{ "cells": [ { "cell_type": "markdown", "id": "ddfa9ae6-69fe-444a-b994-8c4c5970a7ec", "metadata": {}, "source": [ "# Project - Airline AI Assistant\n", "\n", "We'll now bring together what we've learned to make an AI Customer Support assistant for an Airline" ] }, { "cell_type": "code", "execution_count": 2, "id": "8b50bbe2-c0b1-49c3-9a5c-1ba7efa2bcb4", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import json\n", "from dotenv import load_dotenv\n", "from openai import OpenAI\n", "import gradio as gr" ] }, { "cell_type": "code", "execution_count": 3, "id": "747e8786-9da8-4342-b6c9-f5f69c2e22ae", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OpenAI API Key exists and begins sk-proj-\n", "\u001b[?25lpulling manifest ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest \n", "pulling dde5aa3fc5ff... 100% ▕████████████████▏ 2.0 GB \n", "pulling 966de95ca8a6... 100% ▕████████████████▏ 1.4 KB \n", "pulling fcc5a6bec9da... 100% ▕████████████████▏ 7.7 KB \n", "pulling a70ff7e570d9... 100% ▕████████████████▏ 6.0 KB \n", "pulling 56bb8bd477a5... 100% ▕████████████████▏ 96 B \n", "pulling 34bb5ab01051... 100% ▕████████████████▏ 561 B \n", "verifying sha256 digest \n", "writing manifest \n", "success \u001b[?25h\n" ] } ], "source": [ "# Initialization\n", "\n", "load_dotenv()\n", "\n", "openai_api_key = os.getenv('OPENAI_API_KEY')\n", "if openai_api_key:\n", " print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", "else:\n", " print(\"OpenAI API Key not set\")\n", " \n", "MODEL = \"gpt-4o-mini\"\n", "openai = OpenAI()\n", "\n", "# As an alternative, if you'd like to use Ollama instead of OpenAI\n", "# Check that Ollama is running for you locally (see week1/day2 exercise) then uncomment these next 2 lines\n", "MODEL = \"llama3.2\"\n", "openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", "\n", "# Let's just make sure the model is loaded\n", "\n", "!ollama pull llama3.2" ] }, { "cell_type": "code", "execution_count": 4, "id": "0a521d84-d07c-49ab-a0df-d6451499ed97", "metadata": {}, "outputs": [], "source": [ "system_message = \"You are a helpful assistant for an Airline called FlightAI. \"\n", "system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n", "system_message += \"Always be accurate. If you don't know the answer, say so.\"" ] }, { "cell_type": "code", "execution_count": 5, "id": "61a2a15d-b559-4844-b377-6bd5cb4949f6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7865\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This function looks rather simpler than the one from my video, because we're taking advantage of the latest Gradio updates\n", "\n", "def chat(message, history):\n", " messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", " response = openai.chat.completions.create(model=MODEL, messages=messages)\n", " return response.choices[0].message.content\n", "\n", "gr.ChatInterface(fn=chat, type=\"messages\").launch()" ] }, { "cell_type": "markdown", "id": "36bedabf-a0a7-4985-ad8e-07ed6a55a3a4", "metadata": {}, "source": [ "## Tools\n", "\n", "Tools are an incredibly powerful feature provided by the frontier LLMs.\n", "\n", "With tools, you can write a function, and have the LLM call that function as part of its response.\n", "\n", "Sounds almost spooky.. we're giving it the power to run code on our machine?\n", "\n", "Well, kinda." ] }, { "cell_type": "code", "execution_count": 8, "id": "0696acb1-0b05-4dc2-80d5-771be04f1fb2", "metadata": {}, "outputs": [], "source": [ "# Let's start by making a useful function\n", "\n", "ticket_prices = {\"london\": \"$799\", \"paris\": \"$899\", \"tokyo\": \"$1400\", \"berlin\": \"$499\"}\n", "\n", "def get_ticket_price(destination_city):\n", " print(f\"Tool get_ticket_price called for {destination_city}\")\n", " city = destination_city.lower()\n", " return ticket_prices.get(city, \"Unknown\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "80ca4e09-6287-4d3f-997d-fa6afbcf6c85", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tool get_ticket_price called for Berlin\n" ] }, { "data": { "text/plain": [ "'$499'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_ticket_price(\"Berlin\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "4afceded-7178-4c05-8fa6-9f2085e6a344", "metadata": {}, "outputs": [], "source": [ "# There's a particular dictionary structure that's required to describe our function:\n", "\n", "price_function = {\n", " \"name\": \"get_ticket_price\",\n", " \"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", " \"parameters\": {\n", " \"type\": \"object\",\n", " \"properties\": {\n", " \"destination_city\": {\n", " \"type\": \"string\",\n", " \"description\": \"The city that the customer wants to travel to\",\n", " },\n", " },\n", " \"required\": [\"destination_city\"],\n", " \"additionalProperties\": False\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 11, "id": "bdca8679-935f-4e7f-97e6-e71a4d4f228c", "metadata": {}, "outputs": [], "source": [ "# And this is included in a list of tools:\n", "\n", "tools = [{\"type\": \"function\", \"function\": price_function}]" ] }, { "cell_type": "markdown", "id": "c3d3554f-b4e3-4ce7-af6f-68faa6dd2340", "metadata": {}, "source": [ "## Getting OpenAI to use our Tool\n", "\n", "There's some fiddly stuff to allow OpenAI \"to call our tool\"\n", "\n", "What we actually do is give the LLM the opportunity to inform us that it wants us to run the tool.\n", "\n", "Here's how the new chat function looks:" ] }, { "cell_type": "code", "execution_count": 13, "id": "ce9b0744-9c78-408d-b9df-9f6fd9ed78cf", "metadata": {}, "outputs": [], "source": [ "def chat(message, history):\n", " messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", " response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n", "\n", " if response.choices[0].finish_reason==\"tool_calls\":\n", " message = response.choices[0].message\n", " response, city = handle_tool_call(message)\n", " messages.append(message)\n", " messages.append(response)\n", " response = openai.chat.completions.create(model=MODEL, messages=messages)\n", " \n", " return response.choices[0].message.content" ] }, { "cell_type": "code", "execution_count": 14, "id": "b0992986-ea09-4912-a076-8e5603ee631f", "metadata": {}, "outputs": [], "source": [ "# We have to write that function handle_tool_call:\n", "\n", "def handle_tool_call(message):\n", " tool_call = message.tool_calls[0]\n", " arguments = json.loads(tool_call.function.arguments)\n", " city = arguments.get('destination_city')\n", " price = get_ticket_price(city)\n", " response = {\n", " \"role\": \"tool\",\n", " \"content\": json.dumps({\"destination_city\": city,\"price\": price}),\n", " \"tool_call_id\": tool_call.id\n", " }\n", " return response, city" ] }, { "cell_type": "code", "execution_count": 15, "id": "f4be8a71-b19e-4c2f-80df-f59ff2661f14", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7866\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Tool get_ticket_price called for Tokyo\n", "Tool get_ticket_price called for London\n", "Tool get_ticket_price called for London\n", "Tool get_ticket_price called for Tokyo\n" ] } ], "source": [ "gr.ChatInterface(fn=chat, type=\"messages\").launch()" ] } ], "metadata": { "kernelspec": { "display_name": "llms", "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 }