building a microservice utilising ASI-1

Introduction

LLM Assistants are everywhere, and building one with ASI-1 couldn’t be simpler.

Installation

Head over to as1.ai, and create an api key.
Make sure you have Flask installed .
Make sure you have Pydantic installed .

Server

Create new file called simple_server.py and copy the below code in and save.

simple_server.py


 
from flask import Flask, request, jsonify
from pydantic import BaseModel, ValidationError
import json
import requests
 
app = Flask(__name__)
 
 
class QuestionRequest(BaseModel):
    question: str
 
 
class ResponseObject(BaseModel):
    response: str
 
 
@app.route('/ask', methods=['POST'])
def ask():
    try:
        question_data = QuestionRequest(**request.json)
 
        HEADERS = {
            'Content-Type': 'application/json',
            'Accept': 'application/json',
            'Authorization': 'bearer {ASI_KEY_HERE}'
        }
 
        URL = "https://api.asi1.ai/v1/chat/completions"
 
        MODEL = "asi1-mini"
 
        payload = json.dumps({
            "model": MODEL,
            "messages": [
                {
                    "role": "user",
                    "content": question_data.question
                }
            ],
            "temperature": 0,
            "stream": False,
            "max_tokens": 0
        })
 
        response = requests.request("POST", URL, headers=HEADERS, data=payload)
 
        return jsonify(ResponseObject(response=response.json()["choices"][0]["message"]["content"]).model_dump())
 
    except ValidationError as e:
        return jsonify({"error": e.errors()}), 400
 
 
if __name__ == '__main__':
    app.run(debug=True)

The above is a simple rest endpoint example, we assume you know how to use Flask, so let’s just get into the extras:


class QuestionRequest(BaseModel):
    question: str
 
class ResponseObject(BaseModel):
    response: str

We define two objects that represent the JSON object we receive, and the JSON object we return.

Inside /ask:


@app.route('/ask', methods=['POST'])
def ask():

We create our call to ASI-1:


HEADERS = {
    'Content-Type': 'application/json',
    'Accept': 'application/json',
    'Authorization': 'bearer {ASI_KEY_HERE}'
}
 
URL = "https://api.asi1.ai/v1/chat/completions"
 
MODEL = "asi1-mini"
 
payload = json.dumps({
    "model": MODEL,
    "messages": [
        {
            "role": "user",
            "content": question_data.question
        }
    ],
    "temperature": 0,
    "stream": False,
    "max_tokens": 5
})

Our payload is simple with only a few things to know:

messages[n]["role"] - in a chat based system, always give the role of user, then the AI will respond as the “teacher”.

temperature - the lower this is, the less random, great for strict Q&A.

stream - either returns the response in one go, or partially as the AI reasons. In this example we set it to False, so the response is returned in one. You can see the response from Stream=True here.

max_tokens - how much you are prepared to pay for the response.

Finally, we return the response to the user.

return jsonify(ResponseObject(response=response.json()["choices"][0]["message"]["content"]).model_dump())

For clarity, here is the output of response.json() :


 
{
  'model': 'asi1-mini',
  'id': 'id_g04OXB7PVmubCj8pY',
  'executable_data': [],
  'conversation_id': None,
  'thought': [
    'The user is asking for the capital of France. This is a straightforward question, and the answer is well-known and verifiable within the knowledge cutoff date. No complex reasoning or steps are needed here—just provide the correct and concise answer.'
  ],
  'tool_thought': [],
  'choices': [
    {
      'index': 0,
      'finish_reason': 'stop',
      'message': {
        'role': 'assistant',
        'content': 'The capital of France is **Paris**.'
      }
    }
  ],
  'usage': {
    'prompt_tokens': 44,
    'completion_tokens': 65,
    'total_tokens': 109
  }
}

Let’s start the server with python app.py

The server will start on http://127.0.0.1:5000/ .

Testing

Let’s curl a request:

example


curl -X POST http://127.0.0.1:5000/ask \
     -H "Content-Type: application/json" \
     -d '{"question": "What is the capital of France?"}'

and our response:

response


curl -X POST http://127.0.0.1:5000/ask \
     -H "Content-Type: application/json" \
     -d '{"question": "What is the capital of France?"}'
{
  "response": "The capital of France is Paris."
}

Next steps

That’s all that is required for a Flask server to use ASI-1. We hope this example gives you the information you need to build some incredible applications and microservices utilising ASI-1.

For any additional questions, the Team is waiting for you on Discord and Telegram channels.