update api to match langchain

src/api_demo.py

@@ -14,7 +14,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from contextlib import asynccontextmanager
 from transformers import TextIteratorStreamer
 from sse_starlette import EventSourceResponse
-from typing import Any, Dict, List, Literal, Optional, Union
+from typing import Any, Dict, List, Literal, Optional
 
 from utils import (
     Template,
@@ -46,17 +46,17 @@ app.add_middleware(
 
 class ModelCard(BaseModel):
     id: str
-    object: str = "model"
+    object: Optional[str] = "model"
-    created: int = Field(default_factory=lambda: int(time.time()))
+    created: Optional[int] = Field(default_factory=lambda: int(time.time()))
-    owned_by: str = "owner"
+    owned_by: Optional[str] = "owner"
     root: Optional[str] = None
     parent: Optional[str] = None
-    permission: Optional[list] = None
+    permission: Optional[list] = []
 
 
 class ModelList(BaseModel):
-    object: str = "list"
+    object: Optional[str] = "list"
-    data: List[ModelCard] = []
+    data: Optional[List[ModelCard]] = []
 
 
 class ChatMessage(BaseModel):
@@ -74,8 +74,8 @@ class ChatCompletionRequest(BaseModel):
     messages: List[ChatMessage]
     temperature: Optional[float] = None
     top_p: Optional[float] = None
-    max_length: Optional[int] = None
+    n: Optional[int] = 1
-    max_new_tokens: Optional[int] = None
+    max_tokens: Optional[int] = None
     stream: Optional[bool] = False
 
 
@@ -88,14 +88,30 @@ class ChatCompletionResponseChoice(BaseModel):
 class ChatCompletionResponseStreamChoice(BaseModel):
     index: int
     delta: DeltaMessage
-    finish_reason: Optional[Literal["stop", "length"]]
+    finish_reason: Optional[Literal["stop", "length"]] = None
+
+
+class ChatCompletionResponseUsage(BaseModel):
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
 
 
 class ChatCompletionResponse(BaseModel):
-    model: str
+    id: Optional[str] = "chatcmpl-default"
-    object: Literal["chat.completion", "chat.completion.chunk"]
+    object: Literal["chat.completion"]
-    choices: List[Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]]
     created: Optional[int] = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[ChatCompletionResponseChoice]
+    usage: ChatCompletionResponseUsage
+
+
+class ChatCompletionStreamResponse(BaseModel):
+    id: Optional[str] = "chatcmpl-default"
+    object: Literal["chat.completion.chunk"]
+    created: Optional[int] = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[ChatCompletionResponseStreamChoice]
 
 
 @app.get("/v1/models", response_model=ModelList)
@@ -135,12 +151,10 @@ async def create_chat_completion(request: ChatCompletionRequest):
         "top_p": request.top_p if request.top_p else gen_kwargs["top_p"],
         "logits_processor": get_logits_processor()
     })
-    if request.max_length:
+
-        gen_kwargs.pop("max_new_tokens", None)
+    if request.max_tokens:
-        gen_kwargs["max_length"] = request.max_length
-    if request.max_new_tokens:
         gen_kwargs.pop("max_length", None)
-        gen_kwargs["max_new_tokens"] = request.max_new_tokens
+        gen_kwargs["max_new_tokens"] = request.max_tokens
 
     if request.stream:
         generate = predict(gen_kwargs, request.model)
@@ -150,13 +164,19 @@ async def create_chat_completion(request: ChatCompletionRequest):
     outputs = generation_output.tolist()[0][len(inputs["input_ids"][0]):]
     response = tokenizer.decode(outputs, skip_special_tokens=True)
 
+    usage = ChatCompletionResponseUsage(
+        prompt_tokens=len(inputs["input_ids"][0]),
+        completion_tokens=len(outputs),
+        total_tokens=len(inputs["input_ids"][0]) + len(outputs)
+    )
+
     choice_data = ChatCompletionResponseChoice(
         index=0,
         message=ChatMessage(role="assistant", content=response),
         finish_reason="stop"
     )
 
-    return ChatCompletionResponse(model=request.model, choices=[choice_data], object="chat.completion")
+    return ChatCompletionResponse(model=request.model, choices=[choice_data], usage=usage, object="chat.completion")
 
 
 async def predict(gen_kwargs: Dict[str, Any], model_id: str):
@@ -173,7 +193,7 @@ async def predict(gen_kwargs: Dict[str, Any], model_id: str):
         delta=DeltaMessage(role="assistant"),
         finish_reason=None
     )
-    chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
+    chunk = ChatCompletionStreamResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
     yield chunk.json(exclude_unset=True, ensure_ascii=False)
 
     for new_text in streamer:
@@ -185,7 +205,7 @@ async def predict(gen_kwargs: Dict[str, Any], model_id: str):
             delta=DeltaMessage(content=new_text),
             finish_reason=None
         )
-        chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
+        chunk = ChatCompletionStreamResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
         yield chunk.json(exclude_unset=True, ensure_ascii=False)
 
     choice_data = ChatCompletionResponseStreamChoice(
@@ -193,7 +213,7 @@ async def predict(gen_kwargs: Dict[str, Any], model_id: str):
         delta=DeltaMessage(),
         finish_reason="stop"
     )
-    chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
+    chunk = ChatCompletionStreamResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
     yield chunk.json(exclude_unset=True, ensure_ascii=False)
     yield "[DONE]"