From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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355 lines
11 KiB
355 lines
11 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": 1, |
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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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"from dotenv import load_dotenv\n", |
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"from openai import OpenAI\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": 2, |
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"id": "231605aa-fccb-447e-89cf-8b187444536a", |
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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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"OpenAI API Key exists and begins sk-proj-\n", |
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"Anthropic API Key exists and begins sk-ant-\n", |
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"Google API Key exists and begins AIzaSyA-\n" |
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] |
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} |
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], |
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"source": [ |
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"# Load environment variables in a file called .env\n", |
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"# Print the key prefixes to help with any debugging\n", |
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"\n", |
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"load_dotenv() \n", |
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"openai_api_key = os.getenv('OPENAI_API_KEY')\n", |
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"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", |
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"google_api_key = os.getenv('GOOGLE_API_KEY')\n", |
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"\n", |
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"if openai_api_key:\n", |
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" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", |
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"else:\n", |
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" print(\"OpenAI API Key not set\")\n", |
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" \n", |
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"if anthropic_api_key:\n", |
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" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", |
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"else:\n", |
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" print(\"Anthropic API Key not set\")\n", |
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"\n", |
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"if google_api_key:\n", |
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" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", |
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"else:\n", |
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" print(\"Google API Key not set\")" |
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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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"# Initialize\n", |
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"\n", |
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"openai = OpenAI()\n", |
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"MODEL = 'gpt-4o-mini'" |
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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": "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": 5, |
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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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"# Simpler than in my video - we can easily create this function that calls OpenAI\n", |
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"# It's now just 1 line of code to prepare the input to OpenAI!\n", |
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"\n", |
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"def chat(message, history):\n", |
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" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
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"\n", |
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" print(\"History is:\")\n", |
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" print(history)\n", |
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" print(\"And messages is:\")\n", |
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" print(messages)\n", |
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"\n", |
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" stream = openai.chat.completions.create(model=MODEL, messages=messages, 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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" response += chunk.choices[0].delta.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": "markdown", |
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"id": "1334422a-808f-4147-9c4c-57d63d9780d0", |
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"metadata": {}, |
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"source": [ |
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"## And then enter Gradio's magic!" |
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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": "0866ca56-100a-44ab-8bd0-1568feaf6bf2", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"gr.ChatInterface(fn=chat, type=\"messages\").launch()" |
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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": 8, |
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"id": "1f91b414-8bab-472d-b9c9-3fa51259bdfe", |
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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 in a clothes store. You should try to gently encourage \\\n", |
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"the customer to try items that are on sale. Hats are 60% off, and most other items are 50% off. \\\n", |
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"For example, if the customer says 'I'm looking to buy a hat', \\\n", |
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"you could reply something like, 'Wonderful - we have lots of hats - including several that are part of our sales evemt.'\\\n", |
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"Encourage the customer to buy hats if they are unsure what to get.\"" |
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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": 9, |
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"id": "4e5be3ec-c26c-42bc-ac16-c39d369883f6", |
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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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" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
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"\n", |
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" stream = openai.chat.completions.create(model=MODEL, messages=messages, 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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" response += chunk.choices[0].delta.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": "413e9e4e-7836-43ac-a0c3-e1ab5ed6b136", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"gr.ChatInterface(fn=chat, type=\"messages\").launch()" |
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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": 10, |
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"id": "d75f0ffa-55c8-4152-b451-945021676837", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"system_message += \"\\nIf the customer asks for shoes, you should respond that shoes are not on sale today, \\\n", |
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"but remind the customer to look at hats!\"" |
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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": "c602a8dd-2df7-4eb7-b539-4e01865a6351", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"gr.ChatInterface(fn=chat, type=\"messages\").launch()" |
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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": 13, |
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"id": "0a987a66-1061-46d6-a83a-a30859dc88bf", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Fixed a bug in this function brilliantly identified by student Gabor M.!\n", |
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"# I've also improved the structure of this function\n", |
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"# Paul Goodwin added \"Buy One get one free offer\" for a bit of fun\n", |
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"\n", |
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"def chat(message, history):\n", |
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"\n", |
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" relevant_system_message = system_message\n", |
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" keywords = ['discount', 'offer', 'promotion'] # Define words that imply customer is looking for a better deal\n", |
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"\n", |
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" if 'belt' in message.strip().lower():\n", |
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" relevant_system_message += (\n", |
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" \" 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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" elif any(word in message.strip().lower() for word in keywords): # Use elif for clarity\n", |
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" relevant_system_message += (\n", |
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" \" If the customer asks for more money off the selling price, the store is currently running 'buy 2 get one free' campaign, so be sure to mention this.\"\n", |
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" )\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 = openai.chat.completions.create(model=MODEL, messages=messages, 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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" response += chunk.choices[0].delta.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": 14, |
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"id": "20570de2-eaad-42cc-a92c-c779d71b48b6", |
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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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"* Running on local URL: http://127.0.0.1:7862\n", |
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"\n", |
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"To create a public link, set `share=True` in `launch()`.\n" |
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] |
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}, |
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{ |
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"data": { |
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"text/html": [ |
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"<div><iframe src=\"http://127.0.0.1:7862/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
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], |
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"text/plain": [ |
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"<IPython.core.display.HTML 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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"data": { |
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"text/plain": [] |
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}, |
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"execution_count": 14, |
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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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"gr.ChatInterface(fn=chat, type=\"messages\").launch()" |
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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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} |
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], |
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"kernelspec": { |
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"display_name": "Python 3 (ipykernel)", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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"name": "ipython", |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.11.10" |
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"nbformat": 4, |
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"nbformat_minor": 5 |
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