feat: openai.Model.list() (#499)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>

openllm-python/src/openllm/_service.py

@@ -1,5 +1,6 @@
 # mypy: disable-error-code="call-arg,misc,attr-defined,type-abstract,type-arg,valid-type,arg-type"
 from __future__ import annotations
+import logging
 import typing as t
 import warnings
 
@@ -23,6 +24,8 @@ warnings.filterwarnings('ignore', message='MatMul8bitLt: inputs will be cast fro
 warnings.filterwarnings('ignore', message='MatMul8bitLt: inputs will be cast from torch.bfloat16 to float16 during quantization')
 warnings.filterwarnings('ignore', message='The installed version of bitsandbytes was compiled without GPU support.')
 
+logger = logging.getLogger(__name__)
+
 model = svars.model
 model_id = svars.model_id
 adapter_map = svars.adapter_map
@@ -62,6 +65,8 @@ async def generate_stream_v1(input_dict: dict[str, t.Any]) -> t.AsyncGenerator[s
          input=bentoml.io.JSON.from_sample(openllm.utils.bentoml_cattr.unstructure(openllm.openai.CompletionRequest(prompt='What is 1+1?', model=runner.llm_type))),
          output=bentoml.io.Text())
 async def completion_v1(input_dict: dict[str, t.Any], ctx: bentoml.Context) -> str | t.AsyncGenerator[str, None]:
+  _model = input_dict.get('model', None)
+  if _model is not runner.llm_type: logger.warning("Model '%s' is not supported. Run openai.Model.list() to see all supported models.", _model)
   prompt = input_dict.pop('prompt', None)
   if prompt is None: raise ValueError("'prompt' should not be None.")
   stream = input_dict.pop('stream', False)
@@ -118,6 +123,8 @@ async def completion_v1(input_dict: dict[str, t.Any], ctx: bentoml.Context) -> s
                  }], model=runner.llm_type))),
          output=bentoml.io.Text())
 async def chat_completion_v1(input_dict: dict[str, t.Any], ctx: bentoml.Context) -> str | t.AsyncGenerator[str, None]:
+  _model = input_dict.get('model', None)
+  if _model is not runner.llm_type: logger.warning("Model '%s' is not supported. Run openai.Model.list() to see all supported models.", _model)
   prompt = openllm.openai.messages_to_prompt(input_dict['messages'])
   stream = input_dict.pop('stream', False)
   config = {
@@ -158,12 +165,17 @@ async def chat_completion_v1(input_dict: dict[str, t.Any], ctx: bentoml.Context)
       responses = await runner.generate.async_run(prompt, **config)
     return orjson.dumps(
         openllm.utils.bentoml_cattr.unstructure(
-            openllm.openai.ChatCompletionResponse(choices=[
-                openllm.openai.ChatCompletionChoice(index=i, message=openllm.openai.Message(role='assistant', content=response)) for i, response in enumerate(responses)
-            ],
-                                                  model=model)  # TODO: logprobs, finish_reason and usage
+            openllm.openai.ChatCompletionResponse(
+                choices=[openllm.openai.ChatCompletionChoice(index=i, message=openllm.openai.Message(role='assistant', content=response)) for i, response in enumerate(responses)],
+                model=runner.llm_type)  # TODO: logprobs, finish_reason and usage
         )).decode('utf-8')
 
+def models_v1(_: Request) -> Response:
+  return JSONResponse(openllm.utils.bentoml_cattr.unstructure(openllm.openai.ModelList(data=[openllm.openai.ModelCard(id=runner.llm_type)])), status_code=200)
+
+openai_app = Starlette(debug=True, routes=[Route('/models', models_v1, methods=['GET'])])
+svc.mount_asgi_app(openai_app, path='/v1')
+
 @svc.api(route='/v1/metadata',
          input=bentoml.io.Text(),
          output=bentoml.io.JSON.from_sample({

openllm-python/src/openllm/protocol/openai.py

@@ -119,6 +119,18 @@ class ChatCompletionResponseStream:
   id: str = attr.field(default=attr.Factory(lambda: openllm_core.utils.gen_random_uuid('chatcmpl')))
   created: int = attr.field(default=attr.Factory(lambda: int(time.time())))
 
+@attr.define
+class ModelCard:
+  id: str
+  object: str = 'model'
+  created: int = attr.field(default=attr.Factory(lambda: int(time.time())))
+  owned_by: str = 'na'
+
+@attr.define
+class ModelList:
+  object: str = 'list'
+  data: t.List[ModelCard] = attr.field(factory=list)
+
 def messages_to_prompt(messages: list[Message]) -> str:
   formatted = '\n'.join([f"{message['role']}: {message['content']}" for message in messages])
   return f'{formatted}\nassistant:'