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|
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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "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(override=True)\n", |
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"\n", |
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"openai_api_key = os.getenv('OPENAI_API_KEY')\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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"MODEL = \"gpt-4o-mini\"\n", |
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"openai = OpenAI()\n", |
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"\n", |
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"# As an alternative, if you'd like to use Ollama instead of OpenAI\n", |
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"# Check that Ollama is running for you locally (see week1/day2 exercise) then uncomment these next 2 lines\n", |
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"# MODEL = \"llama3.2\"\n", |
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"# openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\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": "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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"# This function looks rather simpler than the one from my video, because we're taking advantage of the latest Gradio updates\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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" 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, 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": "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(\"Berlin\")" |
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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": "b6c73b6b-1f9f-4df3-bd36-f6c8bf45a670", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"from datetime import date, datetime\n", |
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"\n", |
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"availabilities = {\"london\": \"01/01/2025-31/03/2025\", \"paris\": \"01/01/2025-31/12/2025\", \"tokyo\": \"05/06/2024-04/06/2025\", \"berlin\": \"01/12/2024-30/09/2025\"}\n", |
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"\n", |
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"def get_availability_period(destination_city, flight_date):\n", |
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" \"\"\"Check for dates as LLM answers on this are not reliable.\"\"\"\n", |
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" print(f\"Tool get_availability_period called for {destination_city}\")\n", |
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" \n", |
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" city = destination_city.lower()\n", |
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" availability = availabilities.get(city)\n", |
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" \n", |
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" if availability:\n", |
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" current_date = date.today()\n", |
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" if len(flight_date.split(\"-\")) == 2:\n", |
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" flight_date = f\"{flight_date}-{str(current_date.year)}\"\n", |
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" flight_date = datetime.strptime(flight_date, \"%d-%m-%Y\").date()\n", |
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" \n", |
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" start_str, end_str = availability.split(\"-\")\n", |
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" start_date = datetime.strptime(start_str, \"%d/%m/%Y\").date()\n", |
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" end_date = datetime.strptime(end_str, \"%d/%m/%Y\").date()\n", |
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" \n", |
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" if start_date < flight_date < end_date:\n", |
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" return \"Available\"\n", |
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" else:\n", |
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" return \"Not available for requested flight dates\"" |
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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": "9680afe1-65d8-4094-8a09-6ef37ebdad2b", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"\n", |
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"availability_function = {\n", |
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" \"name\": \"get_availability_period\",\n", |
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" \"description\": \"Check if the requested flight dates are within the availability period of the flight schedule to the destination city. Call this whenever you need to know whether there is a flight to the requested city at the provided travel dates of the user, for example when a customer asks 'I want to book a flight to Tokyo on 5 Nov 2025'. \\\n", |
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" If the user's date does not include a year, the date will refer to a date after the current date. State in your response whether there will be a flight to the destination city at the requested flight date. \\\n", |
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" For example, an availability of '01/08/2025-31/10/2025' means that there are flights between 01 Aug 25 and 31 Oct 2025. \\\n", |
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" Parse the user's flight date as string in the format DD-MM-YYYY or DD-MM if no year is given\",\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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" \"flight_date\": {\n", |
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" \"type\": \"string\",\n", |
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" \"description\": \"The date on which the customer wants to travel\",\n", |
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" },\n", |
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" },\n", |
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" \"required\": [\"destination_city\", \"flight_date\"],\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}, {\"type\": \"function\", \"function\": availability_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}] + history + [{\"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 = handle_tools(message)\n", |
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" messages.append(message)\n", |
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" print(f\"Message: \\n {messages}\")\n", |
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" messages.append(response)\n", |
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" print(f\"Tool call response: \\n {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\": tool_call.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": 1, |
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"id": "d772048a-310e-4ff1-8c20-3dd7edb14a19", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Extended function to handle two tools\n", |
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"\n", |
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"def handle_tools(message):\n", |
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" print(f\"message.tool_calls:\\n {message.tool_calls}\")\n", |
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" tool_call = message.tool_calls[0] \n", |
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" if message.tool_calls[0].function.name == \"get_ticket_price\":\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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" content = json.dumps({\"destination_city\": city,\"price\": price})\n", |
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" elif message.tool_calls[0].function.name == \"get_availability_period\":\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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" flight_date = arguments.get('flight_date')\n", |
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" availability_dates = get_availability_period(city, flight_date)\n", |
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" content = json.dumps({\"destination_city\": city,\"availability_dates\": availability_dates})\n", |
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" \n", |
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" response = {\n", |
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" \"role\": \"tool\",\n", |
