Refine user query by another AI agent instead of providing list of dictionry
This commit is contained in:
codenigma1
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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": "75e2ef28-594f-4c18-9d22-c6b8cd40ead2",
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"metadata": {},
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"source": [
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"# Day 3 - Conversational AI - aka Chatbot!"
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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": "70e39cd8-ec79-4e3e-9c26-5659d42d0861",
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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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"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": "231605aa-fccb-447e-89cf-8b187444536a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load environment variables in a file called .env\n",
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"# Print the key prefixes to help with any debugging\n",
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"\n",
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"load_dotenv()\n",
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"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
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"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
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"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
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"\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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"if anthropic_api_key:\n",
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" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
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"else:\n",
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" print(\"Anthropic API Key not set\")\n",
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"\n",
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"if google_api_key:\n",
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" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
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"else:\n",
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" print(\"Google API Key not set\")"
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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": 3,
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"id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Initialize\n",
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"\n",
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"openai = OpenAI()\n",
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"MODEL = 'gpt-4o-mini'"
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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": 4,
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"id": "e16839b5-c03b-4d9d-add6-87a0f6f37575",
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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\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "98e97227-f162-4d1a-a0b2-345ff248cbe7",
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"metadata": {},
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"source": [
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"# Please read this! A change from the video:\n",
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"\n",
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"In the video, I explain how we now need to write a function called:\n",
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"\n",
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"`chat(message, history)`\n",
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"\n",
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"Which expects to receive `history` in a particular format, which we need to map to the OpenAI format before we call OpenAI:\n",
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"\n",
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"```\n",
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"[\n",
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" {\"role\": \"system\", \"content\": \"system message here\"},\n",
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" {\"role\": \"user\", \"content\": \"first user prompt here\"},\n",
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" {\"role\": \"assistant\", \"content\": \"the assistant's response\"},\n",
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" {\"role\": \"user\", \"content\": \"the new user prompt\"},\n",
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"]\n",
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"```\n",
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"\n",
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"But Gradio has been upgraded! Now it will pass in `history` in the exact OpenAI format, perfect for us to send straight to OpenAI.\n",
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"\n",
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"So our work just got easier!\n",
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"\n",
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"We will write a function `chat(message, history)` where: \n",
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"**message** is the prompt to use \n",
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"**history** is the past conversation, in OpenAI format \n",
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"\n",
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"We will combine the system message, history and latest message, then call OpenAI."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "1eacc8a4-4b48-4358-9e06-ce0020041bc1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Simpler than in my video - we can easily create this function that calls OpenAI\n",
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"# It's now just 1 line of code to prepare the input to OpenAI!\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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"\n",
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" print(\"History is:\")\n",
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" print(history)\n",
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" print(\"And messages is:\")\n",
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" print(messages)\n",
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"\n",
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" stream = openai.chat.completions.create(model=MODEL, messages=messages, stream=True)\n",
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"\n",
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" response = \"\"\n",
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" for chunk in stream:\n",
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" response += chunk.choices[0].delta.content or ''\n",
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" yield response"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1334422a-808f-4147-9c4c-57d63d9780d0",
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"metadata": {},
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"source": [
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"## And then enter Gradio's magic!"
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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": "0866ca56-100a-44ab-8bd0-1568feaf6bf2",
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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": 7,
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"id": "1f91b414-8bab-472d-b9c9-3fa51259bdfe",
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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 in a clothes store. You should try to gently encourage \\\n",
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"the customer to try items that are on sale. Hats are 60% off, and most other items are 50% off. \\\n",
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"For example, if the customer says 'I'm looking to buy a hat', \\\n",
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"you could reply something like, 'Wonderful - we have lots of hats - including several that are part of our sales event.'\\\n",
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"Encourage the customer to buy hats if they are unsure what to get.\""
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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": 8,
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"id": "4e5be3ec-c26c-42bc-ac16-c39d369883f6",
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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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"\n",
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" stream = openai.chat.completions.create(model=MODEL, messages=messages, stream=True)\n",
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"\n",
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" response = \"\"\n",
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" for chunk in stream:\n",
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" response += chunk.choices[0].delta.content or ''\n",
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" yield 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": "413e9e4e-7836-43ac-a0c3-e1ab5ed6b136",
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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": 10,
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"id": "d75f0ffa-55c8-4152-b451-945021676837",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_message += \"\\nIf the customer asks for shoes, you should respond that shoes are not on sale today, \\\n",
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"but remind the customer to look at hats!\""
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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": "c602a8dd-2df7-4eb7-b539-4e01865a6351",
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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": 14,
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"id": "ce43fe80",
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"metadata": {},
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"outputs": [],
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"source": [
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"ollama_via_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": 15,
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"id": "4f28e3a8",
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"metadata": {},
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"outputs": [],
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"source": [
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"ollama_system_prompt = \"\"\"You assistant only to refine user query like check grammatical, capital, lower case that make sophisticated prompt.\"\"\""
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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": 16,
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"id": "667a7fbb",
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"metadata": {},
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"outputs": [],
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"source": [
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"ollama_user_prompt = \"\"\"You assistant like RAG technology use to add more informaiton about the content in the user query. \n",
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"Please look at some content as following we don't have in our store:\n",
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"pants\n",
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"sunglasses\n",
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"watch\n",
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"underwear\n",
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"If you find this items in user query, answer gently like The store does not sell like 'e.g. pants'; if they are asked for 'pants', be sure to point out other items on sale.\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": 17,
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"id": "4632f16b",
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"metadata": {},
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"outputs": [],
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"source": [
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"user_messages = [{\"role\": \"system\", \"content\": ollama_system_prompt}, {\"role\": \"user\", \"content\": ollama_user_prompt},]"
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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": 23,
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"id": "0a987a66-1061-46d6-a83a-a30859dc88bf",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Fixed a bug in this function brilliantly identified by student Gabor M.!\n",
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"# I've also improved the structure of this function\n",
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"\n",
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"def chat(message, history):\n",
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" relevant_system_message = system_message\n",
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"\n",
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" # Refine the user query\n",
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" try:\n",
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" refine_query = ollama_via_openai.chat.completions.create(model='llama3.2', messages=user_messages)\n",
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" refined_content = refine_query.choices[0].message.content\n",
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" except Exception as e:\n",
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" print(f\"Error in refinement: {e}\")\n",
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" refined_content = \"Error in refining query.\"\n",
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"\n",
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" # Log the original and refined queries\n",
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" # print(f\"Original User Query: {message}\")\n",
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" # print(f\"Refined Query: {refined_content}\")\n",
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" # print(\"============================== END of REFINE CODE ===================================\")\n",
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"\n",
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" relevant_system_message += refined_content\n",
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" \n",
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" messages = [{\"role\": \"system\", \"content\": relevant_system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
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"\n",
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" stream = openai.chat.completions.create(model=MODEL, messages=messages, stream=True)\n",
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"\n",
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" response = \"\"\n",
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" for chunk in stream:\n",
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" response += chunk.choices[0].delta.content or ''\n",
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" yield 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": "20570de2-eaad-42cc-a92c-c779d71b48b6",
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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": "markdown",
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"id": "82a57ee0-b945-48a7-a024-01b56a5d4b3e",
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"metadata": {},
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"source": [
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"<table style=\"margin: 0; text-align: left;\">\n",
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" <tr>\n",
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" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
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" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
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" </td>\n",
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" <td>\n",
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" <h2 style=\"color:#181;\">Business Applications</h2>\n",
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" <span style=\"color:#181;\">Conversational Assistants are of course a hugely common use case for Gen AI, and the latest frontier models are remarkably good at nuanced conversation. And Gradio makes it easy to have a user interface. Another crucial skill we covered is how to use prompting to provide context, information and examples.\n",
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"<br/><br/>\n",
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"Consider how you could apply an AI Assistant to your business, and make yourself a prototype. Use the system prompt to give context on your business, and set the tone for the LLM.</span>\n",
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" </td>\n",
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" </tr>\n",
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"</table>"
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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": "6dfb9e21-df67-4c2b-b952-5e7e7961b03d",
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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": "llm_env",
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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": "3.11.9"
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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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Reference in New Issue
Block a user