docs(routing.md): update routing docs

docs/my-website/docs/routing.md

@@ -247,16 +247,24 @@ print(f"response: {response}")
 
 If you want a server to just route requests to different LLM APIs, use our [OpenAI Proxy Server](./simple_proxy.md#multiple-instances-of-1-model)
 
-## Queuing
-
-### Quick Start 
+## Queuing (Beta)
 
 This requires a [Redis DB](https://redis.com/) to work. 
 
 Our implementation uses LiteLLM's proxy server + Celery workers to process up to 100 req./s
 
+:::info
+
+This is pretty new, and might have bugs. Any contributions to improving our implementation are welcome
+
+:::
+
+
 [**See Code**](https://github.com/BerriAI/litellm/blob/fbf9cab5b9e35df524e2c9953180c58d92e4cd97/litellm/proxy/proxy_server.py#L589)
 
+
+### Quick Start 
+
 1. Add Redis credentials in a .env file
 
 ```python

litellm/proxy/proxy_cli.py

@@ -139,11 +139,6 @@ def run_server(host, port, api_base, api_version, model, alias, add_key, headers
                     status = polling_response["status"]
                     if status == "finished":
                         llm_response = polling_response["result"]
-                        with open("response_log.txt", "a") as log_file:
-                            log_file.write(
-                                f"Response ID: {llm_response.get('id', 'NA')}\nLLM Response: {llm_response}\nTime: {end_time - start_time:.2f} seconds\n\n"
-                            )
-
                         break
                     print(f"POLLING JOB{polling_url}\nSTATUS: {status}, \n Response {polling_response}")
                     time.sleep(0.5)
@@ -156,7 +151,7 @@ def run_server(host, port, api_base, api_version, model, alias, add_key, headers
 
         # List to store the futures of concurrent calls
         futures = []
-
+        start_time = time.time()
         # Make concurrent calls
         with concurrent.futures.ThreadPoolExecutor(max_workers=concurrent_calls) as executor:
             for _ in range(concurrent_calls):
@@ -175,7 +170,8 @@ def run_server(host, port, api_base, api_version, model, alias, add_key, headers
                     successful_calls += 1
                 else:
                     failed_calls += 1
-
+        end_time = time.time()
+        print(f"Elapsed Time: {end_time-start_time}")
         print(f"Load test Summary:")
         print(f"Total Requests: {concurrent_calls}")
         print(f"Successful Calls: {successful_calls}")

litellm/proxy/proxy_server.py

@@ -618,7 +618,7 @@ async def async_queue_response(request: Request, task_id: str):
     try: 
         job = async_result(task_id, app=celery_app_conn)
         if job.ready():
-            return job.result
+            return {"status": "finished", "result": job.result}
         else:
             return {'status': 'queued'}
     except Exception as e: