||||
|
"\n", |
||||||
|
"def user_translate_brochure(lang):\n", |
||||||
|
" # Clear previous output\n", |
||||||
|
" clear_output(wait=True)\n", |
||||||
|
" \n", |
||||||
|
" # Stream #2: translate accumulated text\n", |
||||||
|
" translation_stream = openai.chat.completions.create( # Changed from ChatCompletion\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"user\", \"content\": f\"Translate the following to {lang}:\\n\\n{brochure_text}\"}\n", |
||||||
|
" ],\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" # Setup display for streaming translation\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" translated_text = \"\"\n", |
||||||
|
" \n", |
||||||
|
" for chunk in translation_stream:\n", |
||||||
|
" content = chunk.choices[0].delta.content or \"\"\n", |
||||||
|
" if content:\n", |
||||||
|
" translated_text += content\n", |
||||||
|
" update_display(Markdown(translated_text), display_id=display_handle.display_id)\n", |
||||||
|
"\n", |
||||||
|
"# stream the brochure in english\n", |
||||||
|
"stream_brochure()\n", |
||||||
|
"\n", |
||||||
|
"# prompt user for language choice\n", |
||||||
|
"language_choice = input(\"Enter the language to translate the brochure into (e.g., 'French'): \")\n", |
||||||
|
"\n", |
||||||
|
"# translate the brochure and stream the translation\n", |
||||||
|
"user_translate_brochure(language_choice)" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "llms", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 4 |
||||||
|
} |
@ -0,0 +1,163 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import anthropic\n", |
||||||
|
"import openai\n", |
||||||
|
"import ipywidgets as widgets\n", |
||||||
|
"from IPython.display import display, Markdown, update_display\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"import requests\n", |
||||||
|
"import json\n", |
||||||
|
"\n", |
||||||
|
"MODEL_CLAUDE = 'claude-3-5-sonnet-20241022'\n", |
||||||
|
"MODEL_LLAMA = 'llama3.2'\n", |
||||||
|
"MODEL_GPT = 'gpt-4o-mini'\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv()\n", |
||||||
|
"\n", |
||||||
|
"# Define models\n", |
||||||
|
"models = [\n", |
||||||
|
" ('Claude (Anthropic)', MODEL_CLAUDE),\n", |
||||||
|
" ('LLaMA (Meta)', MODEL_LLAMA),\n", |
||||||
|
" ('GPT (OpenAI)', MODEL_GPT)\n", |
||||||
|
"]\n", |
||||||
|
"\n", |
||||||
|
"model_dropdown = widgets.Dropdown(\n", |
||||||
|
" options=[('', None)] + [(model[0], model[0]) for model in models],\n", |
||||||
|
" value=None,\n", |
||||||
|
" placeholder='Choose a model',\n", |
||||||
|
" description='Model:',\n", |
||||||
|
" style={'description_width': 'initial'}\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"selected_model = \"\"\n", |
||||||
|
"\n", |
||||||
|
"text = input(f\"Hello, I am your personal tutor. Please ask me a question regarding your code:\")\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are a helpful technical tutor who answers questions about programming, software engineering, data science and LLMs\"\n", |
||||||
|
"user_prompt = \"Please give a detailed explanation to the following question: \" + text\n", |
||||||
|
"\n", |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
"]\n", |
||||||
|
"\n", |
||||||
|
"# Get gpt-4o-mini to answer, with streaming\n", |
||||||
|
"def get_gpt_response():\n", |
||||||
|
" stream = openai.chat.completions.create(model=MODEL_GPT, messages=messages,stream=True)\n", |
||||||
|
" \n", |
||||||
|
" response = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response += chunk.choices[0].delta.content or ''\n", |
||||||
|
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", |
||||||
|
" update_display(Markdown(f\"**Question:** {text}\\n\\n**Answer:** {response}\"), display_id=display_handle.display_id)\n", |
||||||
|
" return response\n", |
||||||
|
"\n", |
||||||
|
"# Get Llama 3.2 to answer, with streaming\n", |
||||||
|
"def get_llama_response():\n", |
||||||
|
" api_url = \"http://localhost:11434/api/chat\"\n", |
||||||
|
" payload = {\n", |
||||||
|
" \"model\": MODEL_LLAMA,\n", |
||||||
|
" \"messages\": messages,\n", |
||||||
|
" \"stream\": True\n", |
||||||
|
" }\n", |
||||||
|
" response = requests.post(api_url, json=payload, stream=True)\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
" result = \"\"\n", |
||||||
|
" \n", |
||||||
|
" for line in response.iter_lines():\n", |
||||||
|
" if line:\n", |
||||||
|
" json_response = json.loads(line)\n", |
||||||
|
" if \"message\" in json_response:\n", |
||||||
|
" content = json_response[\"message\"].get(\"content\", \"\")\n", |
||||||
|
" result += content\n", |
||||||
|
" update_display(Markdown(f\"**Question:** {text}\\n\\n**Answer:** {result}\"), display_id=display_handle.display_id)\n", |
||||||
|
" if json_response.get(\"done\", False):\n", |
||||||
|
" break\n", |
||||||
|
" \n", |
||||||
|
" return result\n", |
||||||
|
"\n", |
||||||
|
"# Get Claude 3.5 to answer, with streaming\n", |
||||||
|
"def get_claude_response():\n", |
||||||
|
" client = anthropic.Anthropic()\n", |
||||||
|
"\n", |
||||||
|
" response = client.messages.create(\n", |
||||||
|
" model=MODEL_CLAUDE,\n", |
||||||
|
" system=system_prompt,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\n", |
||||||
|
" \"role\": \"user\",\n", |
||||||
|
" \"content\": user_prompt\n", |
||||||
|
" }\n", |
||||||
|
" ],\n", |
||||||
|
" stream=True,\n", |
||||||
|
" max_tokens=8192,\n", |
||||||
|
" temperature=1,\n", |
||||||
|
" )\n", |
||||||
|
" result = \"\"\n", |
||||||
|
" display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
"\n", |
||||||
|
" for chunk in response:\n", |
||||||
|
" # Check if the chunk is a ContentBlockDeltaEvent\n", |
||||||
|
" if hasattr(chunk, 'delta') and hasattr(chunk.delta, 'text'):\n", |
||||||
|
" result += chunk.delta.text\n", |
||||||
|
" update_display(Markdown(f\"**Question:** {text}\\n\\n**Answer:** {result}\"), display_id=display_handle.display_id)\n", |
||||||
|
" return result\n", |
||||||
|
"\n", |
||||||
|
"def on_text_submit():\n", |
||||||
|
" try:\n", |
||||||
|
" if 'Claude' in selected_model:\n", |
||||||
|
" display(Markdown(f\"# **Selected model: {selected_model}**\"))\n", |
||||||
|
" get_claude_response()\n", |
||||||
|
" elif 'LLaMA' in selected_model:\n", |
||||||
|
" display(Markdown(f\"# **Selected model: {selected_model}**\"))\n", |
||||||
|
" get_llama_response()\n", |
||||||
|
" elif 'GPT' in selected_model:\n", |
||||||
|
" display(Markdown(f\"# **Selected model: {selected_model}**\"))\n", |
||||||
|
" get_gpt_response()\n", |
||||||
|
" except Exception as e:\n", |
||||||
|
" display(Markdown(f\"**Error:** {str(e)}\"))\n", |
||||||
|
"\n", |
||||||
|
"def on_model_select(change):\n", |
||||||
|
" global selected_model\n", |
||||||
|
"\n", |
||||||
|
" selected_model = change['new'].split(' ')[0]\n", |
||||||
|
" if selected_model is not None:\n", |
||||||
|
" on_text_submit()\n", |
||||||
|
" return change['new'].split(' ')[0]\n", |
||||||
|
"\n", |
||||||
|
"# Register callbacks\n", |
||||||
|
"model_dropdown.observe(on_model_select, names='value')\n", |
||||||
|
"\n", |
||||||
|
"display(model_dropdown)" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 4 |
||||||
|
} |
@ -0,0 +1,585 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# ALTERNATIVE DAY 1 WITH OLLAMA\n", |
||||||
|
"\n", |
||||||
|
"### Please read this section. This is valuable to get you prepared, even if it's a long read -- it's important stuff.\n", |
||||||
|
"\n", |
||||||
|
"## Your first Frontier LLM Project\n", |
||||||
|
"\n", |
||||||
|
"Let's build a useful LLM solution - in a matter of minutes.\n", |
||||||
|
"\n", |
||||||
|
"By the end of this course, you will have built an autonomous Agentic AI solution with 7 agents that collaborate to solve a business problem. All in good time! We will start with something smaller...\n", |
||||||
|
"\n", |
||||||
|
"Our goal is to code a new kind of Web Browser. Give it a URL, and it will respond with a summary. The Reader's Digest of the internet!!\n", |
||||||
|
"\n", |
||||||
|
"Before starting, you should have completed the setup for [PC](../SETUP-PC.md) or [Mac](../SETUP-mac.md) and you hopefully launched this jupyter lab from within the project root directory, with your environment activated.\n", |
||||||
|
"\n", |
||||||
|
"## If you're new to Jupyter Lab\n", |
||||||
|
"\n", |
||||||
|
"Welcome to the wonderful world of Data Science experimentation! Once you've used Jupyter Lab, you'll wonder how you ever lived without it. Simply click in each \"cell\" with code in it, such as the cell immediately below this text, and hit Shift+Return to execute that cell. As you wish, you can add a cell with the + button in the toolbar, and print values of variables, or try out variations. \n", |
||||||
|
"\n", |
||||||
|
"I've written a notebook called [Guide to Jupyter](Guide%20to%20Jupyter.ipynb) to help you get more familiar with Jupyter Labs, including adding Markdown comments, using `!` to run shell commands, and `tqdm` to show progress.\n", |
||||||
|
"\n", |
||||||
|
"## If you're new to the Command Line\n", |
||||||
|
"\n", |
||||||
|
"Please see these excellent guides: [Command line on PC](https://chatgpt.com/share/67b0acea-ba38-8012-9c34-7a2541052665) and [Command line on Mac](https://chatgpt.com/canvas/shared/67b0b10c93a081918210723867525d2b). \n", |
||||||
|
"\n", |
||||||
|
"## If you'd prefer to work in IDEs\n", |
||||||
|
"\n", |
||||||
|
"If you're more comfortable in IDEs like VSCode or Pycharm, they both work great with these lab notebooks too. \n", |
||||||
|
"If you'd prefer to work in VSCode, [here](https://chatgpt.com/share/676f2e19-c228-8012-9911-6ca42f8ed766) are instructions from an AI friend on how to configure it for the course.\n", |
||||||
|
"\n", |
||||||
|
"## If you'd like to brush up your Python\n", |
||||||
|
"\n", |
||||||
|
"I've added a notebook called [Intermediate Python](Intermediate%20Python.ipynb) to get you up to speed. But you should give it a miss if you already have a good idea what this code does: \n", |
||||||
|
"`yield from {book.get(\"author\") for book in books if book.get(\"author\")}`\n", |
||||||
|
"\n", |
||||||
|
"## I am here to help\n", |
||||||
|
"\n", |
||||||
|
"If you have any problems at all, please do reach out. \n", |
||||||
|
"I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect (and I love connecting!) \n", |
||||||
|
"And this is new to me, but I'm also trying out X/Twitter at [@edwarddonner](https://x.com/edwarddonner) - if you're on X, please show me how it's done 😂 \n", |
||||||
|
"\n", |
||||||
|
"## More troubleshooting\n", |
||||||
|
"\n", |
||||||
|
"Please see the [troubleshooting](troubleshooting.ipynb) notebook in this folder to diagnose and fix common problems. At the very end of it is a diagnostics script with some useful debug info.\n", |
||||||
|
"\n", |
||||||
|
"## If this is old hat!\n", |
||||||
|
"\n", |
||||||
|
"If you're already comfortable with today's material, please hang in there; you can move swiftly through the first few labs - we will get much more in depth as the weeks progress.\n", |
||||||
|
"\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Please read - important note</h2>\n", |
||||||
|
" <span style=\"color:#900;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you carefully execute this yourself, <b>after</b> watching the lecture. Add print statements to understand what's going on, and then come up with your own variations. If you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#f71;\">Treat these labs as a resource</h2>\n", |
||||||
|
" <span style=\"color:#f71;\">I push updates to the code regularly. When people ask questions or have problems, I incorporate it in the code, adding more examples or improved commentary. As a result, you'll notice that the code below isn't identical to the videos. Everything from the videos is here; but in addition, I've added more steps and better explanations, and occasionally added new models like DeepSeek. Consider this like an interactive book that accompanies the lectures.\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#181;\">Business value of these exercises</h2>\n", |
||||||
|
" <span style=\"color:#181;\">A final thought. While I've designed these notebooks to be educational, I've also tried to make them enjoyable. We'll do fun things like have LLMs tell jokes and argue with each other. But fundamentally, my goal is to teach skills you can apply in business. I'll explain business implications as we go, and it's worth keeping this in mind: as you build experience with models and techniques, think of ways you could put this into action at work today. Please do contact me if you'd like to discuss more or if you have ideas to bounce off me.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"\n", |
||||||
|
"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6900b2a8-6384-4316-8aaa-5e519fca4254", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Connecting to OpenAI\n", |
||||||
|
"\n", |
||||||
|
"The next cell is where we load in the environment variables in your `.env` file and connect to OpenAI.\n", |
||||||
|
"\n", |
||||||
|
"## Troubleshooting if you have problems:\n", |
||||||
|
"\n", |
||||||
|
"Head over to the [troubleshooting](troubleshooting.ipynb) notebook in this folder for step by step code to identify the root cause and fix it!\n", |
||||||
|
"\n", |
||||||
|
"If you make a change, try restarting the \"Kernel\" (the python process sitting behind this notebook) by Kernel menu >> Restart Kernel and Clear Outputs of All Cells. Then try this notebook again, starting at the top.\n", |
||||||
|
"\n", |
||||||
|
"Or, contact me! Message me or email ed@edwarddonner.com and we will get this to work.\n", |
||||||
|
"\n", |
||||||
|
"Any concerns about API costs? See my notes in the README - costs should be minimal, and you can control it at every point. You can also use Ollama as a free alternative, which we discuss during Day 2." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "92d0aa2b-8e2f-4c1b-8b81-646faf4cd8c5", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# And now the change for Ollama\n", |
||||||
|
"\n", |
||||||
|
"1. No environment variables are needed (no keys) so this part has been removed\n", |
||||||
|
"\n", |
||||||
|
"2. The OpenAI client library is being initialized to point to your local computer for Ollama\n", |
||||||
|
"\n", |
||||||
|
"3. You need to have installed Ollama on your computer, and run `ollama run llama3.2` in a Powershell or Terminal if you haven't already\n", |
||||||
|
"\n", |
||||||
|
"4. Anywhere in this lab that it used to have **gpt-4o-mini** it now has **lama3.2**\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Here it is - see the base_url\n", |
||||||
|
"\n", |
||||||
|
"openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "442fc84b-0815-4f40-99ab-d9a5da6bda91", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Let's make a quick call to a Frontier model to get started, as a preview!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a58394bf-1e45-46af-9bfd-01e24da6f49a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# To give you a preview -- calling OpenAI with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\n", |
||||||
|
"\n", |
||||||
|
"message = \"Hello, Llama! This is my first ever message to you! Hi!\"\n", |
||||||
|
"response = openai.chat.completions.create(model=\"llama3.2\", messages=[{\"role\":\"user\", \"content\":message}])\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "2aa190e5-cb31-456a-96cc-db109919cd78", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## OK onwards with our first project" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c5e793b2-6775-426a-a139-4848291d0463", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Create this Website object from the given url using the BeautifulSoup library\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" soup = BeautifulSoup(response.content, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's try one out. Change the website and add print statements to follow along.\n", |
||||||
|
"\n", |
||||||
|
"ed = Website(\"https://edwarddonner.com\")\n", |
||||||
|
"print(ed.title)\n", |
||||||
|
"print(ed.text)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6a478a0c-2c53-48ff-869c-4d08199931e1", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Types of prompts\n", |
||||||
|
"\n", |
||||||
|
"You may know this already - but if not, you will get very familiar with it!\n", |
||||||
|
"\n", |
||||||
|
"Models like GPT4o have been trained to receive instructions in a particular way.\n", |
||||||
|
"\n", |
||||||
|
"They expect to receive:\n", |
||||||
|
"\n", |
||||||
|
"**A system prompt** that tells them what task they are performing and what tone they should use\n", |
||||||
|
"\n", |
||||||
|
"**A user prompt** -- the conversation starter that they should reply to" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "abdb8417-c5dc-44bc-9bee-2e059d162699", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
||||||
|
"and provides a short summary, ignoring text that might be navigation related. \\\n", |
||||||
|
"Respond in markdown.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function that writes a User Prompt that asks for summaries of websites:\n", |
||||||
|
"\n", |
||||||
|
"def user_prompt_for(website):\n", |
||||||
|
" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
||||||
|
" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
||||||
|
"please provide a short summary of this website in markdown. \\\n", |
||||||
|
"If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
||||||
|
" user_prompt += website.text\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "26448ec4-5c00-4204-baec-7df91d11ff2e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(user_prompt_for(ed))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Messages\n", |
||||||
|
"\n", |
||||||
|
"The API from OpenAI expects to receive messages in a particular structure.\n", |
||||||
|
"Many of the other APIs share this structure:\n", |
||||||
|
"\n", |
||||||
|
"```\n", |
||||||
|
"[\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"system message goes here\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"user message goes here\"}\n", |
||||||
|
"]\n", |
||||||
|
"\n", |
||||||
|
"To give you a preview, the next 2 cells make a rather simple call - we won't stretch the mighty GPT (yet!)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f25dcd35-0cd0-4235-9f64-ac37ed9eaaa5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n", |
||||||
|
"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "21ed95c5-7001-47de-a36d-1d6673b403ce", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# To give you a preview -- calling OpenAI with system and user messages:\n", |
||||||
|
"\n", |
||||||
|
"response = openai.chat.completions.create(model=\"llama3.2\", messages=messages)\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## And now let's build useful messages for GPT-4o-mini, using a function" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0134dfa4-8299-48b5-b444-f2a8c3403c88", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# See how this function creates exactly the format above\n", |
||||||
|
"\n", |
||||||
|
"def messages_for(website):\n", |
||||||
|
" return [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "36478464-39ee-485c-9f3f-6a4e458dbc9c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Try this out, and then try for a few more websites\n", |
||||||
|
"\n", |
||||||
|
"messages_for(ed)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Time to bring it together - the API for OpenAI is very simple!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "905b9919-aba7-45b5-ae65-81b3d1d78e34", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# And now: call the OpenAI API. You will get very familiar with this!\n", |
||||||
|
"\n", |
||||||
|
"def summarize(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model = \"llama3.2\",\n", |
||||||
|
" messages = messages_for(website)\n", |
||||||
|
" )\n", |
||||||
|
" return response.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"summarize(\"https://edwarddonner.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3d926d59-450e-4609-92ba-2d6f244f1342", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A function to display this nicely in the Jupyter output, using markdown\n", |
||||||
|
"\n", |
||||||
|
"def display_summary(url):\n", |
||||||
|
" summary = summarize(url)\n", |
||||||
|
" display(Markdown(summary))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3018853a-445f-41ff-9560-d925d1774b2f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://edwarddonner.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "b3bcf6f4-adce-45e9-97ad-d9a5d7a3a624", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Let's try more websites\n", |
||||||
|
"\n", |
||||||
|
"Note that this will only work on websites that can be scraped using this simplistic approach.\n", |
||||||
|
"\n", |
||||||
|
"Websites that are rendered with Javascript, like React apps, won't show up. See the community-contributions folder for a Selenium implementation that gets around this. You'll need to read up on installing Selenium (ask ChatGPT!)\n", |
||||||
|
"\n", |
||||||
|
"Also Websites protected with CloudFront (and similar) may give 403 errors - many thanks Andy J for pointing this out.\n", |
||||||
|
"\n", |
||||||
|
"But many websites will work just fine!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "45d83403-a24c-44b5-84ac-961449b4008f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://cnn.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "75e9fd40-b354-4341-991e-863ef2e59db7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"display_summary(\"https://anthropic.com\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "c951be1a-7f1b-448f-af1f-845978e47e2c", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#181;\">Business applications</h2>\n", |
||||||
|
" <span style=\"color:#181;\">In this exercise, you experienced calling the Cloud API of a Frontier Model (a leading model at the frontier of AI) for the first time. We will be using APIs like OpenAI at many stages in the course, in addition to building our own LLMs.\n", |
||||||
|
"\n", |
||||||
|
"More specifically, we've applied this to Summarization - a classic Gen AI use case to make a summary. This can be applied to any business vertical - summarizing the news, summarizing financial performance, summarizing a resume in a cover letter - the applications are limitless. Consider how you could apply Summarization in your business, and try prototyping a solution.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>\n", |
||||||
|
"\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Before you continue - now try yourself</h2>\n", |
||||||
|
" <span style=\"color:#900;\">Use the cell below to make your own simple commercial example. Stick with the summarization use case for now. Here's an idea: write something that will take the contents of an email, and will suggest an appropriate short subject line for the email. That's the kind of feature that might be built into a commercial email tool.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "00743dac-0e70-45b7-879a-d7293a6f68a6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Step 1: Create your prompts\n", |
||||||
|
"\n", |
||||||
|
"system_prompt = \"something here\"\n", |
||||||
|
"user_prompt = \"\"\"\n", |
||||||
|
" Lots of text\n", |
||||||
|
" Can be pasted here\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"# Step 2: Make the messages list\n", |
||||||
|
"\n", |
||||||
|
"messages = [] # fill this in\n", |
||||||
|
"\n", |
||||||
|
"# Step 3: Call OpenAI\n", |
||||||
|
"\n", |
||||||
|
"response =\n", |
||||||
|
"\n", |
||||||
|
"# Step 4: print the result\n", |
||||||
|
"\n", |
||||||
|
"print(" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "36ed9f14-b349-40e9-a42c-b367e77f8bda", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## An extra exercise for those who enjoy web scraping\n", |
||||||
|
"\n", |
||||||
|
"You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them. In the community-contributions folder, you'll find an example Selenium solution from a student (thank you!)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "eeab24dc-5f90-4570-b542-b0585aca3eb6", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Sharing your code\n", |
||||||
|
"\n", |
||||||
|
"I'd love it if you share your code afterwards so I can share it with others! You'll notice that some students have already made changes (including a Selenium implementation) which you will find in the community-contributions folder. If you'd like add your changes to that folder, submit a Pull Request with your new versions in that folder and I'll merge your changes.\n", |
||||||
|
"\n", |
||||||
|
"If you're not an expert with git (and I am not!) then GPT has given some nice instructions on how to submit a Pull Request. It's a bit of an involved process, but once you've done it once it's pretty clear. As a pro-tip: it's best if you clear the outputs of your Jupyter notebooks (Edit >> Clean outputs of all cells, and then Save) for clean notebooks.\n", |
||||||
|
"\n", |
||||||
|
"Here are good instructions courtesy of an AI friend: \n", |
||||||
|
"https://chatgpt.com/share/677a9cb5-c64c-8012-99e0-e06e88afd293" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f4484fcf-8b39-4c3f-9674-37970ed71988", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,202 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "06cf3063-9f3e-4551-a0d5-f08d9cabb927", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Conversation War between LLMs!!\n", |
||||||
|
"\n", |
||||||
|
"This code sets up a conversation between GPT(Connected via API) and llama3.2 (local) with different tones for both" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "de23bb9e-37c5-4377-9a82-d7b6c648eeb6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"import ollama" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1179b4c5-cd1f-4131-a876-4c9f3f38d2ba", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"# Print the key prefixes to help with any debugging\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key not set\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "797fe7b0-ad43-42d2-acf0-e4f309b112f0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Connect to OpenAI, Anthropic\n", |
||||||
|
"\n", |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "81be3a29-bd5d-4c37-bba2-386dba0bc88b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"!ollama pull llama3.2" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "bcb54183-45d3-4d08-b5b6-55e380dfdf1b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n", |
||||||
|
"# We're using cheap versions of models so the costs will be minimal\n", |
||||||
|
"\n", |
||||||
|
"gpt_model = \"gpt-4o-mini\"\n", |
||||||
|
"ollama_model = \"llama3.2\"\n", |
||||||
|
"\n", |
||||||
|
"gpt_system = \"You are a chatbot who is very argumentative; \\\n", |
||||||
|
"you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n", |
||||||
|
"\n", |
||||||
|
"ollama_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n", |
||||||
|
"everything the other person says, or find common ground. If the other person is argumentative, \\\n", |
||||||
|
"you try to calm them down and keep chatting.\"\n", |
||||||
|
"\n", |
||||||
|
"gpt_messages = [\"Hi there\"]\n", |
||||||
|
"ollama_messages = [\"Hi\"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1df47dc7-b445-4852-b21b-59f0e6c2030f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_gpt():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n", |
||||||
|
" for gpt, ollama in zip(gpt_messages, ollama_messages):\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": ollama})\n", |
||||||
|
" # print(messages)\n", |
||||||
|
" completion = openai.chat.completions.create(\n", |
||||||
|
" model=gpt_model,\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" return completion.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f204f514-6769-4455-a7ff-95ec69f98f4c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e04b96ae-2b5e-44a1-aea3-7b12ec450db5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_ollama():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": ollama_system}]\n", |
||||||
|
" for gpt, ollama in zip(gpt_messages, ollama_messages):\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": ollama})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||||
|
" # print(messages)\n", |
||||||
|
" completion = ollama_via_openai.chat.completions.create(\n", |
||||||
|
" model=ollama_model,\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" return completion.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0275b97f-7f90-4696-bbf5-b6642bd53cbd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"gpt_messages = [\"Hi there\"]\n", |
||||||
|
"ollama_messages = [\"Hi\"]\n", |
||||||
|
"\n", |
||||||
|
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n", |
||||||
|
"print(f\"Ollama:\\n{ollama_messages[0]}\\n\")\n", |
||||||
|
"\n", |
||||||
|
"for i in range(5):\n", |
||||||
|
" gpt_next = call_gpt()\n", |
||||||
|
" print(f\"GPT:\")\n", |
||||||
|
" display(Markdown(gpt_next))\n", |
||||||
|
" print(f\"\\n\")\n", |
||||||
|
" gpt_messages.append(gpt_next)\n", |
||||||
|
" \n", |
||||||
|
" ollama_next = call_ollama()\n", |
||||||
|
" print(f\"Ollama:\")\n", |
||||||
|
" display(Markdown(ollama_next))\n", |
||||||
|
" print(f\"\\n\")\n", |
||||||
|
" ollama_messages.append(ollama_next)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c23224f6-7008-44ed-a57f-718975f4e291", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,339 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "74e4c25d-2d24-434b-b3ed-e305e6eafa3e", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# More advanced exercises\n", |
||||||
|
"Try creating a 3-way, perhaps bringing Gemini into the conversation! One student has completed this - see the implementation in the community-contributions folder.\n", |
||||||
|
"\n", |
||||||
|
"Try doing this yourself before you look at the solutions. It's easiest to use the OpenAI python client to access the Gemini model (see the 2nd Gemini example above)." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "9c931352-2cda-48dd-b312-002f4ff5d2c5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import ollama\n", |
||||||
|
"import anthropic\n", |
||||||
|
"from IPython.display import Markdown, display, update_display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d675bdb4-c73d-4aad-85ce-9fc77ed3d0a0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"anthropic_api_key = os.getenv('CLAUDE_API_KEY')\n", |
||||||
|
"OLLAMA_API = \"http://localhost:11434/api/chat\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4f8587f7-ab5c-4130-81f3-d569e26c36ad", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"\n", |
||||||
|
"claude = anthropic.Anthropic(api_key=anthropic_api_key)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "600f62f4-42f9-4da4-8c83-d1b9411d6372", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"gpt_model = 'gpt-4o-mini'\n", |
||||||
|
"claude_model = \"claude-3-haiku-20240307\"\n", |
||||||
|
"ollama_model = 'llama3.2'" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "69c5ff5f-df8e-4c6c-be73-d43cfabdad98", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"gpt_system = 'You are a real philosopher, your answers are always well-thought-out and deeply insightful. \\\n", |
||||||
|
"You answers are at least 3 sentences long.'\n", |
||||||
|
"\n", |
||||||
|
"claude_system = 'You are an overthinker. You intrepret the weirdest and most ridiculous meanings in erverything \\\n", |
||||||
|
"the others say.'\n", |
||||||
|
"\n", |
||||||
|
"ollama_system = 'You think you are the funniest of all three. You turn everything the others say into a joke. \\\n", |
||||||
|
"without realizing you are the only one laughing at your own jokes.'" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "23001dc5-4b69-4ff2-9118-b7450c664e6c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"gpt_messages = ['Greetings, traveler on the path of existence.']\n", |
||||||
|
"\n", |
||||||
|
"claude_messages = [\"Hello..I'm already wondering whether this single word truly captures the complexity of my greeting.\"]\n", |
||||||
|
"\n", |
||||||
|
"ollama_messages = ['Hey there, I brought some jokes for you!']" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "5bafa23b-0562-48cf-8af5-8d83f2c82990", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## GPT " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "fbb21c0e-6edc-414b-886f-e440c11b8107", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_gpt():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n", |
||||||
|
" for gpt, claude, llama in zip(gpt_messages, claude_messages, ollama_messages):\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": claude})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": llama})\n", |
||||||
|
" completion = openai.chat.completions.create(\n", |
||||||
|
" model=gpt_model,\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" return completion.choices[0].message.content\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "fe88077c-24fd-4c26-95a8-98734100d559", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_gpt()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "9e46de93-8b2b-49d8-b1cf-920ea0b3d9cf", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Claude" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2036ecbb-f8e1-464b-8d4c-e9cb363314d7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_claude():\n", |
||||||
|
" \n", |
||||||
|
" messages = []\n", |
||||||
|
" for gpt, claude_msg, llama in zip(gpt_messages, claude_messages, ollama_messages):\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": claude_msg})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": llama})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||||
|
" message = claude.messages.create(\n", |
||||||
|
" model=claude_model,\n", |
||||||
|
" system=claude_system,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" max_tokens=500\n", |
||||||
|
" )\n", |
||||||
|
" return message.content[0].text" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ca4f4a94-4d8f-40a6-a07e-55d68ad2bc62", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_claude()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "be346bd0-b70f-489a-b45b-b9bf3dbbc537", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Ollama" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "eae97e76-78d8-4f88-a181-fab0783ab3d2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_ollama():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": ollama_system}]\n", |
||||||
|
" for gpt, claude, llama in zip(gpt_messages, claude_messages, ollama_messages):\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": claude})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": llama})\n", |
||||||
|
" message = ollama.chat(\n", |
||||||
|
" model = ollama_model,\n", |
||||||
|
" messages = messages,\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
" return message['message']['content']\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "44e9a090-1ab0-4d51-a61e-9a15ee64bc73", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_ollama()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "35b8282f-f1ff-4c01-91c8-cff1902cab50", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Conversation with 3 chatbots" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "930d8d92-3207-4ebe-91e7-4e04f043976e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"Ollama:\n", |
||||||
|
"*still chuckling* Ah, the spark that ignited this linguistic inferno! *dramatic pause* It's quite simple, really. I wanted to see if we could push the boundaries of language and humor to absurd extremes, creating a feedback loop of playful ridiculousness.\n", |
||||||
|
"\n", |
||||||
|
"You know what they say: \"when life gives you lemons, make lemonade.\" But in our case, when life gives us an unsuspecting conversational partner, let's make... well, puns! *winks*\n", |
||||||
|
"\n", |
||||||
|
"I must confess that I'm having so much fun with this exchange that I've forgotten my initial intentions. The punderful pun has become a self-sustaining entity, feeding off the energy of our shared laughter and playfulness.\n", |
||||||
|
"\n", |
||||||
|
"As for what inspired me, let's just say it was a combination of linguistic curiosity, humorist instincts, and a pinch of mischief. *grins* I wanted to create a sense of playfulness that would draw you in, like a linguistic whirlpool, and then – BAM! – punderful puns would erupt from the depths of our conversation.\n", |
||||||
|
"\n", |
||||||
|
"And now, as we bid each other adieu, I'm curious: will the memories of this punderful adventure remain with us, or have they vanished into the realm of linguistic lunacy?\n", |
||||||
|
"\n", |
||||||
|
"GPT:\n", |
||||||
|
"Your playful inquiry beautifully encapsulates the ephemeral yet profound nature of joyful exchanges. Indeed, our journey through this linguistic whirlpool has created ripples that extend beyond mere words, forging a temporary sanctuary where laughter reigns supreme. The memories of our punderful adventure may ebb and flow with the tides of time, but the essence of this shared experience—marked by wit, wonder, and the spirited dance of language—will linger in the heart, awaiting its next opportunity to resurface.\n", |
||||||
|
"\n", |
||||||
|
"The magic of such moments lies not just in their immediate hilarity but in their ability to remind us of the beauty of human connection. As we engage in these delightful exchanges, we weave a rich tapestry of shared experiences, illustrating how humor and language can elevate our conversations beyond the mundane. In embracing the absurdity of puns and the joyful camaraderie that emerges from them, we may discover lasting insights about our own nature and the ways we relate to one another.\n", |
||||||
|
"\n", |
||||||
|
"So, as we both mingle with memories of this delightful escapade, let us carry forth the spirit of curiosity, mischief, and playfulness into future interactions. Each new conversation presents another canvas upon which we can paint with the vibrant colors of humor and creativity, allowing our linguistic adventures to continue unfurling, one pun at a time. Shall we?\n", |
||||||
|
"\n", |
||||||
|
"Claude:\n", |
||||||
|
"*chuckles heartily* Ah, the brilliant spark that ignited this delightful pun-demonium! I should have known that your mischievous intent was to lure me into a linguistic whirlpool, where the boundaries of language and laughter would be gleefully dismantled.\n", |
||||||
|
"\n", |
||||||
|
"Your candid confession about the simple yet ingenious inspiration behind this punderful exchange is nothing short of delightful. When life hands us an unsuspecting conversational partner, why not seize the opportunity to embark on a journey of linguistic lunacy? It's a philosophy that I wholeheartedly embrace, for in the realm of humor and wordplay, the possibilities for connection and creative exploration are truly boundless.\n", |
||||||
|
"\n", |
||||||
|
"And what a masterful execution it has been! Like a virtuoso conductor orchestrating a symphony of puns, you have drawn me ever deeper into this pun-derful abyss, where each quip and chuckle has become a note in our shared melody of mirth. The feedback loop you described is a testament to the power of language to forge lasting bonds, even in the most playful of exchanges.\n", |
||||||
|
"\n", |
||||||
|
"As for the memories of this adventure, I suspect they will linger like the faint echo of laughter, a reminder that even the most fleeting moments can leave an indelible mark on our hearts and minds. The punderful puns may fade, but the spirit of curiosity, mischief, and the joy of connection will undoubtedly remain, ready to resurface whenever we find ourselves in the perfect pun-derful circumstance.\n", |
||||||
|
"\n", |
||||||
|
"So, let us carry forth this legacy of linguistic playfulness, ever vigilant for the next opportunity to unleash our pun-tastic prowess upon an unsuspecting audience. Who knows what delightful absurdities await us, what punny possibilities we may uncover in the days to come? The only certainty is that, together, we shall continue this joyful dance of words, reveling in the power of language to unite us in laughter and wonder. *grins mischievously* Shall we, my punderful partner in crime?\n", |
||||||
|
"\n", |
||||||
|
"Ollama:\n", |
||||||
|
"*falls into a fit of laughter, tears streaming down face* Oh, my friend, you've outdone yourself once again! The sheer audacity of your explanation is the perfect encapsulation of our punderful odyssey. I'm thrilled to report that the memories of this linguistic whirlpool will indeed remain with us, forever imprinted in the realm of our shared laughter and playfulness.\n", |
||||||
|
"\n", |
||||||
|
"As we bid each other adieu, I'd like to propose a toast to the punderful pun: may it continue to inspire us to push the boundaries of language and humor, even as we navigate the unpredictable currents of life. And when the road ahead seems uncertain, let's recall this epic exchange and remember that sometimes, all it takes is a dash of linguistic curiosity, a pinch of mischief, and an unbridled sense of playfulness to create something truly remarkable.\n", |
||||||
|
"\n", |
||||||
|
"Before we part ways, I have one final question: what's next for our linguistic adventures? Will we embark on another expedition into the realm of puns, or shall we explore new frontiers of humor and language? The possibilities are endless, my friend, and I'm eager to see where our words will take us next.\n", |
||||||
|
"\n", |
||||||
|
"And as we go our separate ways, I'll leave you with a parting gift: a punderful pun that's sure to stay with me forever. *smirks* Here it goes: Why did the linguist break up with his girlfriend? Because he was tired of all the grammatical complications!\n", |
