Update fastapi server example

examples/high_level_api/fastapi_server.py

@@ -13,7 +13,8 @@ Then visit http://localhost:8000/docs to see the interactive API docs.
 """
 import os
 import json
-from typing import List, Optional, Literal, Union, Iterator
+from typing import List, Optional, Literal, Union, Iterator, Dict
+from typing_extensions import TypedDict
 
 import llama_cpp
 
@@ -64,13 +65,24 @@ class CreateCompletionRequest(BaseModel):
     max_tokens: int = 16
     temperature: float = 0.8
     top_p: float = 0.95
-    logprobs: Optional[int] = Field(None)
     echo: bool = False
     stop: List[str] = []
-    repeat_penalty: float = 1.1
-    top_k: int = 40
     stream: bool = False
 
+    # ignored or currently unsupported
+    model: Optional[str] = Field(None)
+    n: Optional[int] = 1
+    logprobs: Optional[int] = Field(None)
+    presence_penalty: Optional[float] = 0
+    frequency_penalty: Optional[float] = 0
+    best_of: Optional[int] = 1
+    logit_bias: Optional[Dict[str, float]] = Field(None)
+    user: Optional[str] = Field(None)
+
+    # llama.cpp specific parameters
+    top_k: int = 40
+    repeat_penalty: float = 1.1
+
     class Config:
         schema_extra = {
             "example": {
@@ -91,7 +103,20 @@ def create_completion(request: CreateCompletionRequest):
     if request.stream:
         chunks: Iterator[llama_cpp.CompletionChunk] = llama(**request.dict())  # type: ignore
         return EventSourceResponse(dict(data=json.dumps(chunk)) for chunk in chunks)
-    return llama(**request.dict())
+    return llama(
+        **request.dict(
+            exclude={
+                "model",
+                "n",
+                "logprobs",
+                "frequency_penalty",
+                "presence_penalty",
+                "best_of",
+                "logit_bias",
+                "user",
+            }
+        )
+    )
 
 
 class CreateEmbeddingRequest(BaseModel):
@@ -132,6 +157,16 @@ class CreateChatCompletionRequest(BaseModel):
     stream: bool = False
     stop: List[str] = []
     max_tokens: int = 128
+
+    # ignored or currently unsupported
+    model: Optional[str] = Field(None)
+    n: Optional[int] = 1
+    presence_penalty: Optional[float] = 0
+    frequency_penalty: Optional[float] = 0
+    logit_bias: Optional[Dict[str, float]] = Field(None)
+    user: Optional[str] = Field(None)
+
+    # llama.cpp specific parameters
     repeat_penalty: float = 1.1
 
     class Config:
@@ -160,7 +195,16 @@ async def create_chat_completion(
     request: CreateChatCompletionRequest,
 ) -> Union[llama_cpp.ChatCompletion, EventSourceResponse]:
     completion_or_chunks = llama.create_chat_completion(
-        **request.dict(exclude={"model"}),
+        **request.dict(
+            exclude={
+                "model",
+                "n",
+                "presence_penalty",
+                "frequency_penalty",
+                "logit_bias",
+                "user",
+            }
+        ),
     )
 
     if request.stream:
@@ -179,3 +223,40 @@ async def create_chat_completion(
         )
     completion: llama_cpp.ChatCompletion = completion_or_chunks  # type: ignore
     return completion
+
+
+class ModelData(TypedDict):
+    id: str
+    object: Literal["model"]
+    owned_by: str
+    permissions: List[str]
+
+
+class ModelList(TypedDict):
+    object: Literal["list"]
+    data: List[ModelData]
+
+
+GetModelResponse = create_model_from_typeddict(ModelList)
+
+
+@app.get("/v1/models", response_model=GetModelResponse)
+def get_models() -> ModelList:
+    return {
+        "object": "list",
+        "data": [
+            {
+                "id": llama.model_path,
+                "object": "model",
+                "owned_by": "me",
+                "permissions": [],
+            }
+        ],
+    }
+
+
+if __name__ == "__main__":
+    import os
+    import uvicorn
+
+    uvicorn.run(app, host=os.getenv("HOST", "localhost"), port=os.getenv("PORT", 8000))