Streaming Responses

[nhtkid]

Dear all,

I used this Langchain doc example, hoping to stream the response when using the QA chain.

I seem to have issue with the two import:
from langchain.chat_models import ChatOpenAI
from langchain.callbacks.base import CallbackManager

They cannot be imported.
from langchain.callbacks.base import CallbackManager
ImportError: cannot import name 'CallbackManager' from 'langchain.callbacks.base'

Is there anything I missed?
Thanks

[dosubot[bot]]

Answer generated by a 🤖

Answer

I understand that you're having trouble importing ChatOpenAI and CallbackManager from the LangChain library. The error message you're seeing suggests that these modules are not available for import.

Based on the context you've provided, it seems you're trying to stream responses using the QA chain. However, the modules you're trying to import are not part of the example you're following.

Here's how you can correct your code:

from langchain.chains.llm import LLMChain
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains.conversational_retrieval.prompts import (
    CONDENSE_QUESTION_PROMPT,
    QA_PROMPT,
)
from langchain.chains.question_answering import load_qa_chain

# Construct a ConversationalRetrievalChain with a streaming llm for combine docs
# and a separate, non-streaming llm for question generation
llm = OpenAI(temperature=0, openai_api_key=openai_api_key)
streaming_llm = OpenAI(
    streaming=True,
    callbacks=[StreamingStdOutCallbackHandler()],
    temperature=0,
    openai_api_key=openai_api_key,
)

question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(streaming_llm, chain_type="stuff", prompt=QA_PROMPT)

In this code, replace openai_api_key with your actual OpenAI API key. Also, replace chain_type in the load_qa_chain function with the actual chain type you want to use.

To set your OpenAI API key, you can use the getpass function and set it as an environment variable like this:

from getpass import getpass

OPENAI_API_KEY = getpass()
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

After setting the environment variable, you can remove the openai_api_key=openai_api_key part from the OpenAI class initialization as it will automatically use the API key from the environment variable.

This solution should resolve the import errors you're experiencing. However, if you still encounter issues or if your experience contradicts this solution, I recommend checking if you're using the latest version of LangChain. If not, upgrading might help.

If you believe this is a bug that could impact other users, feel free to make a pull request with the necessary changes. Your contributions to improving LangChain are always welcome!

Sources

• docs/extras/ecosystem/integrations/vectara/vectara_chat.ipynb
• docs/extras/modules/model_io/models/llms/integrations/openai.ipynb

---

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

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

[nhtkid]

Thanks Bot.
Can I use the streaming with ChatOpenAI model?

[dosubot[bot]]

Answer generated by a 🤖

Answer

I understand that you're interested in using streaming with the ChatOpenAI model in the LangChain Python framework, and you've previously encountered issues with importing ChatOpenAI and CallbackManager.

Yes, you can definitely use streaming with the ChatOpenAI model in LangChain. LangChain supports streaming for various implementations including OpenAI, ChatOpenAI, and ChatAnthropic.

To use streaming, you'll need to implement a CallbackHandler that uses on_llm_new_token. For instance, you can use StreamingStdOutCallbackHandler as shown in the example below:

from langchain.llms import ChatOpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

llm = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)
resp = llm("Write me a song about sparkling water.")

Your previous issues with importing ChatOpenAI and CallbackManager might be due to incorrect import statements or a version mismatch. Please ensure that you're importing the correct classes from the right modules and that your version of LangChain supports these classes.

Here's an example of how you can adapt streaming with OpenAI in a ConversationalRetrievalChain for ChatOpenAI:

from langchain.chains.llm import LLMChain
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.llms import ChatOpenAI
from langchain.chains.conversational_retrieval.prompts import (
    CONDENSE_QUESTION_PROMPT,
    QA_PROMPT,
)
from langchain.chains.question_answering import load_qa_chain

# Construct a ConversationalRetrievalChain with a streaming llm for combine docs
# and a separate, non-streaming llm for question generation
llm = ChatOpenAI(temperature=0)
streaming_llm = ChatOpenAI(
    streaming=True,
    callbacks=[StreamingStdOutCallbackHandler()],
    temperature=0,
)

question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(streaming_llm, chain_type="stuff", prompt=QA_PROMPT)

Please note that this is one potential solution based on the information provided. If you find this solution helpful and believe it could benefit other users, I encourage you to make a pull request to update the LangChain documentation. This way, you can contribute to the community and help others who might encounter the same issue.

Sources

• docs/snippets/modules/model_io/models/llms/how_to/streaming_llm.mdx
• docs/extras/ecosystem/integrations/vectara/vectara_chat.ipynb

---

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

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

[nhtkid]

Quick update, I am using the following now.
It does not give me error but also it doesn't streaming the responses.
I am using Streamlit as the Chat UI.
`
from langchain.llms import ChatOpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)
qa = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever())

Rest of the code:
result = qa({"question": prompt, "chat_history": chat_history_tuples})
Rest of the code.
`

Thank for any help!

[mettafore]

This snippet may be useful

https://gist.github.com/ninely/88485b2e265d852d3feb8bd115065b1a

Instead of model.agenerate; do qa.arun({...}, callbacks=[AsyncIteratorCallbackHandler()])