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How can I stream a response from LangChain‘s OpenAI using Flask API?

article 2024/12/24 11:30:26

题意：怎样在 Flask API 中使用 LangChain 的 OpenAI 模型流式传输响应

问题背景：

I am using Python Flask app for chat over data. In the console I am getting streamable response directly from the OpenAI since I can enable streming with a flag streaming=True.

我正在使用 Python Flask 应用程序进行数据聊天。在控制台中，我直接从 OpenAI 获取流式响应，因为我可以通过设置 `streaming=True` 来启用流式传输。

The problem is, that I can't "forward" the stream or "show" the stream than in my API call.

问题是，我无法在 API 调用中“转发”或“显示”这个流式响应。

Code for the processing OpenAI and chain is:

处理 OpenAI 和链的代码如下：

def askQuestion(self, collection_id, question):
    collection_name = "collection-" + str(collection_id)
    self.llm = ChatOpenAI(model_name=self.model_name, temperature=self.temperature, openai_api_key=os.environ.get('OPENAI_API_KEY'), streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
    self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True,  output_key='answer')
    
    chroma_Vectorstore = Chroma(collection_name=collection_name, embedding_function=self.embeddingsOpenAi, client=self.chroma_client)


    self.chain = ConversationalRetrievalChain.from_llm(self.llm, chroma_Vectorstore.as_retriever(similarity_search_with_score=True),
                                                        return_source_documents=True,verbose=VERBOSE, 
                                                        memory=self.memory)
    

    result = self.chain({"question": question})
    
    res_dict = {
        "answer": result["answer"],
    }

    res_dict["source_documents"] = []

    for source in result["source_documents"]:
        res_dict["source_documents"].append({
            "page_content": source.page_content,
            "metadata":  source.metadata
        })

    return res_dict

and the API route code: 以及 API 路由的代码：

@app.route("/collection/<int:collection_id>/ask_question", methods=["POST"])
def ask_question(collection_id):
    question = request.form["question"]
    # response_generator = document_thread.askQuestion(collection_id, question)
    # return jsonify(response_generator)

    def stream(question):
        completion = document_thread.askQuestion(collection_id, question)
        for line in completion['answer']:
            yield line

I am testing my endpoint with curl and I am passing flag -N to curl, so I should get the streamable response, if it is possible.

我正在使用 curl 测试我的端点，并传递了 `-N` 标志，因此如果可能的话，我应该能得到流式响应。

When I make API call first the endpoint is waiting to process the data (I can see in my terminal in VS code the streamable answer) and when finished, I get everything displayed in one go.

当我发起 API 调用时，端点首先等待处理数据（我可以在 VS Code 的终端中看到流式的回答），处理完成后，所有内容一次性显示出来。

问题解决：

With the usage of threading and callback we can have a streaming response from flask API.

通过使用 `threading` 和 `callback`，我们可以在 Flask API 中实现流式响应。

In flask API, you may create a queue to register tokens through langchain's callback.

在 Flask API 中，可以创建一个队列，通过 LangChain 的回调函数来注册令牌。

class StreamingHandler(BaseCallbackHandler):
    ...

    def on_llm_new_token(self, token: str, **kwargs) -> None:
        self.queue.put(token)

You may get tokens from the same queue in your flask route.

你可以在 Flask 路由中从同一个队列获取令牌。

from flask import Response, stream_with_context
import threading 

@app.route(....):
def stream_output():
   q = Queue()
   
   def generate(rq: Queue):
      ...
      # add your logic to prevent while loop
      # to run indefinitely  
      while( ...):
          yield rq.get()
   
   callback_fn = StreamingHandler(q)
   
   threading.Thread(target= askQuestion, args=(collection_id, question, callback_fn))
   return Response(stream_with_context(generate(q))

In your langchain's ChatOpenAI add the above custom callback StreamingHandler.

在你的 LangChain 的 `ChatOpenAI` 中添加上述自定义回调 `StreamingHandler`。

self.llm = ChatOpenAI(
  model_name=self.model_name, 
  temperature=self.temperature, 
  openai_api_key=os.environ.get('OPENAI_API_KEY'), 
  streaming=True, 
  callback=[callback_fn,]
)

For reference: 参考如下

https://python.langchain.com/en/latest/modules/callbacks/getting_started.html#creating-a-custom-handler
Streaming Contents — Flask Documentation (2.3.x)