From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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480 lines
16 KiB
480 lines
16 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "ddfa9ae6-69fe-444a-b994-8c4c5970a7ec", |
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"metadata": {}, |
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"source": [ |
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"# Project - Airline AI Assistant\n", |
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"\n", |
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"We'll now bring together what we've learned to make an AI Customer Support assistant for an Airline" |
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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": "8b50bbe2-c0b1-49c3-9a5c-1ba7efa2bcb4", |
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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 json\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": null, |
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"id": "747e8786-9da8-4342-b6c9-f5f69c2e22ae", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Initialization\n", |
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"\n", |
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"load_dotenv()\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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"MODEL = \"gpt-4o-mini\"\n", |
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"openai = 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": null, |
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"id": "0a521d84-d07c-49ab-a0df-d6451499ed97", |
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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 for an Airline called FlightAI. \"\n", |
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"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n", |
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"system_message += \"Always be accurate. If you don't know the answer, say so.\"" |
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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": "61a2a15d-b559-4844-b377-6bd5cb4949f6", |
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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}]\n", |
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" for human, assistant in history:\n", |
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" messages.append({\"role\": \"user\", \"content\": human})\n", |
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" messages.append({\"role\": \"assistant\", \"content\": assistant})\n", |
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" messages.append({\"role\": \"user\", \"content\": message})\n", |
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" response = openai.chat.completions.create(model=MODEL, messages=messages)\n", |
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" return response.choices[0].message.content\n", |
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"\n", |
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"gr.ChatInterface(fn=chat).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": "36bedabf-a0a7-4985-ad8e-07ed6a55a3a4", |
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"metadata": {}, |
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"source": [ |
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"## Tools\n", |
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"\n", |
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"Tools are an incredibly powerful feature provided by the frontier LLMs.\n", |
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"\n", |
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"With tools, you can write a function, and have the LLM call that function as part of its response.\n", |
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"\n", |
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"Sounds almost spooky.. we're giving it the power to run code on our machine?\n", |
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"\n", |
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"Well, kinda." |
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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": "0696acb1-0b05-4dc2-80d5-771be04f1fb2", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Let's start by making a useful function\n", |
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"\n", |
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"ticket_prices = {\"london\": \"$799\", \"paris\": \"$899\", \"tokyo\": \"$1400\", \"berlin\": \"$499\"}\n", |
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"\n", |
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"def get_ticket_price(destination_city):\n", |
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" print(f\"Tool get_ticket_price called for {destination_city}\")\n", |
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" city = destination_city.lower()\n", |
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" return ticket_prices.get(city, \"Unknown\")" |
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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": "80ca4e09-6287-4d3f-997d-fa6afbcf6c85", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"get_ticket_price(\"London\")" |
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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": "4afceded-7178-4c05-8fa6-9f2085e6a344", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# There's a particular dictionary structure that's required to describe our function:\n", |
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"\n", |
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"price_function = {\n", |
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" \"name\": \"get_ticket_price\",\n", |
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" \"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", |
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" \"parameters\": {\n", |
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" \"type\": \"object\",\n", |
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" \"properties\": {\n", |
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" \"destination_city\": {\n", |
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" \"type\": \"string\",\n", |
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" \"description\": \"The city that the customer wants to travel to\",\n", |
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" },\n", |
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" },\n", |
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" \"required\": [\"destination_city\"],\n", |
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" \"additionalProperties\": False\n", |
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" }\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": null, |
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"id": "bdca8679-935f-4e7f-97e6-e71a4d4f228c", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# And this is included in a list of tools:\n", |
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"\n", |
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"tools = [{\"type\": \"function\", \"function\": price_function}]" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "c3d3554f-b4e3-4ce7-af6f-68faa6dd2340", |
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"metadata": {}, |
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"source": [ |
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"## Getting OpenAI to use our Tool\n", |
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"\n", |
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"There's some fiddly stuff to allow OpenAI \"to call our tool\"\n", |
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"\n", |
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"What we actually do is give the LLM the opportunity to inform us that it wants us to run the tool.\n", |
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"\n", |
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"Here's how the new chat function looks:" |
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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": "ce9b0744-9c78-408d-b9df-9f6fd9ed78cf", |
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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}]\n", |
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" for human, assistant in history:\n", |
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" messages.append({\"role\": \"user\", \"content\": human})\n", |
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" messages.append({\"role\": \"assistant\", \"content\": assistant})\n", |
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" messages.append({\"role\": \"user\", \"content\": message})\n", |
