Building an AI Language Tutor uAgent with ASI-1 Mini

This guide explains how to create a simple uAgent that serves as an AI language tutor using the ASI-1 Mini API. The agent accepts a user query and returns language learning assistance—such as translation, grammar correction, or pronunciation tips—based on the provided prompt.

Overview

Purpose

This project demonstrates how to integrate ASI-1 Mini, Fetch.ai's Web3-native large language model, into a uAgent. The agent, named AI_Language_Tutor, is designed to:

• Call the ASI-1 Mini API with a prompt tailored for language learning.
• Provide translation, grammar correction, or pronunciation tips based on user input.
• Log the response upon agent startup.

Prerequisites

Before running this project, ensure you have:

• Python 3.11 installed.
• uAgents library installed. If not already installed, use:

pip install uagents

• Requests library installed:

pip install requests

• A valid API key for ASI-1 Mini. Get your API Key here.

Script Breakdown

1. Importing Required Libraries

The script begins by importing the necessary modules:

• requests: To perform HTTP requests to the ASI-1 Mini API.
• json: For handling JSON responses and potential JSON decode errors.
• uagents: To create the uAgent and manage events.
• Context: Provides access to the agent's runtime context, including logging.

import requests
import json
from uagents import Agent, Context

2. Initializing the uAgent

An instance of the uAgent is created with a name, port, and endpoint.

agent = Agent(
    name="AI_Language_Tutor",
    port=8000,  # You can change this to any available port
    endpoint="http://localhost:8000/submit"
)

3. Defining the ASI-1 Mini API Helper Function

The function get_language_help constructs a custom prompt and sends a POST request to the ASI-1 Mini API. Based on the user's query and target language, it asks the API to either translate, correct grammar, or offer pronunciation tips.

def get_language_help(query: str, target_language: str = "Spanish") -> str:
    url = "https://api.asi1.ai/v1/chat/completions"
    headers = {
        'Content-Type': 'application/json',
        'Authorization': f'Bearer <Your_ASI1_Mini_API_Key>'  # Replace with your API Key
    }

    prompt = f"""You are an AI language tutor. Help the user with their language learning request:

    - If they ask for a **translation**, provide it in {target_language}.
    - If they provide a sentence, **correct any grammar mistakes**.
    - If they ask for pronunciation tips, explain how to say it.

    User request: "{query}"
    """

    payload = {
        "model": "asi1-mini",
        "messages": [{"role": "user", "content": prompt}],
        "temperature": 0.7,
        "max_tokens": 0
    }

    try:
        response = requests.post(url, headers=headers, json=payload)
        response.raise_for_status()
        return response.json()['choices'][0]['message']['content']

    except requests.exceptions.RequestException as e:
        return f"API Request Error: {str(e)}"

    except json.JSONDecodeError:
        return "API Error: Unable to parse JSON response"

4. Startup Event Handler

The agent registers an on_event("startup") handler. When the agent starts, it:

• Logs a sample query.
• Calls the get_language_help function with an example query.
• Logs the returned response from the ASI-1 Mini API.

@agent.on_event("startup")
async def language_tutor_demo(ctx: Context):
    query = "How do you say 'Good morning' in French?"  # Example query
    ctx.logger.info(f"User query: {query}")
    response = get_language_help(query, target_language="French")
    ctx.logger.info(f"🌍 AI Tutor Response: {response}")

5. Running the Agent

The script concludes by running the agent, which starts the server and registers the startup handler.

if __name__ == "__main__":
    agent.run()

Running the Agent

1. Activate Your Virtual Environment:

source venv/bin/activate   # On Windows: venv\Scripts\activate

2. Run the Script:

python my_language_tutor.py

3. Expected Output:

Upon startup, you should see log messages similar to:

INFO: [AI_Language_Tutor]: Starting agent with address: agent1...
INFO: [AI_Language_Tutor]: User query: How do you say 'Good morning' in French?
INFO: [AI_Language_Tutor]: 🌍 AI Tutor Response: Bonjour (Good morning/Good day).

GitHub Repository

For the complete code and additional examples, visit the ASI-1-Mini-simple-Examples repository.

note

Note: You can learn more about ASI-1 Mini APIs here.