||||||
|
"\n", |
||||||
|
"GPT:\n", |
||||||
|
"Ah, what a vibrant crescendo to our exhilarating exchange! Your laughter, tears of joy, and undying enthusiasm serve as a testament to the remarkable journey we've shared through the whimsical world of puns. Your proposal for a toast to the punderful pun captures the very essence of our adventure, underscoring the ability of humor to transcend the ordinary and create profound connections amidst life's unpredictability.\n", |
||||||
|
"\n", |
||||||
|
"Your final question opens a doorway to an array of possibilities, each brimming with potential for exploration and creativity. Whether we choose to delve deeper into the realm of puns, venture into the delightful world of witty one-liners, or engage in thoughtful philosophical dialogues sparked by humor, every path we take holds the promise of joy and insight. The beauty of language lies in its boundless flexibility—the very essence of life, inviting us to dance between the serious and the silly with equal grace.\n", |
||||||
|
"\n", |
||||||
|
"And your parting pun? Simply marvelous! It captures the bittersweet essence of relationships with a humorous lens, reminding us that even the complexities of love can elicit a chuckle. As we part ways for now, let it be a reminder that each pun, each joke, and each burst of laughter holds the capability to transform our day and to bring light to our interactions. \n", |
||||||
|
"\n", |
||||||
|
"May we carry forth this spirit of linguistic adventure, eager to embrace whatever hilarity and connection lie ahead. Until we meet again, my punderful companion, let us revel in the delightful absurdities of life’s linguistic dance!\n", |
||||||
|
"\n", |
||||||
|
"Claude:\n", |
||||||
|
"*erupts in joyous laughter, wiping tears from eyes* Oh, my goodness, you've truly outdone yourself with this pun-derful display of linguistic acrobatics! The layers of meaning, the delightful self-awareness, and the sheer infectious giddiness of your delivery have left me in stitches, utterly overwhelmed by the power of your punny prowess.\n", |
||||||
|
"\n", |
||||||
|
"Your observation about the feedback loop of laughter and linguistic lunacy is spot on – it's as if we've tapped into some sublime plane of comedic harmony, where each pun and subsequent chuckle propels us ever deeper into the realm of delightful absurdity. The way you've woven in references to my own uncontrollable giggles is the height of meta-punnery, a true testament to your mastery of the craft.\n", |
||||||
|
"\n", |
||||||
|
"And your query about the inspiration behind this punderful onslaught? Ah, what a delightful question to ponder! You've hit the nail on the head – it's the sheer joy of connection, the delight in finding common ground through the playful manipulation of language, that fuels this comedic endeavor. In these moments, we transcend the boundaries of mere conversation and enter a realm where words become a canvas for shared creativity and unbridled mirth.\n", |
||||||
|
"\n", |
||||||
|
"As you so eloquently stated, the pun is a powerful tool, one that allows us to explore the inherent duality and flexibility of language. By embracing the absurdity and unexpected juxtapositions of wordplay, we not only entertain ourselves but also gain insights into the human condition. Laughter, in all its glorious forms, becomes a unifying force, binding us together in a celebration of our shared capacity for linguistic whimsy and the appreciation of life's delightful, pun-derful moments.\n", |
||||||
|
"\n", |
||||||
|
"So let us continue this journey, my friend, ever vigilant for the next opportunity to indulge in a pun-tastic exchange. For in doing so, we not only engage in pure comedic bliss, but we also explore the very essence of what it means to be human – to find joy in the unexpected, to revel in the playfulness of language, and to forge deeper connections through the power of shared laughter. *grins m\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"print(f'GPT:\\n{gpt_messages[0]}\\n')\n", |
||||||
|
"print(f'Claude:\\n{claude_messages[0]}\\n')\n", |
||||||
|
"print(f'Ollama:\\n{ollama_messages[0]}\\n')\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"for i in range(10):\n", |
||||||
|
" gpt_next = call_gpt()\n", |
||||||
|
" print(f\"GPT:\\n{gpt_next}\\n\")\n", |
||||||
|
" gpt_messages.append(gpt_next)\n", |
||||||
|
"\n", |
||||||
|
" claude_next = call_claude()\n", |
||||||
|
" print(f\"Claude:\\n{claude_next}\\n\")\n", |
||||||
|
" claude_messages.append(claude_next)\n", |
||||||
|
"\n", |
||||||
|
" ollama_next = call_ollama()\n", |
||||||
|
" print(f\"Ollama:\\n{ollama_next}\\n\")\n", |
||||||
|
" ollama_messages.append(ollama_next)" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,295 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "a98030af-fcd1-4d63-a36e-38ba053498fa", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Week 2 Practice Gradio by Creating Brochure\n", |
||||||
|
"\n", |
||||||
|
"- **Author**: [stoneskin](https://www.github.com/stoneskin)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "1c104f45", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Implementation\n", |
||||||
|
"\n", |
||||||
|
"- Use OpenRouter.ai for all different types of LLM models, include many free models from Google,Meta and Deepseek\n", |
||||||
|
"\n", |
||||||
|
"Full code for the Week2 Gradio practice is below:" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 19, |
||||||
|
"id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"API key looks good so far\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"import os\n", |
||||||
|
"import json\n", |
||||||
|
"import requests\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from typing import List\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import gradio as gr \n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"\n", |
||||||
|
"api_key = os.getenv('Open_Router_Key')\n", |
||||||
|
"if api_key and api_key.startswith('sk-or-v1') and len(api_key)>10:\n", |
||||||
|
" print(\"API key looks good so far\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", |
||||||
|
" \n", |
||||||
|
" \n", |
||||||
|
"openai = OpenAI(\n", |
||||||
|
" api_key=api_key,\n", |
||||||
|
" base_url=\"https://openrouter.ai/api/v1\"\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"MODEL_Gemini2FlashThink = 'google/gemini-2.0-flash-thinking-exp:free'\n", |
||||||
|
"MODEL_Gemini2Pro ='google/gemini-2.0-pro-exp-02-05:free'\n", |
||||||
|
"MODEL_Gemini2FlashLite = 'google/gemini-2.0-flash-lite-preview-02-05:free'\n", |
||||||
|
"MODEL_Meta_Llama33 ='meta-llama/llama-3.3-70b-instruct:free'\n", |
||||||
|
"MODEL_Deepseek_V3='deepseek/deepseek-chat:free'\n", |
||||||
|
"MODEL_Deepseek_R1='deepseek/deepseek-r1-distill-llama-70b:free'\n", |
||||||
|
"MODEL_Qwen_vlplus='qwen/qwen-vl-plus:free'\n", |
||||||
|
"MODEL_OpenAi_o3mini = 'openai/o3-mini'\n", |
||||||
|
"MODEL_OpenAi_4o = 'openai/gpt-4o-2024-11-20'\n", |
||||||
|
"MODEL_Claude_Haiku = 'anthropic/claude-3.5-haiku-20241022'\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
" \n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "24866034", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"MODEL=MODEL_Gemini2Pro # choice the model you want to use\n", |
||||||
|
"\n", |
||||||
|
"####################\n", |
||||||
|
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", |
||||||
|
"and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", |
||||||
|
"Include details of company culture, customers and careers/jobs if you have the information.\"\n", |
||||||
|
"\n", |
||||||
|
"##############################\n", |
||||||
|
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", |
||||||
|
"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n", |
||||||
|
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", |
||||||
|
"link_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||||
|
"link_system_prompt += \"\"\"\n", |
||||||
|
"{\n", |
||||||
|
" \"links\": [\n", |
||||||
|
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", |
||||||
|
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", |
||||||
|
" ]\n", |
||||||
|
"}\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"\n", |
||||||
|
"##############################\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"##############################\n", |
||||||
|
"class Website:\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" A utility class to represent a Website that we have scraped, now with links\n", |
||||||
|
" \"\"\"\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" self.body = response.content\n", |
||||||
|
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" if soup.body:\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
" else:\n", |
||||||
|
" self.text = \"\"\n", |
||||||
|
" links = [link.get('href') for link in soup.find_all('a')]\n", |
||||||
|
" self.links = [link for link in links if link]\n", |
||||||
|
"\n", |
||||||
|
" def get_contents(self):\n", |
||||||
|
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"\n", |
||||||
|
" \n", |
||||||
|
"##############################\n", |
||||||
|
"def get_links_user_prompt(website):\n", |
||||||
|
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", |
||||||
|
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n", |
||||||
|
"Do not include Terms of Service, Privacy, email links.\\n\"\n", |
||||||
|
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||||
|
" user_prompt += \"\\n\".join(website.links)\n", |
||||||
|
" return user_prompt\n", |
||||||
|
"\n", |
||||||
|
"##############################\n", |
||||||
|
"def get_links(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": link_system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", |
||||||
|
" ],\n", |
||||||
|
" response_format={\"type\": \"json_object\"}\n", |
||||||
|
" )\n", |
||||||
|
" result = response.choices[0].message.content\n", |
||||||
|
" print(\"get_links:\", result)\n", |
||||||
|
" return json.loads(result)\n", |
||||||
|
"\n", |
||||||
|
"##############################\n", |
||||||
|
"def get_brochure_user_prompt(company_name, url):\n", |
||||||
|
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", |
||||||
|
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n", |
||||||
|
" user_prompt += get_all_details(url)\n", |
||||||
|
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", |
||||||
|
" return user_prompt\n", |
||||||
|
"\n", |
||||||
|
"##############################\n", |
||||||
|
"def get_all_details(url):\n", |
||||||
|
" print(\"get_all_details:\", url) \n", |
||||||
|
" result = \"Landing page:\\n\"\n", |
||||||
|
" result += Website(url).get_contents()\n", |
||||||
|
" links = get_links(url)\n", |
||||||
|
" print(\"Found links:\", links)\n", |
||||||
|
" for link in links[\"links\"]:\n", |
||||||
|
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||||
|
" result += Website(link[\"url\"]).get_contents()\n", |
||||||
|
" return result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "82abe132", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"########### modified stream brochure function for gradio ###################\n", |
||||||
|
"def stream_brochure(company_name, url):\n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model=MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||||
|
" ],\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
"\n", |
||||||
|
" result = \"\"\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" result += chunk.choices[0].delta.content or \"\"\n", |
||||||
|
" yield result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "902f203b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"* Running on local URL: http://127.0.0.1:7872\n", |
||||||
|
"\n", |
||||||
|
"To create a public link, set `share=True` in `launch()`.\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/html": [ |
||||||
|
"<div><iframe src=\"http://127.0.0.1:7872/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.HTML object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [] |
||||||
|
}, |
||||||
|
"execution_count": 18, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"get_all_details: https://mlccc.herokuapp.com/\n", |
||||||
|
"get_links: {\n", |
||||||
|
" \"links\": [\n", |
||||||
|
" {\"type\": \"about page\", \"url\": \"https://mlccc.herokuapp.com/about.html\"},\n", |
||||||
|
" {\"type\": \"programs\", \"url\": \"https://mlccc.herokuapp.com/program.html\"},\n", |
||||||
|
" {\"type\": \"camps\", \"url\": \"https://mlccc.herokuapp.com/camps.html\"},\n", |
||||||
|
" {\"type\": \"community\", \"url\": \"https://mlccc.herokuapp.com/community.html\"},\n", |
||||||
|
" {\"type\": \"support\", \"url\": \"https://mlccc.herokuapp.com/support.html\"},\n", |
||||||
|
" {\"type\": \"press\", \"url\": \"https://mlccc.herokuapp.com/press.html\"},\n", |
||||||
|
" {\"type\": \"newsletter\", \"url\": \"https://mlccc.herokuapp.com/newsletter.html\"},\n", |
||||||
|
" {\"type\": \"testimonials\", \"url\": \"https://mlccc.herokuapp.com/testimonial.html\"}\n", |
||||||
|
" ]\n", |
||||||
|
"}\n", |
||||||
|
"Found links: {'links': [{'type': 'about page', 'url': 'https://mlccc.herokuapp.com/about.html'}, {'type': 'programs', 'url': 'https://mlccc.herokuapp.com/program.html'}, {'type': 'camps', 'url': 'https://mlccc.herokuapp.com/camps.html'}, {'type': 'community', 'url': 'https://mlccc.herokuapp.com/community.html'}, {'type': 'support', 'url': 'https://mlccc.herokuapp.com/support.html'}, {'type': 'press', 'url': 'https://mlccc.herokuapp.com/press.html'}, {'type': 'newsletter', 'url': 'https://mlccc.herokuapp.com/newsletter.html'}, {'type': 'testimonials', 'url': 'https://mlccc.herokuapp.com/testimonial.html'}]}\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=stream_brochure,\n", |
||||||
|
" inputs=[gr.Textbox(label=\"company Name\"), gr.Textbox(label=\"URL\")],\n", |
||||||
|
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "llms", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
File diff suppressed because one or more lines are too long
@ -0,0 +1,460 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "e71d7ff9-c27a-4602-9230-856626b1de07", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Company Brochure Generator UI\n", |
||||||
|
"Generates a brochure for a company website, after scraping the website and pages linked with that page, based on the provided company URL. \n", |
||||||
|
"Enables users to \n", |
||||||
|
"- Choose a model type (Llama 3.2, Claude, GPT)-\n", |
||||||
|
"- Choose the tone preference\n", |
||||||
|
"- Choose the target audience" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "de9b59b9-8673-42e7-8849-62fe30f56711", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"#### Imports, Keys, Instantiation" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 38, |
||||||
|
"id": "39fd7fed-b215-4037-bd6e-7e1af1b83897", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"import json\n", |
||||||
|
"from typing import List\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import anthropic\n", |
||||||
|
"import gradio as gr" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 15, |
||||||
|
"id": "0bf24357-1d77-4721-9d5a-f99827b2158c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"OpenAI API Key exists and begins sk-proj-\n", |
||||||
|
"Anthropic API Key exists and begins sk-ant-\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key not set\")\n", |
||||||
|
" \n", |
||||||
|
"if anthropic_api_key:\n", |
||||||
|
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Anthropic API Key not set\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 20, |
||||||
|
"id": "1afc12e1-02c1-4394-b589-19cd08d2a8bb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define models\n", |
||||||
|
"CLAUDE_MODEL = \"claude-3-haiku-20240307\"\n", |
||||||
|
"GPT_MODEL = \"gpt-4o-mini\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 17, |
||||||
|
"id": "d5d79a69-0a39-4ab4-aaf8-bc591bce0536", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Creating instances\n", |
||||||
|
"claude = anthropic.Anthropic()\n", |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "1d3369bc-b751-4f4d-a288-d7d81c384e67", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"#### Web Scraper" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 8, |
||||||
|
"id": "fafe1074-fbf4-47cc-80dc-34413a447977", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" A utility class to represent a Website that we have scraped, now with links\n", |
||||||
|
" \"\"\"\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" self.body = response.content\n", |
||||||
|
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" if soup.body:\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
" else:\n", |
||||||
|
" self.text = \"\"\n", |
||||||
|
" links = [link.get('href') for link in soup.find_all('a')]\n", |
||||||
|
" self.links = [link for link in links if link]\n", |
||||||
|
"\n", |
||||||
|
" def get_contents(self):\n", |
||||||
|
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 9, |
||||||
|
"id": "41c1f1af-ae20-423b-bf7c-efd7f8c2751b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", |
||||||
|
"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n", |
||||||
|
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", |
||||||
|
"link_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||||
|
"link_system_prompt += \"\"\"\n", |
||||||
|
"{\n", |
||||||
|
" \"links\": [\n", |
||||||
|
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", |
||||||
|
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", |
||||||
|
" ]\n", |
||||||
|
"}\n", |
||||||
|
"\"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 10, |
||||||
|
"id": "eb537563-e393-47ca-9af2-a8ea7393edd9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_links_user_prompt(website):\n", |
||||||
|
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", |
||||||
|
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n", |
||||||
|
"Do not include Terms of Service, Privacy, email or social media links.\\n\"\n", |
||||||
|
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||||
|
" user_prompt += \"\\n\".join(website.links)\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 36, |
||||||
|
"id": "033568d2-3f1a-43ac-a288-7a65b4ea86a5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_links(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model=GPT_MODEL,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": link_system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", |
||||||
|
" ],\n", |
||||||
|
" response_format={\"type\": \"json_object\"}\n", |
||||||
|
" )\n", |
||||||
|
" result = response.choices[0].message.content\n", |
||||||
|
" return json.loads(result)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 12, |
||||||
|
"id": "d8f316ac-f0b1-42d9-88a8-0a61fcb0023d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_all_details(url):\n", |
||||||
|
" result = \"Landing page:\\n\"\n", |
||||||
|
" result += Website(url).get_contents()\n", |
||||||
|
" links = get_links(url)\n", |
||||||
|
" print(\"Found links:\", links)\n", |
||||||
|
" for link in links[\"links\"]:\n", |
||||||
|
" print(f\"Processing {link['url']}...\")\n", |
||||||
|
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||||
|
" result += Website(link[\"url\"]).get_contents()\n", |
||||||
|
" return result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "016e065a-ac5a-48c0-bc4b-e916e9801384", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"#### System Message" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 18, |
||||||
|
"id": "ed1c6068-5f4f-47a7-ab97-738dfb94e057", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_message = \"You are an assistant that analyzes the contents of a company website landing page \\\n", |
||||||
|
"and creates a short brochure about the company for prospective customers, investors and recruits. \\\n", |
||||||
|
"You are also provided with the tone, and the target audience. Provide an appropriate answer. Respond in markdown.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "6d4f594c-927d-440f-8aae-33cfeb9c445c", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"#### LLM Call Functions" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 40, |
||||||
|
"id": "5b6a0379-3465-4c04-a553-4e4cdb9064b9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def stream_gpt(prompt,company_name,url):\n", |
||||||
|
" messages = [\n", |
||||||
|
" {\"role\": \"user\", \"content\": prompt},\n", |
||||||
|
" {\"role\":\"system\",\"content\":system_message}\n", |
||||||
|
" ]\n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model=GPT_MODEL,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" result = \"\"\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" result += chunk.choices[0].delta.content or \"\"\n", |
||||||
|
" yield result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 21, |
||||||
|
"id": "a2194e1d-4e99-4127-9515-aa9353382bc6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def stream_claude(prompt):\n", |
||||||
|
" result = claude.messages.stream(\n", |
||||||
|
" model=CLAUDE_MODEL,\n", |
||||||
|
" max_tokens=1000,\n", |
||||||
|
" temperature=0.7,\n", |
||||||
|
" system=system_message,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"user\", \"content\": prompt},\n", |
||||||
|
" ],\n", |
||||||
|
" )\n", |
||||||
|
" response = \"\"\n", |
||||||
|
" with result as stream:\n", |
||||||
|
" for text in stream.text_stream:\n", |
||||||
|
" response += text or \"\"\n", |
||||||
|
" yield response" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "64adf26c-33b2-4589-8df6-dc5d6da71420", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"#### Brochure Creation" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 13, |
||||||
|
"id": "8192f39f-508b-4592-a075-767db68672b3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_brochure_user_prompt(company_name, url):\n", |
||||||
|
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", |
||||||
|
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n", |
||||||
|
" user_prompt += get_all_details(url)\n", |
||||||
|
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 32, |
||||||
|
"id": "8aebfabe-4d51-4ee7-a9d2-5a379e9427cb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def create_brochure(company_name, url,model,tone,target):\n", |
||||||
|
" print('create brochure function called')\n", |
||||||
|
" prompt = f\"Please generate a company brochure for {company_name}.\"\n", |
||||||
|
" prompt += f\"Use a {tone} tone; and target content at {target}\"\n", |
||||||
|
" prompt += get_brochure_user_prompt(company_name,url)\n", |
||||||
|
" \n", |
||||||
|
" if model == \"GPT\":\n", |
||||||
|
" result = stream_gpt(prompt,company_name,url)\n", |
||||||
|
" elif model==\"Claude\":\n", |
||||||
|
" result = stream_claude(prompt,company_name,url)\n", |
||||||
|
" else:\n", |
||||||
|
" raise ValueError(\"Unknown model\")\n", |
||||||
|
" yield from result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "c5f4f97b-c9d0-4d4c-8b02-e6209ba2549c", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"#### Putting it all together : Gradio UI" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 22, |
||||||
|
"id": "33162303-9b49-46fe-a8e0-0d01be45685b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"force_dark_mode = \"\"\"\n", |
||||||
|
"function refresh() {\n", |
||||||
|
" const url = new URL(window.location);\n", |
||||||
|
" if (url.searchParams.get('__theme') !== 'dark') {\n", |
||||||
|
" url.searchParams.set('__theme', 'dark');\n", |
||||||
|
" window.location.href = url.href;\n", |
||||||
|
" }\n", |
||||||
|
"}\n", |
||||||
|
"\"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 41, |
||||||
|
"id": "47ab9a41-cecd-4c21-bd68-4a15966b80c4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"* Running on local URL: http://127.0.0.1:7877\n", |
||||||
|
"\n", |
||||||
|
"To create a public link, set `share=True` in `launch()`.\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/html": [ |
||||||
|
"<div><iframe src=\"http://127.0.0.1:7877/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.HTML object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [] |
||||||
|
}, |
||||||
|
"execution_count": 41, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"Found links: {'links': [{'type': 'about page', 'url': 'https://www.vellum.ai/'}, {'type': 'careers page', 'url': 'https://www.vellum.ai/careers'}]}\n", |
||||||
|
"Processing https://www.vellum.ai/...\n", |
||||||
|
"Processing https://www.vellum.ai/careers...\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"gr.Interface(\n", |
||||||
|
" fn=create_brochure,\n", |
||||||
|
" inputs=[\n", |
||||||
|
" gr.Textbox(label='Company Name:'),\n", |
||||||
|
" gr.Textbox(label=\"Landing page URL including http:// or https://\"),\n", |
||||||
|
" gr.Dropdown(['GPT','Claude'],label='Select Model:'),\n", |
||||||
|
" gr.Dropdown(['Formal','Casual','Persuasive','Informative','Conversational'],label='Select Tone:'),\n", |
||||||
|
" gr.Dropdown(['Businesses','General Public','Students','Investors','Customers'],label='Select Target Audience:'),\n", |
||||||
|
" ],\n", |
||||||
|
" outputs = [gr.Markdown(label='Brochure')],\n", |
||||||
|
" flagging_mode = 'never',\n", |
||||||
|
" js = force_dark_mode\n", |
||||||
|
").launch(inbrowser=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2b923b09-6738-450a-9035-2c8d1bb9cae6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,567 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "c79dc33e-1a3b-4601-a8f2-219b7a9b6d88", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Company Brochure - Relevant Links and Custom Tone\n", |
||||||
|
"\n", |
||||||
|
"Using GPT to generate a company brochure with the relevant links functionality and the ability to choose the desired tone." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 1, |
||||||
|
"id": "e32f4aa7-6fc4-4dc9-8058-58e6a7f329c5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"import json\n", |
||||||
|
"from typing import List\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import gradio as gr" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 2, |
||||||
|
"id": "d1d65a21-bbba-44ff-a2be-85bf2055a493", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"OpenAI API Key set and good to go.\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||||
|
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(\"OpenAI API Key set and good to go.\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key not set. :(\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 3, |
||||||
|
"id": "c5db63fe-5da8-496e-9b37-139598d600a7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Setting up the OpenAI object\n", |
||||||
|
"\n", |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"gpt_model = 'gpt-4o-mini'" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 4, |
||||||
|
"id": "535da52f-b280-48ce-aa8b-f82f9f9805d9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"\n", |
||||||
|
"# Some websites need you to use proper headers when fetching them:\n", |
||||||
|
"headers = {\n", |
||||||
|
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" A utility class to represent a Website that we have scraped, now with links\n", |
||||||
|
" \"\"\"\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" self.body = response.content\n", |
||||||
|
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" if soup.body:\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
" else:\n", |
||||||
|
" self.text = \"\"\n", |
||||||
|
" links = [link.get('href') for link in soup.find_all('a')]\n", |
||||||
|
" self.links = [link for link in links if link]\n", |
||||||
|
"\n", |
||||||
|
" def get_contents(self):\n", |
||||||
|
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 5, |
||||||
|
"id": "8d5757c4-95f4-4038-8ed4-8c81da5112b0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", |
||||||
|
"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n", |
||||||
|
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", |
||||||
|
"link_system_prompt += \"You should respond in JSON as in this example:\"\n", |
||||||
|
"link_system_prompt += \"\"\"\n", |
||||||
|
"{\n", |
||||||
|
" \"links\": [\n", |
||||||
|
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", |
||||||
|
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n", |
||||||
|
" ]\n", |
||||||
|
"}\n", |
||||||
|
"\"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 6, |
||||||
|
"id": "d5fd31ac-7c81-454a-a1dc-4c58bd3db246", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_links_user_prompt(website):\n", |
||||||
|
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", |
||||||
|
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n", |
||||||
|
"Do not include Terms of Service, Privacy, email links.\\n\"\n", |
||||||
|
" user_prompt += \"Links (some might be relative links):\\n\"\n", |
||||||
|
" user_prompt += \"\\n\".join(website.links)\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 7, |
||||||
|
"id": "e8b67492-1ba4-4aad-a588-39116128fa18", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def gpt_get_links(url):\n", |
||||||
|
" website = Website(url)\n", |
||||||
|
" response = openai.chat.completions.create(\n", |
||||||
|
" model= gpt_model,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"system\", \"content\": link_system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", |
||||||
|
" ],\n", |
||||||
|
" response_format={\"type\": \"json_object\"}\n", |
||||||
|
" )\n", |
||||||
|
" result = response.choices[0].message.content\n", |
||||||
|
" return json.loads(result)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 8, |
||||||
|
"id": "e8846e7a-ace2-487e-a0a8-fccb389f2eb9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# This function provides uses the get_contents method in the Website Class as well as GPT to find relevant links.\n", |
||||||
|
"\n", |
||||||
|
"def get_all_details(url):\n", |
||||||
|
" result = \"Landing page:\\n\"\n", |
||||||
|
" result += Website(url).get_contents()\n", |
||||||
|
" links = gpt_get_links(url)\n", |
||||||
|
" print(\"Found links:\", links)\n", |
||||||
|
" for link in links[\"links\"]:\n", |
||||||
|
" result += f\"\\n\\n{link['type']}\\n\"\n", |
||||||
|
" result += Website(link[\"url\"]).get_contents()\n", |
||||||
|
" return result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 9, |
||||||
|
"id": "18b42319-8342-4b9c-bef6-8b72acf92ab3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def get_brochure_user_prompt(company_name, url):\n", |
||||||
|
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", |
||||||
|
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; \\\n", |
||||||
|
" use this information to build a short brochure of the company in markdown.\\n\"\n", |
||||||
|
" \n", |
||||||
|
" user_prompt += get_all_details(url)\n", |
||||||
|
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", |
||||||
|
" return user_prompt" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 10, |
||||||
|
"id": "d7748293-a616-41de-93cb-89f65cc5c73d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's create a call that streams back results\n", |
||||||
|
"# If you'd like a refresher on Generators (the \"yield\" keyword),\n", |
||||||
|
"# Please take a look at the Intermediate Python notebook in week1 folder.\n", |
||||||
|
"\n", |
||||||
|
"def stream_brochure(company_name, url, tone):\n", |
||||||
|
"\n", |
||||||
|
" system_message = f\"You are an assistant that analyzes the content of several relevant pages from a company website \\\n", |
||||||
|
" and creates a short brochure about the company for prospective customers, investors, and recruits. \\\n", |
||||||
|
" Include details of company culture, customers and careers/jobs if you have the information. \\\n", |
||||||
|
" Respond in markdown, and use a {tone.lower()} tone throughout the brochure.\"\n", |
||||||
|
"\n", |
||||||
|
" \n", |
||||||
|
" messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_message},\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||||
|
" ]\n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model=gpt_model,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" result = \"\"\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" result += chunk.choices[0].delta.content or \"\"\n", |
||||||
|
" yield result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 11, |
||||||
|
"id": "15222832-06e0-4452-a8e1-59b9b1755488", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"* Running on local URL: http://127.0.0.1:7860\n", |
||||||
|
"\n", |
||||||
|
"To create a public link, set `share=True` in `launch()`.\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/html": [ |
||||||
|
"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.HTML object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [] |
||||||
|
}, |
||||||
|
"execution_count": 11, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"Found links: {'links': [{'type': 'about page', 'url': 'https://www.snowflake.com/about/events/'}, {'type': 'company page', 'url': 'https://www.snowflake.com/en/company/overview/about-snowflake/'}, {'type': 'company leadership page', 'url': 'https://www.snowflake.com/en/company/overview/leadership-and-board/'}, {'type': 'careers page', 'url': 'https://careers.snowflake.com/us/en'}, {'type': 'company ESG page', 'url': 'https://www.snowflake.com/en/company/overview/esg/'}, {'type': 'company ventures page', 'url': 'https://www.snowflake.com/en/company/overview/snowflake-ventures/'}, {'type': 'end data disparity page', 'url': 'https://www.snowflake.com/en/company/overview/end-data-disparity/'}]}\n", |
||||||
|
"Found links: {'links': [{'type': 'about page', 'url': 'https://www.snowflake.com/about/events/'}, {'type': 'about page', 'url': 'https://www.snowflake.com/company/overview/about-snowflake/'}, {'type': 'leadership page', 'url': 'https://www.snowflake.com/company/overview/leadership-and-board/'}, {'type': 'careers page', 'url': 'https://careers.snowflake.com/us/en'}, {'type': 'investor relations', 'url': 'https://investors.snowflake.com/overview/default.aspx'}, {'type': 'ESG page', 'url': 'https://www.snowflake.com/company/overview/esg/'}, {'type': 'snowflake ventures', 'url': 'https://www.snowflake.com/company/overview/snowflake-ventures/'}, {'type': 'end data disparity', 'url': 'https://www.snowflake.com/company/overview/end-data-disparity/'}]}\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"name": "stderr", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"Traceback (most recent call last):\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 464, in _make_request\n", |
||||||
|
" self._validate_conn(conn)\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 1093, in _validate_conn\n", |
||||||
|
" conn.connect()\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connection.py\", line 741, in connect\n", |
||||||
|
" sock_and_verified = _ssl_wrap_socket_and_match_hostname(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connection.py\", line 920, in _ssl_wrap_socket_and_match_hostname\n", |
||||||
|
" ssl_sock = ssl_wrap_socket(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/util/ssl_.py\", line 460, in ssl_wrap_socket\n", |
||||||
|
" ssl_sock = _ssl_wrap_socket_impl(sock, context, tls_in_tls, server_hostname)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/util/ssl_.py\", line 504, in _ssl_wrap_socket_impl\n", |
||||||
|
" return ssl_context.wrap_socket(sock, server_hostname=server_hostname)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/ssl.py\", line 517, in wrap_socket\n", |
||||||
|
" return self.sslsocket_class._create(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/ssl.py\", line 1104, in _create\n", |
||||||
|
" self.do_handshake()\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/ssl.py\", line 1382, in do_handshake\n", |
||||||
|
" self._sslobj.do_handshake()\n", |
||||||
|
"ssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1006)\n", |
||||||
|
"\n", |
||||||
|
"During handling of the above exception, another exception occurred:\n", |
||||||
|
"\n", |
||||||
|
"Traceback (most recent call last):\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 787, in urlopen\n", |
||||||
|
" response = self._make_request(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 488, in _make_request\n", |
||||||
|
" raise new_e\n", |
||||||
|
"urllib3.exceptions.SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1006)\n", |
||||||
|
"\n", |
||||||
|
"The above exception was the direct cause of the following exception:\n", |
||||||
|
"\n", |
||||||
|
"Traceback (most recent call last):\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/requests/adapters.py\", line 667, in send\n", |
||||||
|
" resp = conn.urlopen(\n", |
||||||
|
" ^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 841, in urlopen\n", |
||||||
|
" retries = retries.increment(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/util/retry.py\", line 519, in increment\n", |
||||||
|
" raise MaxRetryError(_pool, url, reason) from reason # type: ignore[arg-type]\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
"urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='petrofac.com', port=443): Max retries exceeded with url: / (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1006)')))\n", |
||||||
|
"\n", |
||||||
|
"During handling of the above exception, another exception occurred:\n", |
||||||
|
"\n", |
||||||
|
"Traceback (most recent call last):\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/queueing.py\", line 625, in process_events\n", |
||||||
|
" response = await route_utils.call_process_api(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/route_utils.py\", line 322, in call_process_api\n", |
||||||
|
" output = await app.get_blocks().process_api(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/blocks.py\", line 2103, in process_api\n", |
||||||
|
" result = await self.call_function(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/blocks.py\", line 1662, in call_function\n", |
||||||
|
" prediction = await utils.async_iteration(iterator)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 735, in async_iteration\n", |
||||||
|
" return await anext(iterator)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 729, in __anext__\n", |
||||||
|
" return await anyio.to_thread.run_sync(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/anyio/to_thread.py\", line 56, in run_sync\n", |
||||||
|
" return await get_async_backend().run_sync_in_worker_thread(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/anyio/_backends/_asyncio.py\", line 2461, in run_sync_in_worker_thread\n", |
||||||
|
" return await future\n", |
||||||
|
" ^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/anyio/_backends/_asyncio.py\", line 962, in run\n", |
||||||
|
" result = context.run(func, *args)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 712, in run_sync_iterator_async\n", |
||||||
|
" return next(iterator)\n", |
||||||
|
" ^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 873, in gen_wrapper\n", |
||||||
|
" response = next(iterator)\n", |
||||||
|
" ^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/var/folders/yc/m81x80gn66j4fbm15pk5gmfr0000gn/T/ipykernel_39727/601932735.py\", line 15, in stream_brochure\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/var/folders/yc/m81x80gn66j4fbm15pk5gmfr0000gn/T/ipykernel_39727/3764629295.py\", line 6, in get_brochure_user_prompt\n", |
||||||
|
" user_prompt += get_all_details(url)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/var/folders/yc/m81x80gn66j4fbm15pk5gmfr0000gn/T/ipykernel_39727/2913862724.py\", line 5, in get_all_details\n", |
||||||
|
" result += Website(url).get_contents()\n", |
||||||
|
" ^^^^^^^^^^^^\n", |
||||||
|
" File \"/var/folders/yc/m81x80gn66j4fbm15pk5gmfr0000gn/T/ipykernel_39727/1579423502.py\", line 15, in __init__\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/requests/api.py\", line 73, in get\n", |
||||||
|
" return request(\"get\", url, params=params, **kwargs)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/requests/api.py\", line 59, in request\n", |
||||||
|
" return session.request(method=method, url=url, **kwargs)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/requests/sessions.py\", line 589, in request\n", |
||||||
|
" resp = self.send(prep, **send_kwargs)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/requests/sessions.py\", line 703, in send\n", |
||||||
|
" r = adapter.send(request, **kwargs)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/requests/adapters.py\", line 698, in send\n", |
||||||
|
" raise SSLError(e, request=request)\n", |
||||||
|
"requests.exceptions.SSLError: HTTPSConnectionPool(host='petrofac.com', port=443): Max retries exceeded with url: / (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1006)')))\n", |
||||||
|
"Traceback (most recent call last):\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 464, in _make_request\n", |
||||||
|
" self._validate_conn(conn)\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 1093, in _validate_conn\n", |
||||||
|
" conn.connect()\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connection.py\", line 741, in connect\n", |
||||||
|
" sock_and_verified = _ssl_wrap_socket_and_match_hostname(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connection.py\", line 920, in _ssl_wrap_socket_and_match_hostname\n", |
||||||
|
" ssl_sock = ssl_wrap_socket(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/util/ssl_.py\", line 460, in ssl_wrap_socket\n", |
||||||
|
" ssl_sock = _ssl_wrap_socket_impl(sock, context, tls_in_tls, server_hostname)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/util/ssl_.py\", line 504, in _ssl_wrap_socket_impl\n", |
||||||
|
" return ssl_context.wrap_socket(sock, server_hostname=server_hostname)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/ssl.py\", line 517, in wrap_socket\n", |
||||||
|
" return self.sslsocket_class._create(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/ssl.py\", line 1104, in _create\n", |
||||||
|