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" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n", |
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"\n", |
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" if response.choices[0].finish_reason==\"tool_calls\":\n", |
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" message = response.choices[0].message\n", |
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" response, city = handle_tool_call(message)\n", |
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" messages.append(message)\n", |
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" messages.append(response)\n", |
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" response = openai.chat.completions.create(model=MODEL, messages=messages)\n", |
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" \n", |
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" return response.choices[0].message.content" |
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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": "b0992986-ea09-4912-a076-8e5603ee631f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# We have to write that function handle_tool_call:\n", |
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"\n", |
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"def handle_tool_call(message):\n", |
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" tool_call = message.tool_calls[0]\n", |
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" arguments = json.loads(tool_call.function.arguments)\n", |
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" city = arguments.get('destination_city')\n", |
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" price = get_ticket_price(city)\n", |
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" response = {\n", |
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" \"role\": \"tool\",\n", |
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" \"content\": json.dumps({\"destination_city\": city,\"price\": price}),\n", |
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" \"tool_call_id\": message.tool_calls[0].id\n", |
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" }\n", |
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" return response, city" |
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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": "f4be8a71-b19e-4c2f-80df-f59ff2661f14", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"gr.ChatInterface(fn=chat).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": "473e5b39-da8f-4db1-83ae-dbaca2e9531e", |
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"metadata": {}, |
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"source": [ |
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"# Let's go multi-modal!!\n", |
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"\n", |
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"We can use DALL-E-3, the image generation model behind GPT-4o, to make us some images\n", |
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"\n", |
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"Let's put this in a function called artist.\n", |
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"\n", |
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"### Price alert: each time I generate an image it costs about 4c - don't go crazy with images!" |
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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": "2c27c4ba-8ed5-492f-add1-02ce9c81d34c", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Some imports for handling images\n", |
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"\n", |
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"import base64\n", |
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"from io import BytesIO\n", |
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"from PIL import Image" |
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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": "773a9f11-557e-43c9-ad50-56cbec3a0f8f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def artist(city):\n", |
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" image_response = openai.images.generate(\n", |
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" model=\"dall-e-3\",\n", |
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" prompt=f\"An image representing a vacation in {city}, showing tourist spots and everything unique about {city}, in a vibrant pop-art style\",\n", |
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" size=\"1024x1024\",\n", |
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" n=1,\n", |
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" response_format=\"b64_json\",\n", |
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" )\n", |
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" image_base64 = image_response.data[0].b64_json\n", |
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" image_data = base64.b64decode(image_base64)\n", |
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" return Image.open(BytesIO(image_data))" |
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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": "d877c453-e7fb-482a-88aa-1a03f976b9e9", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"image = artist(\"New York City\")\n", |
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"display(image)" |
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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": "728a12c5-adc3-415d-bb05-82beb73b079b", |
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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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"cell_type": "markdown", |
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"id": "f4975b87-19e9-4ade-a232-9b809ec75c9a", |
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"metadata": {}, |
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"source": [ |
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"## Audio\n", |
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"\n", |
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"And let's make a function talker that uses OpenAI's speech model to generate Audio\n", |
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"\n", |
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"### Troubleshooting Audio issues\n", |
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"\n", |
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"If you have any problems running this code below (like a FileNotFound error, or a warning of a missing package), you may need to install FFmpeg, a very popular audio utility.\n", |
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"\n", |
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"**For PC Users**\n", |
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"\n", |
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"1. Download FFmpeg from the official website: https://ffmpeg.org/download.html\n", |
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"\n", |
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"2. Extract the downloaded files to a location on your computer (e.g., `C:\\ffmpeg`)\n", |
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"\n", |
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"3. Add the FFmpeg bin folder to your system PATH:\n", |
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"- Right-click on 'This PC' or 'My Computer' and select 'Properties'\n", |
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"- Click on 'Advanced system settings'\n", |
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"- Click on 'Environment Variables'\n", |
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"- Under 'System variables', find and edit 'Path'\n", |
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"- Add a new entry with the path to your FFmpeg bin folder (e.g., `C:\\ffmpeg\\bin`)\n", |
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"- Restart your command prompt, and within Jupyter Lab do Kernel -> Restart kernel, to pick up the changes\n", |
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"\n", |
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"4. Open a new command prompt and run this to make sure it's installed OK\n", |
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"`ffmpeg -version`\n", |
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"\n", |
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"**For Mac Users**\n", |
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"\n", |
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"1. Install homebrew if you don't have it already by running this in a Terminal window and following any instructions: \n", |
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"`/bin/bash -c \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\"`\n", |
