Add eCommerce Assistant notebook for product price retrieval week2
This commit is contained in:
ariel1985
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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": "e3a5643a-c247-4a9b-8c57-ec9b1e89c088",
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"metadata": {},
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"source": [
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"# Week 2 - eCommerce Assistant for products price from dictionary\n",
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"\n",
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"An eCommerce assitant that can get a product price\n",
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"\n",
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"Gradio for chat box"
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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": "756573b3-72b2-4102-a773-91c278e5c4fd",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# !ollama pull 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": "57ae8d30-f7aa-47a3-bab8-b7002e87a8f7",
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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": "ef7f8b2b-1d6a-4bbd-858a-be187ccfc02a",
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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')"
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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": "7b46dd52-8a3c-42d1-ac24-59f5eb5aaba1",
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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 online store called CommerceAI. \"\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": "31a6431b-215d-4f46-b813-971d8af7e034",
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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": "d3586bfb-acc3-4b5e-95be-02120b696f98",
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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": "c9ac43e8-9880-44f6-b03a-6d1ab05bbb94",
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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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"items_prices = {f\"item{i}\": f\"{i*100}\" for i in range(1,6)}\n",
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"\n",
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"items_prices = {\"printer\": \"$500\", \"paper\": \"$10\", \"mini printer\": \"$50\", \"label printer\": \"$60\", \"sticker-paper\": \"$5\"}\n",
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"\n",
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"def get_item_price(product):\n",
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" print(f\"Tool get_item_price called for {product}\")\n",
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" item = product.lower()\n",
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" return items_prices.get(item, \"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": "88d249f7-787d-4750-b5b9-7df108da1b57",
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"metadata": {},
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"outputs": [],
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"source": [
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"items_prices"
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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": "ef3c3897-9a57-4f04-b5d0-f9ac8bb02d00",
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"metadata": {},
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"outputs": [],
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"source": [
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"get_item_price(\"mini printer\")"
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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": "511ef9b8-bec0-4f14-b647-057e14c849cc",
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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_item_price\",\n",
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" \"description\": \"Get the price of an item in the store. \\\n",
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" Call this whenever you need to know the store item price , \\\n",
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" for example when a customer asks 'How much is a mini printer' \",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"product\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The item that the customer wants to buy\"\n",
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" },\n",
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" },\n",
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" \"required\": [\"product\"],\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": "86f674a4-4b7c-443d-b025-0f016932508a",
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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": "724d0f89-8a86-493e-8cd1-73814688a70b",
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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": "2d67fb72-132e-499e-9931-86cb71b634b6",
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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, item = handle_tool_call(message)\n",
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" print('response', response, 'item', item)\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": "3b4de767-954a-4077-a5f7-0055a0b90393",
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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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" item = arguments.get('product') \n",
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" print('product', item)\n",
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" price = get_item_price(item)\n",
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" response = {\n",
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" \"role\": \"tool\",\n",
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" \"content\": json.dumps({\"item\": item,\"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, item\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": "5ffda702-6ac5-4d13-9703-a14fa93aea68",
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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": "0ae2edbf-de58-43fa-b380-267cfc1755de",
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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": "3.11.11"
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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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