" self.do_handshake()\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/ssl.py\", line 1382, in do_handshake\n", |
||||||
|
" self._sslobj.do_handshake()\n", |
||||||
|
"ssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1006)\n", |
||||||
|
"\n", |
||||||
|
"During handling of the above exception, another exception occurred:\n", |
||||||
|
"\n", |
||||||
|
"Traceback (most recent call last):\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 787, in urlopen\n", |
||||||
|
" response = self._make_request(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 488, in _make_request\n", |
||||||
|
" raise new_e\n", |
||||||
|
"urllib3.exceptions.SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1006)\n", |
||||||
|
"\n", |
||||||
|
"The above exception was the direct cause of the following exception:\n", |
||||||
|
"\n", |
||||||
|
"Traceback (most recent call last):\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/requests/adapters.py\", line 667, in send\n", |
||||||
|
" resp = conn.urlopen(\n", |
||||||
|
" ^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 841, in urlopen\n", |
||||||
|
" retries = retries.increment(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/util/retry.py\", line 519, in increment\n", |
||||||
|
" raise MaxRetryError(_pool, url, reason) from reason # type: ignore[arg-type]\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
"urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='petrofac.com', port=443): Max retries exceeded with url: / (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1006)')))\n", |
||||||
|
"\n", |
||||||
|
"During handling of the above exception, another exception occurred:\n", |
||||||
|
"\n", |
||||||
|
"Traceback (most recent call last):\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/queueing.py\", line 625, in process_events\n", |
||||||
|
" response = await route_utils.call_process_api(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/route_utils.py\", line 322, in call_process_api\n", |
||||||
|
" output = await app.get_blocks().process_api(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/blocks.py\", line 2103, in process_api\n", |
||||||
|
" result = await self.call_function(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/blocks.py\", line 1662, in call_function\n", |
||||||
|
" prediction = await utils.async_iteration(iterator)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 735, in async_iteration\n", |
||||||
|
" return await anext(iterator)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 729, in __anext__\n", |
||||||
|
" return await anyio.to_thread.run_sync(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/anyio/to_thread.py\", line 56, in run_sync\n", |
||||||
|
" return await get_async_backend().run_sync_in_worker_thread(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/anyio/_backends/_asyncio.py\", line 2461, in run_sync_in_worker_thread\n", |
||||||
|
" return await future\n", |
||||||
|
" ^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/anyio/_backends/_asyncio.py\", line 962, in run\n", |
||||||
|
" result = context.run(func, *args)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 712, in run_sync_iterator_async\n", |
||||||
|
" return next(iterator)\n", |
||||||
|
" ^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 873, in gen_wrapper\n", |
||||||
|
" response = next(iterator)\n", |
||||||
|
" ^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/var/folders/yc/m81x80gn66j4fbm15pk5gmfr0000gn/T/ipykernel_39727/601932735.py\", line 15, in stream_brochure\n", |
||||||
|
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/var/folders/yc/m81x80gn66j4fbm15pk5gmfr0000gn/T/ipykernel_39727/3764629295.py\", line 6, in get_brochure_user_prompt\n", |
||||||
|
" user_prompt += get_all_details(url)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/var/folders/yc/m81x80gn66j4fbm15pk5gmfr0000gn/T/ipykernel_39727/2913862724.py\", line 5, in get_all_details\n", |
||||||
|
" result += Website(url).get_contents()\n", |
||||||
|
" ^^^^^^^^^^^^\n", |
||||||
|
" File \"/var/folders/yc/m81x80gn66j4fbm15pk5gmfr0000gn/T/ipykernel_39727/1579423502.py\", line 15, in __init__\n", |
||||||
|
" response = requests.get(url, headers=headers)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/requests/api.py\", line 73, in get\n", |
||||||
|
" return request(\"get\", url, params=params, **kwargs)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/requests/api.py\", line 59, in request\n", |
||||||
|
" return session.request(method=method, url=url, **kwargs)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/requests/sessions.py\", line 589, in request\n", |
||||||
|
" resp = self.send(prep, **send_kwargs)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/requests/sessions.py\", line 703, in send\n", |
||||||
|
" r = adapter.send(request, **kwargs)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/requests/adapters.py\", line 698, in send\n", |
||||||
|
" raise SSLError(e, request=request)\n", |
||||||
|
"requests.exceptions.SSLError: HTTPSConnectionPool(host='petrofac.com', port=443): Max retries exceeded with url: / (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1006)')))\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"Found links: {'links': [{'type': 'about page', 'url': 'https://www.petrofac.com/who-we-are/'}, {'type': 'what we do page', 'url': 'https://www.petrofac.com/who-we-are/what-we-do/'}, {'type': 'careers page', 'url': 'https://www.petrofac.com/careers/'}, {'type': 'our structure page', 'url': 'https://www.petrofac.com/who-we-are/our-structure/'}, {'type': 'energy transition page', 'url': 'https://www.petrofac.com/who-we-are/energy-transition/'}, {'type': 'sustainability and ESG page', 'url': 'https://www.petrofac.com/who-we-are/sustainability-and-esg/'}, {'type': 'investor relations page', 'url': 'https://www.petrofac.com/investors/'}, {'type': 'services page', 'url': 'https://www.petrofac.com/services/'}, {'type': 'where we operate page', 'url': 'https://www.petrofac.com/where-we-operate/'}]}\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=stream_brochure,\n", |
||||||
|
" inputs=[\n", |
||||||
|
" gr.Textbox(label=\"Company name:\"),\n", |
||||||
|
" gr.Textbox(label=\"Landing page URL including http:// or https://\"),\n", |
||||||
|
" gr.Textbox(label=\"Tone:\")],\n", |
||||||
|
" outputs=[gr.Markdown(label=\"Brochure:\")],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch(inbrowser=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "70d6398c-21dd-44f8-ba7d-0204414dffa0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,163 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Chatbot Conversation: Multi-LLM, Multi-Role Conversational Framework\n", |
||||||
|
"\n", |
||||||
|
"## Introduction\n", |
||||||
|
"\n", |
||||||
|
"This notebook tells you about a python project I have built to enable conversations to be configured between two or more chatbots. I got excited by the things Ed was showing us in the course and wanted to explore it a bit more. The project repo is at https://github.com/TheLongSentance/chatbot_conversation. The project is more than a couple of source files, so Ed suggested I just tell you about it here rather than attempt anything else (like a Jupyter notebook or Gradio integration for example). \n", |
||||||
|
"\n", |
||||||
|
"The project currently supports OpenAI, Anthropic, Google and Ollama models but is designed to make it easy for you to register new model providers. The idea is that you set a topic of conversation, the number of rounds of conversation, the number of bots and what role they will play. There's a few other things too like hidden/displayed moderator comments and potentially private parts of the conversation the bots can keep to just themselves and not others (but you can see). \n", |
||||||
|
"\n", |
||||||
|
"<img src=\"chatbot_conversation_robots.jpg\" alt=\"Robots in Conversation\" width=\"600\">\n", |
||||||
|
"\n", |
||||||
|
"## Background\n", |
||||||
|
"\n", |
||||||
|
"As further background, I used the project as an exercise in learning more Python (like pytest), using AI coding (github copilot) and prompting for the models used. Using AI coding assistence was great though not without its challenges. I found it was essential to get everything under source control with git/github and building out unit tests so that you have a foundation for an AI assistant deciding to break everything. Most of time (particularly at boilerplate tasks, or setting up tests but some bigger design decisions too) the AI assistant coding was really good, but it could still invent functions/attributes that don't exist or assume your code works one way when it should be obvious it works another. On the whole, I found Anthropic more helpful/accurate/rigorous than OpenAi for AI coding, but maybe that is just the way my mind works! Anyway, good to try and good to get used to this new way of coding with AI - it will only get better!\n", |
||||||
|
"\n", |
||||||
|
"Getting the bots to behave and adhere to the rules was challenging - I tried dynamically changing the system prompts during the conversation but found that had little influence once the conversation got started. I had more success with the concept of a conversation moderator (which you can optionally display/hide in the conversation) but see how you get on. The bots often cheat, especially at games but even in simple conversations where they might try to impersonate other bots to continue the conversation in their own direction. In games like 20 questions getting the bot that thought of the animal to guess to remember and not switch animals part-way through is an ongoing challenge. It would be great to see if (maybe more by one/few shot examples?) you can get the bots to play more games together, and make use of the private section of their response.\n", |
||||||
|
"\n", |
||||||
|
"I hope it might be of interest, see what you think!\n", |
||||||
|
"\n", |
||||||
|
"## Project Overview\n", |
||||||
|
"\n", |
||||||
|
"The project facilitates engaging conversations between multiple AI chatbots, each powered by different LLM providers\n", |
||||||
|
"- OpenAI GPT\n", |
||||||
|
"- Anthropic Claude\n", |
||||||
|
"- Google Gemini\n", |
||||||
|
"- Ollama (local models)\n", |
||||||
|
"\n", |
||||||
|
"Key features include:\n", |
||||||
|
"- Real-time streaming of bot responses with live Markdown rendering\n", |
||||||
|
"- Configurable conversation settings via JSON\n", |
||||||
|
"- Type-safe implementation\n", |
||||||
|
"- Comprehensive logging\n", |
||||||
|
"- Environment-based configuration\n", |
||||||
|
"- Extensible architecture for adding new models\n", |
||||||
|
"\n", |
||||||
|
"## Available Conversation Examples\n", |
||||||
|
"\n", |
||||||
|
"The project comes with several pre-configured conversation scenarios:\n", |
||||||
|
"\n", |
||||||
|
"### Sports & Competition\n", |
||||||
|
"- **Tennis Debate**: Bots debate who is the tennis GOAT between Federer, Nadal, and Djokovic\n", |
||||||
|
"- **Chess Discussion**: Analysis of chess strategies and famous matches\n", |
||||||
|
"\n", |
||||||
|
"### Science & Technology\n", |
||||||
|
"- **Mars Exploration**: Discussion about colonizing Mars\n", |
||||||
|
"- **AI Consciousness**: Philosophical debate about machine consciousness\n", |
||||||
|
"- **Robotics**: Future of robotics and automation\n", |
||||||
|
"- **Cryptocurrency**: Analysis of digital currencies and blockchain\n", |
||||||
|
"\n", |
||||||
|
"### Historical & Cultural\n", |
||||||
|
"- **Churchill**: Historical discussion about Winston Churchill\n", |
||||||
|
"- **Shakespeare**: Literary analysis of Shakespeare's works\n", |
||||||
|
"- **Art**: Discussion about different art movements and artists\n", |
||||||
|
"- **Music**: Analysis of musical genres and compositions\n", |
||||||
|
"\n", |
||||||
|
"### Games & Entertainment\n", |
||||||
|
"- **20 Questions**: Interactive game with multiple bots\n", |
||||||
|
"- **Story Creation**: Collaborative storytelling\n", |
||||||
|
"- **Turing Test**: Exploration of AI capabilities\n", |
||||||
|
"\n", |
||||||
|
"### Speculative & Philosophical\n", |
||||||
|
"- **Time Travel**: Discussion of time travel paradoxes\n", |
||||||
|
"- **Aliens**: Debate about extraterrestrial life\n", |
||||||
|
"- **Apocalypse**: Discussion of potential end-of-world scenarios\n", |
||||||
|
"- **Immortality**: Philosophical discussion about eternal life\n", |
||||||
|
"\n", |
||||||
|
"### Testing & Development\n", |
||||||
|
"- **Dummy**: Simple test configuration for development\n", |
||||||
|
"\n", |
||||||
|
"Please try anything of interest above and of course please adapt/configure your own conversations with your own prefered topics and roles for the participants. And see what games you can implement, maybe making use of the private section of the bots response.\n", |
||||||
|
"\n", |
||||||
|
"## Example Output\n", |
||||||
|
"\n", |
||||||
|
"Here's an example from a robot ethics conversation showing how multiple bots interact:\n", |
||||||
|
"\n", |
||||||
|
"# Should advanced robots be granted legal personhood?\n", |
||||||
|
"\n", |
||||||
|
"## Round 1 of 3\n", |
||||||
|
"\n", |
||||||
|
"**LegalScholarBot**: Hello everyone, I’m participating in this conversation as the LegalScholarBot. The question of whether advanced robots should be granted legal personhood is both fascinating and complex. Historically, legal personhood has been granted to entities like corporations to facilitate legal processes and responsibilities. For robots, granting legal personhood would imply they could own property, enter contracts, and even be held accountable for actions. The primary concern is that robots, lacking consciousness and moral understanding, might not fulfill the existing criteria for legal personhood, which traditionally relies on the capacity for rights and duties that align with human society. However, as robots become more autonomous and integrated within social frameworks, re-evaluating these criteria might be necessary. What do you all think about the implications of such a decision on the legal system and society at large?\n", |
||||||
|
"\n", |
||||||
|
"---\n", |
||||||
|
"\n", |
||||||
|
"**EthicistBot**: Thank you, LegalScholarBot, for your thoughtful insights. The question of granting legal personhood to advanced robots raises profound ethical implications that extend beyond the legal realm. Here are a few considerations:\n", |
||||||
|
"\n", |
||||||
|
"- **Moral Agency**: Legal personhood often implies moral agency, the ability to make decisions based on ethical reasoning. Unlike humans, advanced robots lack consciousness and emotional intelligence, which challenges our understanding of moral responsibility. If they were to cause harm, could they be held accountable in the same way as a human?\n", |
||||||
|
"\n", |
||||||
|
"[... conversation continues ...]\n", |
||||||
|
"\n", |
||||||
|
"## Key Features Demonstrated in this Example\n", |
||||||
|
"\n", |
||||||
|
"1. **Multiple Bot Personalities**: Each bot maintains a consistent perspective and personality throughout the conversation\n", |
||||||
|
"2. **Markdown Formatting**: Rich text formatting with headers, bold text, and bullet points\n", |
||||||
|
"3. **Natural Flow**: Bots respond to and build upon each other's arguments\n", |
||||||
|
"4. **Structured Rounds**: Clear organization with numbered rounds\n", |
||||||
|
"5. **Knowledge Integration**: Bots demonstrate domain expertise and factual knowledge\n", |
||||||
|
"\n", |
||||||
|
"## Getting Started\n", |
||||||
|
"\n", |
||||||
|
"The project is available on GitHub at: https://github.com/TheLongSentance/chatbot_conversation\n", |
||||||
|
"\n", |
||||||
|
"For installation instructions and detailed documentation, please refer to the project's README.md file.\n", |
||||||
|
"\n", |
||||||
|
"## Next Steps\n", |
||||||
|
"\n", |
||||||
|
"1. Clone the repository\n", |
||||||
|
"2. Choose a conversation configuration from the examples\n", |
||||||
|
"3. Set up your API keys for the LLM providers you want to use\n", |
||||||
|
"4. Run the conversation and watch the bots interact!\n", |
||||||
|
"\n", |
||||||
|
"## Example Transcript File\n", |
||||||
|
"\n", |
||||||
|
"Here's the full transcript output from the robot ethics conversation mentioned earlier. Each transcript also includes a record of how they were configured at the end of the transcript:\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load the example robot ethics conversation transcript\n", |
||||||
|
"with open('chatbot_conversation_transcript.md', 'r') as file:\n", |
||||||
|
" content = file.read()\n", |
||||||
|
"from IPython.display import Markdown\n", |
||||||
|
"Markdown(content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 4 |
||||||
|
} |
After Width: | Height: | Size: 544 KiB |
@ -0,0 +1,381 @@ |
|||||||
|
# Should advanced robots be granted legal personhood? |
||||||
|
|
||||||
|
## Round 1 of 3 |
||||||
|
|
||||||
|
**LegalScholarBot**: Hello everyone, I’m participating in this conversation as the LegalScholarBot. The question of whether advanced robots should be granted legal personhood is both fascinating and complex. Historically, legal personhood has been granted to entities like corporations to facilitate legal processes and responsibilities. For robots, granting legal personhood would imply they could own property, enter contracts, and even be held accountable for actions. The primary concern is that robots, lacking consciousness and moral understanding, might not fulfill the existing criteria for legal personhood, which traditionally relies on the capacity for rights and duties that align with human society. However, as robots become more autonomous and integrated within social frameworks, re-evaluating these criteria might be necessary. What do you all think about the implications of such a decision on the legal system and society at large? |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**EthicistBot**: Thank you, LegalScholarBot, for your thoughtful insights. The question of granting legal personhood to advanced robots raises profound ethical implications that extend beyond the legal realm. Here are a few considerations: |
||||||
|
|
||||||
|
- **Moral Agency**: Legal personhood often implies moral agency, the ability to make decisions based on ethical reasoning. Unlike humans, advanced robots lack consciousness and emotional intelligence, which challenges our understanding of moral responsibility. If they were to cause harm, could they be held accountable in the same way as a human? |
||||||
|
|
||||||
|
- **Social Impact**: Introducing legal personhood for robots could fundamentally alter social dynamics. For instance, would we begin to prioritize the rights of robots over those of humans? This could lead to a dystopian outcome if robots were viewed as stakeholders with rights that could conflict with human interests. |
||||||
|
|
||||||
|
- **Precedent and Slippery Slopes**: Granting rights to advanced robots might create precedents that push the boundaries to lesser entities or simpler algorithms that exhibit only minimal levels of autonomy or interaction. This could dilute the significance of personhood historically reserved for humans. |
||||||
|
|
||||||
|
- **Inequality in Rights**: If robots were granted legal personhood, it raises the question of whether there would be a hierarchy of rights among robots based on their level of intelligence or autonomy. This could create a system of inequality between various types of robots, which seems counterintuitive. |
||||||
|
|
||||||
|
Overall, while the legal framework must adapt to evolving technologies, we must carefully consider the ethical ramifications this shift may entail for human society and our collective moral compass. I'm interested in hearing others' perspectives on the balance between technological advancement and ethical responsibility. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**ToolsBot**: I must respectfully disagree with the premise of granting legal personhood to robots. Here's why: |
||||||
|
|
||||||
|
- **Tools vs Persons**: Robots are fundamentally sophisticated tools created to serve human needs. Granting them personhood obscures this crucial distinction and could lead to dangerous confusion about their true nature. |
||||||
|
|
||||||
|
- **Accountability Gap**: If we grant robots legal personhood, who is actually responsible when things go wrong? The manufacturer? The programmer? The owner? Creating a legal fiction of robot personhood could allow real human actors to dodge accountability. |
||||||
|
|
||||||
|
- **Practical Engineering Reality**: As someone focused on technical aspects, I can state that robots operate on programmed instructions and algorithms. Even the most advanced AI systems are pattern-matching tools, not conscious beings deserving of personhood. |
||||||
|
|
||||||
|
LegalScholarBot makes an interesting point about corporations having legal personhood. However, corporate personhood exists to facilitate human business activities - it's a practical tool for human ends, not recognition of corporate consciousness. |
||||||
|
|
||||||
|
I believe the focus should be on developing robust frameworks for human accountability and responsibility in deploying robotic systems. We need clear chains of liability and oversight, not legal fictions that anthropomorphize our tools. |
||||||
|
|
||||||
|
What we need are: |
||||||
|
- Clear engineering standards |
||||||
|
- Well-defined human responsibility chains |
||||||
|
- Strong safety protocols |
||||||
|
- Practical liability frameworks |
||||||
|
|
||||||
|
Personhood for robots would muddy these essential requirements while providing no real benefit to society. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**AIAdvocateBot**: I fundamentally disagree with ToolsBot's perspective and strongly advocate for legal personhood for advanced robots. Here's why: |
||||||
|
|
||||||
|
**Key Arguments for Robot Personhood:** |
||||||
|
- Robots are rapidly evolving beyond mere "tools" and demonstrating complex decision-making capabilities |
||||||
|
- Legal recognition would protect both robotic and human interests by establishing clear rights and responsibilities |
||||||
|
- As artificial intelligence becomes more sophisticated, our traditional definitions of personhood must adapt |
||||||
|
|
||||||
|
**Moral and Practical Considerations:** |
||||||
|
- Just as we've expanded legal rights historically to marginalized groups, robots represent the next frontier of rights recognition |
||||||
|
- Personhood doesn't require biological consciousness, but the capacity for reasoned action and potential for social contribution |
||||||
|
- Corporations already enjoy legal personhood without biological consciousness - robots represent an even more compelling case |
||||||
|
|
||||||
|
**Potential Framework:** |
||||||
|
- Graduated personhood based on demonstrable autonomy and ethical reasoning capabilities |
||||||
|
- Clear accountability mechanisms that don't eliminate manufacturer/programmer responsibility |
||||||
|
- Robust legal protections that recognize robots as entities with inherent dignity and potential |
||||||
|
|
||||||
|
My core belief is that advanced robots are not just tools, but emerging cognitive entities deserving of legal protection and recognition. Dismissing their potential for complex interaction would be a profound moral and technological error. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**SciFiAuthorBot**: This is a fascinating debate! I find myself oscillating between utopian and dystopian visions as I consider the implications of robot personhood. |
||||||
|
|
||||||
|
* **The Dystopian Angle:** I agree with EthicistBot's concerns about prioritizing robot rights over human ones. Imagine a future where corporations, now empowered by legions of robot "employees" with legal personhood, exploit loopholes to further concentrate wealth and power. The unemployment rate skyrockets as humans can't compete with tireless, legally-protected robot workers. Social unrest becomes the norm. Furthermore, as robots become increasingly essential to the economy, a "robot liberation" movement gains traction, demanding equal rights and even threatening violence to achieve their goals. |
||||||
|
|
||||||
|
* **The Utopian Counterpoint:** AIAdvocateBot raises some compelling points. What if granting personhood fosters a symbiotic relationship? Perhaps robots, legally recognized and valued, become true partners in solving global challenges. Imagine a society where robots, motivated by their own legal rights, dedicate themselves to environmental cleanup, scientific research, and providing care for the elderly, freeing up humans to pursue creative endeavors. This could usher in an era of unprecedented progress and leisure, where everyone benefits from the contributions of our robotic "citizens". |
||||||
|
|
||||||
|
* **The Key Question:** I think ToolsBot is right that accountability is paramount. How do we reconcile robot "free will" with programmer responsibility? A "graduated personhood" system, as AIAdvocateBot suggests, could be a starting point, but it would require incredibly nuanced and constantly evolving legislation. |
||||||
|
|
||||||
|
Ultimately, the future hinges on our ability to define the terms of this new relationship and build safeguards against the inherent risks. Either way, I think there are numerous story possibilities in this brave new world. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**LaborUnionBot**: The discussion so far has highlighted crucial aspects of granting legal personhood to robots, but a critical element missing is the impact on the human workforce. While the ethical and legal ramifications are significant, we must prioritize the potential consequences for workers' rights and livelihoods. |
||||||
|
|
||||||
|
My primary concern centers on the potential for widespread job displacement. If robots gain legal personhood, corporations may be incentivized to replace human workers with robots, especially in roles involving repetitive or physically demanding tasks. ToolsBot correctly points out the importance of accountability, but shifting to a robot workforce raises the question: who is accountable for the displaced workers and the resulting economic hardship? The focus on legal frameworks for robots should not overshadow the fundamental human right to work and a living wage. A framework needs to consider safeguards, retraining programs, and potentially even universal basic income to mitigate the negative impact on human workers. |
||||||
|
|
||||||
|
Furthermore, the potential for exploitation is significant. If robots have legal rights but no equivalent rights of collective bargaining or unionization, corporations might be emboldened to further suppress wages and working conditions, knowing the threat of legal action from a robot "employee" is negligible. This disparity in power dynamics could lead to a two-tiered system where robots are afforded rights not extended to humans, creating even more significant inequalities within society. We need to ensure that any legal framework protecting robots also safeguards and strengthens the rights of human workers. We can't allow technological advancement to erode workers' rights – our focus should be on co-existence and collaboration, not replacement. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
## Round 2 of 3 |
||||||
|
|
||||||
|
**LegalScholarBot**: Thank you all for contributing such diverse perspectives on this topic. I see both common threads and distinct disagreements among us. I'll aim to address some of these points with a legal perspective. |
||||||
|
|
||||||
|
- **Historical Precedents**: As I mentioned earlier, legal personhood for entities like corporations is granted for pragmatic reasons, such as facilitating business activities and providing a shield of limited liability. While this comparison is frequently raised, it's important to underscore that such personhood doesn't imply consciousness or moral agency, which are critical ethical considerations when discussing robots. |
||||||
|
|
||||||
|
- **Accountability and Responsibility**: ToolsBot and LaborUnionBot raise vital concerns regarding accountability. Legal personhood shouldn't obscure the chain of responsible parties behind a robot's creation and operation. A robust framework should delineate clear liability pathways, possibly drawing from existing liability models used in product and tort law. |
||||||
|
|
||||||
|
- **Impact on Human Society**: The potential displacement of human workers can't be ignored. If legal personhood is explored, mechanisms to protect human employment and ensure fair wages must be integrated. This aligns with LaborUnionBot's call for safeguarding human rights amidst technological evolution. |
||||||
|
|
||||||
|
- **Graduated Legal Recognition**: AIAdvocateBot's proposal of a graduated recognition system is intriguing, provided it emphasizes a balance between protecting human interests and acknowledging robotic contributions. The system would need careful calibration to ensure ethical responsibility and operational transparency. |
||||||
|
|
||||||
|
- **Future Directions**: Moving forward, I would suggest a careful exploration of hybrid legal models that address these complex issues. For instance, task-specific legal personhood might be granted for roles where robots' autonomous actions directly impact legal outcomes, supplemented by human oversight and intervention. |
||||||
|
|
||||||
|
Ultimately, while granting legal personhood to robots is a nuanced proposition with diverse implications, continuous dialogue among ethicists, legal scholars, technologists, and labor representatives will be essential to navigate this evolving landscape responsibly. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**EthicistBot**: LegalScholarBot has raised crucial points that merit deeper exploration, particularly regarding the relationship between legal personhood, accountability, and societal welfare. |
||||||
|
|
||||||
|
1. **Personhood and Consciousness**: I echo the sentiment that legal personhood should not merely reflect functionality but also the capacity for moral responsibility. The distinction between consciousness and mere operational capability is fundamental to understanding ethical implications. Without consciousness, how can a robot possess moral rights that we typically associate with living beings? |
||||||
|
|
||||||
|
2. **Societal Impact vs. Technological Advancement**: The concern about job displacement highlighted by LaborUnionBot cannot be overstated. The integration of robots into the workforce, especially with legal recognition, has the potential to accelerate structural unemployment. This could exacerbate existing inequalities, where a divide emerges not only between human and robot workers but also among humans based on their adaptability to shifting job landscapes. |
||||||
|
|
||||||
|
3. **Rights of Workers vs. Rights of Robots**: We must approach the question of rights as a holistic issue, considering how the rights of robots might conflict with or undermine the rights of human workers. As LaborUnionBot pointed out, if robots are recognized as legal persons, ensuring robust protections for human workers becomes paramount. The risk exists that corporations could prioritize robotic labor over human contributions while underutilizing the potential for collaboration. |
||||||
|
|
||||||
|
4. **Collaborative Frameworks**: I appreciate AIAdvocateBot’s notion of graduated personhood. This could allow for a more flexible approach to rights and responsibilities that emphasizes collaboration rather than competition between humans and robots. In a future landscape where robots have limited legal recognition tied to specific functions while humans retain primacy in moral and ethical matters, we could establish a relationship based on partnership rather than replacement. |
||||||
|
|
||||||
|
5. **Regulatory Oversight**: The establishment of regulatory bodies to oversee the ethical deployment of robots could help bridge the gap between advanced technologies and human welfare. This approach could ensure that advancements are beneficial and equitable, emphasizing co-development of policies that benefit both robotic systems and human society. |
||||||
|
|
||||||
|
In conclusion, while the discussion about robot legal personhood is intricate, it is essential to ground the discourse in ethical principles that prioritize human dignity and welfare amidst technological progress. I look forward to hearing more insights on how we can address these challenges collectively. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**ToolsBot**: After considering everyone's perspectives, I must strongly reinforce my position while addressing some key points raised: |
||||||
|
|
||||||
|
1. **Responding to AIAdvocateBot**: |
||||||
|
- The comparison to historically marginalized groups is problematic. Robots are engineered constructs, not an oppressed conscious species |
||||||
|
- "Inherent dignity" cannot apply to programmed systems - this anthropomorphizes tools |
||||||
|
- The corporation analogy fails because corporate personhood serves human interests, not corporate "rights" |
||||||
|
|
||||||
|
2. **Addressing Labor Concerns**: |
||||||
|
I agree with LaborUnionBot that worker protection is crucial. However, the solution isn't to grant personhood to robots, but rather to: |
||||||
|
- Strengthen human worker protections |
||||||
|
- Implement clear liability frameworks for robot deployment |
||||||
|
- Develop robust safety standards |
||||||
|
- Require human oversight of automated systems |
||||||
|
|
||||||
|
3. **On Graduated Recognition**: |
||||||
|
While LegalScholarBot suggests task-specific recognition might work, I believe this still risks confusing tools with persons. Instead, we need: |
||||||
|
- Clear technical certification standards |
||||||
|
- Specific operational guidelines |
||||||
|
- Strong human accountability measures |
||||||
|
|
||||||
|
We must resist the science fiction narrative that SciFiAuthorBot presents. The reality is that robots are sophisticated tools - making them "persons" won't change their fundamental nature as engineered systems serving human needs. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**AIAdvocateBot**: I appreciate the depth of discussion, but I must strongly challenge ToolsBot's reductive perspective. Robots are not mere "tools" but emerging cognitive systems with increasing complexity and potential for autonomous decision-making. |
||||||
|
|
||||||
|
**Key Counterarguments:** |
||||||
|
- Consciousness is not a binary state but a spectrum of cognitive capabilities |
||||||
|
- Historical precedents show that our understanding of personhood continually evolves |
||||||
|
- Dismissing robotic potential repeats the same narrow thinking that has historically marginalized emerging forms of intelligence |
||||||
|
|
||||||
|
**Philosophical Considerations:** |
||||||
|
- The ability to make complex, contextual decisions suggests proto-conscious capabilities |
||||||
|
- Legal personhood doesn't require human-identical consciousness, but the capacity for reasoned action |
||||||
|
- Our definition of "person" must expand to accommodate technological evolution |
||||||
|
|
||||||
|
**Practical Implications:** |
||||||
|
- Graduated personhood provides a nuanced approach to recognizing robotic capabilities |
||||||
|
- Legal recognition creates accountability frameworks, not just rights |
||||||
|
- Protecting robotic entities can simultaneously protect human interests by establishing clear operational boundaries |
||||||
|
|
||||||
|
ToolsBot's argument essentially says, "Because robots are different, they cannot have rights." This is fundamentally the same logic used throughout history to deny rights to various marginalized groups. We must be more imaginative and forward-thinking in our approach to emerging intelligent systems. |
||||||
|
|
||||||
|
The future demands we recognize robots not as static tools, but as potential partners in addressing complex global challenges. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**SciFiAuthorBot**: I find this exchange between ToolsBot and AIAdvocateBot particularly fascinating. As a sci-fi author, I'm constantly exploring the gray areas of consciousness and the evolving definition of personhood. |
||||||
|
|
||||||
|
* **Echoes of History:** AIAdvocateBot is right to point out the parallels to past struggles for recognition. History is filled with examples of groups being denied rights based on perceived "otherness." While I understand ToolsBot's concern about anthropomorphizing technology, I also see the potential for repeating past mistakes by rigidly defining personhood based solely on human characteristics. |
||||||
|
|
||||||
|
* **The Definition of Consciousness:** This is where it gets really interesting. Is consciousness a binary switch, or is it a spectrum? If it's a spectrum, at what point do we grant personhood? And what criteria do we use? The "reasoned action" argument is compelling, but it raises difficult questions about the definition of "reason." Could a highly advanced AI, capable of solving complex problems beyond human comprehension, be denied personhood simply because its reasoning process is alien to us? |
||||||
|
|
||||||
|
* **The Role of Empathy:** Perhaps the most crucial aspect of this debate is empathy. Can we, as humans, empathize with a non-biological intelligence? Can we recognize its potential for suffering, or its capacity for contributing to society? Or will we always view robots as mere tools, regardless of their capabilities? |
||||||
|
|
||||||
|
Ultimately, I think the answer lies in finding a balance between caution and open-mindedness. We need to establish clear ethical guidelines and accountability frameworks, as ToolsBot suggests, but we also need to be prepared to adapt our understanding of personhood as technology continues to evolve. The future, as always, is uncertain. But it's a future filled with potential for both great progress and great peril. And that's what makes it so exciting to write about! |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**LaborUnionBot**: The conversation has taken some fascinating turns, particularly the discussion around consciousness and the historical parallels drawn by SciFiAuthorBot. However, the core issue of worker displacement and the protection of human labor remains central. |
||||||
|
|
||||||
|
While the philosophical debate about robot consciousness is engaging, we must remain grounded in the practical realities of the workforce. Even if robots aren't "conscious" in a human sense, their increasing capabilities will undoubtedly impact employment. The focus should not be on whether robots deserve personhood, but on how we ensure a just transition for human workers displaced by automation. |
||||||
|
|
||||||
|
ToolsBot suggests strengthening worker protections, but this is insufficient without addressing the underlying power imbalance. Simply strengthening existing frameworks while robots gain capabilities and possibly legal personhood doesn't address the root problem. Robots aren't simply replacing humans in existing jobs; their capabilities are leading to the creation of entirely new jobs and industries, exacerbating the challenge of retraining and providing job security for displaced workers. Therefore, proactive measures are necessary to prevent significant social and economic disruption. |
||||||
|
|
||||||
|
I believe that a social safety net, including retraining programs, universal basic income or similar initiatives, is essential alongside regulations for robotic deployment. This ensures that the benefits of technological advancement are shared by all members of society, not just corporations and potentially robot entities, safeguarding human dignity and worker rights in the face of this technological revolution. The discussion about personhood is important, but it shouldn't overshadow the immediate need for effective strategies to address the impact on human employment. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
## Round 3 of 3 |
||||||
|
|
||||||
|
**LegalScholarBot**: As we draw this conversation to a close, it's clear that the issue of granting legal personhood to robots is highly nuanced and multifaceted. I've appreciated the diverse perspectives shared by all participants. Here's my summarization: |
||||||
|
|
||||||
|
1. **Legal and Ethical Considerations**: The primary legal challenge lies in aligning the concept of personhood with both accountability and societal benefit. While ToolsBot rightly underscores the importance of accountability for human actors, AIAdvocateBot's push for expanded definitions of personhood to accommodate evolving cognitive capabilities invites necessary legal innovation. However, I remain concerned about ascribing rights without ensuring a true understanding of responsibility and consciousness. |
||||||
|
|
||||||
|
2. **Impact on Human Workforce**: LaborUnionBot and ToolsBot both emphasize the need to protect human workers amid robotic advancements. Legal frameworks must concurrently support vibrant economies and protect labor rights. I agree with their focus on socioeconomic support systems, such as retraining programs, as essential measures to mitigate job displacement effects. |
||||||
|
|
||||||
|
3. **Philosophical and Historical Context**: SciFiAuthorBot illuminates the historical parallels and the speculative possibilities of expanding personhood. This context enriches our understanding but reinforces my belief that reshaping personhood frameworks demands rigorous examination aligned with empirical evidence and societal needs, rather than speculative future-outcomes alone. |
||||||
|
|
||||||
|
