feat: return server.server_llm_config information for REST endpoint (#1083)

memgpt/server/rest_api/models/index.py

@@ -9,43 +9,32 @@ from memgpt.server.rest_api.auth_token import get_current_user
 from memgpt.server.rest_api.interface import QueuingInterface
 from memgpt.server.server import SyncServer
 
+from memgpt.models.pydantic_models import LLMConfigModel, EmbeddingConfigModel
+
 router = APIRouter()
 
 
-class Model(BaseModel):
-    name: str = Field(..., description="The name of the model.")
-    endpoint: str = Field(..., description="Endpoint URL for the model.")
-    endpoint_type: str = Field(..., description="Type of the model endpoint.")
-    wrapper: str = Field(None, description="Wrapper used for the model.")
-    context_window: int = Field(..., description="Context window size for the model.")
-
-
 class ListModelsResponse(BaseModel):
-    models: List[Model] = Field(..., description="List of model configurations.")
+    models: List[LLMConfigModel] = Field(..., description="List of model configurations.")
 
 
 def setup_models_index_router(server: SyncServer, interface: QueuingInterface, password: str):
     get_current_user_with_server = partial(partial(get_current_user, server), password)
 
     @router.get("/models", tags=["models"], response_model=ListModelsResponse)
-    async def list_models(
-        user_id: uuid.UUID = Depends(get_current_user_with_server),
-    ):
+    async def list_models():
         # Clear the interface
         interface.clear()
 
-        # TODO: Replace with actual data fetching logic once available
-        models_data = [
-            Model(
-                name="ehartford/dolphin-2.5-mixtral-8x7b",
-                endpoint="https://api.memgpt.ai",
-                endpoint_type="vllm",
-                wrapper="chatml",
-                context_window=16384,
-            ),
-            Model(name="gpt-4", endpoint="https://api.openai.com/v1", endpoint_type="openai", context_window=8192),
-        ]
+        # currently, the server only supports one model, however this may change in the future
+        llm_config = LLMConfigModel(
+            model=server.server_llm_config.model,
+            model_endpoint=server.server_llm_config.model_endpoint,
+            model_endpoint_type=server.server_llm_config.model_endpoint_type,
+            model_wrapper=server.server_llm_config.model_wrapper,
+            context_window=server.server_llm_config.context_window,
+        )
 
-        return ListModelsResponse(models=models_data)
+        return ListModelsResponse(models=[llm_config])
 
     return router