LangChain Callback Handler

When running your LangChain agent, it is essential to provide the appropriate callback handler. This ensures seamless communication between the Chainlit UI and your agent.

There are two ways to pass the callback handler:

1. Pass the callback handler during runtime. This will apply the handler to all calls made by your agent.
2. Pass the callback handler to individual components, such as an llm, allowing you to control what information is returned to the UI.

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Asynchronous Callback Handler

The following code example demonstrates how to pass an asynchronous callback handler.

llm = OpenAI(temperature=0)
llm_math = LLMMathChain.from_llm(llm=llm)

@cl.on_message
async def main(message: cl.Message):
    res = await llm_math.acall(message.content, callbacks=[cl.AsyncLangchainCallbackHandler()])

    await cl.Message(content="Hello").send()

Notice the acall here, that could also be arun or any other async function. Since we are calling the asynchronous implementation of the agent, we need to use the asynchronous callback handler.

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Synchronous Callback Handler

Alternatively, you can pass a synchronous callback handler, as shown in the example below:

llm = OpenAI(temperature=0)
llm_math = LLMMathChain.from_llm(llm=llm)

@cl.on_message
async def main(message: cl.Message):

    res = await cl.make_async(llm_math)(message.content, callbacks=[cl.LangchainCallbackHandler()])

    await cl.Message(content="Hello").send()

Since we are calling a long running synchronous function, we need to wrap it in cl.make_async to make it asynchronous and not block the event loop. The function will still run synchronously in its own thread, hence we use the synchronous callback handler.

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Final Answer streaming

If streaming is enabled at the LLM level, Langchain will only stream the intermediate steps. You can enable final answer streaming by passing stream_final_answer=True to the callback handler.

# Optionally, you can also pass the prefix tokens that will be used to identify the final answer
answer_prefix_tokens=["FINAL", "ANSWER"]

cl.AsyncLangchainCallbackHandler(
        stream_final_answer=True,
        answer_prefix_tokens=answer_prefix_tokens,
    )

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Conclusion

Choose the appropriate callback handler based on your specific requirements, ensuring seamless interaction between your LangChain agent and the Chainlit UI.