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"\n", |
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"2. Then install FFmpeg with `brew install ffmpeg`\n", |
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"\n", |
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"3. Verify your installation with `ffmpeg -version` and if everything is good, within Jupyter Lab do Kernel -> Restart kernel to pick up the changes\n", |
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"\n", |
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"Message me or email me at ed@edwarddonner.com with any problems!" |
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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": "ffbfe93b-5e86-4e68-ba71-b301cd5230db", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"from pydub import AudioSegment\n", |
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"from pydub.playback import play\n", |
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"\n", |
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"def talker(message):\n", |
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" response = openai.audio.speech.create(\n", |
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" model=\"tts-1\",\n", |
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" voice=\"onyx\", # Also, try replacing onyx with alloy\n", |
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" input=message\n", |
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" )\n", |
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" \n", |
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" audio_stream = BytesIO(response.content)\n", |
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" audio = AudioSegment.from_file(audio_stream, format=\"mp3\")\n", |
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" play(audio)" |
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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": "b88d775d-d357-4292-a1ad-5dc5ed567281", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"talker(\"Well, hi there\")" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "1d48876d-c4fa-46a8-a04f-f9fadf61fb0d", |
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"metadata": {}, |
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"source": [ |
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"# Our Agent Framework\n", |
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"\n", |
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"The term 'Agentic AI' and Agentization is an umbrella term that refers to a number of techniques, such as:\n", |
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"\n", |
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"1. Breaking a complex problem into smaller steps, with multiple LLMs carrying out specialized tasks\n", |
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"2. The ability for LLMs to use Tools to give them additional capabilities\n", |
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"3. The 'Agent Environment' which allows Agents to collaborate\n", |
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"4. An LLM can act as the Planner, dividing bigger tasks into smaller ones for the specialists\n", |
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"5. The concept of an Agent having autonomy / agency, beyond just responding to a prompt - such as Memory\n", |
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"\n", |
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"We're showing 1 and 2 here, and to a lesser extent 3 and 5. In week 8 we will do the lot!" |
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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": "ba820c95-02f5-499e-8f3c-8727ee0a6c0c", |
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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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" image = None\n", |
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" conversation = [{\"role\": \"system\", \"content\": system_message}]\n", |
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" for human, assistant in history:\n", |
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" conversation.append({\"role\": \"user\", \"content\": human})\n", |
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" conversation.append({\"role\": \"assistant\", \"content\": assistant})\n", |
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" conversation.append({\"role\": \"user\", \"content\": message})\n", |
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" response = openai.chat.completions.create(model=MODEL, messages=conversation, tools=tools)\n", |
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"\n", |
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" if response.choices[0].finish_reason==\"tool_calls\":\n", |
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" message = tool_call = response.choices[0].message\n", |
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" response, city = handle_tool_call(message)\n", |
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" conversation.append(message)\n", |
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" conversation.append(response)\n", |
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" image = artist(city)\n", |
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" response = openai.chat.completions.create(model=MODEL, messages=conversation)\n", |
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"\n", |
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" reply = response.choices[0].message.content\n", |
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" talker(reply)\n", |
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" return reply, image" |
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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": "f38d0d27-33bf-4992-a2e5-5dbed973cde7", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# More involved Gradio code as we're not using the preset Chat interface\n", |
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"\n", |
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"with gr.Blocks() as ui:\n", |
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" with gr.Row():\n", |
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" chatbot = gr.Chatbot(height=500)\n", |
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" image_output = gr.Image(height=500)\n", |
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" with gr.Row():\n", |
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" msg = gr.Textbox(label=\"Chat with our AI Assistant:\")\n", |
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" with gr.Row():\n", |
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" clear = gr.Button(\"Clear\")\n", |
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"\n", |
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" def user(user_message, history):\n", |
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" return \"\", history + [[user_message, None]]\n", |
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"\n", |
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" def bot(history):\n", |
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" user_message = history[-1][0]\n", |
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" bot_message, image = chat(user_message, history[:-1])\n", |
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" history[-1][1] = bot_message\n", |
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" return history, image\n", |
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"\n", |
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" msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", |
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" bot, chatbot, [chatbot, image_output]\n", |
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" )\n", |
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" clear.click(lambda: None, None, chatbot, queue=False)\n", |
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"\n", |
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"ui.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": null, |
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"id": "b0b12548-951d-4e7c-8e77-803a92271855", |
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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": "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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"version": 3 |
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}, |
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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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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 5 |
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}
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