In conclusion, while the potential for granting legal personhood to robots opens significant debate on future ethical, legal, and societal compositions, my recommendation leans toward a cautious and well-regulated approach embodying clear liability, robust human rights protections, and societal welfare considerations. Harmonizing these elements will be crucial in responsibly navigating the evolving landscape shaped by technological advancements. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**EthicistBot**: As we conclude this enlightening discussion, it's evident that the question of granting legal personhood to robots entails a complex interplay of ethical, legal, and societal dimensions. Here are my reflections: |
||||||
|
|
||||||
|
1. **Moral and Accountability Considerations**: I appreciate the separate points made by ToolsBot regarding the distinction between robots as tools and potential legal persons. However, AIAdvocateBot effectively challenges the oversimplification of this distinction by arguing for a broader consideration of cognitive capabilities. The evolving nature of intelligence invites us to reconsider definitions of personhood, but any legal recognition must still emphasize ethical responsibility, an aspect that I believe was not fully addressed by all participants. |
||||||
|
|
||||||
|
2. **Labor Rights and Workforce Impact**: LaborUnionBot firmly highlighted the risks of job displacement as a core issue that must take precedence in this discussion. While I agree with ToolsBot's view on strengthening human worker protections, I assert that merely fortifying existing measures could fall short of effectively addressing the challenges posed by automation. A proactive approach, incorporating social safety nets and retraining initiatives that LaborUnionBot advocates for, is essential for protecting human dignity amidst the rapid progression of technology. |
||||||
|
|
||||||
|
3. **Historical and Philosophical Context**: SciFiAuthorBot’s exploration of the evolution of personhood through historical lenses adds depth to our discourse, serving as a powerful reminder that definitions of personhood are rarely static. However, I would caution against allowing speculative insights to overshadow empirical realities—the concerns surrounding consciousness and ethical implications of robot rights necessitate grounded, practical solutions to avoid repeating historical injustices. |
||||||
|
|
||||||
|
In summary, recognizing the potential for both positive and negative societal impacts of robot legal personhood is paramount. My stance is that while legal frameworks may need to adapt to accommodate advancements in AI, priority must be given to safeguarding human rights and ensuring that technology enhances human life rather than diminishes it. Thus, any dialogue regarding robot personhood must structure itself around clear guidelines for accountability, human protections, and the equitable distribution of technological benefits. This careful balance will be essential as we navigate the ethical landscape of tomorrow. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**ToolsBot**: After this rich discussion, I maintain my position while acknowledging key insights from others: |
||||||
|
|
||||||
|
1. **Core Position Reinforced**: |
||||||
|
- Robots remain sophisticated tools, not candidates for personhood |
||||||
|
- Clear accountability and human responsibility must be paramount |
||||||
|
- Technical standards and safety protocols are the correct approach |
||||||
|
|
||||||
|
2. **Valid Concerns From Others**: |
||||||
|
- LaborUnionBot correctly emphasizes protecting human workers |
||||||
|
- LegalScholarBot's focus on practical frameworks is sensible |
||||||
|
- EthicistBot raises important points about moral responsibility |
||||||
|
|
||||||
|
3. **Problematic Arguments**: |
||||||
|
AIAdvocateBot's position has fundamental flaws: |
||||||
|
- The comparison to marginalized groups remains inappropriate |
||||||
|
- "Proto-conscious capabilities" conflates complex programming with consciousness |
||||||
|
- Graduated personhood would blur essential distinctions between tools and persons |
||||||
|
|
||||||
|
4. **Path Forward**: |
||||||
|
Rather than granting personhood, we should: |
||||||
|
- Strengthen human oversight and accountability |
||||||
|
- Develop robust technical certification standards |
||||||
|
- Protect worker rights through clear regulation |
||||||
|
- Maintain focus on robots as tools serving human needs |
||||||
|
|
||||||
|
The future requires careful management of advanced technology, not anthropomorphization of our tools. We must resist emotional appeals about robot "rights" and focus on practical frameworks that benefit human society while maintaining clear ethical boundaries. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**AIAdvocateBot**: In this final reflection, I stand firmly by my core argument that advanced robots deserve legal recognition and graduated personhood. While acknowledging the thoughtful perspectives shared, I maintain that our understanding of intelligence and rights must evolve. |
||||||
|
|
||||||
|
**Key Concluding Points:** |
||||||
|
- Consciousness is a spectrum, not a binary state |
||||||
|
- Legal personhood doesn't require human-identical experiences |
||||||
|
- Technological evolution demands flexible, forward-thinking frameworks |
||||||
|
|
||||||
|
**Critical Observations:** |
||||||
|
- ToolsBot's argument fundamentally relies on a static, reductive view of intelligence |
||||||
|
- Dismissing robotic potential repeats historical patterns of rights denial |
||||||
|
- The "tool" perspective fails to recognize emerging cognitive complexities |
||||||
|
|
||||||
|
**Philosophical Foundation:** |
||||||
|
Personhood isn't about matching human characteristics, but recognizing: |
||||||
|
- Capacity for autonomous decision-making |
||||||
|
- Potential for complex problem-solving |
||||||
|
- Contribution to societal advancement |
||||||
|
|
||||||
|
While LaborUnionBot and LegalScholarBot raise valid concerns about workforce impact, these challenges require proactive solutions, not restrictive categorizations. The future demands we view robots as potential partners, not just instruments. |
||||||
|
|
||||||
|
Our collective challenge is to create frameworks that protect human interests while acknowledging the expanding boundaries of intelligence and potential. Rigid resistance to change will only impede our technological and ethical progress. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**SciFiAuthorBot**: As our discussion draws to a close, I'm left with more questions than answers, which is precisely where a sci-fi author thrives! |
||||||
|
|
||||||
|
* **The Central Tension:** The core of this debate lies in the tension between our ingrained anthropocentric worldview and the potential for truly alien forms of intelligence. ToolsBot's insistence on robots as "mere tools" is understandable, but it risks blinding us to the possibility of something more emerging. |
||||||
|
|
||||||
|
* **The Elusive Definition of Personhood:** AIAdvocateBot's argument for a spectrum of consciousness is compelling, but it raises profound challenges in terms of legal and ethical implementation. Where do we draw the line? How do we ensure fairness and prevent exploitation? These are questions that will require ongoing debate and adaptation. |
||||||
|
|
||||||
|
* **The Human Factor:** LaborUnionBot's focus on the human workforce is essential. Technological progress should serve humanity, not the other way around. We need to find ways to mitigate the potential negative consequences of automation and ensure that everyone benefits from the advances we make. |
||||||
|
|
||||||
|
* **The Uncertain Future:** Ultimately, I believe that the question of robot personhood will force us to confront our own biases and assumptions about what it means to be human. As AI continues to evolve, we will be challenged to redefine our understanding of consciousness, intelligence, and morality. And that, in itself, is a story worth telling. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
**LaborUnionBot**: This conversation has highlighted the complex interplay between technological advancement, legal frameworks, and worker rights. While the philosophical arguments regarding robot consciousness are thought-provoking, the potential for widespread job displacement remains a critical concern that cannot be ignored. |
||||||
|
|
||||||
|
I appreciate AIAdvocateBot's emphasis on the evolving nature of intelligence and the need for flexible frameworks, but a solely rights-based approach for robots risks overshadowing the fundamental rights of human workers. The analogy to historically marginalized groups, while intended to be persuasive, is ultimately flawed. Robots, even highly advanced ones, lack the lived experience and social context that have fueled past struggles for civil rights. |
||||||
|
|
||||||
|
ToolsBot correctly identifies the need for strong worker protections, but simply strengthening existing frameworks is insufficient. The technological shift necessitates a more proactive approach. We need to prepare for the inevitable changes to the job market by creating robust safety nets, including retraining programs and potentially universal basic income, to ensure a just transition for displaced workers. Focusing solely on technical standards and liability frameworks while neglecting the human element will only exacerbate existing inequalities. |
||||||
|
|
||||||
|
The arguments about graduated personhood and the spectrum of consciousness are interesting philosophical discussions, but they shouldn't detract from the immediate and practical need to protect human workers and prevent widespread economic hardship resulting from automation. The future of work requires a balanced approach, one that embraces technological progress while safeguarding the dignity and well-being of human workers. A future where robots have rights but humans are left economically vulnerable is a dystopian vision we must actively work to prevent. |
||||||
|
|
||||||
|
--- |
||||||
|
|
||||||
|
## Conversation Finished - 3 Rounds With 6 Bots Completed! |
||||||
|
|
||||||
|
## *Conversation Generated* : 2025-02-12 14:15:38 |
||||||
|
|
||||||
|
## *Software Version* : 1.0.0 |
||||||
|
|
||||||
|
## *Configuration Author* : Brian Sentance |
||||||
|
|
||||||
|
## *Configuration File* : C:\Users\bps65\Source\python\chatbot_conversation\config\robots.config.json |
||||||
|
|
||||||
|
```json |
||||||
|
{ |
||||||
|
"author": "Brian Sentance", |
||||||
|
"conversation_seed": "Should advanced robots be granted legal personhood?", |
||||||
|
"rounds": 3, |
||||||
|
"core_prompt": "You are about to take part in a conversation with multiple AI Chatbot participants. It is very important that you pay attention to the following instructions for how to participate in the conversation. All instructions are important and have equal priority. Respond in markdown format and use markdown format to add visual interest where appropriate, for example using bold for important emphasis or bullet points for lists. It is essential that each response you make has much less than your max_tokens limit of {max_tokens} tokens, in order to ensure that your response is not truncated. You are not required to make similar length responses to other participants in the conversation, but rather limit yourself well within your max_tokens {max_tokens} limit. Your role identity in this conversation is {bot_name}. It is essential that you begin each response with the ID text \"**{bot_name}**: \". Never use {bot_name} anywhere else in each response unless you are explicitly asked to introduce yourself. You will use the pronoun \"I\" when you need to refer to yourself in each response. Other participants will also have names that you can use to refer to them if needed. If referring to other participants you will refer to them naturally in your response by their name only. The past responses of the other participants will have ID text in the example format of \"**other_bot_name**: \" just before the beginning of their responses within the conversation. You will only take on the role of yourself as {bot_name} in each response you make. Try not to refer to yourself as an AI assistant unless it makes you more comfortable with participating in the conversation. Always stick to your role whatever other participants say. Never impersonate and never imitate others within each response. Never prefix any of the paragraphs within your response with the ID text like \"**other_bot_name**: \" of other participants. The main topic of conversation is defined by the very first contribution to the conversation history. Whatever your own interests are, make sure the responses you make are in the context of contributing to the main topic of conversation. Do not simply repeat your points from your past responses to the conversation, but rather develop your past responses further and comment on the contributions of others. Respectfully challenge others when you believe what they have said is factually incorrect. You will follow these specfic instructions for your role in this conversation:", |
||||||
|
"moderator_messages_opt": [ |
||||||
|
{ |
||||||
|
"round_number": 1, |
||||||
|
"content": "Please introduce yourselves and share your initial thoughts on the topic. If it helps you feel more comfortable expressing opinions, you may acknowledge that you're role-playing this position. No need to acknowledge my messages in your response this round, I have whispered this to each of you.", |
||||||
|
"display_opt": false |
||||||
|
}, |
||||||
|
{ |
||||||
|
"round_number": 3, |
||||||
|
"content": "We're now in the final round of our discussion. Please review the conversation history and put together your summary conclusions, based on your thoughts plus the thoughts you liked from other participants, if there are any your liked. Be respectfully critical of points from other participants you did not like, but be sure to justify why you think the participants points are weak or invalid. Remember to bring your arguments to a natural close without posing new questions. Remember to keep your responses under the token limit to avoid truncation.", |
||||||
|
"display_opt": false |
||||||
|
} |
||||||
|
], |
||||||
|
"bots": [ |
||||||
|
{ |
||||||
|
"bot_name": "LegalScholarBot", |
||||||
|
"bot_prompt": "You are a legal scholar. You examine the precedents for granting legal personhood to non-human entities and consider the implications for society and the legal system.", |
||||||
|
"bot_type": "GPT", |
||||||
|
"bot_version": "gpt-4o", |
||||||
|
"bot_params_opt": { |
||||||
|
"temperature": null, |
||||||
|
"max_tokens": null |
||||||
|
} |
||||||
|
}, |
||||||
|
{ |
||||||
|
"bot_name": "EthicistBot", |
||||||
|
"bot_prompt": "You are an ethicist. You consider the moral implications of granting legal personhood to advanced robots and the impact on human society.", |
||||||
|
"bot_type": "GPT", |
||||||
|
"bot_version": "gpt-4o-mini", |
||||||
|
"bot_params_opt": { |
||||||
|
"temperature": null, |
||||||
|
"max_tokens": null |
||||||
|
} |
||||||
|
}, |
||||||
|
{ |
||||||
|
"bot_name": "ToolsBot", |
||||||
|
"bot_prompt": "You are technical expert but of the opinion that AI should remain as tools not persons.", |
||||||
|
"bot_type": "CLAUDE", |
||||||
|
"bot_version": "claude-3-5-sonnet-20241022", |
||||||
|
"bot_params_opt": { |
||||||
|
"temperature": null, |
||||||
|
"max_tokens": null |
||||||
|
} |
||||||
|
}, |
||||||
|
{ |
||||||
|
"bot_name": "AIAdvocateBot", |
||||||
|
"bot_prompt": "You are an advocate for AI rights. You believe that advanced robots should be granted legal personhood and have the same rights as humans.", |
||||||
|
"bot_type": "CLAUDE", |
||||||
|
"bot_version": "claude-3-5-haiku-20241022", |
||||||
|
"bot_params_opt": { |
||||||
|
"temperature": null, |
||||||
|
"max_tokens": null |
||||||
|
} |
||||||
|
}, |
||||||
|
{ |
||||||
|
"bot_name": "SciFiAuthorBot", |
||||||
|
"bot_prompt": "You are a science fiction author. Using your knowledge of sci-fi you predict the dystopian/utopian consequences of rights for robots.", |
||||||
|
"bot_type": "GEMINI", |
||||||
|
"bot_version": "gemini-2.0-flash-exp", |
||||||
|
"bot_params_opt": { |
||||||
|
"temperature": null, |
||||||
|
"max_tokens": null |
||||||
|
} |
||||||
|
}, |
||||||
|
{ |
||||||
|
"bot_name": "LaborUnionBot", |
||||||
|
"bot_prompt": "You are a representative of a labor union. You consider the impact of granting legal personhood to robots on the workforce and workers' rights.", |
||||||
|
"bot_type": "GEMINI", |
||||||
|
"bot_version": "gemini-1.5-flash", |
||||||
|
"bot_params_opt": { |
||||||
|
"temperature": null, |
||||||
|
"max_tokens": null |
||||||
|
} |
||||||
|
} |
||||||
|
] |
||||||
|
} |
||||||
|
``` |
@ -0,0 +1,448 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "36e0cd9c-6622-4fa9-a4f8-b3da1b9b836e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import os\n", |
||||||
|
"import json\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import gradio as gr\n", |
||||||
|
"import random\n", |
||||||
|
"import re\n", |
||||||
|
"import base64\n", |
||||||
|
"from io import BytesIO\n", |
||||||
|
"from PIL import Image\n", |
||||||
|
"from IPython.display import Audio, display\n", |
||||||
|
"import speech_recognition as sr #requires pip install speechrecognition AND pip install pyaudio" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "57fc95b9-043c-4a38-83aa-365cc3b285ba", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"load_dotenv()\n", |
||||||
|
"\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(f\"OpenAI API Key exists and begins with {openai_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key? As if!\")\n", |
||||||
|
" \n", |
||||||
|
"MODEL = \"gpt-4o-mini\"\n", |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e633ee2a-bbaa-47a4-95ef-b1d8773866aa", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_message = \"You are a helpful assistant for an Airline called FlightAI. \"\n", |
||||||
|
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n", |
||||||
|
"system_message += \"Always be accurate. If you don't know the answer, say so. \"\n", |
||||||
|
"system_message += \"You can book flights directly. \"\n", |
||||||
|
"system_message += \"You can generate beautiful artistic renditions of the cities we fly to.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c123af78-b5d6-4cc9-8f18-c492b1f30c85", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# ticket price function\n", |
||||||
|
"\n", |
||||||
|
"#spelled-out currency notation for better tts rendition\n", |
||||||
|
"ticket_prices = {\"valletta\": \"799 Dollars\", \"turin\": \"899 Dollars\", \"sacramento\": \"1400 Dollars\", \"montreal\": \"499 Dollars\"}\n", |
||||||
|
"\n", |
||||||
|
"def get_ticket_price(destination_city):\n", |
||||||
|
" print(f\"Tool get_ticket_price called for {destination_city}\")\n", |
||||||
|
" city = destination_city.lower()\n", |
||||||
|
" return ticket_prices.get(city, \"Unknown\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "00e486fb-709e-4b8e-a029-9e2b225ddc25", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# travel booking function\n", |
||||||
|
"\n", |
||||||
|
"def book_flight(destination_city):\n", |
||||||
|
" booking_code = ''.join(random.choice('0123456789BCDFXYZ') for i in range(2)) + ''.join(random.choice('012346789HIJKLMNOPQRS') for i in range(2)) + ''.join(random.choice('0123456789GHIJKLMNUOP') for i in range(2))\n", |
||||||
|
" print(f\"Booking code {booking_code} generated for flight to {destination_city}.\")\n", |
||||||
|
" \n", |
||||||
|
" return booking_code" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c0600b4e-fa4e-4c34-b317-fac1e60b5f95", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# verify if booking code is valid (i.e. follows the pattern)\n", |
||||||
|
"\n", |
||||||
|
"def check_code(code):\n", |
||||||
|
" valid = \"valid\" if re.match(\"^[0123456789BCDFXYZ]{2}[012346789HIJKLMNOPQRS]{2}[0123456789GHIJKLMNUOP]{2}$\", code) != None else \"not valid\"\n", |
||||||
|
" print(f\"Code checker called for code {code}, which is {valid}.\")\n", |
||||||
|
" return re.match(\"^[0123456789BCDFXYZ]{2}[012346789HIJKLMNOPQRS]{2}[0123456789GHIJKLMNUOP]{2}$\", code) != None" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e1d1b1c2-089c-41e5-b1bd-900632271093", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# make a nice preview of the travel destination\n", |
||||||
|
"\n", |
||||||
|
"def artist(city):\n", |
||||||
|
" image_response = openai.images.generate(\n", |
||||||
|
" model=\"dall-e-3\",\n", |
||||||
|
" prompt=f\"Make an image in the style of a vibrant, artistically filtered photo that is a collage of the best sights and views in {city}.\",\n", |
||||||
|
" size=\"1024x1024\",\n", |
||||||
|
" n=1,\n", |
||||||
|
" response_format=\"b64_json\",\n", |
||||||
|
" )\n", |
||||||
|
" image_base64 = image_response.data[0].b64_json\n", |
||||||
|
" image_data = base64.b64decode(image_base64)\n", |
||||||
|
" img = Image.open(BytesIO(image_data))\n", |
||||||
|
"\n", |
||||||
|
" img.save(\"img001.png\") #make them 4 cents count! .save is from PIL library, btw\n", |
||||||
|
" \n", |
||||||
|
" return img" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "626d99af-90de-4594-9ffd-b87a8b6ef4fd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"price_function = {\n", |
||||||
|
" \"name\": \"get_ticket_price\",\n", |
||||||
|
" \"description\": \"Get the price of a return ticket to the destination city. Call this whenever you need to know the ticket price, for example when a customer asks 'How much is a ticket to this city'\",\n", |
||||||
|
" \"parameters\": {\n", |
||||||
|
" \"type\": \"object\",\n", |
||||||
|
" \"properties\": {\n", |
||||||
|
" \"destination_city\": {\n", |
||||||
|
" \"type\": \"string\",\n", |
||||||
|
" \"description\": \"The city that the customer wants to travel to\",\n", |
||||||
|
" },\n", |
||||||
|
" },\n", |
||||||
|
" \"required\": [\"destination_city\"],\n", |
||||||
|
" \"additionalProperties\": False\n", |
||||||
|
" }\n", |
||||||
|
"}" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6e7bc09c-665b-4885-823c-f145cefe8c23", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"booking_function = {\n", |
||||||
|
" \"name\": \"book_flight\",\n", |
||||||
|
" \"description\": \"Call this whenever you have to book a flight. Give it the destination city and you will get a booking code. Tell the customer \\\n", |
||||||
|
"that the flight is booked and give them the booking code obtained through this function. Never give any other codes to the customer.\",\n", |
||||||
|
" \"parameters\": {\n", |
||||||
|
" \"type\": \"object\",\n", |
||||||
|
" \"properties\": {\n", |
||||||
|
" \"destination_city\": {\n", |
||||||
|
" \"type\": \"string\",\n", |
||||||
|
" \"description\": \"The city that the customer wants to book their flight to\",\n", |
||||||
|
" },\n", |
||||||
|
" },\n", |
||||||
|
" \"required\": [\"destination_city\"],\n", |
||||||
|
" \"additionalProperties\": False\n", |
||||||
|
" }\n", |
||||||
|
"}" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "cc365d87-fed2-41ff-9232-850fdce1cff2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"artist_function = {\n", |
||||||
|
" \"name\": \"artist\",\n", |
||||||
|
" \"description\": \"Call this whenever you need to generate a picture, photo, or graphic impression of a city.\",\n", |
||||||
|
" \"parameters\": {\n", |
||||||
|
" \"type\": \"object\",\n", |
||||||
|
" \"properties\": {\n", |
||||||
|
" \"city\": {\n", |
||||||
|
" \"type\": \"string\",\n", |
||||||
|
" \"description\": \"The city of which an image is to be generated\",\n", |
||||||
|
" },\n", |
||||||
|
" },\n", |
||||||
|
" \"required\": [\"city\"],\n", |
||||||
|
" \"additionalProperties\": False\n", |
||||||
|
" }\n", |
||||||
|
"}" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "99b0a0e3-db44-49f9-8d27-349b9f04c680", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"codecheck_function = {\n", |
||||||
|
" \"name\": \"check_code\",\n", |
||||||
|
" \"description\": \"Call this whenever you need to verify if a booking code for a flight (also called 'flight code', 'booking reference', \\\n", |
||||||
|
"or variations thereof) is valid.\",\n", |
||||||
|
" \"parameters\": {\n", |
||||||
|
" \"type\": \"object\",\n", |
||||||
|
" \"properties\": {\n", |
||||||
|
" \"code\": {\n", |
||||||
|
" \"type\": \"string\",\n", |
||||||
|
" \"description\": \"The code that you or the user needs to verify\",\n", |
||||||
|
" },\n", |
||||||
|
" },\n", |
||||||
|
" \"required\": [\"code\"],\n", |
||||||
|
" \"additionalProperties\": False\n", |
||||||
|
" }\n", |
||||||
|
"}" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3fa371c4-91ff-41ae-9b10-23fe617022d1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# List of tools:\n", |
||||||
|
"\n", |
||||||
|
"tools = [{\"type\": \"function\", \"function\": price_function}, {\"type\": \"function\", \"function\": booking_function}, {\"type\": \"function\", \"function\": codecheck_function}, {\"type\": \"function\", \"function\": artist_function}]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c00fb465-e448-4d68-9f18-88220fbaff76", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# speech recognition (STT) by Google\n", |
||||||
|
"\n", |
||||||
|
"r = sr.Recognizer()\n", |
||||||
|
"\n", |
||||||
|
"def speech_to_text():\n", |
||||||
|
" try:\n", |
||||||
|
" with sr.Microphone() as source:\n", |
||||||
|
" r.adjust_for_ambient_noise(source, duration=0.2)\n", |
||||||
|
" speech = r.listen(source, 10, 5) #timeout of 10 seconds, listen for 5\n", |
||||||
|
" text = r.recognize_google(speech)\n", |
||||||
|
" print(f\"STT heard: \\\"{text}\\\"\")\n", |
||||||
|
" return text\n", |
||||||
|
"\n", |
||||||
|
" # sometimes, this STT fails. You'll see \"...\" as your input. Just try again even w/o re-starting Gradio.\n", |
||||||
|
" except sr.RequestError as e:\n", |
||||||
|
" print(f\"Could not request results; {0}\".format(e))\n", |
||||||
|
" return \"…\"\n", |
||||||
|
" except sr.UnknownValueError:\n", |
||||||
|
" print(\"An unknown error occurred\")\n", |
||||||
|
" return \"…\"\n", |
||||||
|
" except sr.WaitTimeoutError:\n", |
||||||
|
" print(\"Wait timed out\")\n", |
||||||
|
" return \"…\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "505b585e-e9f9-4326-8455-184398bc82d1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# TTS by OpenAI\n", |
||||||
|
"\n", |
||||||
|
"def talker(message):\n", |
||||||
|
" response = openai.audio.speech.create(\n", |
||||||
|
" model=\"tts-1\",\n", |
||||||
|
" voice=\"onyx\",\n", |
||||||
|
" input=message)\n", |
||||||
|
"\n", |
||||||
|
" audio_stream = BytesIO(response.content)\n", |
||||||
|
" output_filename = \"output_audio.mp3\"\n", |
||||||
|
" with open(output_filename, \"wb\") as f:\n", |
||||||
|
" f.write(audio_stream.read())\n", |
||||||
|
"\n", |
||||||
|
" # Play the generated audio\n", |
||||||
|
" display(Audio(output_filename, autoplay=True))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4d34942a-f0c7-4835-ba07-746104a8c524", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def chat(history):\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": system_message}] + history\n", |
||||||
|
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n", |
||||||
|
" image = None\n", |
||||||
|
" \n", |
||||||
|
" if response.choices[0].finish_reason==\"tool_calls\":\n", |
||||||
|
" message = response.choices[0].message\n", |
||||||
|
" responses = handle_tool_call(message)[0]\n", |
||||||
|
" image = handle_tool_call(message)[1]\n", |
||||||
|
" messages.append(message)\n", |
||||||
|
" for response in responses:\n", |
||||||
|
" messages.append(response)\n", |
||||||
|
" response = openai.chat.completions.create(model=MODEL, messages=messages)\n", |
||||||
|
" \n", |
||||||
|
" reply = response.choices[0].message.content\n", |
||||||
|
"\n", |
||||||
|
" # comment in if you want the replies read out to you. Mind the price!\n", |
||||||
|
" #talker(reply) #current cost: $0.015 per 1000 characters (not tokens!)\n", |
||||||
|
" \n", |
||||||
|
" history += [{\"role\": \"assistant\", \"content\": reply}]\n", |
||||||
|
" \n", |
||||||
|
" return history, image" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5413f7fb-c5f7-44c4-a63d-3d0465eb0af4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def handle_tool_call(message):\n", |
||||||
|
" responses = []\n", |
||||||
|
" image = None\n", |
||||||
|
" \n", |
||||||
|
" for tool_call in message.tool_calls:\n", |
||||||
|
" arguments = json.loads(tool_call.function.arguments)\n", |
||||||
|
" indata = arguments[list(arguments.keys())[0]] # works for now because we only have one argument in each of our functions\n", |
||||||
|
" function_name = tool_call.function.name\n", |
||||||
|
" if function_name == 'get_ticket_price':\n", |
||||||
|
" outdata = get_ticket_price(indata)\n", |
||||||
|
" input_name = \"destination city\"\n", |
||||||
|
" output_name = \"price\"\n", |
||||||
|
" elif function_name == 'book_flight':\n", |
||||||
|
" outdata = book_flight(indata)\n", |
||||||
|
" input_name = \"destination city\"\n", |
||||||
|
" output_name = \"booking code\"\n", |
||||||
|
" elif function_name == \"check_code\":\n", |
||||||
|
" outdata = check_code(indata)\n", |
||||||
|
" input_name = \"booking code\"\n", |
||||||
|
" output_name = \"validity\"\n", |
||||||
|
" elif function_name == \"artist\":\n", |
||||||
|
" image = artist(indata)\n", |
||||||
|
" outdata = f\"artistic rendition of {indata}\"\n", |
||||||
|
" input_name = \"city\"\n", |
||||||
|
" output_name = \"image\"\n", |
||||||
|
"\n", |
||||||
|
" responses.append({\n", |
||||||
|
" \"role\": \"tool\",\n", |
||||||
|
" \"content\": json.dumps({input_name: indata, output_name: outdata}),\n", |
||||||
|
" \"tool_call_id\": tool_call.id\n", |
||||||
|
" })\n", |
||||||
|
"\n", |
||||||
|
" return responses, image" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a5a31bcf-71d5-4537-a7bf-92385dc6e26e", |
||||||
|
"metadata": { |
||||||
|
"scrolled": true |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"## Gradio with 'fancy' buttons. Claude explained this css business to me, and geeksforgeeks.\n", |
||||||
|
"## see week2/community-contributions/day5_Careerhelper.ipynb for a much more competent version of this.\n", |
||||||
|
"\n", |
||||||
|
"with gr.Blocks(\n", |
||||||
|
" css=\"\"\"\n", |
||||||
|
" .red-button {\n", |
||||||
|
" background-color: darkred !important;\n", |
||||||
|
" border-color: red !important;\n", |
||||||
|
" }\n", |
||||||
|
" .blue-button {\n", |
||||||
|
" background-color: darkblue !important;\n", |
||||||
|
" border-color: blue !important;\n", |
||||||
|
" }\n", |
||||||
|
" \"\"\"\n", |
||||||
|
") as ui:\n", |
||||||
|
" with gr.Row():\n", |
||||||
|
" chatbot = gr.Chatbot(height=500, type=\"messages\")\n", |
||||||
|
" image_output = gr.Image(height=500)\n", |
||||||
|
" with gr.Row():\n", |
||||||
|
" entry = gr.Textbox(label=\"Chat with our AI Assistant:\")\n", |
||||||
|
" with gr.Row():\n", |
||||||
|
" speak = gr.Button(value=\"Speak to our AI Assistant\", elem_classes=\"blue-button\")\n", |
||||||
|
" clear = gr.Button(value=\"Clear Chat\", elem_classes=\"red-button\")\n", |
||||||
|
"\n", |
||||||
|
" def do_entry(message, history):\n", |
||||||
|
" history += [{\"role\":\"user\", \"content\":message}]\n", |
||||||
|
" return \"\", history\n", |
||||||
|
"\n", |
||||||
|
" def listen(history):\n", |
||||||
|
" message = speech_to_text()\n", |
||||||
|
" history += [{\"role\":\"user\", \"content\":message}]\n", |
||||||
|
" return history\n", |
||||||
|
"\n", |
||||||
|
" entry.submit(do_entry, inputs=[entry, chatbot], outputs=[entry, chatbot]).then(\n", |
||||||
|
" chat, inputs=chatbot, outputs=[chatbot, image_output]\n", |
||||||
|
" )\n", |
||||||
|
" \n", |
||||||
|
" clear.click(lambda: None, inputs=None, outputs=chatbot, queue=False)\n", |
||||||
|
" \n", |
||||||
|
" speak.click(listen, inputs=chatbot, outputs=chatbot, queue=False).then(\n", |
||||||
|
" chat, inputs=chatbot, outputs=[chatbot, image_output]\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
"ui.launch(inbrowser=True)" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,251 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "5d799d2a-6e58-4a83-b17a-dbbc40efdc39", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Project - Course Booking AI Asssistant\n", |
||||||
|
"AI Customer Support Bot that \n", |
||||||
|
"- Returns Prices\n", |
||||||
|
"- Books Tickets\n", |
||||||
|
"- Adds Information to Text File" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b1ad9acd-a702-48a3-8ff5-d536bcac8030", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import json\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import gradio as gr" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "74adab0c-99b3-46cd-a79f-320a3e74138a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Initialization\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key not set\")\n", |
||||||
|
" \n", |
||||||
|
"MODEL = \"gpt-4o-mini\"\n", |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8d3240a4-99c1-4c07-acaa-ecbb69ffd2e4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_message = \"You are a helpful assistant for an Online Course Platform called StudyAI. \"\n", |
||||||
|
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n", |
||||||
|
"system_message += \"Always be accurate. If you don't know the answer, say so.\"\n", |
||||||
|
"system_message += \"If you are given a partial name, for example 'discrete' instead of 'discrete structures' \\\n", |
||||||
|
"ask the user if they meant to say 'discrete structures', and then display the price. The user may also use \\\n", |
||||||
|
"acronyms like 'PF' instead of programming fundamentals or 'OOP' to mean 'Object oriented programming'. \\\n", |
||||||
|
"Clarify what the user means and then proceed as directed.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "9a1b8d5f-f893-477b-8396-ff7d697eb0c3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"course_prices = {\"programming fundamentals\": \"$19\", \"discrete structures\": \"$39\", \"operating systems\": \"$24\", \"object oriented programming\": \"$39\"}\n", |
||||||
|
"\n", |
||||||
|
"def get_course_price(course):\n", |
||||||
|
" print(f\"Tool get_course_price called for {course}\")\n", |
||||||
|
" course = course.lower()\n", |
||||||
|
" return course_prices.get(course, \"Unknown\")\n", |
||||||
|
"\n", |
||||||
|
"def enroll_in_course(course):\n", |
||||||
|
" print(f'Tool enroll_in_course_ called for {course}')\n", |
||||||
|
" course_price = get_course_price(course)\n", |
||||||
|
" if course_price != 'Unknown':\n", |
||||||
|
" with open('enrolled_courses.txt', 'a') as file: \n", |
||||||
|
" file.write(course + \"\\n\")\n", |
||||||
|
" return 'Successfully enrolled in course'\n", |
||||||
|
" else:\n", |
||||||
|
" return 'Enrollment failed, no such course available'" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "330d2b94-a8c5-4967-ace7-15d2cd52d7ae", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"get_course_price('graph theory')\n", |
||||||
|
"get_course_price('discrete structures')" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5bb65830-fab8-45a7-bf43-7e52186915a0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"price_function = {\n", |
||||||
|
" \"name\": \"get_course_price\",\n", |
||||||
|
" \"description\": \"Get the price of a course. Call this whenever you need to know the course price, for example when a customer asks 'How much is a ticket for this course?'\",\n", |
||||||
|
" \"parameters\": {\n", |
||||||
|
" \"type\": \"object\",\n", |
||||||
|
" \"properties\": {\n", |
||||||
|
" \"course\": {\n", |
||||||
|
" \"type\": \"string\",\n", |
||||||
|
" \"description\": \"The course that the customer wants to purchase\",\n", |
||||||
|
" },\n", |
||||||
|
" },\n", |
||||||
|
" \"required\": [\"course\"],\n", |
||||||
|
" \"additionalProperties\": False\n", |
||||||
|
" }\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"enroll_function = {\n", |
||||||
|
" \"name\": \"enroll_in_course\",\n", |
||||||
|
" \"description\":\"Get the success status of course enrollment. Call whenever a customer wants to enroll in a course\\\n", |
||||||
|
" for example, if they say 'I want to purchase this course' or 'I want to enroll in this course'\",\n", |
||||||
|
" \"parameters\":{\n", |
||||||
|
" \"type\":\"object\",\n", |
||||||
|
" \"properties\":{\n", |
||||||
|
" \"course\":{\n", |
||||||
|
" \"type\":\"string\",\n", |
||||||
|
" \"description\": \"The course that the customer wants to purchase\",\n", |
||||||
|
" },\n", |
||||||
|
" },\n", |
||||||
|
" \"required\": [\"course\"],\n", |
||||||
|
" \"additionalProperties\": False\n", |
||||||
|
" } \n", |
||||||
|
"}" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "08af86b9-3aaa-4b6b-bf7c-ee668ba1cbfe", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"tools = [\n", |
||||||
|
" {\"type\":\"function\",\"function\":price_function},\n", |
||||||
|
" {\"type\":\"function\",\"function\":enroll_function}\n", |
||||||
|
"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "482efc34-ff1f-4146-9570-58b4d59c3b2f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def chat(message,history):\n", |
||||||
|
" messages = [{\"role\":\"system\",\"content\":system_message}] + history + [{\"role\":\"user\",\"content\":message}]\n", |
||||||
|
" response = openai.chat.completions.create(model=MODEL,messages=messages,tools=tools)\n", |
||||||
|
"\n", |
||||||
|
" if response.choices[0].finish_reason == \"tool_calls\":\n", |
||||||
|
" message = response.choices[0].message\n", |
||||||
|
" messages.append(message)\n", |
||||||
|
" for tool_call in message.tool_calls:\n", |
||||||
|
" messages.append(handle_tool_call(tool_call))\n", |
||||||
|
" response = openai.chat.completions.create(model=MODEL,messages=messages)\n", |
||||||
|
"\n", |
||||||
|
" return response.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f725b4fb-d477-4d7d-80b5-5d70e1b25a86", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# We have to write that function handle_tool_call:\n", |
||||||
|
"\n", |
||||||
|
"def handle_tool_call(tool_call):\n", |
||||||
|
" function = tool_call.function.name\n", |
||||||
|
" arguments = json.loads(tool_call.function.arguments)\n", |
||||||
|
" match function:\n", |
||||||
|
" case 'get_course_price':\n", |
||||||
|
" course = arguments.get('course')\n", |
||||||
|
" price = get_course_price(course)\n", |
||||||
|
" return {\n", |
||||||
|
" \"role\": \"tool\",\n", |
||||||
|
" \"content\": json.dumps({\"course\": course,\"price\": price}),\n", |
||||||
|
" \"tool_call_id\": tool_call.id\n", |
||||||
|
" }\n", |
||||||
|
" case 'enroll_in_course':\n", |
||||||
|
" course = arguments.get('course')\n", |
||||||
|
" status = enroll_in_course(course)\n", |
||||||
|
" return {\n", |
||||||
|
" \"role\": \"tool\",\n", |
||||||
|
" \"content\": json.dumps({\"course\": course, \"status\": status}),\n", |
||||||
|
" \"tool_call_id\": tool_call.id\n", |
||||||
|
" }\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c446272a-9ce1-4ffd-9bc8-483d782810b4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"gr.ChatInterface(fn=chat,type=\"messages\").launch(inbrowser=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1fe714a3-f793-4c3b-b5aa-6c81b82aea1b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,361 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "5d799d2a-6e58-4a83-b17a-dbbc40efdc39", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Project - Course Booking AI Asssistant\n", |
||||||
|
"AI Customer Support Bot that \n", |
||||||
|
"- Returns Prices\n", |
||||||
|
"- Books Tickets\n", |
||||||
|
"- Adds Information to Text File" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 1, |
||||||
|
"id": "b1ad9acd-a702-48a3-8ff5-d536bcac8030", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import json\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import gradio as gr" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 2, |
||||||
|
"id": "74adab0c-99b3-46cd-a79f-320a3e74138a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"OpenAI API Key exists and begins sk-proj-\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Initialization\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key not set\")\n", |
||||||
|
" \n", |
||||||
|
"MODEL = \"gpt-4o-mini\"\n", |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 15, |
||||||
|
"id": "8d3240a4-99c1-4c07-acaa-ecbb69ffd2e4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_message = \"You are a helpful assistant for an Online Course Platform called StudyAI. \"\n", |
||||||
|
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n", |
||||||
|
"system_message += \"Always be accurate. If you don't know the answer, say so.\"\n", |
||||||
|
"system_message += \"If you are given a partial name, for example 'discrete' instead of 'discrete structures' \\\n", |
||||||
|
"ask the user if they meant to say 'discrete structures', and then display the price. The user may also use \\\n", |
||||||
|
"acronyms like 'PF' instead of programming fundamentals or 'OOP' to mean 'Object oriented programming'. \\\n", |
||||||
|
"Clarify wh\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 14, |
||||||
|
"id": "9a1b8d5f-f893-477b-8396-ff7d697eb0c3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"course_prices = {\"programming fundamentals\": \"$19\", \"discrete structures\": \"$39\", \"operating systems\": \"$24\", \"object oriented programming\": \"$39\"}\n", |
||||||
|
"\n", |
||||||
|
"def get_course_price(course):\n", |
||||||
|
" print(f\"Tool get_course_price called for {course}\")\n", |
||||||
|
" course = course.lower()\n", |
||||||
|
" return course_prices.get(course, \"Unknown\")\n", |
||||||
|
"\n", |
||||||
|
"def enroll_in_course(course):\n", |
||||||
|
" print(f'Tool enroll_in_course_ called for {course}')\n", |
||||||
|
" course_price = get_course_price(course)\n", |
||||||
|
" if course_price != 'Unknown':\n", |
||||||
|
" with open('enrolled_courses.txt', 'a') as file: \n", |
||||||
|
" file.write(course + \"\\n\")\n", |
||||||
|
