[Feature] Simple API token authentication and pluggable middlewares (v…

…llm-project#1106)

docs/source/getting_started/quickstart.rst

@@ -63,38 +63,10 @@ Call ``llm.generate`` to generate the outputs. It adds the input prompts to vLLM
 
 The code example can also be found in `examples/offline_inference.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference.py>`_.
 
-
-API Server
-----------
-
-vLLM can be deployed as an LLM service. We provide an example `FastAPI <https://fastapi.tiangolo.com/>`_ server. Check `vllm/entrypoints/api_server.py <https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/api_server.py>`_ for the server implementation. The server uses ``AsyncLLMEngine`` class to support asynchronous processing of incoming requests.
-
-Start the server:
-
-.. code-block:: console
-
-    $ python -m vllm.entrypoints.api_server
-
-By default, this command starts the server at ``http://localhost:8000`` with the OPT-125M model.
-
-Query the model in shell:
-
-.. code-block:: console
-
-    $ curl http://localhost:8000/generate \
-    $     -d '{
-    $         "prompt": "San Francisco is a",
-    $         "use_beam_search": true,
-    $         "n": 4,
-    $         "temperature": 0
-    $     }'
-
-See `examples/api_client.py <https://github.com/vllm-project/vllm/blob/main/examples/api_client.py>`_ for a more detailed client example.
-
 OpenAI-Compatible Server
 ------------------------
 
-vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API.
+vLLM can be deployed as a server that implements the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API.
 By default, it starts the server at ``http://localhost:8000``. You can specify the address with ``--host`` and ``--port`` arguments. The server currently hosts one model at a time (OPT-125M in the command below) and implements `list models <https://platform.openai.com/docs/api-reference/models/list>`_, `create chat completion <https://platform.openai.com/docs/api-reference/chat/completions/create>`_, and `create completion <https://platform.openai.com/docs/api-reference/completions/create>`_ endpoints. We are actively adding support for more endpoints.
 
 Start the server:
@@ -118,6 +90,8 @@ This server can be queried in the same format as OpenAI API. For example, list t
 
     $ curl http://localhost:8000/v1/models
 
+You can pass in the argument ``--api-key`` or environment variable ``VLLM_API_KEY`` to enable the server to check for API key in the header.
+
 Using OpenAI Completions API with vLLM
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

vllm/entrypoints/openai/api_server.py

@@ -2,6 +2,10 @@
 import asyncio
 import json
 from contextlib import asynccontextmanager
+import os
+import importlib
+import inspect
+
 from aioprometheus import MetricsMiddleware
 from aioprometheus.asgi.starlette import metrics
 import fastapi
@@ -65,6 +69,13 @@ def parse_args():
                         type=json.loads,
                         default=["*"],
                         help="allowed headers")
+    parser.add_argument(
+        "--api-key",
+        type=str,
+        default=None,
+        help=
+        "If provided, the server will require this key to be presented in the header."
+    )
     parser.add_argument("--served-model-name",
                         type=str,
                         default=None,
@@ -95,6 +106,17 @@ def parse_args():
         type=str,
         default=None,
         help="FastAPI root_path when app is behind a path based routing proxy")
+    parser.add_argument(
+        "--middleware",
+        type=str,
+        action="append",
+        default=[],
+        help="Additional ASGI middleware to apply to the app. "
+        "We accept multiple --middleware arguments. "
+        "The value should be an import path. "
+        "If a function is provided, vLLM will add it to the server using @app.middleware('http'). "
+        "If a class is provided, vLLM will add it to the server using app.add_middleware(). "
+    )
 
     parser = AsyncEngineArgs.add_cli_args(parser)
     return parser.parse_args()
@@ -164,6 +186,29 @@ async def create_completion(request: CompletionRequest, raw_request: Request):
         allow_headers=args.allowed_headers,
     )
 
+    if token := os.environ.get("VLLM_API_KEY") or args.api_key:
+
+        @app.middleware("http")
+        async def authentication(request: Request, call_next):
+            if not request.url.path.startswith("/v1"):
+                return await call_next(request)
+            if request.headers.get("Authorization") != "Bearer " + token:
+                return JSONResponse(content={"error": "Unauthorized"},
+                                    status_code=401)
+            return await call_next(request)
+
+    for middleware in args.middleware:
+        module_path, object_name = middleware.rsplit(".", 1)
+        imported = getattr(importlib.import_module(module_path), object_name)
+        if inspect.isclass(imported):
+            app.add_middleware(imported)
+        elif inspect.iscoroutinefunction(imported):
+            app.middleware("http")(imported)
+        else:
+            raise ValueError(
+                f"Invalid middleware {middleware}. Must be a function or a class."
+            )
+
     logger.info(f"args: {args}")
 
     if args.served_model_name is not None: