Shopping Assistance Agent using on_query
Introduction
This example creates a shopping assistant agent using FastAPI and uagents. The agent handles shopping questions, utilizes the OpenAI API to generate responses, and sends personalized shopping advice back to the user. The on_query
method is key to processing and responding to these queries.
Supporting Documents
- Almanac contract overview .
- How to create an agent .
- Registering in the Almanac Contract .
- How to use on_query decorator .
Pre-requisites
- Python : Download and install from Python official website (opens in a new tab).
Shopping Assistance Agent
- models : This code defines two data models,
ShoppingRequest
andShoppingAssistResponse
, using the uAgents library, each with a single string attribute (question and answer, respectively).
models.pyfrom uagents import Model class ShoppingRequest(Model): question: str class ShoppingAssistResponse(Model): answer: str
- Shopping Assistance Agent : This code defines a shopping assistant agent using the uAgents framework. The agent listens for incoming
ShoppingRequest
messages, processes them by sending the user's question to OpenAI's GPT-4 API for a response, and then sends the generated response back to the sender as aShoppingAssistResponse
.
agent.pyfrom uagents import Agent, Context from uagents.setup import fund_agent_if_low from models import ShoppingAssistResponse, ShoppingRequest import requests import json agent = Agent( name="Shopping Assistant", seed="shopping_assistant_seed_phrase", port=8001, endpoint="http://localhost:8001/submit", ) fund_agent_if_low(agent.wallet.address()) YOUR_OPEN_AI_API_KEY = "PASTE_YOUR_OPEN_AI_API_KEY" def get_chat_response(messages): """ Sends a request to the OpenAI GPT-4 API to generate a chat response. Accepts: - messages: A list of dictionaries representing the conversation history, where each dictionary contains a 'role' (e.g., 'system' or 'user') and 'content' (the message text). What it does: - Sends a POST request to the OpenAI API with the conversation history. - Handles the API response, returning the generated response from the model or an error message if the request fails. Returns: - A string containing the model's response if the request is successful. - An error message string if the request fails. """ api_url = "https://api.openai.com/v1/chat/completions" headers = { "Authorization": f"Bearer {YOUR_OPEN_AI_API_KEY}", "Content-Type": "application/json", } data = { "model": "gpt-4", "messages": messages, "max_tokens": 150, "temperature": 0.7, } response = requests.post(api_url, headers=headers, data=json.dumps(data)) if response.status_code == 200: result = response.json() return result["choices"][0]["message"]["content"].strip() else: return f"Error: {response.status_code} - {response.text}" @agent.on_query(model=ShoppingRequest, replies=ShoppingAssistResponse) async def handler(ctx: Context, sender: str, msg: ShoppingRequest): """ Handles incoming queries from other agents, specifically ShoppingRequest messages. Accepts: - ctx: The context object, used for logging and communication. - sender: A string representing the address of the agent that sent the message. - msg: A ShoppingRequest object containing the question from the user. What it does: - Logs the received question. - Prepares a conversation history for the chat model with the role of the assistant and user. - Sends the conversation history to the get_chat_response function to generate a response. - Logs the generated response. - Sends the response back to the sender as a ShoppingAssistResponse. Returns: - None (asynchronous function). """ ctx.logger.info(f"Received message from {sender} with question: {msg.question}") messages = [{"role": "system", "content": "You are a helpful shopping assistant."}] messages.append({"role": "user", "content": msg.question}) response = get_chat_response(messages) ctx.logger.info(f"Question: {msg.question}\nAnswer: {response}") await ctx.send(sender, ShoppingAssistResponse(answer=response)) if __name__ == "__main__": agent.run()
You need to have an OpenAI API key to run this example. You can get it from OpenAI (opens in a new tab) .
