Build an Alpaca/Vicuna 13B Streaming API with Python, FastAPI & Starlette

If you’re looking to build a streaming API for Alpaca or Vicuna 13B using Python, FastAPI and Starlette are the perfect tools for the job. In this article, we’ll demonstrate how to use these powerful frameworks to create a high-performance streaming API that can handle a large number of concurrent connections.

What is Alpaca/Vicuna 13B?

Alpaca and Vicuna 13B are both popular solutions for real-time data streaming and analytics. They are commonly used in financial and trading applications, where low latency and high throughput are crucial. By building a streaming API for Alpaca/Vicuna 13B, you can access real-time market data, execute trades, and perform complex analytics with ease.

Using FastAPI and Starlette

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. It is built on top of Starlette for the web parts and is designed to be easy to use and highly efficient. Together, they provide a powerful and flexible foundation for building streaming APIs.

Building the Streaming API

Here’s a basic example of how to use FastAPI and Starlette to create a simple streaming API:

    
    from fastapi import FastAPI, WebSocket
    import json

    app = FastAPI()

    @app.websocket("/ws")
    async def websocket_endpoint(websocket: WebSocket):
        await websocket.accept()
        while True:
            data = get_data_from_alpaca_or_vicuna()
            await websocket.send_text(json.dumps(data))
    
    

In this example, we define a WebSocket endpoint using the WebSocket class provided by FastAPI. Inside the endpoint function, we use a while loop to continuously fetch data from Alpaca or Vicuna 13B and send it to the connected clients over the WebSocket connection.

Handling Concurrent Connections

One of the key advantages of using FastAPI and Starlette for building a streaming API is their excellent support for handling concurrent connections. These frameworks are designed to be fully asynchronous, which means they can efficiently manage a large number of simultaneous connections without blocking or slowing down.

Conclusion

With the power of Python, FastAPI, and Starlette, you can easily build a high-performance streaming API for Alpaca or Vicuna 13B. By following the example provided in this article, you can create a robust and efficient solution for accessing real-time market data, executing trades, and conducting complex analytics.

So, if you’re looking to harness the power of Alpaca or Vicuna 13B for your financial or trading applications, give FastAPI and Starlette a try and see the difference it can make in your streaming API development.