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" \"content\": content,\n", |
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" \"tool_call_id\": tool_call.id\n", |
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" }\n", |
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"\n", |
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" return 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": "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, type=\"messages\").launch()" |
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] |
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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": "3.11.11" |
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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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|
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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "d006b2ea-9dfe-49c7-88a9-a5a0775185fd", |
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"metadata": {}, |
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"source": [ |
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"# Additional End of week Exercise - week 2\n", |
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"\n", |
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"Now use everything you've learned from Week 2 to build a full prototype for the technical question/answerer you built in Week 1 Exercise.\n", |
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"\n", |
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"This should include a Gradio UI, streaming, use of the system prompt to add expertise, and the ability to switch between models. Bonus points if you can demonstrate use of a tool!\n", |
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"\n", |
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"If you feel bold, see if you can add audio input so you can talk to it, and have it respond with audio. ChatGPT or Claude can help you, or email me if you have questions.\n", |
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"\n", |
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"I will publish a full solution here soon - unless someone beats me to it...\n", |
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"\n", |
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"There are so many commercial applications for this, from a language tutor, to a company onboarding solution, to a companion AI to a course (like this one!) I can't wait to see your results." |
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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": "a07e7793-b8f5-44f4-aded-5562f633271a", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import os\n", |
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"from dotenv import load_dotenv\n", |
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"\n", |
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"import gradio as gr\n", |
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"from IPython.display import Markdown, display, update_display\n", |
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"from openai import OpenAI\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\n", |
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"from IPython.display import Audio, display\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": "5b9b086f-46bb-4801-a13d-d5574c2b8e97", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"MODEL_GPT = 'gpt-4o-mini'\n", |
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"MODEL_LLAMA = 'llama3.2'" |
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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": "38584d6f-3bbd-4b52-9775-1650c9548884", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# set up environment\n", |
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"load_dotenv(override=True)\n", |
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"api_key = os.getenv(\"OPENAI_API_KEY\")\n", |
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"\n", |
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"# set up clients\n", |
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"openai = OpenAI()\n", |
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"ollama = OpenAI(base_url=\"http://localhost:11434/v1\" , api_key=\"ollama\")\n", |
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"\n", |
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"# set up system prompt\n", |
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"system_prompt = \"You are a coding tutor. If the user asks you a question, answer it to the point. If you are asked to create a code snippet, generate the code in Python and then explain it shortly.\"" |
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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": "61851048-d2d1-43ab-86d5-0a4c37181be8", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"\n", |
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"def speak(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=\"alloy\",\n", |
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" input=message)\n", |
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"\n", |
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" audio_stream = BytesIO(response.content)\n", |
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" output_filename = \"output_audio.mp3\"\n", |
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" with open(output_filename, \"wb\") as f:\n", |
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" f.write(audio_stream.read())\n", |
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"\n", |
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" # Play the generated audio\n", |
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" display(Audio(output_filename, autoplay=True))" |
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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": "4b6678d6-de73-4bfd-82c0-4d213f9aa7cd", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def chat(message, history, model, audio=False):\n", |
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" messages = [{\"role\": \"system\", \"content\": system_prompt}] + history + [{\"role\": \"user\", \"content\": message}]\n", |
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" \n", |
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" if model == \"gpt-4o-mini\":\n", |
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" client = openai\n", |
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" elif model == \"llama3.2\":\n", |
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" client = ollama\n", |
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"\n", |
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" stream = client.chat.completions.create(\n", |
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" model = model,\n", |
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" messages = messages,\n", |
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" stream = True\n", |
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" )\n", |
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"\n", |
||||
" response = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" response += chunk.choices[0].delta.content or \"\"\n", |
||||
" yield response\n", |
||||
"\n", |
||||
" if audio:\n", |
||||
" speak(response)\n" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": null, |
||||
"id": "73606604-5462-4699-98b3-d92b4a5b8276", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"with gr.Blocks() as ui:\n", |
||||
" model_selection = gr.Dropdown([\"gpt-4o-mini\", \"llama3.2\"], label=\"Select model\", value=\"llama3.2\")\n", |
||||
" audio_enabled = gr.Checkbox(label=\"Audio enabled\", info=\"Check the box if you want me to speak to you\")\n", |
||||
" \n", |
||||
" gr.ChatInterface(\n", |
||||
" title=\"Coding Friend\",\n", |
||||
" fn=chat,\n", |
||||
" additional_inputs=[model_selection, audio_enabled],\n", |
||||
" type=\"messages\"\n", |
||||
" )\n", |
||||
"ui.launch(inbrowser=True)" |
||||
] |
||||
} |
||||
], |
||||
"metadata": { |
||||
"kernelspec": { |
||||
"display_name": "Python 3 (ipykernel)", |
||||
"language": "python", |
||||
"name": "python3" |
||||
}, |
||||
"language_info": { |
||||
"codemirror_mode": { |
||||
"name": "ipython", |
||||
"version": 3 |
||||
}, |
||||
"file_extension": ".py", |
||||
"mimetype": "text/x-python", |
||||
"name": "python", |
||||
"nbconvert_exporter": "python", |
||||
"pygments_lexer": "ipython3", |
||||
"version": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
Loading…
Reference in new issue