Streaming Data Through Azure Function Apps

Introduction

In this blog post, we will explore how to stream data through Azure Function Apps using Python. I assume the reader is familiar with the basics of Azure Functions.

We'll be implementing an application using the Azure Functions V2 Python programming model and show you how to stream data. In this example, we will use a Fibonacci generator to stream Fibonacci sequence numbers to the client. This can also be used to build a proxy server that streams LLM (GPT-4) data back to a user on your app which gives more control over authentication and allows for adding data to the LLM's messages.

Prerequisites

Before proceeding, make sure you have:

Basic understanding of Azure Functions.
Python 3.8 or later.

Add the following environment variables to your local.settings.json file when running locally, or to your environmental valuables in the Azure Portal when running in the cloud.

{
  "Values": {
    "PYTHON_ISOLATE_WORKER_DEPENDENCIES": "1",
    "PYTHON_ENABLE_INIT_INDEXING": "1"
  }
}
```## Setup

### Step 1: Install Required Packages

First, make sure you have the `azurefunctions-extensions-http-fastapi` package installed. Add it to your `requirements.txt` file:

```azurefunctions-extensions-http-fastapi
```Install the package using pip:

```bash
pip install -r requirements.txt
```### Step 2: Create the Azure Function App

Here, we create an Azure Function App using the following code. This example demonstrates how to stream Fibonacci sequence numbers to the client.

```python
import asyncio
import azure.functions as func
from azurefunctions.extensions.http.fastapi import Request, StreamingResponse

# Azure Function App
app = func.FunctionApp(http_auth_level=func.AuthLevel.ANONYMOUS)

def fibonacci_generator():
    """A generator to yield Fibonacci sequence numbers"""
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

@app.route(route="stream-fibonacci", methods=[func.HttpMethod.GET])
async def stream_fibonacci(req: Request) -> StreamingResponse:
    async def fibonacci_stream():
        for number in fibonacci_generator():
            await asyncio.sleep(
                1
            )  # Ensures that the process yields control and doesn't block
            yield f"{number}\n"

    return StreamingResponse(fibonacci_stream(), media_type="text/event-stream")
```In the example above, we define a Fibonacci generator and use it in the `stream_fibonacci` function, which returns a `StreamingResponse` to the client for continuous data streaming.

### Step 3: Configure Environment Variables

Ensure the following environment variables are set in your Azure Function App settings:

```json
"PYTHON_ISOLATE_WORKER_DEPENDENCIES": "1",
"PYTHON_ENABLE_INIT_INDEXING": "1"
```For local development, add these to the `local.settings.json` file:

```json
{
  "Values": {
    "PYTHON_ISOLATE_WORKER_DEPENDENCIES": "1",
    "PYTHON_ENABLE_INIT_INDEXING": "1"
  }
}
```### Step 4: Deploy Your Function App

Use the Azure CLI or VSCode extensions to deploy your function app.

```## Running the Function App Locally

To run the function locally, use:

```bash
func start
```Now, navigate to `http://localhost:7071/api/stream-fibonacci` to see the streaming Fibonacci sequence.

## Conclusion

In this post, we covered how to create and stream data using Azure Function Apps with the V2 Python programming model. The example demonstrated a Fibonacci sequence generator for streaming. With these steps, you can extend the concept for various data streaming applications.