Add load balancing + websockets example

serverless/load-balancing/build-a-worker.mdx

@@ -247,6 +247,105 @@ async def health_check():
 
 ```
 
+## (Optional) WebSocket support
+
+Load balancing endpoints also support WebSocket connections. This section shows how to add a WebSocket endpoint to your worker and connect to it from a client.
+
+<Tip>
+You can clone the [worker-lb-websocket repository](https://github.com/runpod-workers/worker-lb-websocket) for a complete working example, including scaling tests.
+</Tip>
+
+### Add a WebSocket endpoint
+
+WebSocket endpoints in FastAPI use the `@app.websocket()` decorator. Add the following to your `app.py`:
+
+```python app.py
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+import asyncio
+
+app = FastAPI()
+
+# Track active connections
+active_ws_connections: list[WebSocket] = []
+
+@app.websocket("/ws")
+async def websocket_endpoint(websocket: WebSocket):
+    """Streaming WebSocket endpoint.
+
+    Clients send JSON messages like: {"prompt": "Hello", "max_tokens": 50}
+    Server streams responses back and sends {"done": true} when complete.
+    """
+    await websocket.accept()
+    active_ws_connections.append(websocket)
+
+    try:
+        while True:
+            data = await websocket.receive_json()
+            prompt = data.get("prompt", "")
+
+            if not prompt:
+                await websocket.send_json({"error": "prompt is required"})
+                continue
+
+            # Simulate streaming response (replace with your model)
+            words = f"Response to: {prompt}".split()
+            for i, word in enumerate(words):
+                await websocket.send_json({"token": word, "index": i})
+                await asyncio.sleep(0.05)  # Simulate inference latency
+
+            await websocket.send_json({"done": True})
+
+    except WebSocketDisconnect:
+        pass
+    finally:
+        active_ws_connections.remove(websocket)
+```
+
+### Connect from a client
+
+When connecting to a WebSocket endpoint on a load balancing worker, you must set the `open_timeout` parameter to allow time for workers to scale up. The default timeout of 5 seconds is usually not enough.
+
+```python client.py
+import asyncio
+import json
+import websockets
+
+async def connect_to_worker():
+    url = "wss://ENDPOINT_ID.api.runpod.ai/ws"
+    headers = [("Authorization", "Bearer RUNPOD_API_KEY")]
+
+    # Set open_timeout to allow workers time to scale up (default is ~5s)
+    async with websockets.connect(
+        url,
+        additional_headers=headers,
+        open_timeout=60.0,  # Wait up to 60 seconds for connection
+    ) as ws:
+        # Send a request
+        await ws.send(json.dumps({"prompt": "Hello, world!", "max_tokens": 50}))
+
+        # Receive streaming response
+        while True:
+            response = json.loads(await ws.recv())
+            if response.get("done"):
+                print("Generation complete")
+                break
+            print(response.get("token", ""), end=" ")
+
+asyncio.run(connect_to_worker())
+```
+
+<Warning>
+If you don't set `open_timeout`, connections will fail with a timeout error when workers need to scale up from zero. A value of 60 seconds works for most use cases.
+</Warning>
+
+### Update requirements.txt
+
+Add the `websockets` library to your client's dependencies:
+
+```
+websockets==14.2
+```
+
 ## Troubleshooting
 
 Here are some common issues and methods for troubleshooting:
@@ -256,6 +355,7 @@ Here are some common issues and methods for troubleshooting:
 - **API not accessible**: If your request returns `{"error":"not allowed for QB API"}`, verify that your endpoint type is set to "Load Balancer".
 - **Port issues**: Make sure the environment variable for `PORT` matches what your application is using, and that the `PORT_HEALTH` variable is set to a different port.
 - **Model errors**: Check your model's requirements and whether it's compatible with your GPU.
+- **WebSocket timeout**: If WebSocket connections fail with timeout errors, increase the `open_timeout` parameter in your client code to allow workers time to scale up. See [(Optional) WebSocket support](#optional-websocket-support) for details.
 
 ## Next steps
 

tests/TESTS.md

@@ -129,6 +129,7 @@ Run all smoke tests using local docs
 | serverless-github-deploy | Deploy an endpoint from GitHub | Endpoint from GitHub repo |
 | serverless-ssh-worker | SSH into a running worker for debugging | SSH session established |
 | serverless-metrics | View endpoint metrics (execution time, delay) | Metrics data returned |
+| serverless-lb-websocket | Deploy a load balancing worker with WebSocket support and connect to it | WebSocket connection succeeds and receives streaming response |
 
 ---