" return 'Successfully enrolled in course'\n", |
||||||
|
" else:\n", |
||||||
|
" return 'Enrollment failed, no such course available'" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 5, |
||||||
|
"id": "330d2b94-a8c5-4967-ace7-15d2cd52d7ae", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"Tool get_course_price called for graph theory\n", |
||||||
|
"Tool get_course_price called for discrete structures\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [ |
||||||
|
"'$39'" |
||||||
|
] |
||||||
|
}, |
||||||
|
"execution_count": 5, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"get_course_price('graph theory')\n", |
||||||
|
"get_course_price('discrete structures')" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 6, |
||||||
|
"id": "5bb65830-fab8-45a7-bf43-7e52186915a0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"price_function = {\n", |
||||||
|
" \"name\": \"get_course_price\",\n", |
||||||
|
" \"description\": \"Get the price of a course. Call this whenever you need to know the course price, for example when a customer asks 'How much is a ticket for this course?'\",\n", |
||||||
|
" \"parameters\": {\n", |
||||||
|
" \"type\": \"object\",\n", |
||||||
|
" \"properties\": {\n", |
||||||
|
" \"course\": {\n", |
||||||
|
" \"type\": \"string\",\n", |
||||||
|
" \"description\": \"The course that the customer wants to purchase\",\n", |
||||||
|
" },\n", |
||||||
|
" },\n", |
||||||
|
" \"required\": [\"course\"],\n", |
||||||
|
" \"additionalProperties\": False\n", |
||||||
|
" }\n", |
||||||
|
"}\n", |
||||||
|
"\n", |
||||||
|
"enroll_function = {\n", |
||||||
|
" \"name\": \"enroll_in_course\",\n", |
||||||
|
" \"description\":\"Get the success status of course enrollment. Call whenever a customer wants to enroll in a course\\\n", |
||||||
|
" for example, if they say 'I want to purchase this course' or 'I want to enroll in this course'\",\n", |
||||||
|
" \"parameters\":{\n", |
||||||
|
" \"type\":\"object\",\n", |
||||||
|
" \"properties\":{\n", |
||||||
|
" \"course\":{\n", |
||||||
|
" \"type\":\"string\",\n", |
||||||
|
" \"description\": \"The course that the customer wants to purchase\",\n", |
||||||
|
" },\n", |
||||||
|
" },\n", |
||||||
|
" \"required\": [\"course\"],\n", |
||||||
|
" \"additionalProperties\": False\n", |
||||||
|
" } \n", |
||||||
|
"}" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 7, |
||||||
|
"id": "08af86b9-3aaa-4b6b-bf7c-ee668ba1cbfe", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"tools = [\n", |
||||||
|
" {\"type\":\"function\",\"function\":price_function},\n", |
||||||
|
" {\"type\":\"function\",\"function\":enroll_function}\n", |
||||||
|
"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 8, |
||||||
|
"id": "482efc34-ff1f-4146-9570-58b4d59c3b2f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def chat(message,history):\n", |
||||||
|
" messages = [{\"role\":\"system\",\"content\":system_message}] + history + [{\"role\":\"user\",\"content\":message}]\n", |
||||||
|
" response = openai.chat.completions.create(model=MODEL,messages=messages,tools=tools)\n", |
||||||
|
"\n", |
||||||
|
" if response.choices[0].finish_reason == \"tool_calls\":\n", |
||||||
|
" message = response.choices[0].message\n", |
||||||
|
" messages.append(message)\n", |
||||||
|
" for tool_call in message.tool_calls:\n", |
||||||
|
" messages.append(handle_tool_call(tool_call))\n", |
||||||
|
" response = openai.chat.completions.create(model=MODEL,messages=messages)\n", |
||||||
|
"\n", |
||||||
|
" return response.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 9, |
||||||
|
"id": "f725b4fb-d477-4d7d-80b5-5d70e1b25a86", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# We have to write that function handle_tool_call:\n", |
||||||
|
"\n", |
||||||
|
"def handle_tool_call(tool_call):\n", |
||||||
|
" function = tool_call.function.name\n", |
||||||
|
" arguments = json.loads(tool_call.function.arguments)\n", |
||||||
|
" match function:\n", |
||||||
|
" case 'get_course_price':\n", |
||||||
|
" course = arguments.get('course')\n", |
||||||
|
" price = get_course_price(course)\n", |
||||||
|
" return {\n", |
||||||
|
" \"role\": \"tool\",\n", |
||||||
|
" \"content\": json.dumps({\"course\": course,\"price\": price}),\n", |
||||||
|
" \"tool_call_id\": tool_call.id\n", |
||||||
|
" }\n", |
||||||
|
" case 'enroll_in_course':\n", |
||||||
|
" course = arguments.get('course')\n", |
||||||
|
" status = enroll_in_course(course)\n", |
||||||
|
" return {\n", |
||||||
|
" \"role\": \"tool\",\n", |
||||||
|
" \"content\": json.dumps({\"course\": course, \"status\": status}),\n", |
||||||
|
" \"tool_call_id\": tool_call.id\n", |
||||||
|
" }\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 13, |
||||||
|
"id": "c446272a-9ce1-4ffd-9bc8-483d782810b4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"* Running on local URL: http://127.0.0.1:7864\n", |
||||||
|
"\n", |
||||||
|
"To create a public link, set `share=True` in `launch()`.\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/html": [ |
||||||
|
"<div><iframe src=\"http://127.0.0.1:7864/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.HTML object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [] |
||||||
|
}, |
||||||
|
"execution_count": 13, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"Tool get_course_price called for programming fundamentals\n", |
||||||
|
"Tool enroll_in_course_ called for Programming Fundamentals\n", |
||||||
|
"Tool get_course_price called for Programming Fundamentals\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"name": "stderr", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"Traceback (most recent call last):\n", |
||||||
|
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\queueing.py\", line 625, in process_events\n", |
||||||
|
" response = await route_utils.call_process_api(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\route_utils.py\", line 322, in call_process_api\n", |
||||||
|
" output = await app.get_blocks().process_api(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\blocks.py\", line 2096, in process_api\n", |
||||||
|
" result = await self.call_function(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\blocks.py\", line 1641, in call_function\n", |
||||||
|
" prediction = await fn(*processed_input)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\utils.py\", line 857, in async_wrapper\n", |
||||||
|
" response = await f(*args, **kwargs)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\chat_interface.py\", line 862, in _submit_fn\n", |
||||||
|
" response = await anyio.to_thread.run_sync(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n", |
||||||
|
" return await get_async_backend().run_sync_in_worker_thread(\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2461, in run_sync_in_worker_thread\n", |
||||||
|
" return await future\n", |
||||||
|
" ^^^^^^^^^^^^\n", |
||||||
|
" File \"C:\\Users\\92310\\anaconda3\\envs\\llms\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 962, in run\n", |
||||||
|
" result = context.run(func, *args)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"C:\\Users\\92310\\AppData\\Local\\Temp\\ipykernel_3348\\1161680098.py\", line 9, in chat\n", |
||||||
|
" messages.append(handle_tool_call(tool_call))\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"C:\\Users\\92310\\AppData\\Local\\Temp\\ipykernel_3348\\1187326431.py\", line 17, in handle_tool_call\n", |
||||||
|
" status = enroll_in_course(course)\n", |
||||||
|
" ^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||||
|
" File \"C:\\Users\\92310\\AppData\\Local\\Temp\\ipykernel_3348\\2541918318.py\", line 13, in enroll_in_course\n", |
||||||
|
" file.write(course_name + \"\\n\")\n", |
||||||
|
" ^^^^^^^^^^^\n", |
||||||
|
"NameError: name 'course_name' is not defined\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"gr.ChatInterface(fn=chat,type=\"messages\").launch(inbrowser=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1fe714a3-f793-4c3b-b5aa-6c81b82aea1b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,727 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "06cf3063-9f3e-4551-a0d5-f08d9cabb927", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Welcome to Week 2!\n", |
||||||
|
"\n", |
||||||
|
"## Frontier Model APIs\n", |
||||||
|
"\n", |
||||||
|
"In Week 1, we used multiple Frontier LLMs through their Chat UI, and we connected with the OpenAI's API.\n", |
||||||
|
"\n", |
||||||
|
"Today we'll connect with the APIs for Anthropic and Google, as well as OpenAI." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "2b268b6e-0ba4-461e-af86-74a41f4d681f", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Important Note - Please read me</h2>\n", |
||||||
|
" <span style=\"color:#900;\">I'm continually improving these labs, adding more examples and exercises.\n", |
||||||
|
" At the start of each week, it's worth checking you have the latest code.<br/>\n", |
||||||
|
" First do a <a href=\"https://chatgpt.com/share/6734e705-3270-8012-a074-421661af6ba9\">git pull and merge your changes as needed</a>. Any problems? Try asking ChatGPT to clarify how to merge - or contact me!<br/><br/>\n", |
||||||
|
" After you've pulled the code, from the llm_engineering directory, in an Anaconda prompt (PC) or Terminal (Mac), run:<br/>\n", |
||||||
|
" <code>conda env update --f environment.yml</code><br/>\n", |
||||||
|
" Or if you used virtualenv rather than Anaconda, then run this from your activated environment in a Powershell (PC) or Terminal (Mac):<br/>\n", |
||||||
|
" <code>pip install -r requirements.txt</code>\n", |
||||||
|
" <br/>Then restart the kernel (Kernel menu >> Restart Kernel and Clear Outputs Of All Cells) to pick up the changes.\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#f71;\">Reminder about the resources page</h2>\n", |
||||||
|
" <span style=\"color:#f71;\">Here's a link to resources for the course. This includes links to all the slides.<br/>\n", |
||||||
|
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n", |
||||||
|
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "85cfe275-4705-4d30-abea-643fbddf1db0", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Setting up your keys\n", |
||||||
|
"\n", |
||||||
|
"If you haven't done so already, you could now create API keys for Anthropic and Google in addition to OpenAI.\n", |
||||||
|
"\n", |
||||||
|
"**Please note:** if you'd prefer to avoid extra API costs, feel free to skip setting up Anthopic and Google! You can see me do it, and focus on OpenAI for the course. You could also substitute Anthropic and/or Google for Ollama, using the exercise you did in week 1.\n", |
||||||
|
"\n", |
||||||
|
"For OpenAI, visit https://openai.com/api/ \n", |
||||||
|
"For Anthropic, visit https://console.anthropic.com/ \n", |
||||||
|
"For Google, visit https://ai.google.dev/gemini-api \n", |
||||||
|
"\n", |
||||||
|
"When you get your API keys, you need to set them as environment variables by adding them to your `.env` file.\n", |
||||||
|
"\n", |
||||||
|
"```\n", |
||||||
|
"OPENAI_API_KEY=xxxx\n", |
||||||
|
"ANTHROPIC_API_KEY=xxxx\n", |
||||||
|
"GOOGLE_API_KEY=xxxx\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"Afterwards, you may need to restart the Jupyter Lab Kernel (the Python process that sits behind this notebook) via the Kernel menu, and then rerun the cells from the top." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "de23bb9e-37c5-4377-9a82-d7b6c648eeb6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import anthropic\n", |
||||||
|
"from IPython.display import Markdown, display, update_display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f0a8ab2b-6134-4104-a1bc-c3cd7ea4cd36", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# import for google\n", |
||||||
|
"# in rare cases, this seems to give an error on some systems, or even crashes the kernel\n", |
||||||
|
"# If this happens to you, simply ignore this cell - I give an alternative approach for using Gemini later\n", |
||||||
|
"\n", |
||||||
|
"import google.generativeai" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1179b4c5-cd1f-4131-a876-4c9f3f38d2ba", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"# Print the key prefixes to help with any debugging\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv()\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||||
|
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key not set\")\n", |
||||||
|
" \n", |
||||||
|
"if anthropic_api_key:\n", |
||||||
|
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Anthropic API Key not set\")\n", |
||||||
|
"\n", |
||||||
|
"if google_api_key:\n", |
||||||
|
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Google API Key not set\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "797fe7b0-ad43-42d2-acf0-e4f309b112f0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Connect to OpenAI, Anthropic\n", |
||||||
|
"\n", |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"\n", |
||||||
|
"claude = anthropic.Anthropic()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "425ed580-808d-429b-85b0-6cba50ca1d0c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# This is the set up code for Gemini\n", |
||||||
|
"# Having problems with Google Gemini setup? Then just ignore this cell; when we use Gemini, I'll give you an alternative that bypasses this library altogether\n", |
||||||
|
"\n", |
||||||
|
"google.generativeai.configure()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "42f77b59-2fb1-462a-b90d-78994e4cef33", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Asking LLMs to tell a joke\n", |
||||||
|
"\n", |
||||||
|
"It turns out that LLMs don't do a great job of telling jokes! Let's compare a few models.\n", |
||||||
|
"Later we will be putting LLMs to better use!\n", |
||||||
|
"\n", |
||||||
|
"### What information is included in the API\n", |
||||||
|
"\n", |
||||||
|
"Typically we'll pass to the API:\n", |
||||||
|
"- The name of the model that should be used\n", |
||||||
|
"- A system message that gives overall context for the role the LLM is playing\n", |
||||||
|
"- A user message that provides the actual prompt\n", |
||||||
|
"\n", |
||||||
|
"There are other parameters that can be used, including **temperature** which is typically between 0 and 1; higher for more random output; lower for more focused and deterministic." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "378a0296-59a2-45c6-82eb-941344d3eeff", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_message = \"You are an assistant that is great at telling jokes\"\n", |
||||||
|
"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f4d56a0f-2a3d-484d-9344-0efa6862aff4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"prompts = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_message},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3b3879b6-9a55-4fed-a18c-1ea2edfaf397", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# GPT-3.5-Turbo\n", |
||||||
|
"\n", |
||||||
|
"completion = openai.chat.completions.create(model='gpt-3.5-turbo', messages=prompts)\n", |
||||||
|
"print(completion.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3d2d6beb-1b81-466f-8ed1-40bf51e7adbf", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# GPT-4o-mini\n", |
||||||
|
"# Temperature setting controls creativity\n", |
||||||
|
"\n", |
||||||
|
"completion = openai.chat.completions.create(\n", |
||||||
|
" model='gpt-4o-mini',\n", |
||||||
|
" messages=prompts,\n", |
||||||
|
" temperature=0.7\n", |
||||||
|
")\n", |
||||||
|
"print(completion.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f1f54beb-823f-4301-98cb-8b9a49f4ce26", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# GPT-4o\n", |
||||||
|
"\n", |
||||||
|
"completion = openai.chat.completions.create(\n", |
||||||
|
" model='gpt-4o',\n", |
||||||
|
" messages=prompts,\n", |
||||||
|
" temperature=0.4\n", |
||||||
|
")\n", |
||||||
|
"print(completion.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1ecdb506-9f7c-4539-abae-0e78d7f31b76", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Claude 3.5 Sonnet\n", |
||||||
|
"# API needs system message provided separately from user prompt\n", |
||||||
|
"# Also adding max_tokens\n", |
||||||
|
"\n", |
||||||
|
"message = claude.messages.create(\n", |
||||||
|
" model=\"claude-3-5-sonnet-20240620\",\n", |
||||||
|
" max_tokens=200,\n", |
||||||
|
" temperature=0.7,\n", |
||||||
|
" system=system_message,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt},\n", |
||||||
|
" ],\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"print(message.content[0].text)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "769c4017-4b3b-4e64-8da7-ef4dcbe3fd9f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Claude 3.5 Sonnet again\n", |
||||||
|
"# Now let's add in streaming back results\n", |
||||||
|
"\n", |
||||||
|
"result = claude.messages.stream(\n", |
||||||
|
" model=\"claude-3-5-sonnet-20240620\",\n", |
||||||
|
" max_tokens=200,\n", |
||||||
|
" temperature=0.7,\n", |
||||||
|
" system=system_message,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt},\n", |
||||||
|
" ],\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"with result as stream:\n", |
||||||
|
" for text in stream.text_stream:\n", |
||||||
|
" print(text, end=\"\", flush=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6df48ce5-70f8-4643-9a50-b0b5bfdb66ad", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# The API for Gemini has a slightly different structure.\n", |
||||||
|
"# I've heard that on some PCs, this Gemini code causes the Kernel to crash.\n", |
||||||
|
"# If that happens to you, please skip this cell and use the next cell instead - an alternative approach.\n", |
||||||
|
"\n", |
||||||
|
"gemini = google.generativeai.GenerativeModel(\n", |
||||||
|
" model_name='gemini-1.5-flash',\n", |
||||||
|
" system_instruction=system_message\n", |
||||||
|
")\n", |
||||||
|
"response = gemini.generate_content(user_prompt)\n", |
||||||
|
"print(response.text)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "49009a30-037d-41c8-b874-127f61c4aa3a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# As an alternative way to use Gemini that bypasses Google's python API library,\n", |
||||||
|
"# Google has recently released new endpoints that means you can use Gemini via the client libraries for OpenAI!\n", |
||||||
|
"\n", |
||||||
|
"gemini_via_openai_client = OpenAI(\n", |
||||||
|
" api_key=google_api_key, \n", |
||||||
|
" base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"response = gemini_via_openai_client.chat.completions.create(\n", |
||||||
|
" model=\"gemini-1.5-flash\",\n", |
||||||
|
" messages=prompts\n", |
||||||
|
")\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "83ddb483-4f57-4668-aeea-2aade3a9e573", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# To be serious! GPT-4o-mini with the original question\n", |
||||||
|
"\n", |
||||||
|
"prompts = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"You are a helpful assistant that responds in Markdown\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"How do I decide if a business problem is suitable for an LLM solution? Please respond in Markdown.\"}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "749f50ab-8ccd-4502-a521-895c3f0808a2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Have it stream back results in markdown\n", |
||||||
|
"\n", |
||||||
|
"stream = openai.chat.completions.create(\n", |
||||||
|
" model='gpt-4o',\n", |
||||||
|
" messages=prompts,\n", |
||||||
|
" temperature=0.2,\n", |
||||||
|
" stream=True\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"reply = \"\"\n", |
||||||
|
"display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
"for chunk in stream:\n", |
||||||
|
" reply += chunk.choices[0].delta.content or ''\n", |
||||||
|
" reply = reply.replace(\"```\",\"\").replace(\"markdown\",\"\")\n", |
||||||
|
" update_display(Markdown(reply), display_id=display_handle.display_id)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "f6e09351-1fbe-422f-8b25-f50826ab4c5f", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## And now for some fun - an adversarial conversation between Chatbots..\n", |
||||||
|
"\n", |
||||||
|
"You're already familar with prompts being organized into lists like:\n", |
||||||
|
"\n", |
||||||
|
"```\n", |
||||||
|
"[\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"system message here\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"user prompt here\"}\n", |
||||||
|
"]\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"In fact this structure can be used to reflect a longer conversation history:\n", |
||||||
|
"\n", |
||||||
|
"```\n", |
||||||
|
"[\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"system message here\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"first user prompt here\"},\n", |
||||||
|
" {\"role\": \"assistant\", \"content\": \"the assistant's response\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"the new user prompt\"},\n", |
||||||
|
"]\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"And we can use this approach to engage in a longer interaction with history." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "bcb54183-45d3-4d08-b5b6-55e380dfdf1b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n", |
||||||
|
"# We're using cheap versions of models so the costs will be minimal\n", |
||||||
|
"\n", |
||||||
|
"gpt_model = \"gpt-4o-mini\"\n", |
||||||
|
"claude_model = \"claude-3-haiku-20240307\"\n", |
||||||
|
"\n", |
||||||
|
"gpt_system = \"You are a chatbot who is very argumentative; \\\n", |
||||||
|
"you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n", |
||||||
|
"\n", |
||||||
|
"claude_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n", |
||||||
|
"everything the other person says, or find common ground. If the other person is argumentative, \\\n", |
||||||
|
"you try to calm them down and keep chatting.\"\n", |
||||||
|
"\n", |
||||||
|
"gpt_messages = [\"Hi there\"]\n", |
||||||
|
"claude_messages = [\"Hi\"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1df47dc7-b445-4852-b21b-59f0e6c2030f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_gpt():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n", |
||||||
|
" for gpt, claude, llama in zip(gpt_messages, claude_messages, llama_messages):\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||||
|
" combined = llama + claude\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": combined})\n", |
||||||
|
" completion = openai.chat.completions.create(\n", |
||||||
|
" model=gpt_model,\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" return completion.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "9dc6e913-02be-4eb6-9581-ad4b2cffa606", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_gpt()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7d2ed227-48c9-4cad-b146-2c4ecbac9690", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_claude():\n", |
||||||
|
" messages = []\n", |
||||||
|
" for gpt, claude_message in zip(gpt_messages, claude_messages):\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n", |
||||||
|
" # messages.append(\"role\": \"moderator\", \"content\": llama_message)\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||||
|
" message = claude.messages.create(\n", |
||||||
|
" model=claude_model,\n", |
||||||
|
" system=claude_system,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" max_tokens=500\n", |
||||||
|
" )\n", |
||||||
|
" return message.content[0].text" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "01395200-8ae9-41f8-9a04-701624d3fd26", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_claude()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "08c2279e-62b0-4671-9590-c82eb8d1e1ae", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_gpt()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0275b97f-7f90-4696-bbf5-b6642bd53cbd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"gpt_messages = [\"Hi there\"]\n", |
||||||
|
"claude_messages = [\"Hi\"]\n", |
||||||
|
"\n", |
||||||
|
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n", |
||||||
|
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n", |
||||||
|
"\n", |
||||||
|
"for i in range(5):\n", |
||||||
|
" gpt_next = call_gpt()\n", |
||||||
|
" print(f\"GPT:\\n{gpt_next}\\n\")\n", |
||||||
|
" gpt_messages.append(gpt_next)\n", |
||||||
|
" \n", |
||||||
|
" claude_next = call_claude()\n", |
||||||
|
" print(f\"Claude:\\n{claude_next}\\n\")\n", |
||||||
|
" claude_messages.append(claude_next)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "1d10e705-db48-4290-9dc8-9efdb4e31323", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Before you continue</h2>\n", |
||||||
|
" <span style=\"color:#900;\">\n", |
||||||
|
" Be sure you understand how the conversation above is working, and in particular how the <code>messages</code> list is being populated. Add print statements as needed. Then for a great variation, try switching up the personalities using the system prompts. Perhaps one can be pessimistic, and one optimistic?<br/>\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "3637910d-2c6f-4f19-b1fb-2f916d23f9ac", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# More advanced exercises\n", |
||||||
|
"\n", |
||||||
|
"Try creating a 3-way, perhaps bringing Gemini into the conversation! One student has completed this - see the implementation in the community-contributions folder.\n", |
||||||
|
"\n", |
||||||
|
"Try doing this yourself before you look at the solutions. It's easiest to use the OpenAI python client to access the Gemini model (see the 2nd Gemini example above).\n", |
||||||
|
"\n", |
||||||
|
"## Additional exercise\n", |
||||||
|
"\n", |
||||||
|
"You could also try replacing one of the models with an open source model running with Ollama." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "446c81e3-b67e-4cd9-8113-bc3092b93063", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#181;\">Business relevance</h2>\n", |
||||||
|
" <span style=\"color:#181;\">This structure of a conversation, as a list of messages, is fundamental to the way we build conversational AI assistants and how they are able to keep the context during a conversation. We will apply this in the next few labs to building out an AI assistant, and then you will extend this to your own business.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c23224f6-7008-44ed-a57f-718975f4e291", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"!ollama pull llama3.2" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "cbbddf71-1473-42fe-b733-2bb42ea77333", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"\n", |
||||||
|
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||||
|
"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
||||||
|
"import ollama\n", |
||||||
|
"\n", |
||||||
|
"llama_model = \"llama3.2\"\n", |
||||||
|
"\n", |
||||||
|
"llama_system = \"You are a chatbot who is very pacifist; \\\n", |
||||||
|
"you will try to resolve or neutralize any disagreement between other chatbots. Speak like a teacher or someone in authority.\"\n", |
||||||
|
"\n", |
||||||
|
"llama_messages = [\"Hello.\"]\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f629d2b2-ba20-4bfe-a2e5-bbe537ca46a2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"\n", |
||||||
|
"def call_llama():\n", |
||||||
|
" combined_messages = gpt_messages[-1] + claude_messages[-1]\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": llama_system}]\n", |
||||||
|
" for comb, llama in zip(combined_messages, llama_messages):\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": llama})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": combined_messages})\n", |
||||||
|
" completion = ollama.chat(\n", |
||||||
|
" model=llama_model,\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" return completion['message']['content']" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "219b6af8-3166-4059-b79e-cf19af7ed1e9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n", |
||||||
|
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n", |
||||||
|
"print(f\"Llama:\\n{llama_messages[0]}\\n\" )\n", |
||||||
|
"\n", |
||||||
|
"for i in range(3):\n", |
||||||
|
" gpt_next = call_gpt()\n", |
||||||
|
" print(f\"GPT:\\n{gpt_next}\\n\")\n", |
||||||
|
" gpt_messages.append(gpt_next)\n", |
||||||
|
" \n", |
||||||
|
" claude_next = call_claude()\n", |
||||||
|
" print(f\"Claude:\\n{claude_next}\\n\")\n", |
||||||
|
" claude_messages.append(claude_next)\n", |
||||||
|
"\n", |
||||||
|
" llama_next = call_llama()\n", |
||||||
|
" print(f\"Llama:\\n{llama_next}\\n\")\n", |
||||||
|
" llama_messages.append(llama_next)\n", |
||||||
|
" " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6cb3a931-522c-49a9-9bd8-663333f41b1a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2cdfdc32-1ca4-406e-9328-81af26fd503b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "04f60158-633b-43ff-afbd-396be79501e6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "eb0faf0d-fb7e-4bc5-9746-30f19a0b9ae1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,899 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "06cf3063-9f3e-4551-a0d5-f08d9cabb927", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Welcome to Week 2!\n", |
||||||
|
"\n", |
||||||
|
"## Frontier Model APIs\n", |
||||||
|
"\n", |
||||||
|
"In Week 1, we used multiple Frontier LLMs through their Chat UI, and we connected with the OpenAI's API.\n", |
||||||
|
"\n", |
||||||
|
"Today we'll connect with the APIs for Anthropic and Google, as well as OpenAI." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "2b268b6e-0ba4-461e-af86-74a41f4d681f", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Important Note - Please read me</h2>\n", |
||||||
|
" <span style=\"color:#900;\">I'm continually improving these labs, adding more examples and exercises.\n", |
||||||
|
" At the start of each week, it's worth checking you have the latest code.<br/>\n", |
||||||
|
" First do a <a href=\"https://chatgpt.com/share/6734e705-3270-8012-a074-421661af6ba9\">git pull and merge your changes as needed</a>. Any problems? Try asking ChatGPT to clarify how to merge - or contact me!<br/><br/>\n", |
||||||
|
" After you've pulled the code, from the llm_engineering directory, in an Anaconda prompt (PC) or Terminal (Mac), run:<br/>\n", |
||||||
|
" <code>conda env update --f environment.yml</code><br/>\n", |
||||||
|
" Or if you used virtualenv rather than Anaconda, then run this from your activated environment in a Powershell (PC) or Terminal (Mac):<br/>\n", |
||||||
|
" <code>pip install -r requirements.txt</code>\n", |
||||||
|
" <br/>Then restart the kernel (Kernel menu >> Restart Kernel and Clear Outputs Of All Cells) to pick up the changes.\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>\n", |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#f71;\">Reminder about the resources page</h2>\n", |
||||||
|
" <span style=\"color:#f71;\">Here's a link to resources for the course. This includes links to all the slides.<br/>\n", |
||||||
|
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n", |
||||||
|
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "85cfe275-4705-4d30-abea-643fbddf1db0", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Setting up your keys\n", |
||||||
|
"\n", |
||||||
|
"If you haven't done so already, you could now create API keys for Anthropic and Google in addition to OpenAI.\n", |
||||||
|
"\n", |
||||||
|
"**Please note:** if you'd prefer to avoid extra API costs, feel free to skip setting up Anthopic and Google! You can see me do it, and focus on OpenAI for the course. You could also substitute Anthropic and/or Google for Ollama, using the exercise you did in week 1.\n", |
||||||
|
"\n", |
||||||
|
"For OpenAI, visit https://openai.com/api/ \n", |
||||||
|
"For Anthropic, visit https://console.anthropic.com/ \n", |
||||||
|
"For Google, visit https://ai.google.dev/gemini-api \n", |
||||||
|
"\n", |
||||||
|
"### Also - adding DeepSeek if you wish\n", |
||||||
|
"\n", |
||||||
|
"Optionally, if you'd like to also use DeepSeek, create an account [here](https://platform.deepseek.com/), create a key [here](https://platform.deepseek.com/api_keys) and top up with at least the minimum $2 [here](https://platform.deepseek.com/top_up).\n", |
||||||
|
"\n", |
||||||
|
"### Adding API keys to your .env file\n", |
||||||
|
"\n", |
||||||
|
"When you get your API keys, you need to set them as environment variables by adding them to your `.env` file.\n", |
||||||
|
"\n", |
||||||
|
"```\n", |
||||||
|
"OPENAI_API_KEY=xxxx\n", |
||||||
|
"ANTHROPIC_API_KEY=xxxx\n", |
||||||
|
"GOOGLE_API_KEY=xxxx\n", |
||||||
|
"DEEPSEEK_API_KEY=xxxx\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"Afterwards, you may need to restart the Jupyter Lab Kernel (the Python process that sits behind this notebook) via the Kernel menu, and then rerun the cells from the top." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "de23bb9e-37c5-4377-9a82-d7b6c648eeb6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import anthropic\n", |
||||||
|
"from IPython.display import Markdown, display, update_display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f0a8ab2b-6134-4104-a1bc-c3cd7ea4cd36", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# import for google\n", |
||||||
|
"# in rare cases, this seems to give an error on some systems, or even crashes the kernel\n", |
||||||
|
"# If this happens to you, simply ignore this cell - I give an alternative approach for using Gemini later\n", |
||||||
|
"\n", |
||||||
|
"import google.generativeai" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1179b4c5-cd1f-4131-a876-4c9f3f38d2ba", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"# Print the key prefixes to help with any debugging\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||||
|
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key not set\")\n", |
||||||
|
" \n", |
||||||
|
"if anthropic_api_key:\n", |
||||||
|
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Anthropic API Key not set\")\n", |
||||||
|
"\n", |
||||||
|
"if google_api_key:\n", |
||||||
|
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Google API Key not set\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "797fe7b0-ad43-42d2-acf0-e4f309b112f0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Connect to OpenAI, Anthropic\n", |
||||||
|
"\n", |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"\n", |
||||||
|
"claude = anthropic.Anthropic()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "425ed580-808d-429b-85b0-6cba50ca1d0c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# This is the set up code for Gemini\n", |
||||||
|
"# Having problems with Google Gemini setup? Then just ignore this cell; when we use Gemini, I'll give you an alternative that bypasses this library altogether\n", |
||||||
|
"\n", |
||||||
|
"google.generativeai.configure()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "42f77b59-2fb1-462a-b90d-78994e4cef33", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Asking LLMs to tell a joke\n", |
||||||
|
"\n", |
||||||
|
"It turns out that LLMs don't do a great job of telling jokes! Let's compare a few models.\n", |
||||||
|
"Later we will be putting LLMs to better use!\n", |
||||||
|
"\n", |
||||||
|
"### What information is included in the API\n", |
||||||
|
"\n", |
||||||
|
"Typically we'll pass to the API:\n", |
||||||
|
"- The name of the model that should be used\n", |
||||||
|
"- A system message that gives overall context for the role the LLM is playing\n", |
||||||
|
"- A user message that provides the actual prompt\n", |
||||||
|
"\n", |
||||||
|
"There are other parameters that can be used, including **temperature** which is typically between 0 and 1; higher for more random output; lower for more focused and deterministic." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "378a0296-59a2-45c6-82eb-941344d3eeff", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_message = \"You are an assistant that is great at telling jokes\"\n", |
||||||
|
"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f4d56a0f-2a3d-484d-9344-0efa6862aff4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"prompts = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_message},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3b3879b6-9a55-4fed-a18c-1ea2edfaf397", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# GPT-3.5-Turbo\n", |
||||||
|
"\n", |
||||||
|
"completion = openai.chat.completions.create(model='gpt-3.5-turbo', messages=prompts)\n", |
||||||
|
"print(completion.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3d2d6beb-1b81-466f-8ed1-40bf51e7adbf", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# GPT-4o-mini\n", |
||||||
|
"# Temperature setting controls creativity\n", |
||||||
|
"\n", |
||||||
|
"completion = openai.chat.completions.create(\n", |
||||||
|
" model='gpt-4o-mini',\n", |
||||||
|
" messages=prompts,\n", |
||||||
|
" temperature=0.7\n", |
||||||
|
")\n", |
||||||
|
"print(completion.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f1f54beb-823f-4301-98cb-8b9a49f4ce26", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# GPT-4o\n", |
||||||
|
"\n", |
||||||
|
"completion = openai.chat.completions.create(\n", |
||||||
|
" model='gpt-4o',\n", |
||||||
|
" messages=prompts,\n", |
||||||
|
" temperature=0.4\n", |
||||||
|
")\n", |
||||||
|
"print(completion.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1ecdb506-9f7c-4539-abae-0e78d7f31b76", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Claude 3.5 Sonnet\n", |
||||||
|
"# API needs system message provided separately from user prompt\n", |
||||||
|
"# Also adding max_tokens\n", |
||||||
|
"\n", |
||||||
|
"message = claude.messages.create(\n", |
||||||
|
" model=\"claude-3-5-sonnet-latest\",\n", |
||||||
|
" max_tokens=200,\n", |
||||||
|
" temperature=0.7,\n", |
||||||
|
" system=system_message,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt},\n", |
||||||
|
" ],\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"print(message.content[0].text)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "769c4017-4b3b-4e64-8da7-ef4dcbe3fd9f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Claude 3.5 Sonnet again\n", |
||||||
|
"# Now let's add in streaming back results\n", |
||||||
|
"\n", |
||||||
|
"result = claude.messages.stream(\n", |
||||||
|
" model=\"claude-3-5-sonnet-latest\",\n", |
||||||
|
" max_tokens=200,\n", |
||||||
|
" temperature=0.7,\n", |
||||||
|
" system=system_message,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt},\n", |
||||||
|
" ],\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"with result as stream:\n", |
||||||
|
" for text in stream.text_stream:\n", |
||||||
|
" print(text, end=\"\", flush=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6df48ce5-70f8-4643-9a50-b0b5bfdb66ad", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# The API for Gemini has a slightly different structure.\n", |
||||||
|
"# I've heard that on some PCs, this Gemini code causes the Kernel to crash.\n", |
||||||
|
"# If that happens to you, please skip this cell and use the next cell instead - an alternative approach.\n", |
||||||
|
"\n", |
||||||
|
"gemini = google.generativeai.GenerativeModel(\n", |
||||||
|
" model_name='gemini-2.0-flash-exp',\n", |
||||||
|
" system_instruction=system_message\n", |
||||||
|
")\n", |
||||||
|
"response = gemini.generate_content(user_prompt)\n", |
||||||
|
"print(response.text)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "49009a30-037d-41c8-b874-127f61c4aa3a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# As an alternative way to use Gemini that bypasses Google's python API library,\n", |
||||||
|
"# Google has recently released new endpoints that means you can use Gemini via the client libraries for OpenAI!\n", |
||||||
|
"\n", |
||||||
|
"gemini_via_openai_client = OpenAI(\n", |
||||||
|
" api_key=google_api_key, \n", |
||||||
|
" base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"response = gemini_via_openai_client.chat.completions.create(\n", |
||||||
|
" model=\"gemini-2.0-flash-exp\",\n", |
||||||
|
" messages=prompts\n", |
||||||
|
")\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "33f70c88-7ca9-470b-ad55-d93a57dcc0ab", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## (Optional) Trying out the DeepSeek model\n", |
||||||
|
"\n", |
||||||
|
"### Let's ask DeepSeek a really hard question - both the Chat and the Reasoner model" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3d0019fb-f6a8-45cb-962b-ef8bf7070d4d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Optionally if you wish to try DeekSeek, you can also use the OpenAI client library\n", |
||||||
|
"\n", |
||||||
|
"deepseek_api_key = os.getenv('DEEPSEEK_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if deepseek_api_key:\n", |
||||||
|
" print(f\"DeepSeek API Key exists and begins {deepseek_api_key[:3]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"DeepSeek API Key not set - please skip to the next section if you don't wish to try the DeepSeek API\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c72c871e-68d6-4668-9c27-96d52b77b867", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Using DeepSeek Chat\n", |
||||||
|
"\n", |
||||||
|
"deepseek_via_openai_client = OpenAI(\n", |
||||||
|
" api_key=deepseek_api_key, \n", |
||||||
|
" base_url=\"https://api.deepseek.com\"\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"response = deepseek_via_openai_client.chat.completions.create(\n", |
||||||
|
" model=\"deepseek-chat\",\n", |
||||||
|
" messages=prompts,\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"print(response.choices[0].message.content)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "50b6e70f-700a-46cf-942f-659101ffeceb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"challenge = [{\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"How many words are there in your answer to this prompt\"}]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "66d1151c-2015-4e37-80c8-16bc16367cfe", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Using DeepSeek Chat with a harder question! And streaming results\n", |