- main.py : This FastAPI code starts by defining an endpoint
/api/question-answer
that accepts a query parameter message, sends it to an agent for processing, and returns the agent's response.
main.pyfrom fastapi import FastAPI, Query from uagents.query import query from models import ShoppingRequest import json app = FastAPI() DESTINATION = "agent1qg9p5xppsdv6067lg570g8t3lzens32fg6d0fe88jawd2v0lsh8f6ru9ntc" @app.get("/api/question-answer") async def question_answering( message: str = Query(..., description="The message or question you want to ask") ): """ Handles GET requests to the /api/question-answer endpoint. Accepts: - message: A string query parameter representing the message or question that you want to ask. What it does: - Creates a ShoppingRequest object using the provided message. - Sends the ShoppingRequest object to the specified agent (DESTINATION) using the query function. - Decodes the response payload received from the agent and parses it as JSON. Returns: - A dictionary containing the "response" with the answer retrieved from the agent. """ shopping_request = ShoppingRequest(question=message) answer = await query(destination=DESTINATION, message=shopping_request) response_data = json.loads(answer.decode_payload()) return {"response": response_data.get("answer")} @app.get("/") async def root(): """ Handles GET requests to the root endpoint ("/"). What it does: - Returns a simple "Hello World" message. Returns: - A dictionary containing a "message" key with the value "Hello World". """ return {"message": "Hello World"}
- pyproject.toml : Poetry Dependencies
pyproject.toml[tool.poetry.dependencies] python = ">=3.10,<3.12" fastapi = "^0.112.0" uvicorn = {extras = ["standard"], version = "^0.30.5"} uagents = "^0.15.1"
How to Run This Example
To get started with this example, follow these steps:
-
Activate the Poetry Shell and Install Dependencies
Open your terminal and run the following command to activate the Poetry shell and install all dependencies:
poetry shell
-
Start the FastAPI Server
Navigate to the src directory and start the FastAPI server using uvicorn:
cd src uvicorn main:app
-
Run the Agent Code
Execute the agent code with the following command:
python agent.py
Expected output
- Curl Request
curl -G -L --data-urlencode "message=I want to purchase a MacBook" "http://localhost:8000/api/question-answer/"
- Curl Response
{"response":"Great choice! MacBooks are known for their sleek design, impressive performance, and high-quality display. Here are some things to consider before your purchase:\n\n1. **Model**: Apple currently offers MacBook Air and MacBook Pro. Each model comes with different sizes and specifications. The MacBook Air is a slim, lightweight machine, perfect for casual use or on-the-go work. The MacBook Pro is a more powerful machine, suitable for heavy-duty tasks like video editing, graphic design, and professional applications.\n\n2. **Specifications**: Depending on your needs, you might need more or less power. Consider the processor speed, the amount of RAM, the size of the solid-state drive (SSD), and the type of graphics card.\n\n3. **Budget**: Mac"}
- Shopping Assistance Agent
INFO: [Shopping Assistant]: Almanac registration is up to date! INFO: [Shopping Assistant]: Starting server on http://0.0.0.0:8001 (Press CTRL+C to quit) INFO: [Shopping Assistant]: Received message from user1fu0nqpf7mgxyms0wwvphgt8p3a7cvzfqqq6tkmxlhkr740glxzjsmzudat with question: I want to purchase a MacBook INFO: [Shopping Assistant]: Question: I want to purchase a MacBook Answer: Great choice! MacBooks are known for their sleek design, impressive performance, and high-quality display. Here are some things to consider before your purchase: 1. **Model**: Apple currently offers MacBook Air and MacBook Pro. Each model comes with different sizes and specifications. The MacBook Air is a slim, lightweight machine, perfect for casual use or on-the-go work. The MacBook Pro is a more powerful machine, suitable for heavy-duty tasks like video editing, graphic design, and professional applications. 2. **Specifications**: Depending on your needs, you might need more or less power. Consider the processor speed, the amount of RAM, the size of the solid-state drive (SSD), and the type of graphics card. 3. **Budget**: Mac