add stop word on openai api ChatCompletion

README.md

@@ -319,6 +319,7 @@ for chunk in openai.ChatCompletion.create(
         {"role": "user", "content": "你好"}
     ],
     stream=True 
+    # Specifying stop words in streaming output format is not yet supported and is under development.
 ):
     if hasattr(chunk.choices[0].delta, "content"):
         print(chunk.choices[0].delta.content, end="", flush=True)
@@ -329,7 +330,8 @@ response = openai.ChatCompletion.create(
     messages=[
         {"role": "user", "content": "你好"}
     ],
-    stream=False
+    stream=False,
+    stop=[] # You can add custom stop words here, e.g., stop=["Observation:"] for ReAct prompting.
 )
 print(response.choices[0].message.content)
 ```

README_CN.md

@@ -323,6 +323,7 @@ for chunk in openai.ChatCompletion.create(
         {"role": "user", "content": "你好"}
     ],
     stream=True
+    # 流式输出的自定义stopwords功能尚未支持，正在开发中
 ):
     if hasattr(chunk.choices[0].delta, "content"):
         print(chunk.choices[0].delta.content, end="", flush=True)
@@ -333,7 +334,8 @@ response = openai.ChatCompletion.create(
     messages=[
         {"role": "user", "content": "你好"}
     ],
-    stream=False
+    stream=False,
+    stop=[] # 在此处添加自定义的stop words 例如ReAct prompting时需要增加： stop=["Observation:"]。
 )
 print(response.choices[0].message.content)
 ```

openai_api.py

@@ -68,6 +68,7 @@ class ChatCompletionRequest(BaseModel):
     top_p: Optional[float] = None
     max_length: Optional[int] = None
     stream: Optional[bool] = False
+    stop: Optional[List[str]] = []
 
 
 class ChatCompletionResponseChoice(BaseModel):
@@ -103,7 +104,8 @@ async def create_chat_completion(request: ChatCompletionRequest):
     if request.messages[-1].role != "user":
         raise HTTPException(status_code=400, detail="Invalid request")
     query = request.messages[-1].content
-
+    stop_words = request.stop
+    stop_words.extend(list(map(lambda x: x[1:], filter(lambda x: x.startswith("\n"), stop_words))))
     prev_messages = request.messages[:-1]
     # Temporarily, the system role does not work as expected. We advise that you write the setups for role-play in your query.
     # if len(prev_messages) > 0 and prev_messages[0].role == "system":
@@ -120,10 +122,18 @@ async def create_chat_completion(request: ChatCompletionRequest):
         raise HTTPException(status_code=400, detail="Invalid request.")
 
     if request.stream:
-        generate = predict(query, history, request.model)
+        generate = predict(query, history, request.model, stop_words)
         return EventSourceResponse(generate, media_type="text/event-stream")
 
+    if stop_words:
+        react_stop_words_tokens = [tokenizer.encode(stop_) for stop_ in stop_words]
+        response, _ = model.chat(tokenizer, query, history=history, stop_words_ids=react_stop_words_tokens)
+        for stop_ in stop_words:
+            if response.endswith(stop_):
+                response = response[:response.find(stop_)]
+    else:
         response, _ = model.chat(tokenizer, query, history=history)
+
     choice_data = ChatCompletionResponseChoice(
         index=0,
         message=ChatMessage(role="assistant", content=response),
@@ -133,9 +143,9 @@ async def create_chat_completion(request: ChatCompletionRequest):
     return ChatCompletionResponse(model=request.model, choices=[choice_data], object="chat.completion")
 
 
-async def predict(query: str, history: List[List[str]], model_id: str):
+async def predict(query: str, history: List[List[str]], model_id: str, stop_words: List[str]):
     global model, tokenizer
-
+    assert stop_words == [], "in stream format, stop word is output"
     choice_data = ChatCompletionResponseStreamChoice(
         index=0,
         delta=DeltaMessage(role="assistant"),
@@ -145,8 +155,13 @@ async def predict(query: str, history: List[List[str]], model_id: str):
     yield "{}".format(chunk.model_dump_json(exclude_unset=True))
 
     current_length = 0
+    if stop_words:
+        react_stop_words_tokens = [tokenizer.encode(stop_) for stop_ in stop_words]
+        response_generator = model.chat_stream(tokenizer, query, history=history, stop_words_ids=react_stop_words_tokens)
+    else:
+        response_generator = model.chat_stream(tokenizer, query, history=history)
 
-    for new_response in model.chat_stream(tokenizer, query, history):
+    for new_response in response_generator:
         if len(new_response) == current_length:
             continue