when i try to use the stream and history to create a chatting robot ，but it consoles” Error in RootListenersTracer.on_chain_end callback: ValueError()”

[future-mike]

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Example Code

def invoke_model(question_text,result_question,question,prompt,session_id):
    messages=[]
    if result_question is not None and result_question:
        for item in result_question:
            messages.append(HumanMessage(content=prompt.format(json.loads(item[5])['content'], item[1])))
            messages.append(AIMessage(content=item[2]))

    prompttemplate = ChatPromptTemplate.from_messages(
        [
            (
                "system",
                "You are a helpful assistant. Answer all questions to the best of your ability.",
            ),
            MessagesPlaceholder(variable_name="messages"),
        ]
    )
    parser = StrOutputParser()
    chain = prompttemplate | chat | parser
    with_message_history = RunnableWithMessageHistory(
        chain,
        get_session_history,
        input_messages_key="messages",
    )
    config = {"configurable": {"session_id": str(session_id)}}
    responses = with_message_history.stream(
        {
            "messages": messages + [HumanMessage(content=prompt.format(question_text, question))]
        },
        config=config,
    )
    result=""
    for response in responses:
        result+=response
        print(response, end="")
    return result

Description

its error is at ：
for response in responses:
result+=respons

System Info

windows python 3.9

[dosubot[bot]]

Hey there, @future-mike! I'm here to assist you with any bugs, questions, or contributions while you wait for a human maintainer. Let's tackle this issue together!

To resolve the "Error in RootListenersTracer.on_chain_end callback: ValueError()" when using the stream and history to create a chatting robot in your Python code, you should ensure that your environment variables for LangSmith tracing are correctly set. Here is how you can set them in a Jupyter notebook:

import getpass
import os

os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()

Make sure to replace the getpass.getpass() with your actual LangChain API key if you are not running this in an interactive environment. This setup is crucial for logging traces and debugging the chain or agent.

Additionally, ensure that you have correctly installed the necessary packages and set up your message history and streaming as shown in the provided tutorial. Here is a snippet to help you set up the streaming part correctly:

config = {"configurable": {"session_id": "abc15"}}
for r in with_message_history.stream(
    {
        "messages": [HumanMessage(content="hi! I'm todd. tell me a joke")],
        "language": "English",
    },
    config=config,
):
    print(r.content, end="|")

If you are using the Coze API for your chatbot, ensure that you have correctly configured the ChatCoze class and its parameters. Here is an example of how to set up the ChatCoze class:

from langchain_core.messages import HumanMessage
from langchain_community.chat_models.coze import ChatCoze

chat_model = ChatCoze(
    coze_api_key="your_coze_api_key",
    bot_id="your_bot_id",
    conversation_id="your_conversation_id",
    user="your_user_id",
    streaming=True
)

messages = [HumanMessage(content="hi! I'm todd. tell me a joke")]

for response in chat_model._stream(messages=messages):
    print(response.message.content, end="|")

To further ensure that your environment is correctly set up, you can follow the guidelines from the LangChain chat bot feedback template. Make sure to set the necessary environment variables:

export OPENAI_API_KEY=sk-...
export LANGSMITH_API_KEY=...
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_PROJECT=my-project # Set to the project you want to save to

Additionally, you can use the tracing_v2_enabled callback manager to get the run ID of the call, which you can provide in subsequent calls in the same chat thread. This helps the evaluator assign feedback to the appropriate trace:

from functools import partial
from typing import Optional, Callable, List
from langserve import RemoteRunnable
from langchain.callbacks.manager import tracing_v2_enabled
from langchain_core.messages import BaseMessage, AIMessage, HumanMessage

# Update with the URL provided by your LangServe server
chain = RemoteRunnable("http://127.0.0.1:8031/chat-bot-feedback")

def stream_content(
    text: str,
    chat_history: Optional[List[BaseMessage]] = None,
    last_run_id: Optional[str] = None,
    on_chunk: Callable = None,
):
    results = []
    with tracing_v2_enabled() as cb:
        for chunk in chain.stream(
            {"text": text, "chat_history": chat_history, "last_run_id": last_run_id},
        ):
            on_chunk(chunk)
            results.append(chunk)
        last_run_id = cb.latest_run.id if cb.latest_run else None
    return last_run_id, "".join(results)

chat_history = []
text = "Where are my keys?"
last_run_id, response_message = stream_content(text, on_chunk=partial(print, end=""))
print()
chat_history.extend([HumanMessage(content=text), AIMessage(content=response_message)])
text = "I CAN'T FIND THEM ANYWHERE"
last_run_id, response_message = stream_content(
    text,
    chat_history=chat_history,
    last_run_id=str(last_run_id),
    on_chunk=partial(print, end=""),
)
print()
chat_history.extend([HumanMessage(content=text), AIMessage(content=response_message)])

If you are using the DeepInfra API for your chatbot, ensure that you have correctly configured the ChatDeepInfra class and its parameters. Here is an example of how to set up the ChatDeepInfra class:

from langchain_core.messages import HumanMessage
from langchain_community.chat_models.deepinfra import ChatDeepInfra

chat_model = ChatDeepInfra(
    deepinfra_api_token="your_deepinfra_api_token",
    model_name="meta-llama/Llama-2-70b-chat-hf",
    streaming=True
)

messages = [HumanMessage(content="hi! I'm todd. tell me a joke")]

for response in chat_model._stream(messages=messages):
    print(response.message.content, end="|")

If you are using the MiniMax API for your chatbot, ensure that you have correctly configured the MiniMaxChat class and its parameters. Here is an example of how to set up the MiniMaxChat class:

from langchain_core.messages import HumanMessage
from langchain_community.chat_models.minimax import MiniMaxChat

chat_model = MiniMaxChat(
    api_key="your_minimax_api_key",
    model="abab6.5-chat",
    streaming=True
)

messages = [HumanMessage(content="hi! I'm todd. tell me a joke")]

for response in chat_model._stream(messages=messages):
    print(response.message.content, end="|")

By following these steps and ensuring that your environment variables and API configurations are correctly set, you should be able to resolve the error and have a functioning chatbot with streaming capabilities ^{[1][2][3][4][5]}.

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