How return LLMChain.run() stream data?? it did not store stream text in variable rather it just print??

[faridelya]

Hi Everone Hope You all doing well.

i am using this LLMChain.run("hi i am Max)function for streaming text when streaming start it just print in colab or .py module terminal.

But i want like this.

llm_chain = LLMChain(prompt=prompt, llm=llm ,memory = memory)

response = llm_chain.run(question)

for chunk in response:
  print(chunk, end='')

i am returning this streaming data in Flasksocketio ``` emit('response' , chunk, room=id)

As i can do for chatgpt but here i am stuck the above code show twice stream text data
if i remove for loop then it still print output but i want to return stream data like i use chatgpt api.

Any Help would be appreciated

[dosubot[bot]]

Answer generated by a 🤖

Answer

Hello,

Based on the context provided, it seems you want to return the streaming data from LLMChain.run() instead of printing it. This can be achieved by using Python's built-in yield keyword, which allows a function to return a stream of data, one item at a time.

In the LangChain codebase, the stream method in the BaseLLM class is already implemented as a generator function using yield. This function takes an input and config as arguments, and yields the text of each chunk generated by the _stream method.

To use this with Flask-SocketIO's emit function, you can call the stream method in a loop and emit each item as it is yielded. Here's an example of how you might do this:

from flask_socketio import SocketIO, emit

socketio = SocketIO(app)

@app.route('/stream')
def stream_data():
    def generate():
        llm = BaseLLM()  # Initialize your LLM here
        input = LanguageModelInput()  # Provide your input here
        config = RunnableConfig()  # Provide your config here
        for chunk in llm.stream(input, config):
            yield chunk
    return Response(generate(), mimetype='text/plain')

@socketio.on('start_stream')
def handle_start_stream():
    for chunk in stream_data():
        emit('stream_data', {'data': chunk})

In this example, the stream_data function is a Flask route that calls the stream method in a loop and yields each chunk of data. The handle_start_stream function is a SocketIO event handler that emits each chunk of data as it is yielded by the stream_data function.

Please note that you'll need to replace the placeholders in the generate function with your actual LLM, input, and config.

Also, there was a similar issue titled Support for streaming when using LLMchain? which was solved by setting verbose=True when defining the model and verbose=False when instantiating the LLMChain.

I hope this helps! If you have any other questions, feel free to ask.

Sources

• libs/langchain/langchain/llms/base.py

---

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[faridelya]

i did not work how you can do with this code to convert for streaming rather printing

llm = LlamaCpp(
    model_path=model_path,
    max_tokens=2024,
    n_gpu_layers=n_gpu_layers,
    n_batch=n_batch,
    callback_manager=callback_manager,
    verbose=False,
)

llm_chain = LLMChain(prompt=prompt, llm=llm ,memory = memory)

question = "hi my name is farid"


response = llm_chain.run(question) # this statement print stream output however i want to use for loop for returning streaming data?

output

 Hello Farid! It's nice to meet you too! I'm here to help answer any questions you may have, while ensuring a safe and respectful conversation. Please keep in mind that I cannot provide personal advice or engage in discussions that promote harmful or inappropriate content.
Could you please share your question or topic you would like to discuss? I'll do my best to provide a helpful and accurate response while adhering to ethical and socially unbiased standards. 

[dosubot]

Answer generated by a 🤖

Hello,

Based on a similar issue found in the LangChain repository, you can enable streaming with LLMChain by setting verbose=True when defining your model and verbose=False when instantiating your LLMChain. Here is the corrected code:

# Defining model 
llm = LlamaCpp(
    model_path=model_path,
    max_tokens=2024,
    n_gpu_layers=n_gpu_layers,
    n_batch=n_batch,
    callback_manager=callback_manager,
    verbose=True, # set verbose to True here
)

# Create chain
llm_chain = LLMChain(prompt=prompt, llm=llm ,memory = memory, verbose=False) # set verbose to False here

# Get response    
response = llm_chain.run(question)

In this case, verbose=True in the model definition is for LlamaCpp to stream the response, and verbose=False in the LLMChain instantiation is to prevent streaming of the LangChain thought process. You can find more details in this issue.

If you want to handle the streaming data with a for loop, you can use the _stream method in the LlamaCpp class. Here is an example:

from langchain.llms import LlamaCpp

llm = LlamaCpp(model_path="/path/to/llama/model")

for chunk in llm._stream(prompt="This is a prompt."):
    # handle the chunk data here
    print(chunk.text)

Please note that the _stream method is a private method, which means it's not intended to be used directly outside of the class. If you want to use this functionality in a more proper way, you might want to create a new public method in the LlamaCpp class that calls _stream and use that method instead.

I hope this helps! Let me know if you have any other questions.

Sources

• Support for streaming when using LLMchain?
• libs/langchain/langchain/llms/llamacpp.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

If you you want to continue the conversation, start your reply with start your reply @dosu-bot. I'll only respond to replies from the issue author.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

[hpxiong]

The second part of the solution provided didn't use LLMChain instead using LLM directly. Also, this solution appears to be working only for llamacpp but not for models like GPT4All, cTransformers, etc.

