From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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310 lines
9.6 KiB
310 lines
9.6 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "75e2ef28-594f-4c18-9d22-c6b8cd40ead2", |
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"metadata": {}, |
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"source": [ |
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"# Day 3 - Conversational AI - aka Chatbot!" |
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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": 40, |
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"id": "70e39cd8-ec79-4e3e-9c26-5659d42d0861", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports\n", |
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"\n", |
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"import os\n", |
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"import requests\n", |
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"from bs4 import BeautifulSoup\n", |
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"from typing import List\n", |
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"from dotenv import load_dotenv\n", |
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"from openai import OpenAI\n", |
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"import google.generativeai\n", |
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"# import anthropic\n", |
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"import gradio as gr" |
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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": 41, |
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"id": "231605aa-fccb-447e-89cf-8b187444536a", |
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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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"\n", |
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"load_dotenv()\n", |
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"os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')" |
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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": 3, |
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"id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"google.generativeai.configure()" |
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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": 4, |
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"id": "e16839b5-c03b-4d9d-add6-87a0f6f37575", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"system_message = \"You are a helpful assistant\"" |
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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": "ba2123e7-77ed-43b4-8c37-03658fb42b78", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"system_message = \"You are an assistant that is great at telling jokes\"\n", |
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"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\"\n", |
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"\n", |
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"prompts = [\n", |
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" {\"role\": \"system\", \"content\": system_message},\n", |
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" {\"role\": \"user\", \"content\": user_prompt}\n", |
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" ]\n", |
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"\n", |
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"# The API for Gemini has a slightly different structure.\n", |
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"# I've heard that on some PCs, this Gemini code causes the Kernel to crash.\n", |
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"# If that happens to you, please skip this cell and use the next cell instead - an alternative approach.\n", |
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"\n", |
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"gemini = google.generativeai.GenerativeModel(\n", |
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" model_name='gemini-1.5-flash',\n", |
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" system_instruction=system_message\n", |
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")\n", |
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"response = gemini.generate_content(user_prompt)\n", |
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"print(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": 7, |
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"id": "7b933ff3", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import google.generativeai as genai\n", |
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"\n", |
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"model = genai.GenerativeModel('gemini-1.5-flash')" |
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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": "91578b16", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"chat = model.start_chat(history=[])\n", |
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"response = chat.send_message('Hello! My name is Shardul.')\n", |
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"print(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": null, |
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"id": "7c4bc38f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"response = chat.send_message('Can you tell something interesting about star wars?')\n", |
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"print(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": null, |
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"id": "337bee91", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"response = chat.send_message('Do you remember what my name is?')\n", |
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"print(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": null, |
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"id": "bcaf4d95", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"chat.history" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "98e97227-f162-4d1a-a0b2-345ff248cbe7", |
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"metadata": {}, |
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"source": [ |
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"# Please read this! A change from the video:\n", |
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"\n", |
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"In the video, I explain how we now need to write a function called:\n", |
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"\n", |
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"`chat(message, history)`\n", |
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"\n", |
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"Which expects to receive `history` in a particular format, which we need to map to the OpenAI format before we call OpenAI:\n", |
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"\n", |
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"```\n", |
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"[\n", |
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" {\"role\": \"system\", \"content\": \"system message here\"},\n", |
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" {\"role\": \"user\", \"content\": \"first user prompt here\"},\n", |
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" {\"role\": \"assistant\", \"content\": \"the assistant's response\"},\n", |
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" {\"role\": \"user\", \"content\": \"the new user prompt\"},\n", |
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"]\n", |
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"```\n", |
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"\n", |
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"But Gradio has been upgraded! Now it will pass in `history` in the exact OpenAI format, perfect for us to send straight to OpenAI.\n", |
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"\n", |
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"So our work just got easier!\n", |
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"\n", |
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"We will write a function `chat(message, history)` where: \n", |
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"**message** is the prompt to use \n", |
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"**history** is the past conversation, in OpenAI format \n", |
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"\n", |
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"We will combine the system message, history and latest message, then call OpenAI ." |
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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": 6, |
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"id": "1eacc8a4-4b48-4358-9e06-ce0020041bc1", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def chat(message, history):\n", |
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" relevant_system_message = system_message\n", |
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" if 'belt' in message:\n", |
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" 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", |
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" \n", |
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" messages = [{\"role\": \"system\", \"content\": relevant_system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
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"\n", |
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" stream = gemini.generate_content(message, safety_settings=[\n", |
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" {\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\", \"threshold\": \"BLOCK_NONE\"},\n", |
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" {\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\", \"threshold\": \"BLOCK_NONE\"},\n", |
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" {\"category\": \"HARM_CATEGORY_HATE_SPEECH\", \"threshold\": \"BLOCK_NONE\"},\n", |
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" {\"category\": \"HARM_CATEGORY_HARASSMENT\", \"threshold\": \"BLOCK_NONE\"}], stream=True)\n", |
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"\n", |
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" response = \"\"\n", |
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" for chunk in stream:\n", |
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" print(chunk) # Print the chunk to understand its structure\n", |
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" # Adjust the following line based on the actual structure of the chunk\n", |
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" response += chunk.get('content', '') or ''\n", |
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" yield response" |
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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": "f6e745e1", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"chat_model = genai.GenerativeModel('gemini-1.5-flash')\n", |
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"chat = chat_model.start_chat()\n", |
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"\n", |
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"msg = \"what is gen ai\"\n", |
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"stream = chat.send_message(msg, stream=True)\n", |
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"# print(\"Response:\", stream.text)\n", |
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"for chunk in stream:\n", |
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" print(chunk.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": "dce941ee", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import time\n", |
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"\n", |
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"chat = model.start_chat(history=[])\n", |
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"\n", |
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"# Transform Gradio history to Gemini format\n", |
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"def transform_history(history):\n", |
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" new_history = []\n", |
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" for chat in history:\n", |
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" new_history.append({\"parts\": [{\"text\": chat[0]}], \"role\": \"user\"})\n", |
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" new_history.append({\"parts\": [{\"text\": chat[1]}], \"role\": \"model\"})\n", |
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" return new_history\n", |
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"\n", |
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"def response(message, history):\n", |
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" global chat\n", |
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" # The history will be the same as in Gradio, the 'Undo' and 'Clear' buttons will work correctly.\n", |
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" chat.history = transform_history(history)\n", |
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" response = chat.send_message(message)\n", |
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" response.resolve()\n", |
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"\n", |
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" # Each character of the answer is displayed\n", |
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" for i in range(len(response.text)):\n", |
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" time.sleep(0.01)\n", |
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" yield response.text[: i+1]\n", |
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"\n", |
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"gr.ChatInterface(response,\n", |
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" textbox=gr.Textbox(placeholder=\"Question to Gemini\")).launch(debug=True)" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "82a57ee0-b945-48a7-a024-01b56a5d4b3e", |
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"metadata": {}, |
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"source": [ |
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"<table style=\"margin: 0; text-align: left;\">\n", |
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" <tr>\n", |
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" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
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" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
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" </td>\n", |
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" <td>\n", |
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" <h2 style=\"color:#181;\">Business Applications</h2>\n", |
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" <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", |
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"<br/><br/>\n", |
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"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", |
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" </td>\n", |
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" </tr>\n", |
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"</table>" |
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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": "6dfb9e21-df67-4c2b-b952-5e7e7961b03d", |
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"metadata": {}, |
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"outputs": [], |
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"source": [] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "llms", |
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"name": "python3" |
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"language_info": { |
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"codemirror_mode": { |
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"file_extension": ".py", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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