Merge pull request #271 from simonsteinberg/community-contributions-branch
The Airline AI Assistant can now compare prices
This commit is contained in:
Ed Donner
GitHub
275
week2/community-contributions/day4_compare_prices.ipynb
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275
week2/community-contributions/day4_compare_prices.ipynb
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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 be\\\\gins {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 = [\n",
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" {\"role\": \"system\", \"content\": system_message}\n",
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" ] + history + [\n",
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" {\"role\": \"user\", \"content\": message}\n",
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" ]\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": "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": "ad32321f-083a-4462-a6d6-7bb3b0f5d10a",
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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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" responses = []\n",
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" for tool_call in message.tool_calls: \n",
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" if tool_call.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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" 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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" responses.append(response)\n",
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" return responses"
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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 = [\n",
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" {\"role\": \"system\", \"content\": system_message}\n",
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" ] + history + [\n",
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" {\"role\": \"user\", \"content\": message}\n",
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" ]\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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" # Tool usage\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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" responses = handle_tool_call(message)\n",
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" messages.append(message) # That's the assistant asking us to run a tool\n",
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" for response in responses:\n",
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" messages.append(response) # That's the result of the tool calls\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": "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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"cell_type": "code",
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"execution_count": null,
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"id": "8dc18486-4d6b-4cbf-a6b8-16d08d7c4f54",
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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": "3.13.2"
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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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