[sultf2]

I followed your suggestion and implemented the following using your socketio code as a basis and enabling streaming in langchain. Streaming is succesful in the console but fails to connect with socketio, see below

def stream(input_text):
    def get_openai_answer(input_text):
        
            embeddings = OpenAIEmbeddings(
            model = 'text-embedding-ada-002',
            openai_api_key=OPENAI_API_KEY
            )
                
        db = Chroma(
            persist_directory=persist_directory, 
            embedding_function=embeddings
            )
        
        retriever = db.as_retriever(
            search_type="similarity", 
            search_kwargs={"k":4}
            )
        
        llm = ChatOpenAI(
            model_name="gpt-4", 
            temperature=0,
            openai_api_key=OPENAI_API_KEY,
            streaming=True, 
            callbacks=[handler]
            )
    
        # create a chain to answer questions 
        qa = RetrievalQA.from_chain_type(
            llm=llm, 
            chain_type="stuff", 
            retriever=retriever, 
            return_source_documents=True, 
            verbose=False
            )
        
        for chunk in qa({"query": input_text}):
            yield chunk

    return Response(get_openai_answer(input_text), mimetype='text/event-stream')
    
@socketio.on('run_query')
def handle_start_stream(input_text): 
    print("i'm here")
    for chunk in stream(input_text):
        emit('stream_data', {'data': chunk})

However I got the following error

TypeError: 'Response' object is not iterable

Any ideas?

[pmespresso]

Response object doesn't implement iterable

Try StreamingResponse

[reflexiveprophecy]

Hope this helps. Had to implement custom handler with a queue to store tokens and a thread in Flask. Also posted my response on Stack Overflow: https://stackoverflow.com/questions/76284412/stream-a-response-from-langchains-openai-with-pyton-flask-api/78411799#78411799

import threading
from queue import Queue, Empty
from flask import Flask, request, jsonify, Response, stream_with_context
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from langchain.schema.output import LLMResult
from langchain.chains import LLMChain
from langchain_core.callbacks import (
    CallbackManager, 
    StreamingStdOutCallbackHandler,
    BaseCallbackHandler
)

token_queue = Queue() #from queue import Queue

class LLMTokenQueueHandler(BaseCallbackHandler): 
    """
    This is to change the behavior of LLMChain to 
    store the outputted tokens to a queue
    """ 
    def on_llm_new_token(
        self, 
        token: str, 
        **kwargs
        ) -> None:    
        token_queue.put({"type": "token", "value": token})  

    def on_llm_end(
        self, 
        response: LLMResult, 
        **kwargs
        ) -> None:
        token_queue.put({'type': 'end'})


def generate_text_response(
    input_query: str
    ) -> None:
    """
    Generate text response from LLM
    note that we are not streaming from this 
    function but from the stream_tokens() function
    """
    prompt_template = """
    input your prompt template

    Chat History: 
    {chat_history}

    Human Input: 
    {input_query}
    """

    #adding the LLMTokenQueueHandler to the callback manager
    #so now the tokens are automatically stored into token_queue
    gptchat = ChatOpenAI(
        model_name='model_name', 
        temperature= 0.25, 
        openai_api_key=os.environ.get('OPENAI_API_KEY'), 
        streaming = True,
        callback_manager=CallbackManager([LLMTokenQueueHandler()])
    )

    prompt = PromptTemplate(
        input_variables=[
        "chat_history", 
        "input_query"
        #add more variables if needed
        ],
        template=prompt_template
    )

    llm_chain = LLMChain(
        llm=gptchat,
        prompt=prompt,
        memory=global_memory,
        verbose = False
    )

    #this streaming call triggers the process to 
    #store answer tokens to queue
    for chunk_response in llm_chain.stream(
        {
            "input_query": input_query, 
        }
    ):
        print(chunk_response)


def stream_tokens():  
    """Generator function to stream tokens."""  
    while True:  
        # Wait for a token to be available in the queue and retrieve it  
        token_dict = token_queue.get()  
        print("token_dict: ", token_dict)

        if token_dict["type"] == "token":
            # encode str as byte  
            yield token_dict['value'].encode('utf-8')

        #we need to implement when streaming ends
        #with the 'end' token, then break out of loop
        elif token_dict["type"] == "end":
            break


@app.route('/stream', methods=['POST'])  
def stream_text_response():  
    """
    Stream text response with user input query
    """
    input_json = request.get_json()  
    input_query = input_json.get('stream', '')  
  
    # Start generate_text_response in a separate thread to avoid blocking  
    threading.Thread(
        target=generate_text_response, 
        args=(input_query,)
    ).start()  
  
    # Stream tokens back to the client as they are produced
    # not streaming generate_text_response as it doesn't produce
    # the streamed tokens directly  
    return Response(
        stream_with_context(stream_tokens())
    )