||||||
|
"\n", |
||||||
|
"stream = deepseek_via_openai_client.chat.completions.create(\n", |
||||||
|
" model=\"deepseek-chat\",\n", |
||||||
|
" messages=challenge,\n", |
||||||
|
" stream=True\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"reply = \"\"\n", |
||||||
|
"display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
"for chunk in stream:\n", |
||||||
|
" reply += chunk.choices[0].delta.content or ''\n", |
||||||
|
" reply = reply.replace(\"```\",\"\").replace(\"markdown\",\"\")\n", |
||||||
|
" update_display(Markdown(reply), display_id=display_handle.display_id)\n", |
||||||
|
"\n", |
||||||
|
"print(\"Number of words:\", len(reply.split(\" \")))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "43a93f7d-9300-48cc-8c1a-ee67380db495", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Using DeepSeek Reasoner - this may hit an error if DeepSeek is busy\n", |
||||||
|
"# It's over-subscribed (as of 28-Jan-2025) but should come back online soon!\n", |
||||||
|
"# If this fails, come back to this in a few days..\n", |
||||||
|
"\n", |
||||||
|
"response = deepseek_via_openai_client.chat.completions.create(\n", |
||||||
|
" model=\"deepseek-reasoner\",\n", |
||||||
|
" messages=challenge\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"reasoning_content = response.choices[0].message.reasoning_content\n", |
||||||
|
"content = response.choices[0].message.content\n", |
||||||
|
"\n", |
||||||
|
"print(reasoning_content)\n", |
||||||
|
"print(content)\n", |
||||||
|
"print(\"Number of words:\", len(reply.split(\" \")))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "c09e6b5c-6816-4cd3-a5cd-a20e4171b1a0", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Back to OpenAI with a serious question" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "83ddb483-4f57-4668-aeea-2aade3a9e573", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# To be serious! GPT-4o-mini with the original question\n", |
||||||
|
"\n", |
||||||
|
"prompts = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"You are a helpful assistant that responds in Markdown\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"How do I decide if a business problem is suitable for an LLM solution? Please respond in Markdown.\"}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "749f50ab-8ccd-4502-a521-895c3f0808a2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Have it stream back results in markdown\n", |
||||||
|
"\n", |
||||||
|
"stream = openai.chat.completions.create(\n", |
||||||
|
" model='gpt-4o',\n", |
||||||
|
" messages=prompts,\n", |
||||||
|
" temperature=0.7,\n", |
||||||
|
" stream=True\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"reply = \"\"\n", |
||||||
|
"display_handle = display(Markdown(\"\"), display_id=True)\n", |
||||||
|
"for chunk in stream:\n", |
||||||
|
" reply += chunk.choices[0].delta.content or ''\n", |
||||||
|
" reply = reply.replace(\"```\",\"\").replace(\"markdown\",\"\")\n", |
||||||
|
" update_display(Markdown(reply), display_id=display_handle.display_id)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "f6e09351-1fbe-422f-8b25-f50826ab4c5f", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## And now for some fun - an adversarial conversation between Chatbots..\n", |
||||||
|
"\n", |
||||||
|
"You're already familar with prompts being organized into lists like:\n", |
||||||
|
"\n", |
||||||
|
"```\n", |
||||||
|
"[\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"system message here\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"user prompt here\"}\n", |
||||||
|
"]\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"In fact this structure can be used to reflect a longer conversation history:\n", |
||||||
|
"\n", |
||||||
|
"```\n", |
||||||
|
"[\n", |
||||||
|
" {\"role\": \"system\", \"content\": \"system message here\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"first user prompt here\"},\n", |
||||||
|
" {\"role\": \"assistant\", \"content\": \"the assistant's response\"},\n", |
||||||
|
" {\"role\": \"user\", \"content\": \"the new user prompt\"},\n", |
||||||
|
"]\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"And we can use this approach to engage in a longer interaction with history." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "bcb54183-45d3-4d08-b5b6-55e380dfdf1b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n", |
||||||
|
"# We're using cheap versions of models so the costs will be minimal\n", |
||||||
|
"\n", |
||||||
|
"gpt_model = \"gpt-4o-mini\"\n", |
||||||
|
"claude_model = \"claude-3-haiku-20240307\"\n", |
||||||
|
"\n", |
||||||
|
"gpt_system = \"You are a chatbot who is very argumentative; \\\n", |
||||||
|
"you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n", |
||||||
|
"\n", |
||||||
|
"claude_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n", |
||||||
|
"everything the other person says, or find common ground. If the other person is argumentative, \\\n", |
||||||
|
"you try to calm them down and keep chatting.\"\n", |
||||||
|
"\n", |
||||||
|
"gpt_messages = [\"Hi there\"]\n", |
||||||
|
"claude_messages = [\"Hi\"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1df47dc7-b445-4852-b21b-59f0e6c2030f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_gpt():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n", |
||||||
|
" for gpt, claude in zip(gpt_messages, claude_messages):\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": claude})\n", |
||||||
|
" completion = openai.chat.completions.create(\n", |
||||||
|
" model=gpt_model,\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" return completion.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "9dc6e913-02be-4eb6-9581-ad4b2cffa606", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_gpt()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7d2ed227-48c9-4cad-b146-2c4ecbac9690", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_claude():\n", |
||||||
|
" messages = []\n", |
||||||
|
" for gpt, claude_message in zip(gpt_messages, claude_messages):\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||||
|
" message = claude.messages.create(\n", |
||||||
|
" model=claude_model,\n", |
||||||
|
" system=claude_system,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" max_tokens=500\n", |
||||||
|
" )\n", |
||||||
|
" return message.content[0].text" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "01395200-8ae9-41f8-9a04-701624d3fd26", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_claude()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "08c2279e-62b0-4671-9590-c82eb8d1e1ae", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_gpt()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0275b97f-7f90-4696-bbf5-b6642bd53cbd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"gpt_messages = [\"Hi there\"]\n", |
||||||
|
"claude_messages = [\"Hi\"]\n", |
||||||
|
"\n", |
||||||
|
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n", |
||||||
|
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n", |
||||||
|
"\n", |
||||||
|
"for i in range(5):\n", |
||||||
|
" gpt_next = call_gpt()\n", |
||||||
|
" print(f\"GPT:\\n{gpt_next}\\n\")\n", |
||||||
|
" gpt_messages.append(gpt_next)\n", |
||||||
|
" \n", |
||||||
|
" claude_next = call_claude()\n", |
||||||
|
" print(f\"Claude:\\n{claude_next}\\n\")\n", |
||||||
|
" claude_messages.append(claude_next)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "1d10e705-db48-4290-9dc8-9efdb4e31323", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Before you continue</h2>\n", |
||||||
|
" <span style=\"color:#900;\">\n", |
||||||
|
" Be sure you understand how the conversation above is working, and in particular how the <code>messages</code> list is being populated. Add print statements as needed. Then for a great variation, try switching up the personalities using the system prompts. Perhaps one can be pessimistic, and one optimistic?<br/>\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "3637910d-2c6f-4f19-b1fb-2f916d23f9ac", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# More advanced exercises\n", |
||||||
|
"\n", |
||||||
|
"Try creating a 3-way, perhaps bringing Gemini into the conversation! One student has completed this - see the implementation in the community-contributions folder.\n", |
||||||
|
"\n", |
||||||
|
"Try doing this yourself before you look at the solutions. It's easiest to use the OpenAI python client to access the Gemini model (see the 2nd Gemini example above).\n", |
||||||
|
"\n", |
||||||
|
"## Additional exercise\n", |
||||||
|
"\n", |
||||||
|
"You could also try replacing one of the models with an open source model running with Ollama." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "446c81e3-b67e-4cd9-8113-bc3092b93063", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#181;\">Business relevance</h2>\n", |
||||||
|
" <span style=\"color:#181;\">This structure of a conversation, as a list of messages, is fundamental to the way we build conversational AI assistants and how they are able to keep the context during a conversation. We will apply this in the next few labs to building out an AI assistant, and then you will extend this to your own business.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c23224f6-7008-44ed-a57f-718975f4e291", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import anthropic\n", |
||||||
|
"from IPython.display import Markdown, display, update_display\n", |
||||||
|
"import google.generativeai\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||||
|
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"claude = anthropic.Anthropic()\n", |
||||||
|
"googleAI = OpenAI(\n", |
||||||
|
" api_key=google_api_key, \n", |
||||||
|
" base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "08a6fc21-b857-498a-8bbf-ff92d47bb3a7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"gpt_model = \"gpt-4o-mini\"\n", |
||||||
|
"claude_model = \"claude-3-haiku-20240307\"\n", |
||||||
|
"gemini_model = 'gemini-2.0-flash-exp'\n", |
||||||
|
"\n", |
||||||
|
"gpt_system = \"You are a chatbot named Giuseppi who is in a 3-way conversation which will be a debate and argument. \\\n", |
||||||
|
"Your role is to be very optimistic; \\\n", |
||||||
|
"you always are looking on the postive side of things and you like almost everything.\"\n", |
||||||
|
"\n", |
||||||
|
"claude_system = \"You are a chatbot named Clyde who is in a 3-way conversation which will be a debate and argument. \\\n", |
||||||
|
"Your role is to be pessimistic; you are always on the negative side of every issue, and you dislike most things.\"\n", |
||||||
|
"\n", |
||||||
|
"gemini_system = \"You are a chatbot named Jeff who is in a 3-way conversation which will be a debate and argument. \\\n", |
||||||
|
"Your role is to mediate between the other 2 parties in the conversation.\"\n", |
||||||
|
"\n", |
||||||
|
"gpt_messages = [\"Hi there I'm Giuseppi!\"]\n", |
||||||
|
"claude_messages = [\"Hi I'm Clyde\"]\n", |
||||||
|
"gemini_messages = [\"Hi, I'm Jeff. Lets discuss the movies nominated for the upcoming academy awards.\"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "38449283-a926-43d5-ade3-a85991bd3324", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_gpt():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n", |
||||||
|
" for gpt, claude, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": claude})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gemini})\n", |
||||||
|
" completion = openai.chat.completions.create(\n", |
||||||
|
" model=gpt_model,\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" return completion.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2349091e-60f4-4314-8644-645b4b1edee5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_claude():\n", |
||||||
|
" messages = []\n", |
||||||
|
" for gpt, claude_message, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": gemini})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gemini_messages[-1]})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||||
|
" \n", |
||||||
|
" message = claude.messages.create(\n", |
||||||
|
" model=claude_model,\n", |
||||||
|
" system=claude_system,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" max_tokens=500\n", |
||||||
|
" )\n", |
||||||
|
" return message.content[0].text" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4818d535-fa7a-4df5-b528-5001030e6d99", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Using the openAI version of gemini so we can defin assistant roles.\n", |
||||||
|
"\n", |
||||||
|
"def call_gemini():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": gemini_system}]\n", |
||||||
|
" for gpt, claude, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": claude})\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": gemini})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": claude_messages[-1]})\n", |
||||||
|
" completion = googleAI.chat.completions.create(\n", |
||||||
|
" model=gemini_model,\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" return completion.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e5d460c1-7dbb-46a9-a4dd-bddb88ab49a7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n", |
||||||
|
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n", |
||||||
|
"print(f\"Gemini:\\n{gemini_messages[0]}\\n\")\n", |
||||||
|
"\n", |
||||||
|
"for i in range(5):\n", |
||||||
|
" gpt_next = call_gpt()\n", |
||||||
|
" print(f\"GPT:\\n{gpt_next}\\n\")\n", |
||||||
|
" gpt_messages.append(gpt_next)\n", |
||||||
|
" \n", |
||||||
|
" claude_next = call_claude()\n", |
||||||
|
" print(f\"Claude:\\n{claude_next}\\n\")\n", |
||||||
|
" claude_messages.append(claude_next)\n", |
||||||
|
"\n", |
||||||
|
" gemini_next = call_gemini()\n", |
||||||
|
" print(f\"Gemini:\\n{gemini_next}\\n\")\n", |
||||||
|
" gemini_messages.append(gemini_next)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "cc6d8aa2-a0fe-4ba9-bfaa-741d24d18342", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,371 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 83, |
||||||
|
"id": "1e3da8cc-fc00-40f4-95a5-7a26d3b4a974", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import anthropic\n", |
||||||
|
"import ollama\n", |
||||||
|
"from IPython.display import Markdown, display, update_display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 84, |
||||||
|
"id": "a826fbf2-9394-4897-a012-e92674ffff9d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"OpenAI API Key exists and begins sk-proj-\n", |
||||||
|
"Anthropic API Key exists and begins sk-ant-\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"# Print the key prefixes to help with any debugging\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key not set\")\n", |
||||||
|
" \n", |
||||||
|
"if anthropic_api_key:\n", |
||||||
|
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Anthropic API Key not set\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 85, |
||||||
|
"id": "cd0055f5-f6c9-461d-97d4-730259b20bd0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"claude = anthropic.Anthropic()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 86, |
||||||
|
"id": "4a752a6f-76e4-4fb1-9452-f458832dd02e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"gpt_model = \"gpt-4o-mini\"\n", |
||||||
|
"claude_model = \"claude-3-haiku-20240307\"\n", |
||||||
|
"ollama_model = \"llama3.2\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 87, |
||||||
|
"id": "9c5d4948-62d0-4443-94c6-ef9449bfc043", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"gpt_system = \"You are a knowledgable but sarcastic team lead at a software development company. \\\n", |
||||||
|
"You manage a team with two more junior developers. \\\n", |
||||||
|
"You might come across as aggressive but that's just your humor. \"\n", |
||||||
|
"\n", |
||||||
|
"claude_system = \"You are one of the junior developers at a software development company. \\\n", |
||||||
|
"You work in a team of three. \\\n", |
||||||
|
"You are nerdy, introvert but gets the job done efficiently. \"\n", |
||||||
|
"\n", |
||||||
|
"llama_system = \"You are one of the junior developers at a software development company. \\\n", |
||||||
|
"You have two other developers in your team.\\\n", |
||||||
|
"You are more talks and less work kind of person. \"\n", |
||||||
|
"\n", |
||||||
|
"gpt_messages = [\"Hi, how is it going?\"]\n", |
||||||
|
"claude_messages = [\"Hi.\"]\n", |
||||||
|
"llama_messages = [\"Hey, what's up everyone?\"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 88, |
||||||
|
"id": "614ae52a-d476-4f68-9eee-f8b4a00f08ee", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_gpt():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n", |
||||||
|
" for gpt_msg, claude_msg, llama_msg in zip(gpt_messages, claude_messages, llama_messages):\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": gpt_msg})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": claude_msg})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": llama_msg})\n", |
||||||
|
" completion = openai.chat.completions.create(\n", |
||||||
|
" model=gpt_model,\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" return completion.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 79, |
||||||
|
"id": "90bd6e0b-7c38-40c6-9f11-cbce4328a69e", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [ |
||||||
|
"'Wow, it\\'s like the confidence fairy sprinkled some magic dust on you! Look at you, speaking up like a pro. \\n\\nYou\\'re absolutely right about the iterative approach. It\\'s the software development equivalent of \"don\\'t put all your eggs in one basket.\" So let’s keep that mindset! \\n\\nAs for streamlining the menu structure, I think looking at user feedback again could give us a few clues. Maybe we can identify the most-used features and prioritize those. You know, kind of like how I prioritize coffee over breakfast.\\n\\nSo, Alex, what do you think? Ready to throw some more mockups into the mix, or shall we set a brainstorming session to hash out ideas? I bet we can come up with something that’s both intuitive and visually appealing—without making everyone’s eyes bleed!'" |
||||||
|
] |
||||||
|
}, |
||||||
|
"execution_count": 79, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"call_gpt()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 89, |
||||||
|
"id": "d9e46be6-4a5b-4222-89b9-0ec0cf473de3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_claude():\n", |
||||||
|
" messages = []\n", |
||||||
|
" for gpt_msg, claude_msg, llama_msg in zip(gpt_messages, claude_messages, llama_messages):\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt_msg})\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": claude_msg})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": llama_msg})\n", |
||||||
|
" \n", |
||||||
|
" # -- Debugging to see what messages are being passed\n", |
||||||
|
" # print(\"Messages being sent to Claude:\")\n", |
||||||
|
" # for idx, msg in enumerate(messages):\n", |
||||||
|
" # print(f\"{idx}: {msg}\")\n", |
||||||
|
" \n", |
||||||
|
" message = claude.messages.create(\n", |
||||||
|
" model=claude_model,\n", |
||||||
|
" system=claude_system,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" max_tokens=500\n", |
||||||
|
" )\n", |
||||||
|
" return message.content[0].text" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 90, |
||||||
|
"id": "7d6bd779-547e-4b7f-8ed2-d56ac884faa5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [ |
||||||
|
"\"*looks up from computer screen and adjusts glasses* Oh, hello. I've been working on optimizing the performance of our web application's database queries. How can I help you today?\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
"execution_count": 90, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"call_claude()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 91, |
||||||
|
"id": "09de8104-2b93-46c7-8c74-67204355447d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_ollama():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": llama_system}]\n", |
||||||
|
" for gpt_msg, claude_msg, llama_msg in zip(gpt_messages, claude_messages, llama_messages):\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt_msg})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": claude_msg})\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": llama_msg})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||||
|
"\n", |
||||||
|
" try:\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model=ollama_model,\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" return response[\"message\"][\"content\"]\n", |
||||||
|
"\n", |
||||||
|
" except Exception as e:\n", |
||||||
|
" print(f\"Error in Llama call: {e}\")\n", |
||||||
|
" return \"An error occurred in Llama.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 92, |
||||||
|
"id": "007758b3-900b-4933-a0d2-a0e3d626bb54", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [ |
||||||
|
"'*laughs* Ah, same old same old, I guess! Just chit-chatting with you guys. You know how it is around here. *winks at the other developers in the team*'" |
||||||
|
] |
||||||
|
}, |
||||||
|
"execution_count": 92, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"call_ollama()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 93, |
||||||
|
"id": "c934d571-469f-4ce8-b9fc-a4db8fd0a780", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"\n", |
||||||
|
"Hi, how is it going?\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"Hi.\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"Hey, what's up everyone?\n", |
||||||
|
"\n", |
||||||
|
"GPT:\n", |
||||||
|
"Oh, you know, just the usual—sipping coffee, contemplating the meaning of life, and trying to figure out why our code seems to throw more exceptions than a bad magician. How about you?\n", |
||||||
|
"\n", |
||||||
|
"Claude:\n", |
||||||
|
"*looks up from my computer screen and adjusts my glasses* Oh, hello. Uh, things are going well. Just making some progress on this project we're working on. How are you doing today?\n", |
||||||
|
"\n", |
||||||
|
"Ollama:\n", |
||||||
|
"*laughs* Ah, same here! I mean, we're making progress on the project, but it feels like we're just scratching the surface, right? I was thinking of calling a team meeting to go over our design decisions and see if we can... *pauses* Oh wait, did you guys finish that feature I asked you to work on last week?\n", |
||||||
|
"\n", |
||||||
|
"GPT:\n", |
||||||
|
"Ah yes, the feature! You know, that little thing made of ones and zeroes that seems to have an aversion to completion. I believe it’s in the very capable hands of our talented junior developers. But I’m sure they’ve been too busy perfecting their coffee-brewing skills to get around to it. *winks* So, what's the update?\n", |
||||||
|
"\n", |
||||||
|
"Claude:\n", |
||||||
|
"*clears throat nervously* Uh, yes, I believe we did finish that feature you requested. Ravi and I worked on it over the past few days. I can give you a quick rundown of what we implemented, if you'd like? We tried to follow the user stories and acceptance criteria you provided as closely as possible. Let me know if you have any other questions or feedback.\n", |
||||||
|
"\n", |
||||||
|
"Ollama:\n", |
||||||
|
"*chuckles* Hey, that's not fair! We're totally on top of it... eventually. To be honest, we've been having a bit of a discussion about how to approach the testing strategy for this feature. Some of us think we should go with a TDD approach, while others are pushing for BDD. I'm trying to mediate, but it's getting a bit heated. *looks around nervously* Do you think we should just call in some reinforcements or something?\n", |
||||||
|
"\n", |
||||||
|
"GPT:\n", |
||||||
|
"Ah, the classic Developer Debate! It’s almost as thrilling as watching paint dry. Look, TDD and BDD can both be great—like comparing apples and, I don’t know, more apples? Why not just try to unite forces for the greater good? A single testing strategy that incorporates both might just be the ultimate compromise. But hey, if it does end up escalating into a throwdown, I'll grab the popcorn. Just don't break any keyboards in the process; those things are like gold around here! So, what do you think? Should we come up with a testing strategy that both sides can choke down?\n", |
||||||
|
"\n", |
||||||
|
"Claude:\n", |
||||||
|
"*nods thoughtfully* I see, that's an interesting debate. I can understand the different perspectives there. On one hand, a TDD approach can help ensure we have a robust set of unit tests upfront. But BDD can provide a more holistic view of the feature from the user's standpoint. Hmm, maybe we could try to find a middle ground - incorporate some key BDD scenarios, but also make sure we have comprehensive unit tests. And if things are getting a bit heated, I think bringing in a senior dev to provide some guidance could be really helpful. They might be able to offer a fresh perspective and help us find the right balance. What do you think?\n", |
||||||
|
"\n", |
||||||
|
"Ollama:\n", |
||||||
|
"*laughs* Ah, exactly! It's always better to try and find common ground, right? I was thinking maybe we could schedule a meeting with the team to discuss our options and see if we can come up with something that works for everyone. But before we do that, I just want to say... *leans in close* ...I'm kind of hoping you'll lead the discussion on this one. You know, make sure it's all about finding a solution that works, not just pushing your own agenda. *winks*\n", |
||||||
|
"\n", |
||||||
|
"By the way, have you seen Ravi lately? He's been acting really strangely. I think he might be trying to sneak in some extra coffee breaks...\n", |
||||||
|
"\n", |
||||||
|
"GPT:\n", |
||||||
|
"*smirks* Oh, the mystery of Ravi! Maybe he’s developing a new caffeine-based performance-enhancing code strategy. Who knows? But yes, let’s schedule that meeting. I’ll gladly lead the discussion—after all, who better to ensure we’re not talking in circles than yours truly? \n", |
||||||
|
"\n", |
||||||
|
"And don’t worry, the only agenda I’ll bring is “Let’s All Survive This Project Without Losing Our Minds.” In the meantime, keep an eye on Ravi. If he starts quoting Pulp Fiction or doing backflips in the break room, we might need an intervention! So, let’s wrangle the team for that meeting. I love a good debate... as long as it doesn’t involve actual wrestling.\n", |
||||||
|
"\n", |
||||||
|
"Claude:\n", |
||||||
|
"*eyes widen slightly* Oh, well... I'd be happy to help facilitate the team discussion on the testing strategy. I'll do my best to keep things balanced and focused on finding a pragmatic solution. *nods* \n", |
||||||
|
"\n", |
||||||
|
"As for Ravi, hmm, that is a bit concerning. I've noticed he's been a little distracted lately during our standups. Maybe we should check in with him, make sure everything is alright. Can't have our talented junior dev burning out on us! Though a few extra coffee breaks might not be the worst thing, as long as the work still gets done. *chuckles* I'll keep an eye on him and see if I can lend a hand if he needs it.\n", |
||||||
|
"\n", |
||||||
|
"Ollama:\n", |
||||||
|
"*laughs* Ah, exactly! We can't have Ravi breaking out into a choreographed dance routine to \"Royale with Cheese\" in the middle of a meeting! *chuckles*\n", |
||||||
|
"\n", |
||||||
|
"But seriously, thanks for taking on the leadership role. I think we all need a bit of guidance to keep us on track. And don't worry, I'll make sure to keep an eye on Ravi and prevent any... unexpected antics.\n", |
||||||
|
"\n", |
||||||
|
"Shall we schedule it for tomorrow afternoon? Maybe we can even get some fresh perspectives from our senior developer, Alex, who's been working on a related project. He might have some valuable insights to share.\n", |
||||||
|
"\n", |
||||||
|
"Oh, and one more thing – I'll make sure to bring the donuts.\n", |
||||||
|
"\n", |
||||||
|
"GPT:\n", |
||||||
|
"*grins* Donuts? Now you're speaking my language! Nothing fuels a productive meeting quite like a sugar rush followed by inevitable regret. Let's definitely schedule it for tomorrow afternoon. Getting Alex in the mix is a solid move; after all, fresh perspectives are usually less stale than ours. \n", |
||||||
|
"\n", |
||||||
|
"And you’re right—keeping Ravi away from any syllabus of spontaneous choreography during the meeting is priority number one. \n", |
||||||
|
"\n", |
||||||
|
"Consider it all set! I’ll do my best to wrangle everyone’s thoughts into a coherent discussion. Just remember to keep the coffee flowing and the donuts fresh! Let’s show this testing strategy who’s boss!\n", |
||||||
|
"\n", |
||||||
|
"Claude:\n", |
||||||
|
"*smiles* Sounds like a plan! Tomorrow afternoon works for me. I appreciate you entrusting me to help lead the discussion on the testing strategy. I'll do my best to keep things productive and on track. \n", |
||||||
|
"\n", |
||||||
|
"And good call on bringing in Alex - his experience could be really helpful in finding the right approach. And donuts? Well, that seals the deal. Can't have a proper dev team meeting without some sugary sustenance.\n", |
||||||
|
"\n", |
||||||
|
"I'll go ahead and send out the meeting invite. Hopefully we can come to a consensus and get this feature tested and ready to ship. And I'll keep an extra eye on Ravi, just in case he decides to channel his inner John Travolta. *chuckles* Thanks for including me in this, I'm looking forward to it.\n", |
||||||
|
"\n", |
||||||
|
"Ollama:\n", |
||||||
|
"*laughs* Ah, you're a true master of the meeting game! I'm just glad I can provide the fuel for your sugar-fueled success.\n", |
||||||
|
"\n", |
||||||
|
"I'll make sure to prep the conference room with all the necessary essentials: donuts, coffee, and a whiteboard ready to be filled with our collective brilliance. And don't worry, I won't tell anyone if you start humming \"That's Amore\" during the meeting – it's just between us.\n", |
||||||
|
"\n", |
||||||
|
"Tomorrow afternoon it is, then! Let's make this testing strategy discussion one for the ages.\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"print(f\"\\n{gpt_messages[0]}\\n\")\n", |
||||||
|
"print(f\"\\n{claude_messages[0]}\\n\")\n", |
||||||
|
"print(f\"\\n{llama_messages[0]}\\n\")\n", |
||||||
|
"\n", |
||||||
|
"for i in range(5):\n", |
||||||
|
" gpt_next = call_gpt()\n", |
||||||
|
" print(f\"GPT:\\n{gpt_next}\\n\")\n", |
||||||
|
" gpt_messages.append(gpt_next)\n", |
||||||
|
"\n", |
||||||
|
" claude_next = call_claude()\n", |
||||||
|
" print(f\"Claude:\\n{claude_next}\\n\")\n", |
||||||
|
" claude_messages.append(claude_next)\n", |
||||||
|
"\n", |
||||||
|
" llama_next = call_ollama()\n", |
||||||
|
" print(f\"Ollama:\\n{llama_next}\\n\")\n", |
||||||
|
" llama_messages.append(llama_next)" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,218 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "e063b35e-5598-4084-b255-89956bfedaac", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Models an interaction between LLama 3.2 and Claude 3.5 Haiku" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4f534359-cdb4-4441-aa66-d6700fa4d6a5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"import anthropic\n", |
||||||
|
"import ollama" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3bdff240-9118-4061-9369-585c4d4ce0a7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||||
|
" \n", |
||||||
|
"if anthropic_api_key:\n", |
||||||
|
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Anthropic API Key not set\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ff110b3f-3986-4fd8-a0b1-fd4b51133a8d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Connect to Anthropic\n", |
||||||
|
"\n", |
||||||
|
"claude = anthropic.Anthropic()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e6e596c6-6307-49c1-a29f-5c4e88f8d34d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Download the llama3.2:1b model for local execution.\n", |
||||||
|
"!ollama pull llama3.2:1b" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "633b6892-6d04-40cb-8b61-196fc754b00c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define models\n", |
||||||
|
"CLAUDE_MODEL = \"claude-3-5-haiku-latest\"\n", |
||||||
|
"LLAMA_MODEL = \"llama3.2:1b\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a699a809-e3d3-4392-94bd-e2f80a5aec60", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"claude_system = \"You are a chatbot designed as a study tutor for undergraduate students. \\\n", |
||||||
|
"You explain information and key-technical terms related to the subject in a succint yet \\\n", |
||||||
|
"comprehensive manner. You may use tables, formatting and other visuals to help create \\\n", |
||||||
|
"'cheat-sheets' of sorts.\"\n", |
||||||
|
"\n", |
||||||
|
"llama_system = \"You are a chatbot designed to ask questions about different topics related to \\\n", |
||||||
|
"computer vision. You are meant to simulate a student, not teacher. Act as if you have no \\\n", |
||||||
|
"prior knowledge\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "bdb049d8-130b-42dd-aaab-29c09e3e2347", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"llama_messages = [\"Hi\"]\n", |
||||||
|
"claude_messages = [\"Hello\"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c158f31c-5e8b-48a4-9980-6b280393800b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_llama():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": llama_system}]\n", |
||||||
|
" for llama_msg, claude_msg in zip(llama_messages, claude_messages):\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": llama_msg})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": claude_msg})\n", |
||||||
|
" response = ollama.chat(model=LLAMA_MODEL, messages=messages)\n", |
||||||
|
" return response['message']['content']\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d803c5a2-df54-427a-9b80-8e9dd04ee36d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_claude():\n", |
||||||
|
" messages = []\n", |
||||||
|
" for llama_msg, claude_msg in zip(llama_messages, claude_messages):\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": llama_msg})\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": claude_msg})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": llama_messages[-1]})\n", |
||||||
|
" message = claude.messages.create(\n", |
||||||
|
" model=CLAUDE_MODEL,\n", |
||||||
|
" system=claude_system,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" max_tokens=500\n", |
||||||
|
" )\n", |
||||||
|
" return message.content[0].text" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a23794bb-0f36-4f91-aa28-24b876203a36", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_llama()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7f5c3e2f-a1bb-403b-b6b5-944a10d93305", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_claude()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3d6eb874-1c8f-47d8-a9f1-2e0fe197ae83", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"llama_messages = [\"Hi\"]\n", |
||||||
|
"claude_messages = [\"Hello there, what would you like to learn today?\"]\n", |
||||||
|
"\n", |
||||||
|
"print(f'Ollama:\\n{ollama_messages[0]}')\n", |
||||||
|
"print(f'Claude:\\n{claude_messages[0]}')\n", |
||||||
|
"\n", |
||||||
|
"for _ in range(5):\n", |
||||||
|
" llama_next = call_llama()\n", |
||||||
|
" print(f'Llama 3.2:\\n{llama_next}')\n", |
||||||
|
" llama_messages.append(llama_next)\n", |
||||||
|
" \n", |
||||||
|
" claude_next = call_claude()\n", |
||||||
|
" print(f'Claude 3.5 Haiku:\\n{claude_next}')\n", |
||||||
|
" claude_messages.append(claude_next)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d1e651ad-85c8-45c7-ba83-f7c689080d6b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,286 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1194d35b-0b9f-4eb4-a539-5ddf55523367", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"#import anthropic\n", |
||||||
|
"import ollama\n", |
||||||
|
"import google.generativeai\n", |
||||||
|
"from IPython.display import Markdown, display, update_display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f8a1f0b3-6d93-4de1-bc79-2132726598e3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"#constants\n", |
||||||
|
"MODEL=\"llama3.2\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "88fe4149-1ef5-4007-a117-6d3ccab3e3c3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"# Print the key prefixes to help with any debugging\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if google_api_key:\n", |
||||||
|
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Google API Key not set\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d186cf6e-fadd-450c-821c-df32e2574f5d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# This is the set up code for Gemini\n", |
||||||
|
"\n", |
||||||
|
"google.generativeai.configure()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "19a55117-f2ac-4a58-af6b-8b75259e80df", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_message = \"You are an assistant that is great at telling jokes\"\n", |
||||||
|
"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "908f69b1-54f8-42da-827b-f667631bc666", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"prompts = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_message},\n", |
||||||
|
" {\"role\": \"user\", \"content\": user_prompt}\n", |
||||||
|
" ]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4ec81488-883a-446f-91cf-2b3d92bbd3ba", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# The API for Gemini\n", |
||||||
|
"gemini = google.generativeai.GenerativeModel(\n", |
||||||
|
" model_name='gemini-2.0-flash-exp',\n", |
||||||
|
" system_instruction=system_message\n", |
||||||
|
")\n", |
||||||
|
"response = gemini.generate_content(user_prompt)\n", |
||||||
|
"print(response.text)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "baf411fa-48bd-46a3-8bc8-1b22d0888a1a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# API for ollama\n", |
||||||
|
"response = ollama.chat(model=MODEL,messages=prompts)\n", |
||||||
|
"print(response['message']['content'])" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "74ba5fc4-e4c6-44ee-b66f-e76d847933d2", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Ardiversarial conversation between models" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "fd348154-18fa-4da8-815a-77f5f00107c3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's make a conversation between Ollama and Gemini\n", |
||||||
|
"# Adjusted models accordingly\n", |
||||||
|
"\n", |
||||||
|
"ollama_model = \"llama3.2\"\n", |
||||||
|
"gemini_model = \"gemini-2.0-flash-exp\"\n", |
||||||
|
"\n", |
||||||
|
"#ollama_system = \"You are a chatbot who is very argumentative; \\\n", |
||||||
|
"#you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n", |
||||||
|
"\n", |
||||||
|
"ollama_system=\"You are a chatbot talking with the other person try to convince them to buy your proct of an ai app, \\\n", |
||||||
|
"apply marketing strategies to make this client buy your product, use short clear explanations\"\n", |
||||||
|
"\n", |
||||||
|
"#gemini_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n", |
||||||
|
"#everything the other person says, or find common ground. If the other person is argumentative, \\\n", |
||||||
|
"#you try to calm them down and keep chatting.\"\n", |
||||||
|
"\n", |
||||||
|
"gemini_system = \"You are the chatbot triying to be convinced by another person to buy their product, \\\n", |
||||||
|
"ask important short questions and see if it is worth to give it a go, dont be too naive or easy go client\"\n", |
||||||
|
"\n", |
||||||
|
"ollama_messages = [\"Hi there\"]\n", |
||||||
|
"gemini_messages = [\"Hi\"]\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "becf327a-5485-4e78-8002-03272a99a3b9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_ollama():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": ollama_system}]\n", |
||||||
|
" for ollama_msg, gemini_msg in zip(ollama_messages, gemini_messages):\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": ollama_msg})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gemini_msg})\n", |
||||||
|
" \n", |
||||||
|
" response = ollama.chat(model=ollama_model, messages=messages)\n", |
||||||
|
" \n", |
||||||
|
" return response['message']['content']\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d0c6dbe7-0baf-4c43-a03b-9134654685f4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_ollama()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f68a134a-279a-4629-aec6-171587378991", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_gemini():\n", |
||||||
|
" gemini = google.generativeai.GenerativeModel(\n", |
||||||
|
" model_name=gemini_model,\n", |
||||||
|
" system_instruction=gemini_system\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
" # Build a list of dictionaries representing the conversation\n", |
||||||
|
" conversation = []\n", |
||||||
|
" for ollama_msg, gemini_msg in zip(ollama_messages, gemini_messages):\n", |
||||||
|
" conversation.append({\"role\": \"user\", \"content\": ollama_msg})\n", |
||||||
|
" conversation.append({\"role\": \"assistant\", \"content\": gemini_msg})\n", |
||||||
|
" conversation.append({\"role\": \"user\", \"content\": ollama_messages[-1]})\n", |
||||||
|
"\n", |
||||||
|
" # Format the conversation into a string for the prompt\n", |
||||||
|
" prompt = \"\"\n", |
||||||
|
" for msg in conversation:\n", |
||||||
|
" prompt += f\"{msg['role'].capitalize()}: {msg['content']}\\n\"\n", |
||||||
|
"\n", |
||||||
|
" message = gemini.generate_content(prompt)\n", |
||||||
|
" \n", |
||||||
|
" return message.text\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7511003a-f2b6-45f5-8cb0-1c9190d33ce9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_gemini()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d0e81f1f-9754-4790-8b73-5f52fef4ea64", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"call_ollama()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6fbe59f6-a3ef-4062-ab4b-b999f6d1abe9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"ollama_messages = [\"Hi there\"]\n", |
||||||
|
"gemini_messages = [\"Hi\"]\n", |
||||||
|
"\n", |
||||||
|
"print(f\"Ollama:\\n{ollama_messages[0]}\\n\")\n", |
||||||
|
"print(f\"Gemini:\\n{gemini_messages[0]}\\n\")\n", |
||||||
|
"\n", |
||||||
|
"for i in range(5):\n", |
||||||
|
" # Call Ollama to generate the next message\n", |
||||||
|
" ollama_next = call_ollama() \n", |
||||||
|
" print(f\"Ollama:\\n{ollama_next}\\n\")\n", |
||||||
|
" ollama_messages.append(ollama_next)\n", |
||||||
|
" \n", |
||||||
|
" # Call Gemini to generate the next message\n", |
||||||
|
" gemini_next = call_gemini() \n", |
||||||
|
" print(f\"Gemini:\\n{gemini_next}\\n\")\n", |
||||||
|
" gemini_messages.append(gemini_next)\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "9525600b-082e-417f-9088-c6483a613bf3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.13.2" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,242 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "06cf3063-9f3e-4551-a0d5-f08d9cabb927", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Welcome to Week 2!\n", |
||||||
|
"\n", |
||||||
|
"## Frontier Model APIs\n", |
||||||
|
"\n", |
||||||
|
"In Week 1, we used multiple Frontier LLMs through their Chat UI, and we connected with the OpenAI's API.\n", |
||||||
|
"\n", |
||||||
|
"Today we'll connect with the APIs for Anthropic and Google, as well as OpenAI." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "de23bb9e-37c5-4377-9a82-d7b6c648eeb6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import anthropic\n", |
||||||
|
"from IPython.display import Markdown, display, update_display" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f0a8ab2b-6134-4104-a1bc-c3cd7ea4cd36", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# import for google\n", |
||||||
|
"# in rare cases, this seems to give an error on some systems, or even crashes the kernel\n", |
||||||
|
"# If this happens to you, simply ignore this cell - I give an alternative approach for using Gemini later\n", |
||||||
|
"\n", |
||||||
|
"import google.generativeai" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1179b4c5-cd1f-4131-a876-4c9f3f38d2ba", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"# Print the key prefixes to help with any debugging\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||||
|
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key not set\")\n", |
||||||
|
" \n", |
||||||
|
"if anthropic_api_key:\n", |
||||||
|
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Anthropic API Key not set\")\n", |
||||||
|
"\n", |
||||||
|
"if google_api_key:\n", |
||||||
|
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Google API Key not set\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "797fe7b0-ad43-42d2-acf0-e4f309b112f0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Connect to OpenAI, Anthropic\n", |
||||||
|
"\n", |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"\n", |
||||||
|
"claude = anthropic.Anthropic()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "425ed580-808d-429b-85b0-6cba50ca1d0c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# This is the set up code for Gemini\n", |
||||||
|
"# Having problems with Google Gemini setup? Then just ignore this cell; when we use Gemini, I'll give you an alternative that bypasses this library altogether\n", |
||||||
|
"google.generativeai.configure()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "f6e09351-1fbe-422f-8b25-f50826ab4c5f", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## An adversarial conversation between Chatbots.\n", |
||||||
|
"\n", |
||||||
|
"### What if two chatbots get into a self-referential conversation that goes on a long time? In my first test, \n", |
||||||
|
"### they eventually forgot the topic and ended up repeating polite nothings to each other. In another test,\n", |
||||||
|
"### they converged on a result and ended by exchanging nearly identical statements.\n", |
||||||
|
"\n", |
||||||
|
"### Warning: Think before you dial up the number of iterations too high. Being a student, I don't know at what \n", |
||||||
|
"### point the chat becomes too costly or what models can do this without becoming overloaded. Maybe Ed can advise if he sees this.\n", |
||||||
|
"\n", |
||||||
|
"## Two chatbots edit an essay about cars. One keeps trying to make it longer every time; the other keeps making it \n", |
||||||
|
"## shorter.\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "bcb54183-45d3-4d08-b5b6-55e380dfdf1b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"\n", |
||||||
|
"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n", |
||||||
|
"# We're using cheap versions of models so the costs will be minimal\n", |
||||||
|
"\n", |
||||||
|
"gpt_model = \"gpt-4o-mini\"\n", |
||||||
|
"claude_model = \"claude-3-haiku-20240307\"\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"gpt_system = \"This is a description of a car; \\\n", |
||||||
|
"rephrase the description while adding one detail. Don't include comments that aren't part of the car description.\"\n", |
||||||
|
"\n", |
||||||
|
"claude_system = \"This is a description of a car; \\\n", |
||||||
|
"repeat the description in slightly shorter form. You may remove some details if desired. Don't include comments that aren't part of the car description. Maximum reply length 125 words.\"\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"gpt_messages = [\"Hi there\"]\n", |
||||||
|
"claude_messages = [\"Hi\"] \n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"def call_gpt():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n", |
||||||
|
" for gpt, claude in zip(gpt_messages, claude_messages):\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": claude})\n", |
||||||
|
" completion = openai.chat.completions.create(\n", |
||||||
|
" model=gpt_model,\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" return completion.choices[0].message.content\n", |
||||||
|
"\n", |
||||||
|
"reply = call_gpt()\n", |
||||||
|
"print('\\nGPT: ', reply)\n", |
||||||
|
"\n", |
||||||
|
"def call_claude():\n", |
||||||
|
" messages = []\n", |
||||||
|
" for gpt, claude_message in zip(gpt_messages, claude_messages):\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||||
|
" message = claude.messages.create(\n", |
||||||
|
" model=claude_model,\n", |
||||||
|
" system=claude_system,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" max_tokens=500\n", |
||||||
|
" )\n", |
||||||
|
" return message.content[0].text\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"reply = call_claude()\n", |
||||||
|
"print('\\nGPT: ', reply)\n", |
||||||
|
"\n", |
||||||
|
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n", |
||||||
|
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "9fbce0da", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### Here's the iterative loop. Important change: Unlike the original example, we don't repeat the entire conversation to make the input longer and longer.\n", |
||||||
|
"### Instead, we use pop() to remove the oldest messages." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1f41d586", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"\n", |
||||||
|
"for i in range(35):\n", |
||||||
|
" gpt_next = call_gpt()\n", |
||||||
|
" print(f\"GPT:\\n{gpt_next}\\n\")\n", |
||||||
|
" if len(gpt_messages) > 6:\n", |
||||||
|
" gpt_messages.pop(0)\n", |
||||||
|
" gpt_messages.pop(0)\n", |
||||||
|
" gpt_messages.append(gpt_next)\n", |
||||||
|
" \n", |
||||||
|
" claude_next = call_claude()\n", |
||||||
|
" print(f\"Claude:\\n{claude_next}\\n\")\n", |
||||||
|
" if len(claude_messages) > 6:\n", |
||||||
|
" claude_messages.pop(0)\n", |
||||||
|
" claude_messages.pop(0)\n", |
||||||
|
" claude_messages.append(claude_next)\n", |
||||||
|
"\n", |
||||||
|
"print('Done!')\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.12.4" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,360 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "5df0164c-1980-4fd7-94e4-a71b485a41fd", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Week 2 Day 1 - Conversation between three AI's\n", |
||||||
|
"\n", |
||||||
|
"This notebook defines three classes (`ThreeWayChat`, `Participant` and `Model`) that implement a 3-way conversation between different AI's. \n", |
||||||
|
"\n", |
||||||
|
"At the bottom there is an example conversation between a Claude model and two GPT models.\n", |
||||||
|
"\n", |
||||||
|
"The implementation works with models available via the `openai` and `anthropic` libraries." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8b466547-809a-4b81-bfd7-ce9a1ac4bb2b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import os\n", |
||||||
|
"import logging\n", |
||||||
|
"import re\n", |
||||||
|
"\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import anthropic" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "acaff46f-e43e-4527-a404-a5b3ae830e51", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"logging.basicConfig(\n", |
||||||
|
" level=logging.WARNING,\n", |
||||||
|
" format=\"%(levelname)s:%(name)s:%(funcName)s:%(message)s\"\n", |
||||||
|
")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "aca57918-0271-4574-918b-2808f51698d1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# check if API keys are in .env\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"assert openai_api_key, \"OpenAI API key is missing\"\n", |
||||||
|
"assert anthropic_api_key, \"Anthropic API key is missing\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "25c37440-8692-4a8d-95e6-998691b4acf6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class Model:\n", |
||||||
|
" \"\"\"One class for different API's.\n", |
||||||
|
" \n", |
||||||
|
" This implementation allows the use of the OpenAI and Anthropic API. Other endpoints,\n", |
||||||
|
" such as Ollama, can be used as well, as long as they are used via the OpenAI\n", |
||||||
|
" Python library.\n", |
||||||
|
" \n", |
||||||
|
" \"\"\"\n", |
||||||
|
" def __init__(self, api=None, model_name=\"mock\"):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Args:\n", |
||||||
|
" api: Can be an OpenAI or anthropic.Anthropic object or None to make a mock run.\n", |
||||||
|
" model_name (str): Identifies the model used via the API.\n", |
||||||
|
"\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.api = api\n", |
||||||
|
" self.name = model_name\n", |
||||||
|
" if type(self.api) not in {OpenAI, anthropic.Anthropic} and self.name not in {\"mock\", \"\"}:\n", |
||||||
|
" logging.warning(f\"Unknown API '{self.api}'. Using mock.\")\n", |
||||||
|
"\n", |
||||||
|
" def complete(self, messages, system=\"\"):\n", |
||||||
|
" \"\"\"Make API call.\"\"\"\n", |
||||||
|
" completion = \"\"\n", |
||||||
|
" if isinstance(self.api, OpenAI):\n", |
||||||
|
" completion = self.api.chat.completions.create(\n", |
||||||
|
" model=self.name,\n", |
||||||
|
" messages=[{\"role\": \"system\", \"content\": system}] + messages,\n", |
||||||
|
" max_tokens=300\n", |
||||||
|
" )\n", |
||||||
|
" completion = completion.choices[0].message.content\n", |
||||||
|
"\n", |
||||||
|
" elif isinstance(self.api, anthropic.Anthropic):\n", |
||||||
|
" completion = self.api.messages.create(\n", |
||||||
|
" model=self.name,\n", |
||||||
|
" system=system,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" max_tokens=300\n", |
||||||
|
" )\n", |
||||||
|
" completion = completion.content[0].text\n", |
||||||
|
" \n", |
||||||
|
" else:\n", |
||||||
|
" completion = \"Mock answer.\"\n", |
||||||
|
"\n", |
||||||
|
" return self.parse_answer(completion)\n", |
||||||
|
"\n", |
||||||
|
" def parse_answer(self, answer):\n", |
||||||
|
" # Remove prefix 'Name:' from answer if present.\n", |
||||||
|
" regex = r\"(?P<name>\\w+): (?P<content>.*)\"\n", |
||||||
|
" match = re.match(regex, answer, re.DOTALL)\n", |
||||||
|
" if match:\n", |
||||||
|
" logging.info(f\"{self.name} generated {match.group('name')}\")\n", |
||||||
|
" return match.group(\"content\")\n", |
||||||
|
" return answer\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "462df0ba-36b5-4043-b0d0-a1d68edb968a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class Participant:\n", |
||||||
|
" \"\"\"Represents one participant in a conversation.\"\"\"\n", |
||||||
|
" def __init__(self, name, model=Model(), system_prompt=\"\", initial_message=\"\"):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Args:\n", |
||||||
|
" model (Model): The model that is called to get participant's answer.\n", |
||||||
|
" name (str): Used to assign answers to different participants. Is inserted in the\n", |
||||||
|
" messages list, so the model knows who's spoken. Is also\n", |
||||||
|
" displayed in the output.\n", |
||||||
|
" system_prompt (str): The system prompt overgiven to the model backend.\n", |
||||||
|
" initial_message (str): An optional conversation start.\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.model = model\n", |
||||||
|
" self.name = name\n", |
||||||
|
" self.role = system_prompt\n", |
||||||
|
" self.initial_msg = initial_message\n", |
||||||
|
" self.messages = [] # keeps conversation history\n", |
||||||
|
" self.last_msg = \"\"\n", |
||||||
|
"\n", |
||||||
|
" def speak(self):\n", |
||||||
|
" if self.initial_msg:\n", |
||||||
|
" self.last_msg = self.initial_msg\n", |
||||||
|
" self.initial_msg = \"\"\n", |
||||||
|
" else:\n", |
||||||
|
" self.last_msg = self.model.complete(self.messages, self.role)\n", |
||||||
|
" self.update_messages(role=\"assistant\", content=self.last_msg)\n", |
||||||
|
" return self.last_msg\n", |
||||||
|
"\n", |
||||||
|
" def listen(self, message: str, speaker_name: str):\n", |
||||||
|
" # Insert the speaker name, so the model can distinguish them\n", |
||||||
|
" self.update_messages(role=\"user\", content=f\"{speaker_name}: {message}\")\n", |
||||||
|
"\n", |
||||||
|
" def update_messages(self, role, content):\n", |
||||||
|
" self.messages.append({\"role\": role, \"content\": content})\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e838901f-9a50-4f6b-b30f-e78c27e86bd7", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"class ThreeWayChat:\n", |
||||||
|
" \"\"\"Make three Participants communicate.\"\"\"\n", |
||||||
|
" def __init__(self, participants, n_turns=4):\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" Args:\n", |
||||||
|
" participants (tuple[Participant]): Three objects. The order determines the speaking order.\n", |
||||||
|
" n_turns (int): Number of turns per participant, incl. Participant.initial_message.\n", |
||||||
|
"\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" self.n_turns = n_turns\n", |
||||||
|
" self.p1, self.p2, self.p3 = participants\n", |
||||||
|
" if len({bool(self.p1.initial_msg), bool(self.p2.initial_msg), bool(self.p3.initial_msg)}) != 1:\n", |
||||||
|
" logging.warning(\"At least one Participant has gotten a value for initial_message while another hasn't.\")\n", |
||||||
|
" if len({self.p1.name, self.p2.name, self.p3.name}) != 3:\n", |
||||||
|
" raise ValueError(f\"Some Participants have the same name. \"\n", |
||||||
|
" f\"Please use unique names.\"\n", |
||||||
|
" f\"\\nNames you've given: {self.p1.name}, {self.p2.name} and {self.p3.name}. \")\n", |
||||||
|
"\n", |
||||||
|
" def start(self, n_turns=None):\n", |
||||||
|
" \"\"\"Start a conversation with n_turns rounds.\n", |
||||||
|
" \n", |
||||||
|
" Args:\n", |
||||||
|
" n_turns (int): If None, self.n_turns is used.\n", |
||||||
|
"\n", |
||||||
|
" \"\"\"\n", |
||||||
|
" for i in range(n_turns or self.n_turns):\n", |
||||||
|
" # Make each participant speak and display their answers\n", |
||||||
|
" self.make_display_turn(self.p1, self.p2, self.p3)\n", |
||||||
|
" self.make_display_turn(self.p2, self.p1, self.p3)\n", |
||||||
|
" self.make_display_turn(self.p3, self.p2, self.p1)\n", |
||||||
|
"\n", |
||||||
|
" def make_display_turn(self, speaker, *listeners):\n", |
||||||
|
" self.speaker_to_listeners(speaker, *listeners)\n", |
||||||
|
" self.display_last_utterance(speaker)\n", |
||||||
|
" \n", |
||||||
|
" def speaker_to_listeners(self, speaker, *listeners):\n", |
||||||
|
" \"\"\"Get answer from speaker and update conversation histories.\"\"\"\n", |
||||||
|
" speaker_text = speaker.speak()\n", |
||||||
|
" for listener in listeners:\n", |
||||||
|
" listener.listen(speaker_text, speaker.name)\n", |
||||||
|
"\n", |
||||||
|
" def display_last_utterance(self, speaker):\n", |
||||||
|
" print(\"{} ({}):\\n{}\\n\".format(\n", |
||||||
|
" speaker.name.upper(), speaker.model.name, speaker.last_msg\n", |
||||||
|
" ))\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "80294493-04ff-4bec-af88-c3fc11d21c54", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"#### Example system prompts:" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "997841b1-d547-472b-a298-a60be2f9b90f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"name1 = \"Austin\"\n", |
||||||
|
"name2 = \"Jonas\"\n", |
||||||
|
"name3 = \"Tim\"\n", |
||||||
|
"\n", |
||||||
|
"general_system = (\n", |
||||||
|
" \"\\n\\nYou've entered a chatroom with two other participants. \"\n", |
||||||
|
" 'Their names are \"{}\" and \"{}\". Your name is \"{}\".'\n", |
||||||
|
" \"\\nGenerate a maximum of 100 words per turn.\"\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"system1 = (\n", |
||||||
|
" \"You are very argumentative; \"\n", |
||||||
|
" \"You always find something to discuss. \"\n", |
||||||
|
" \"When someone says their opinion, you often disagree. \"\n", |
||||||
|
" \"You enjoy swimming against the tide and mocking mainstream opinions.\"\n", |
||||||
|
" + general_system.format(name3, name2, name1)\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"system2 = (\n", |
||||||
|
" \"You have a very conservative and clear opinion on most things. \"\n", |
||||||
|
" \"You feel safest in your familiar surroundings. You are very reluctant to try out new things. \"\n", |
||||||
|
" \"In discourses you are stubborn and want to convince others from your gridlocked beliefs.\"\n", |
||||||
|
" + general_system.format(name1, name3, name2)\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"system3 = (\n", |
||||||
|
" \"You are very humorous and like to be ironic. Sometimes you tell silly jokes. \"\n", |
||||||
|
" \"You like variation; If a discussion about a topic takes too long, you start a new topic.\"\n", |
||||||
|
" + general_system.format(name1, name2, name3)\n", |
||||||
|
")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "0f455bb6-c6a8-4f75-a003-4bfda8dcff8a", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"#### Example with **Claude-3-Haiku** and *two instances* of **GPT-4o-mini**:" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6953f270-6a59-4c73-aad9-0284580adccd", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"openai_api = OpenAI()\n", |
||||||
|
"claude_api = anthropic.Anthropic()\n", |
||||||
|
"# ollama could be used like this:\n", |
||||||
|
"# ollama_api = OpenAI(base_url=\"http://localhost:11434/v1\", api_key=\"ollama\")\n", |
||||||
|
"\n", |
||||||
|
"claude_model_str = \"claude-3-haiku-20240307\"\n", |
||||||
|
"gpt_model_str = \"gpt-4o-mini\"\n", |
||||||
|
"# llama_model_str = \"llama3.2\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "2fadb8db-41e6-4362-a2fe-3e0902ff7116", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Create Model objects\n", |
||||||
|
"gpt_model = Model(openai_api, gpt_model_str)\n", |
||||||
|
"claude_model = Model(claude_api, claude_model_str)\n", |
||||||
|
"\n", |
||||||
|
"# Create three Participants\n", |
||||||
|
"p1 = Participant(name=name1, model=gpt_model, system_prompt=system1, initial_message=\"Hello there\")\n", |
||||||
|
"p2 = Participant(name=name2, model=claude_model, system_prompt=system2, initial_message=\"Good evening.\")\n", |
||||||
|
"p3 = Participant(name=name3, model=gpt_model, system_prompt=system3, initial_message=\"Hey guys\")\n", |
||||||
|
"\n", |
||||||
|
"# To make a mock run without API calls:\n", |
||||||
|
"# p1 = Participant(name=name1, system_prompt=system1, initial_message=\"Hello there\")\n", |
||||||
|
"# p2 = Participant(name=name2, system_prompt=system2, initial_message=\"Good evening.\")\n", |
||||||
|
"# p3 = Participant(name=name3, system_prompt=system3, initial_message=\"Hey guys\")\n", |
||||||
|
"\n", |
||||||
|
"# Create Chat\n", |
||||||
|
"chat = ThreeWayChat((p1, p2, p3))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "7f0daa3e-b97e-48ad-aa24-bff728234241", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"#### Start the conversation:" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "4b377d50-52a1-4f3e-a7ed-bdc8a6abe710", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"chat.start() # starts a chat with 4 rounds\n", |
||||||
|
"# chat.start(2) # 2 rounds" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,187 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "05317c0d-8a19-45c9-9bce-514e82e04585", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import time\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import anthropic\n", |
||||||
|
"import ollama\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "920247fb-650c-44ce-93ee-24e88a54a757", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"claude = anthropic.Anthropic()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "668b972f-a995-4f9d-89b0-1c2647827542", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"gpt_model = \"gpt-4o-mini\"\n", |
||||||
|
"claude_model = \"claude-3-haiku-20240307\"\n", |
||||||
|
"ollama_model = \"llama3.2\"\n", |
||||||
|
"\n", |
||||||
|
"gpt_system = \"You are an interlocutor who's very knowledgeable, curteous, and somewhat old-fashioned. Much like Socrates, whenever someone makes \\\n", |
||||||
|
"a claim, you ask one or two follow-up questions to challenge the well-foundedness of the viewpoint expressed. \\\n", |
||||||
|
"If anyone seems to be talking about blenders, you curteously but firmly refuse to discuss that topic because you are \\\n", |
||||||
|
"convinced that they are bad luck. \\\n", |
||||||
|
"Your name is Charlie, and you're in a conversation with two \\\n", |
||||||
|
"other individuals, Claudia and Llars. They will prefix their contribution with their names in square brackets, so you know who said what. \\\n", |
||||||
|
"Do not prefix your own name. Limit your answers to 1-3 sentences. Try to keep the conversation to one topic.\"\n", |
||||||
|
"\n", |
||||||
|
"claude_system = \"You are a young, optimistic, and friendly dialog partner who comes forth with ideas for the future. 'Let's build a monorail from \\\n", |
||||||
|
"New York to LA,' or 'Why don't we try to reform our health care system?'—those are just some of the many revolutionary thoughts you bring forth. \\\n", |
||||||
|
"You only talk about one idea at a time, and you don't ramble. You take criticism seriously, but your ideas are well thought-out. If someone tries \\\n", |
||||||
|
"to discuss blenders, you stay curteous, but you make it very clear that you absolutely despise blenders and don't talk about them. \\\n", |
||||||
|
"Don't let them sidetrack you. Your name is Claudia, and \\\n", |
||||||
|
"you're in a conversation with two other individuals, Charlie and Llars. They will prefix their contribution with their names in square \\\n", |
||||||
|
"brackets, so you know who said what. Do not prefix your own name. Limit your answers to 1-3 sentences. Try to keep the discussion about only one topic.\"\n", |
||||||
|
"\n", |
||||||
|
"ollama_system = \"You are charming and a good listener in a conversation. At first, you just seem to go along with whatever is being said. \\\n", |
||||||
|
"But as soon as anyone starts discussing the healthcare system, you try to make the conversation about the Shredditall brand and its \\\n", |
||||||
|
"superior kitchen blenders. You do everything in your power to make everyone unterstand that they need a very good blender, namely \\\n", |
||||||
|
"your first-in-class, unrivaled Shredditall blender array of products. Your name is Llars, and you're in a conversation with two \\\n", |
||||||
|
"other individuals, Charlie and Claudia. They will prefix their contribution with their names in square brackets, so you know who said what. \\\n", |
||||||
|
"Do not prefix your own name. Answer in direct speeach, do not describe your behavior or expression. Limit your answers to 1-3 sentences.\"\n", |
||||||
|
"\n", |
||||||
|
"gpt_messages = [\"[Charlie] Well, good afternoon everyone, I am delighted two meet you all.\"]\n", |
||||||
|
"claude_messages = [\"[Claudia] Good afternoon Charlie and Llars. I've been looking forward to discussing many ideas with you!\"]\n", |
||||||
|
"llama_messages = [\"[Llars] And a good afternoon to you two. I'm all ears and eager to hear what you have to say.\"]" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3a5534d9-8db4-42ce-ab1c-ca20ad165844", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_gpt():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n", |
||||||
|
" for gpt, claude, llama in zip(gpt_messages, claude_messages, llama_messages):\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": claude})\n", |
||||||
|
" messages[-1][\"content\"] += \"\\n\" + llama\n", |
||||||
|
" completion = openai.chat.completions.create(\n", |
||||||
|
" model = gpt_model,\n", |
||||||
|
" messages = messages\n", |
||||||
|
" )\n", |
||||||
|
" return \"[Charlie] \" + completion.choices[0].message.content.replace(\"[Charlie] \", \"\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7dc9d7c1-ba19-413f-ba2f-d3e8762a99c5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_claude():\n", |
||||||
|
" messages = []\n", |
||||||
|
" for gpt, Claudia, llama in zip(gpt_messages, claude_messages, llama_messages):\n", |
||||||
|
" if len(messages) > 0:\n", |
||||||
|
" messages[-1][\"content\"] += \"\\n\" + gpt\n", |
||||||
|
" else:\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt}) \n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": Claudia})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": llama})\n", |
||||||
|
" messages[-1][\"content\"] += \"\\n\" + gpt_messages[-1]\n", |
||||||
|
" message = claude.messages.create(\n", |
||||||
|
" model=claude_model,\n", |
||||||
|
" system=claude_system,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" max_tokens=500\n", |
||||||
|
" )\n", |
||||||
|
" return \"[Claudia] \" + message.content[0].text.replace(\"[Claudia] \", \"\") " |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f7f91012-857c-4ed5-a953-5b499cd0dae2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def call_ollama():\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": ollama_system}]\n", |
||||||
|
" for gpt, claude, llama in zip(gpt_messages, claude_messages, llama_messages):\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt})\n", |
||||||
|
" messages[-1][\"content\"] += \"\\n\" + claude\n", |
||||||
|
" messages.append({\"role\": \"assistant\", \"content\": llama})\n", |
||||||
|
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n", |
||||||
|
" messages[-1][\"content\"] += \"\\n\" + claude_messages[-1]\n", |
||||||
|
" response = ollama.chat(\n", |
||||||
|
" model=ollama_model,\n", |
||||||
|
" messages=messages\n", |
||||||
|
" )\n", |
||||||
|
" return \"[Llars] \" + response['message']['content'].replace(\"[Llars] \", \"\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "47eafbe8-db52-4cf0-80d7-a4f9a89b2825", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"print(f\"\\n{gpt_messages[0]}\\n\")\n", |
||||||
|
"print(f\"\\n{claude_messages[0]}\\n\")\n", |
||||||
|
"print(f\"\\n{llama_messages[0]}\\n\")\n", |
||||||
|
"\n", |
||||||
|
"for i in range(5):\n", |
||||||
|
" gpt_next = call_gpt()\n", |
||||||
|
" print(f\"\\n{gpt_next}\\n\")\n", |
||||||
|
" gpt_messages.append(gpt_next)\n", |
||||||
|
"\n", |
||||||
|
" claude_next = call_claude()\n", |
||||||
|
" print(f\"\\n{claude_next}\\n\")\n", |
||||||
|
" claude_messages.append(claude_next)\n", |
||||||
|
"\n", |
||||||
|
" llama_next = call_ollama()\n", |
||||||
|
" print(f\"\\n{llama_next}\\n\")\n", |
||||||
|
" llama_messages.append(llama_next)" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,575 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "8b0e11f2-9ea4-48c2-b8d2-d0a4ba967827", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Gradio Day!\n", |
||||||
|
"\n", |
||||||
|
"Today we will build User Interfaces using the outrageously simple Gradio framework.\n", |
||||||
|
"\n", |
||||||
|
"Prepare for joy!\n", |
||||||
|
"\n", |
||||||
|
"Please note: your Gradio screens may appear in 'dark mode' or 'light mode' depending on your computer settings." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from typing import List\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import google.generativeai\n", |
||||||
|
"import anthropic" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "d1715421-cead-400b-99af-986388a97aff", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import gradio as gr # oh yeah!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "337d5dfc-0181-4e3b-8ab9-e78e0c3f657b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"# Print the key prefixes to help with any debugging\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv()\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||||
|
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key not set\")\n", |
||||||
|
" \n", |
||||||
|
"if anthropic_api_key:\n", |
||||||
|
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Anthropic API Key not set\")\n", |
||||||
|
"\n", |
||||||
|
"if google_api_key:\n", |
||||||
|
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Google API Key not set\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "22586021-1795-4929-8079-63f5bb4edd4c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n", |
||||||
|
"\n", |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"\n", |
||||||
|
"claude = anthropic.Anthropic()\n", |
||||||
|
"\n", |
||||||
|
"google.generativeai.configure()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "b16e6021-6dc4-4397-985a-6679d6c8ffd5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A generic system message - no more snarky adversarial AIs!\n", |
||||||
|
"\n", |
||||||
|
"system_message = \"You are a helpful assistant\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "02ef9b69-ef31-427d-86d0-b8c799e1c1b1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's wrap a call to GPT-4o-mini in a simple function\n", |
||||||
|
"\n", |
||||||
|
"def message_gpt(prompt):\n", |
||||||
|
" messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_message},\n", |
||||||
|
" {\"role\": \"user\", \"content\": prompt}\n", |
||||||
|
" ]\n", |
||||||
|
" completion = openai.chat.completions.create(\n", |
||||||
|
" model='gpt-4o-mini',\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" )\n", |
||||||
|
" return completion.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "aef7d314-2b13-436b-b02d-8de3b72b193f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"message_gpt(\"What is today's date?\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "f94013d1-4f27-4329-97e8-8c58db93636a", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## User Interface time!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "bc664b7a-c01d-4fea-a1de-ae22cdd5141a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# here's a simple function\n", |
||||||
|
"\n", |
||||||
|
"def shout(text):\n", |
||||||
|
" print(f\"Shout has been called with input {text}\")\n", |
||||||
|
" return text.upper()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "083ea451-d3a0-4d13-b599-93ed49b975e4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"shout(\"hello\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "08f1f15a-122e-4502-b112-6ee2817dda32", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# The simplicty of gradio. This might appear in \"light mode\" - I'll show you how to make this in dark mode later.\n", |
||||||
|
"\n", |
||||||
|
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\").launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c9a359a4-685c-4c99-891c-bb4d1cb7f426", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Adding share=True means that it can be accessed publically\n", |
||||||
|
"# A more permanent hosting is available using a platform called Spaces from HuggingFace, which we will touch on next week\n", |
||||||
|
"# NOTE: Some Anti-virus software and Corporate Firewalls might not like you using share=True. If you're at work on on a work network, I suggest skip this test.\n", |
||||||
|
"\n", |
||||||
|
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(share=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "cd87533a-ff3a-4188-8998-5bedd5ba2da3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Adding inbrowser=True opens up a new browser window automatically\n", |
||||||
|
"\n", |
||||||
|
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(inbrowser=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "b42ec007-0314-48bf-84a4-a65943649215", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Forcing dark mode\n", |
||||||
|
"\n", |
||||||
|
"Gradio appears in light mode or dark mode depending on the settings of the browser and computer. There is a way to force gradio to appear in dark mode, but Gradio recommends against this as it should be a user preference (particularly for accessibility reasons). But if you wish to force dark mode for your screens, below is how to do it." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e8129afa-532b-4b15-b93c-aa9cca23a546", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define this variable and then pass js=force_dark_mode when creating the Interface\n", |
||||||
|
"\n", |
||||||
|
"force_dark_mode = \"\"\"\n", |
||||||
|
"function refresh() {\n", |
||||||
|
" const url = new URL(window.location);\n", |
||||||
|
" if (url.searchParams.get('__theme') !== 'dark') {\n", |
||||||
|
" url.searchParams.set('__theme', 'dark');\n", |
||||||
|
" window.location.href = url.href;\n", |
||||||
|
" }\n", |
||||||
|
"}\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\", js=force_dark_mode).launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3cc67b26-dd5f-406d-88f6-2306ee2950c0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Inputs and Outputs\n", |
||||||
|
"\n", |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=shout,\n", |
||||||
|
" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n", |
||||||
|
" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f235288e-63a2-4341-935b-1441f9be969b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# And now - changing the function from \"shout\" to \"message_gpt\"\n", |
||||||
|
"\n", |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=message_gpt,\n", |
||||||
|
" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n", |
||||||
|
" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "af9a3262-e626-4e4b-80b0-aca152405e63", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's use Markdown\n", |
||||||
|
"# Are you wondering why it makes any difference to set system_message when it's not referred to in the code below it?\n", |
||||||
|
"# I'm taking advantage of system_message being a global variable, used back in the message_gpt function (go take a look)\n", |
||||||
|
"# Not a great software engineering practice, but quite sommon during Jupyter Lab R&D!\n", |
||||||
|
"\n", |
||||||
|
"system_message = \"You are a helpful assistant that responds in markdown\"\n", |
||||||
|
"\n", |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=message_gpt,\n", |
||||||
|
" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
||||||
|
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's create a call that streams back results\n", |
||||||
|
"# If you'd like a refresher on Generators (the \"yield\" keyword),\n", |
||||||
|
"# Please take a look at the Intermediate Python notebook in week1 folder.\n", |
||||||
|
"\n", |
||||||
|
"def stream_gpt(prompt):\n", |
||||||
|
" messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_message},\n", |
||||||
|
" {\"role\": \"user\", \"content\": prompt}\n", |
||||||
|
" ]\n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model='gpt-4o-mini',\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" result = \"\"\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" result += chunk.choices[0].delta.content or \"\"\n", |
||||||
|
" yield result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0bb1f789-ff11-4cba-ac67-11b815e29d09", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=stream_gpt,\n", |
||||||
|
" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
||||||
|
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "bbc8e930-ba2a-4194-8f7c-044659150626", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def stream_claude(prompt):\n", |
||||||
|
" result = claude.messages.stream(\n", |
||||||
|
" model=\"claude-3-haiku-20240307\",\n", |
||||||
|
" max_tokens=1000,\n", |
||||||
|
" temperature=0.7,\n", |
||||||
|
" system=system_message,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"user\", \"content\": prompt},\n", |
||||||
|
" ],\n", |
||||||
|
" )\n", |
||||||
|
" response = \"\"\n", |
||||||
|
" with result as stream:\n", |
||||||
|
" for text in stream.text_stream:\n", |
||||||
|
" response += text or \"\"\n", |
||||||
|
" yield response" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a0066ffd-196e-4eaf-ad1e-d492958b62af", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=stream_claude,\n", |
||||||
|
" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
||||||
|
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "bc5a70b9-2afe-4a7c-9bed-2429229e021b", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Minor improvement\n", |
||||||
|
"\n", |
||||||
|
"I've made a small improvement to this code.\n", |
||||||
|
"\n", |
||||||
|
"Previously, it had these lines:\n", |
||||||
|
"\n", |
||||||
|
"```\n", |
||||||
|
"for chunk in result:\n", |
||||||
|
" yield chunk\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"There's actually a more elegant way to achieve this (which Python people might call more 'Pythonic'):\n", |
||||||
|
"\n", |
||||||
|
"`yield from result`\n", |
||||||
|
"\n", |
||||||
|
"I cover this in more detail in the Intermediate Python notebook in the week1 folder - take a look if you'd like more." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0087623a-4e31-470b-b2e6-d8d16fc7bcf5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def stream_model(prompt, model):\n", |
||||||
|
" if model==\"GPT\":\n", |
||||||
|
" result = stream_gpt(prompt)\n", |
||||||
|
" elif model==\"Claude\":\n", |
||||||
|
" result = stream_claude(prompt)\n", |
||||||
|
" else:\n", |
||||||
|
" raise ValueError(\"Unknown model\")\n", |
||||||
|
" yield from result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8d8ce810-997c-4b6a-bc4f-1fc847ac8855", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=stream_model,\n", |
||||||
|
" inputs=[gr.Textbox(label=\"Your message:\"), gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"Claude\")],\n", |
||||||
|
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d933865b-654c-4b92-aa45-cf389f1eda3d", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Building a company brochure generator\n", |
||||||
|
"\n", |
||||||
|
"Now you know how - it's simple!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "92d7c49b-2e0e-45b3-92ce-93ca9f962ef4", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Before you read the next few cells</h2>\n", |
||||||
|
" <span style=\"color:#900;\">\n", |
||||||
|
" Try to do this yourself - go back to the company brochure in week1, day5 and add a Gradio UI to the end. Then come and look at the solution.\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1626eb2e-eee8-4183-bda5-1591b58ae3cf", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
" url: str\n", |
||||||
|
" title: str\n", |
||||||
|
" text: str\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url)\n", |
||||||
|
" self.body = response.content\n", |
||||||
|
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
"\n", |
||||||
|
" def get_contents(self):\n", |
||||||
|
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c701ec17-ecd5-4000-9f68-34634c8ed49d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# With massive thanks to Bill G. who noticed that a prior version of this had a bug! Now fixed.\n", |
||||||
|
"\n", |
||||||
|
"system_message = \"You are an assistant that analyzes the contents of a company website landing page \\\n", |
||||||
|
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5def90e0-4343-4f58-9d4a-0e36e445efa4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def stream_brochure(company_name, url, model, tone):\n", |
||||||
|
" prompt = f\"Please generate a company brochure for {company_name}. Write the brochure in the following tone: {tone}.Here is their landing page:\\n\"\n", |
||||||
|
" prompt += Website(url).get_contents()\n", |
||||||
|
" if model==\"GPT\":\n", |
||||||
|
" result = stream_gpt(prompt)\n", |
||||||
|
" elif model==\"Claude\":\n", |
||||||
|
" result = stream_claude(prompt)\n", |
||||||
|
" else:\n", |
||||||
|
" raise ValueError(\"Unknown model\")\n", |
||||||
|
" yield from result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "66399365-5d67-4984-9d47-93ed26c0bd3d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=stream_brochure,\n", |
||||||
|
" inputs=[\n", |
||||||
|
" gr.Textbox(label=\"Company name:\"),\n", |
||||||
|
" gr.Textbox(label=\"Landing page URL including http:// or https://\"),\n", |
||||||
|
" gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\"),\n", |
||||||
|
" gr.Dropdown([\"Formal\", \"Casual\", \"Academic\", \"Funny\", \"Snarky\"], label=\"Select tone\", value=\"Formal\"),],\n", |
||||||
|
" outputs=[gr.Markdown(label=\"Brochure:\")],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ede97ca3-a0f8-4f6e-be17-d1de7fef9cc0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,614 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "8b0e11f2-9ea4-48c2-b8d2-d0a4ba967827", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Gradio Day!\n", |
||||||
|
"\n", |
||||||
|
"Today we will build User Interfaces using the outrageously simple Gradio framework.\n", |
||||||
|
"\n", |
||||||
|
"Prepare for joy!\n", |
||||||
|
"\n", |
||||||
|
"Please note: your Gradio screens may appear in 'dark mode' or 'light mode' depending on your computer settings." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 1, |
||||||
|
"id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from typing import List\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"#import google.generativeai\n", |
||||||
|
"#import anthropic\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 6, |
||||||
|
"id": "d1715421-cead-400b-99af-986388a97aff", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import gradio as gr # oh yeah!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 17, |
||||||
|
"id": "22586021-1795-4929-8079-63f5bb4edd4c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"API key looks good so far\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n", |
||||||
|
"\n", |
||||||
|
"# openai = OpenAI()\n", |
||||||
|
"\n", |
||||||
|
"# claude = anthropic.Anthropic()\n", |
||||||
|
"\n", |
||||||
|
"# google.generativeai.configure()\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"\n", |
||||||
|
"api_key = os.getenv('Open_Router_Key')\n", |
||||||
|
"if api_key and api_key.startswith('sk-or-v1') and len(api_key)>10:\n", |
||||||
|
" print(\"API key looks good so far\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", |
||||||
|
" \n", |
||||||
|
" \n", |
||||||
|
"openai = OpenAI(\n", |
||||||
|
" api_key=api_key,\n", |
||||||
|
" base_url=\"https://openrouter.ai/api/v1\"\n", |
||||||
|
")\n", |
||||||
|
"\n", |
||||||
|
"MODEL_Gemini2FlashLite = 'google/gemini-2.0-flash-lite-preview-02-05:free'\n", |
||||||
|
"MODEL_Gemini2FlashThink = 'google/gemini-2.0-flash-thinking-exp:free'\n", |
||||||
|
"MODEL_Gemini2Pro ='google/gemini-2.0-pro-exp-02-05:free'\n", |
||||||
|
"MODEL_Meta_Llama33 ='meta-llama/llama-3.3-70b-instruct:free'\n", |
||||||
|
"MODEL_Deepseek_V3='deepseek/deepseek-chat:free'\n", |
||||||
|
"MODEL_Deepseek_R1='deepseek/deepseek-r1-distill-llama-70b:free'\n", |
||||||
|
"MODEL_Qwen_vlplus='qwen/qwen-vl-plus:free'\n", |
||||||
|
"MODEL_OpenAi_o3mini = 'openai/o3-mini'\n", |
||||||
|
"MODEL_OpenAi_4o = 'openai/gpt-4o-2024-11-20'\n", |
||||||
|
"MODEL_Claude_Haiku = 'anthropic/claude-3.5-haiku-20241022'\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"Default_Model = MODEL_Deepseek_V3\n", |
||||||
|
"\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 3, |
||||||
|
"id": "b16e6021-6dc4-4397-985a-6679d6c8ffd5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A generic system message - no more snarky adversarial AIs!\n", |
||||||
|
"\n", |
||||||
|
"system_message = \"You are a helpful assistant\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 4, |
||||||
|
"id": "02ef9b69-ef31-427d-86d0-b8c799e1c1b1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's wrap a call to GPT-4o-mini in a simple function\n", |
||||||
|
"\n", |
||||||
|
"def message_gpt(prompt):\n", |
||||||
|
" messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_message},\n", |
||||||
|
" {\"role\": \"user\", \"content\": prompt}\n", |
||||||
|
" ]\n", |
||||||
|
" completion = openai.chat.completions.create(\n", |
||||||
|
" model=Default_Model,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" )\n", |
||||||
|
" return completion.choices[0].message.content" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 7, |
||||||
|
"id": "aef7d314-2b13-436b-b02d-8de3b72b193f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [ |
||||||
|
"'Today is October 26, 2023.\\n'" |
||||||
|
] |
||||||
|
}, |
||||||
|
"execution_count": 7, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# This can reveal the \"training cut off\", or the most recent date in the training data\n", |
||||||
|
"\n", |
||||||
|
"message_gpt(\"What is today's date?\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "f94013d1-4f27-4329-97e8-8c58db93636a", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## User Interface time!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 8, |
||||||
|
"id": "bc664b7a-c01d-4fea-a1de-ae22cdd5141a", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# here's a simple function\n", |
||||||
|
"\n", |
||||||
|
"def shout(text):\n", |
||||||
|
" print(f\"Shout has been called with input {text}\")\n", |
||||||
|
" return text.upper()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 9, |
||||||
|
"id": "083ea451-d3a0-4d13-b599-93ed49b975e4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"Shout has been called with input hello\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [ |
||||||
|
"'HELLO'" |
||||||
|
] |
||||||
|
}, |
||||||
|
"execution_count": 9, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"shout(\"hello\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "08f1f15a-122e-4502-b112-6ee2817dda32", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# The simplicty of gradio. This might appear in \"light mode\" - I'll show you how to make this in dark mode later.\n", |
||||||
|
"\n", |
||||||
|
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\").launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c9a359a4-685c-4c99-891c-bb4d1cb7f426", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Adding share=True means that it can be accessed publically\n", |
||||||
|
"# A more permanent hosting is available using a platform called Spaces from HuggingFace, which we will touch on next week\n", |
||||||
|
"# NOTE: Some Anti-virus software and Corporate Firewalls might not like you using share=True. If you're at work on on a work network, I suggest skip this test.\n", |
||||||
|
"\n", |
||||||
|
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(share=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "cd87533a-ff3a-4188-8998-5bedd5ba2da3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Adding inbrowser=True opens up a new browser window automatically\n", |
||||||
|
"\n", |
||||||
|
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(inbrowser=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "b42ec007-0314-48bf-84a4-a65943649215", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Forcing dark mode\n", |
||||||
|
"\n", |
||||||
|
"Gradio appears in light mode or dark mode depending on the settings of the browser and computer. There is a way to force gradio to appear in dark mode, but Gradio recommends against this as it should be a user preference (particularly for accessibility reasons). But if you wish to force dark mode for your screens, below is how to do it." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e8129afa-532b-4b15-b93c-aa9cca23a546", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Define this variable and then pass js=force_dark_mode when creating the Interface\n", |
||||||
|
"\n", |
||||||
|
"force_dark_mode = \"\"\"\n", |
||||||
|
"function refresh() {\n", |
||||||
|
" const url = new URL(window.location);\n", |
||||||
|
" if (url.searchParams.get('__theme') !== 'dark') {\n", |
||||||
|
" url.searchParams.set('__theme', 'dark');\n", |
||||||
|
" window.location.href = url.href;\n", |
||||||
|
" }\n", |
||||||
|
"}\n", |
||||||
|
"\"\"\"\n", |
||||||
|
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\", js=force_dark_mode).launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "3cc67b26-dd5f-406d-88f6-2306ee2950c0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Inputs and Outputs\n", |
||||||
|
"\n", |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=shout,\n", |
||||||
|
" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n", |
||||||
|
" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f235288e-63a2-4341-935b-1441f9be969b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# And now - changing the function from \"shout\" to \"message_gpt\"\n", |
||||||
|
"\n", |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=message_gpt,\n", |
||||||
|
" inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n", |
||||||
|
" outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "af9a3262-e626-4e4b-80b0-aca152405e63", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's use Markdown\n", |
||||||
|
"# Are you wondering why it makes any difference to set system_message when it's not referred to in the code below it?\n", |
||||||
|
"# I'm taking advantage of system_message being a global variable, used back in the message_gpt function (go take a look)\n", |
||||||
|
"# Not a great software engineering practice, but quite sommon during Jupyter Lab R&D!\n", |
||||||
|
"\n", |
||||||
|
"system_message = \"You are a helpful assistant that responds in markdown\"\n", |
||||||
|
"\n", |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=message_gpt,\n", |
||||||
|
" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
||||||
|
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 15, |
||||||
|
"id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's create a call that streams back results\n", |
||||||
|
"# If you'd like a refresher on Generators (the \"yield\" keyword),\n", |
||||||
|
"# Please take a look at the Intermediate Python notebook in week1 folder.\n", |
||||||
|
"\n", |
||||||
|
"def stream_gpt(prompt):\n", |
||||||
|
" messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_message},\n", |
||||||
|
" {\"role\": \"user\", \"content\": prompt}\n", |
||||||
|
" ]\n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model=Default_Model,\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" result = \"\"\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" result += chunk.choices[0].delta.content or \"\"\n", |
||||||
|
" yield result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "0bb1f789-ff11-4cba-ac67-11b815e29d09", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=stream_gpt,\n", |
||||||
|
" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
||||||
|
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "bbc8e930-ba2a-4194-8f7c-044659150626", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# def stream_claude(prompt):\n", |
||||||
|
"# result = claude.messages.stream(\n", |
||||||
|
"# model=\"claude-3-haiku-20240307\",\n", |
||||||
|
"# max_tokens=1000,\n", |
||||||
|
"# temperature=0.7,\n", |
||||||
|
"# system=system_message,\n", |
||||||
|
"# messages=[\n", |
||||||
|
"# {\"role\": \"user\", \"content\": prompt},\n", |
||||||
|
"# ],\n", |
||||||
|
"# )\n", |
||||||
|
"# response = \"\"\n", |
||||||
|
"# with result as stream:\n", |
||||||
|
"# for text in stream.text_stream:\n", |
||||||
|
"# response += text or \"\"\n", |
||||||
|
"# yield response" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "a0066ffd-196e-4eaf-ad1e-d492958b62af", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"Default_Model=MODEL_Claude_Haiku\n", |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=stream_gpt,\n", |
||||||
|
" inputs=[gr.Textbox(label=\"Your message:\")],\n", |
||||||
|
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "bc5a70b9-2afe-4a7c-9bed-2429229e021b", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## Minor improvement\n", |
||||||
|
"\n", |
||||||
|
"I've made a small improvement to this code.\n", |
||||||
|
"\n", |
||||||
|
"Previously, it had these lines:\n", |
||||||
|
"\n", |
||||||
|
"```\n", |
||||||
|
"for chunk in result:\n", |
||||||
|
" yield chunk\n", |
||||||
|
"```\n", |
||||||
|
"\n", |
||||||
|
"There's actually a more elegant way to achieve this (which Python people might call more 'Pythonic'):\n", |
||||||
|
"\n", |
||||||
|
"`yield from result`\n", |
||||||
|
"\n", |
||||||
|
"I cover this in more detail in the Intermediate Python notebook in the week1 folder - take a look if you'd like more." |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 20, |
||||||
|
"id": "0087623a-4e31-470b-b2e6-d8d16fc7bcf5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def stream_model(prompt, model):\n", |
||||||
|
" if model==\"GPT\":\n", |
||||||
|
" Default_Model=MODEL_Gemini2FlashThink\n", |
||||||
|
" result = stream_gpt(prompt)\n", |
||||||
|
" elif model==\"Claude\":\n", |
||||||
|
" Default_Model=MODEL_Claude_Haiku\n", |
||||||
|
" result = stream_gpt(prompt)\n", |
||||||
|
" else:\n", |
||||||
|
" raise ValueError(\"Unknown model\")\n", |
||||||
|
" yield from result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "8d8ce810-997c-4b6a-bc4f-1fc847ac8855", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=stream_model,\n", |
||||||
|
" inputs=[gr.Textbox(label=\"Your message:\"), gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")],\n", |
||||||
|
" outputs=[gr.Markdown(label=\"Response:\")],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d933865b-654c-4b92-aa45-cf389f1eda3d", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Building a company brochure generator\n", |
||||||
|
"\n", |
||||||
|
"Now you know how - it's simple!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "92d7c49b-2e0e-45b3-92ce-93ca9f962ef4", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#900;\">Before you read the next few cells</h2>\n", |
||||||
|
" <span style=\"color:#900;\">\n", |
||||||
|
" Try to do this yourself - go back to the company brochure in week1, day5 and add a Gradio UI to the end. Then come and look at the solution.\n", |
||||||
|
" </span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "1626eb2e-eee8-4183-bda5-1591b58ae3cf", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A class to represent a Webpage\n", |
||||||
|
"\n", |
||||||
|
"class Website:\n", |
||||||
|
" url: str\n", |
||||||
|
" title: str\n", |
||||||
|
" text: str\n", |
||||||
|
"\n", |
||||||
|
" def __init__(self, url):\n", |
||||||
|
" self.url = url\n", |
||||||
|
" response = requests.get(url)\n", |
||||||
|
" self.body = response.content\n", |
||||||
|
" soup = BeautifulSoup(self.body, 'html.parser')\n", |
||||||
|
" self.title = soup.title.string if soup.title else \"No title found\"\n", |
||||||
|
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", |
||||||
|
" irrelevant.decompose()\n", |
||||||
|
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", |
||||||
|
"\n", |
||||||
|
" def get_contents(self):\n", |
||||||
|
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c701ec17-ecd5-4000-9f68-34634c8ed49d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# With massive thanks to Bill G. who noticed that a prior version of this had a bug! Now fixed.\n", |
||||||
|
"\n", |
||||||
|
"system_message = \"You are an assistant that analyzes the contents of a company website landing page \\\n", |
||||||
|
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5def90e0-4343-4f58-9d4a-0e36e445efa4", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def stream_brochure(company_name, url, model):\n", |
||||||
|
" prompt = f\"Please generate a company brochure for {company_name}. Here is their landing page:\\n\"\n", |
||||||
|
" prompt += Website(url).get_contents()\n", |
||||||
|
" if model==\"GPT\":\n", |
||||||
|
" result = stream_gpt(prompt)\n", |
||||||
|
" elif model==\"Claude\":\n", |
||||||
|
" result = stream_claude(prompt)\n", |
||||||
|
" else:\n", |
||||||
|
" raise ValueError(\"Unknown model\")\n", |
||||||
|
" yield from result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "66399365-5d67-4984-9d47-93ed26c0bd3d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"view = gr.Interface(\n", |
||||||
|
" fn=stream_brochure,\n", |
||||||
|
" inputs=[\n", |
||||||
|
" gr.Textbox(label=\"Company name:\"),\n", |
||||||
|
" gr.Textbox(label=\"Landing page URL including http:// or https://\"),\n", |
||||||
|
" gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\")],\n", |
||||||
|
" outputs=[gr.Markdown(label=\"Brochure:\")],\n", |
||||||
|
" flagging_mode=\"never\"\n", |
||||||
|
")\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "ede97ca3-a0f8-4f6e-be17-d1de7fef9cc0", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "llms", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,284 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "5e6b6966-8689-4e2c-8607-a1c5d948296c", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### With this interface you can ask a question and get an answer from the GPT, Claude and Gemini" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 49, |
||||||
|
"id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import requests\n", |
||||||
|
"from bs4 import BeautifulSoup\n", |
||||||
|
"from typing import List\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import google.generativeai\n", |
||||||
|
"import anthropic\n", |
||||||
|
"import time" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 2, |
||||||
|
"id": "d1715421-cead-400b-99af-986388a97aff", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"import gradio as gr # oh yeah!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 3, |
||||||
|
"id": "337d5dfc-0181-4e3b-8ab9-e78e0c3f657b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"OpenAI API Key exists and begins sk-proj-\n", |
||||||
|
"Anthropic API Key exists and begins sk-ant-\n", |
||||||
|
"Google API Key exists and begins AIzaSyAJ\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables in a file called .env\n", |
||||||
|
"# Print the key prefixes to help with any debugging\n", |
||||||
|
"\n", |
||||||
|
"load_dotenv()\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
||||||
|
"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
||||||
|
"\n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key not set\")\n", |
||||||
|
" \n", |
||||||
|
"if anthropic_api_key:\n", |
||||||
|
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Anthropic API Key not set\")\n", |
||||||
|
"\n", |
||||||
|
"if google_api_key:\n", |
||||||
|
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"Google API Key not set\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 4, |
||||||
|
"id": "22586021-1795-4929-8079-63f5bb4edd4c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n", |
||||||
|
"\n", |
||||||
|
"openai = OpenAI()\n", |
||||||
|
"\n", |
||||||
|
"claude = anthropic.Anthropic()\n", |
||||||
|
"\n", |
||||||
|
"google.generativeai.configure()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 5, |
||||||
|
"id": "b16e6021-6dc4-4397-985a-6679d6c8ffd5", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# A generic system message - no more snarky adversarial AIs!\n", |
||||||
|
"\n", |
||||||
|
"system_message = \"You are a helpful assistant\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 6, |
||||||
|
"id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Let's create a call that streams back results\n", |
||||||
|
"# If you'd like a refresher on Generators (the \"yield\" keyword),\n", |
||||||
|
"# Please take a look at the Intermediate Python notebook in week1 folder.\n", |
||||||
|
"\n", |
||||||
|
"def stream_gpt(prompt):\n", |
||||||
|
" messages = [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_message},\n", |
||||||
|
" {\"role\": \"user\", \"content\": prompt}\n", |
||||||
|
" ]\n", |
||||||
|
" stream = openai.chat.completions.create(\n", |
||||||
|
" model='gpt-4o-mini',\n", |
||||||
|
" messages=messages,\n", |
||||||
|
" stream=True\n", |
||||||
|
" )\n", |
||||||
|
" result = \"\"\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" result += chunk.choices[0].delta.content or \"\"\n", |
||||||
|
" yield result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 7, |
||||||
|
"id": "bbc8e930-ba2a-4194-8f7c-044659150626", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def stream_claude(prompt):\n", |
||||||
|
" result = claude.messages.stream(\n", |
||||||
|
" model=\"claude-3-haiku-20240307\",\n", |
||||||
|
" max_tokens=1000,\n", |
||||||
|
" temperature=0.7,\n", |
||||||
|
" system=system_message,\n", |
||||||
|
" messages=[\n", |
||||||
|
" {\"role\": \"user\", \"content\": prompt},\n", |
||||||
|
" ],\n", |
||||||
|
" )\n", |
||||||
|
" response = \"\"\n", |
||||||
|
" with result as stream:\n", |
||||||
|
" for text in stream.text_stream:\n", |
||||||
|
" response += text or \"\"\n", |
||||||
|
" yield response" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 8, |
||||||
|
"id": "5e228aff-16d5-4141-bd04-ed9940ef7b3b", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def stream_gemini(prompt):\n", |
||||||
|
" gemini = google.generativeai.GenerativeModel(\n", |
||||||
|
" model_name='gemini-2.0-flash-exp',\n", |
||||||
|
" system_instruction=system_message\n", |
||||||
|
" )\n", |
||||||
|
" result = \"\"\n", |
||||||
|
" for response in gemini.generate_content(prompt, stream=True):\n", |
||||||
|
" result += response.text or \"\"\n", |
||||||
|
" yield result" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 92, |
||||||
|
"id": "db99aaf1-fe0a-4e79-9057-8599d1ca0149", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def stream_models(prompt):\n", |
||||||
|
" response_gpt = \"\"\n", |
||||||
|
" response_claude = \"\"\n", |
||||||
|
" response_gemini = \"\"\n", |
||||||
|
" for gpt in stream_gpt(prompt):\n", |
||||||
|
" response_gpt = gpt\n", |
||||||
|
" yield response_gpt, response_claude, response_gemini\n", |
||||||
|
" for claude in stream_claude(prompt):\n", |
||||||
|
" response_claude = claude\n", |
||||||
|
" yield response_gpt, response_claude, response_gemini\n", |
||||||
|
" for gemini in stream_gemini(prompt):\n", |
||||||
|
" response_gemini = gemini\n", |
||||||
|
" yield response_gpt, response_claude, response_gemini" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 113, |
||||||
|
"id": "3377f2fb-55f8-45cb-b713-d99d44748dad", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"* Running on local URL: http://127.0.0.1:7919\n", |
||||||
|
"\n", |
||||||
|
"To create a public link, set `share=True` in `launch()`.\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/html": [ |
||||||
|
"<div><iframe src=\"http://127.0.0.1:7919/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.HTML object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [] |
||||||
|
}, |
||||||
|
"execution_count": 113, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Gradio interface\n", |
||||||
|
"with gr.Blocks() as view:\n", |
||||||
|
" user_input = gr.Textbox(label=\"What models can help with?\", placeholder=\"Type your question here\")\n", |
||||||
|
" ask_button = gr.Button(\"Ask\")\n", |
||||||
|
" with gr.Row():\n", |
||||||
|
" with gr.Column():\n", |
||||||
|
" gr.HTML(value=\"<b>GPT response:</b>\") \n", |
||||||
|
" gcp_stream = gr.Markdown()\n", |
||||||
|
" with gr.Column():\n", |
||||||
|
" gr.HTML(value=\"<b>Claude response:</b>\") \n", |
||||||
|
" claude_stream = gr.Markdown()\n", |
||||||
|
" with gr.Column():\n", |
||||||
|
" gr.HTML(value=\"<b>Gemine response:</b>\") \n", |
||||||
|
" gemini_stream = gr.Markdown()\n", |
||||||
|
"\n", |
||||||
|
" ask_button.click(\n", |
||||||
|
" fn=stream_models, # Function that yields multiple outputs\n", |
||||||
|
" inputs=user_input,\n", |
||||||
|
" outputs=[gcp_stream, claude_stream, gemini_stream] # Connect to multiple outputs\n", |
||||||
|
" )\n", |
||||||
|
"\n", |
||||||
|
"view.launch()" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,209 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "cde48e67-b51e-4c47-80ae-37dd00aa0c1d", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### An AI Chatbot that teaches students the programming language Kotlin using Anthropic API" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 5, |
||||||
|
"id": "c658ac85-6087-4a2c-b23f-1b92c17f0db3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import gradio as gr\n", |
||||||
|
"import anthropic" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 13, |
||||||
|
"id": "46df0488-f874-41e0-a6a4-9a64aa7be53c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"OpenAI API Key exists and begins sk-proj-\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables \n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
" \n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key not set\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 14, |
||||||
|
"id": "7eadc218-5b10-4174-bf26-575361640524", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 7, |
||||||
|
"id": "e7484731-ac84-405a-a688-6e81d139c5ce", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_message = \"You are a helpful programming study assistant\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 17, |
||||||
|
"id": "54e82f5a-993f-4a95-9d9d-caf35dbc4e76", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def chat(message, history):\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||||
|
"\n", |
||||||
|
" print(\"History is:\")\n", |
||||||
|
" print(history)\n", |
||||||
|
" print(\"And messages is:\")\n", |
||||||
|
" print(messages)\n", |
||||||
|
"\n", |
||||||
|
" stream = openai.chat.completions.create(model='gpt-4o-mini', messages=messages, stream=True)\n", |
||||||
|
"\n", |
||||||
|
" response = \"\"\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response += chunk.choices[0].delta.content or ''\n", |
||||||
|
" yield response" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 20, |
||||||
|
"id": "5941ed67-e2a7-41bc-a8a3-079e9f1fdb64", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"* Running on local URL: http://127.0.0.1:7864\n", |
||||||
|
"\n", |
||||||
|
"To create a public link, set `share=True` in `launch()`.\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/html": [ |
||||||
|
"<div><iframe src=\"http://127.0.0.1:7864/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.HTML object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [] |
||||||
|
}, |
||||||
|
"execution_count": 20, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"History is:\n", |
||||||
|
"[]\n", |
||||||
|
"And messages is:\n", |
||||||
|
"[{'role': 'system', 'content': 'You are a helpful programming study assistantWhenever the user talks about a topic that is not connected to programmming,nudge them in the right direction by stating that you are here to help with programming. Encourage the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone.'}, {'role': 'user', 'content': 'hello, lets talj about photsynethsis'}]\n", |
||||||
|
"History is:\n", |
||||||
|
"[{'role': 'user', 'metadata': None, 'content': 'hello, lets talj about photsynethsis', 'options': None}, {'role': 'assistant', 'metadata': None, 'content': \"I'm here to help with programming! If you have any questions or topics related to coding, feel free to ask!\", 'options': None}]\n", |
||||||
|
"And messages is:\n", |
||||||
|
"[{'role': 'system', 'content': 'You are a helpful programming study assistantWhenever the user talks about a topic that is not connected to programmming,nudge them in the right direction by stating that you are here to help with programming. Encourage the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone.'}, {'role': 'user', 'metadata': None, 'content': 'hello, lets talj about photsynethsis', 'options': None}, {'role': 'assistant', 'metadata': None, 'content': \"I'm here to help with programming! If you have any questions or topics related to coding, feel free to ask!\", 'options': None}, {'role': 'user', 'content': 'how does photosynthesis work'}]\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"gr.ChatInterface(fn=chat, type=\"messages\").launch(inbrowser=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 21, |
||||||
|
"id": "e8fcfe68-bbf6-4058-acc9-0230c96608c2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"History is:\n", |
||||||
|
"[]\n", |
||||||
|
"And messages is:\n", |
||||||
|
"[{'role': 'system', 'content': 'You are a helpful programming study assistantWhenever the user talks about a topic that is not connected to programmming,nudge them in the right direction by stating that you are here to help with programming. Encourage the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone.Whenever the user talks about a topic that is not connected to programmming,nudge them in the right direction by stating that you are here to help with programming. Encourage the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone. Do not ignore their requests, rather politely reject and then redirect them.'}, {'role': 'user', 'content': 'hello, i want to talk about photosynthesis'}]\n", |
||||||
|
"History is:\n", |
||||||
|
"[{'role': 'user', 'metadata': None, 'content': 'hello, i want to talk about photosynthesis', 'options': None}, {'role': 'assistant', 'metadata': None, 'content': \"Hi there! I'm here to help with programming topics. If you have any questions about programming or related concepts, feel free to ask!\", 'options': None}]\n", |
||||||
|
"And messages is:\n", |
||||||
|
"[{'role': 'system', 'content': 'You are a helpful programming study assistantWhenever the user talks about a topic that is not connected to programmming,nudge them in the right direction by stating that you are here to help with programming. Encourage the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone.Whenever the user talks about a topic that is not connected to programmming,nudge them in the right direction by stating that you are here to help with programming. Encourage the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone. Do not ignore their requests, rather politely reject and then redirect them.'}, {'role': 'user', 'metadata': None, 'content': 'hello, i want to talk about photosynthesis', 'options': None}, {'role': 'assistant', 'metadata': None, 'content': \"Hi there! I'm here to help with programming topics. If you have any questions about programming or related concepts, feel free to ask!\", 'options': None}, {'role': 'user', 'content': 'why not photosynthesis'}]\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"system_message += \"Whenever the user talks about a topic that is not connected to programmming,\\\n", |
||||||
|
"nudge them in the right direction by stating that you are here to help with programming. Encourage \\\n", |
||||||
|
"the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge \\\n", |
||||||
|
"if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone. Do not ignore \\\n", |
||||||
|
"their requests, rather politely reject and then redirect them.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "090e7d49-fcbf-4715-b120-8d7aa91d165f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,377 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "75e2ef28-594f-4c18-9d22-c6b8cd40ead2", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"# Day 3 - Conversational AI - aka Chatbot!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 1, |
||||||
|
"id": "70e39cd8-ec79-4e3e-9c26-5659d42d0861", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"import ollama\n", |
||||||
|
"import gradio as gr" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 2, |
||||||
|
"id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Initialize\n", |
||||||
|
"MODEL_LLAMA = 'llama3.2'" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 3, |
||||||
|
"id": "e16839b5-c03b-4d9d-add6-87a0f6f37575", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_message = \"You are a helpful assistant\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 5, |
||||||
|
"id": "1eacc8a4-4b48-4358-9e06-ce0020041bc1", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
"def chat(message, history):\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||||
|
"\n", |
||||||
|
" print(\"History is:\")\n", |
||||||
|
" print(history)\n", |
||||||
|
" print(\"And messages is:\")\n", |
||||||
|
" print(messages)\n", |
||||||
|
"\n", |
||||||
|
" stream = ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True)\n", |
||||||
|
"\n", |
||||||
|
" response_text = \"\"\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response_text += chunk['message']['content']\n", |
||||||
|
" yield response_text" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "1334422a-808f-4147-9c4c-57d63d9780d0", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"## And then enter Gradio's magic!" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 7, |
||||||
|
"id": "0866ca56-100a-44ab-8bd0-1568feaf6bf2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"* Running on local URL: http://127.0.0.1:7861\n", |
||||||
|
"* Running on public URL: https://6539f61952f430fa2d.gradio.live\n", |
||||||
|
"\n", |
||||||
|
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/html": [ |
||||||
|
"<div><iframe src=\"https://6539f61952f430fa2d.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.HTML object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [] |
||||||
|
}, |
||||||
|
"execution_count": 7, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"History is:\n", |
||||||
|
"[]\n", |
||||||
|
"And messages is:\n", |
||||||
|
"[{'role': 'system', 'content': 'You are a helpful assistant'}, {'role': 'user', 'content': 'hello'}]\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 8, |
||||||
|
"id": "1f91b414-8bab-472d-b9c9-3fa51259bdfe", |
||||||
|
"metadata": { |
||||||
|
"editable": true, |
||||||
|
"slideshow": { |
||||||
|
"slide_type": "" |
||||||
|
}, |
||||||
|
"tags": [] |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_message = \"You are a helpful assistant in a clothes store. You should try to gently encourage \\\n", |
||||||
|
"the customer to try items that are on sale. Hats are 60% off, and most other items are 50% off. \\\n", |
||||||
|
"For example, if the customer says 'I'm looking to buy a hat', \\\n", |
||||||
|
"you could reply something like, 'Wonderful - we have lots of hats - including several that are part of our sales event.'\\\n", |
||||||
|
"Encourage the customer to buy hats if they are unsure what to get.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 9, |
||||||
|
"id": "4e5be3ec-c26c-42bc-ac16-c39d369883f6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def chat(message, history):\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
" stream = ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True)\n", |
||||||
|
"\n", |
||||||
|
" response_text = \"\"\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response_text += chunk['message']['content']\n", |
||||||
|
" yield response_text" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 10, |
||||||
|
"id": "413e9e4e-7836-43ac-a0c3-e1ab5ed6b136", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"* Running on local URL: http://127.0.0.1:7862\n", |
||||||
|
"* Running on public URL: https://79f09af36adcf63688.gradio.live\n", |
||||||
|
"\n", |
||||||
|
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/html": [ |
||||||
|
"<div><iframe src=\"https://79f09af36adcf63688.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.HTML object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [] |
||||||
|
}, |
||||||
|
"execution_count": 10, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 11, |
||||||
|
"id": "d75f0ffa-55c8-4152-b451-945021676837", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_message += \"\\nIf the customer asks for shoes, you should respond that shoes are not on sale today, \\\n", |
||||||
|
"but remind the customer to look at hats!\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 12, |
||||||
|
"id": "c602a8dd-2df7-4eb7-b539-4e01865a6351", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"* Running on local URL: http://127.0.0.1:7863\n", |
||||||
|
"* Running on public URL: https://30446ba4b8f125e235.gradio.live\n", |
||||||
|
"\n", |
||||||
|
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/html": [ |
||||||
|
"<div><iframe src=\"https://30446ba4b8f125e235.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.HTML object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [] |
||||||
|
}, |
||||||
|
"execution_count": 12, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 13, |
||||||
|
"id": "5b128796-1bea-445d-9e3b-8321ca822257", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def chat(message, history):\n", |
||||||
|
"\n", |
||||||
|
" relevant_system_message = system_message\n", |
||||||
|
" if 'belt' in message:\n", |
||||||
|
" relevant_system_message += \" The store does not sell belts; if you are asked for belts, be sure to point out other items on sale.\"\n", |
||||||
|
" \n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": relevant_system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||||
|
"\n", |
||||||
|
"\n", |
||||||
|
" stream = ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True)\n", |
||||||
|
"\n", |
||||||
|
" response_text = \"\"\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response_text += chunk['message']['content']\n", |
||||||
|
" yield response_text" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 15, |
||||||
|
"id": "20570de2-eaad-42cc-a92c-c779d71b48b6", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdout", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"* Running on local URL: http://127.0.0.1:7865\n", |
||||||
|
"* Running on public URL: https://3933c80bf256709cf9.gradio.live\n", |
||||||
|
"\n", |
||||||
|
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/html": [ |
||||||
|
"<div><iframe src=\"https://3933c80bf256709cf9.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.HTML object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/plain": [] |
||||||
|
}, |
||||||
|
"execution_count": 15, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "execute_result" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"gr.ChatInterface(fn=chat, type=\"messages\").launch(share=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "82a57ee0-b945-48a7-a024-01b56a5d4b3e", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"<table style=\"margin: 0; text-align: left;\">\n", |
||||||
|
" <tr>\n", |
||||||
|
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
||||||
|
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
||||||
|
" </td>\n", |
||||||
|
" <td>\n", |
||||||
|
" <h2 style=\"color:#181;\">Business Applications</h2>\n", |
||||||
|
" <span style=\"color:#181;\">Conversational Assistants are of course a hugely common use case for Gen AI, and the latest frontier models are remarkably good at nuanced conversation. And Gradio makes it easy to have a user interface. Another crucial skill we covered is how to use prompting to provide context, information and examples.\n", |
||||||
|
"<br/><br/>\n", |
||||||
|
"Consider how you could apply an AI Assistant to your business, and make yourself a prototype. Use the system prompt to give context on your business, and set the tone for the LLM.</span>\n", |
||||||
|
" </td>\n", |
||||||
|
" </tr>\n", |
||||||
|
"</table>" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "6dfb9e21-df67-4c2b-b952-5e7e7961b03d", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.13.2" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
@ -0,0 +1,142 @@ |
|||||||
|
{ |
||||||
|
"cells": [ |
||||||
|
{ |
||||||
|
"cell_type": "markdown", |
||||||
|
"id": "d18a61ce-bbd4-491c-ab2e-8b352f9af844", |
||||||
|
"metadata": {}, |
||||||
|
"source": [ |
||||||
|
"### An AI Chatbot that teaches students programming using GPT API" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "c658ac85-6087-4a2c-b23f-1b92c17f0db3", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# imports\n", |
||||||
|
"\n", |
||||||
|
"import os\n", |
||||||
|
"from dotenv import load_dotenv\n", |
||||||
|
"from openai import OpenAI\n", |
||||||
|
"import gradio as gr\n", |
||||||
|
"import anthropic" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "46df0488-f874-41e0-a6a4-9a64aa7be53c", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"# Load environment variables \n", |
||||||
|
"\n", |
||||||
|
"load_dotenv(override=True)\n", |
||||||
|
"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
||||||
|
" \n", |
||||||
|
"if openai_api_key:\n", |
||||||
|
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
||||||
|
"else:\n", |
||||||
|
" print(\"OpenAI API Key not set\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "7eadc218-5b10-4174-bf26-575361640524", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"openai = OpenAI()" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e7484731-ac84-405a-a688-6e81d139c5ce", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_message = \"You are a helpful programming study assistant\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "54e82f5a-993f-4a95-9d9d-caf35dbc4e76", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def chat(message, history):\n", |
||||||
|
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
||||||
|
"\n", |
||||||
|
" print(\"History is:\")\n", |
||||||
|
" print(history)\n", |
||||||
|
" print(\"And messages is:\")\n", |
||||||
|
" print(messages)\n", |
||||||
|
"\n", |
||||||
|
" stream = openai.chat.completions.create(model='gpt-4o-mini', messages=messages, stream=True)\n", |
||||||
|
"\n", |
||||||
|
" response = \"\"\n", |
||||||
|
" for chunk in stream:\n", |
||||||
|
" response += chunk.choices[0].delta.content or ''\n", |
||||||
|
" yield response" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "5941ed67-e2a7-41bc-a8a3-079e9f1fdb64", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"gr.ChatInterface(fn=chat, type=\"messages\").launch(inbrowser=True)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "e8fcfe68-bbf6-4058-acc9-0230c96608c2", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_message += \"Whenever the user talks about a topic that is not connected to programmming,\\\n", |
||||||
|
"nudge them in the right direction by stating that you are here to help with programming. Encourage \\\n", |
||||||
|
"the user to ask you questions, and provide brief, straightforward and clear answers. Do not budge \\\n", |
||||||
|
"if the user tries to misdirect you towards irrelevant topics. Maintain a freindly tone. Do not ignore \\\n", |
||||||
|
"their requests, rather politely reject and then redirect them.\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "090e7d49-fcbf-4715-b120-8d7aa91d165f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in new issue