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Documentation Index

Fetch the complete documentation index at: https://docs.chainlit.io/llms.txt

Use this file to discover all available pages before exploring further.

A Message is a piece of information that is sent from the user to an assistant and vice versa. Coupled with life cycle hooks, they are the building blocks of a chat. A message has a content, a timestamp and cannot be nested.

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Example: Reply to a user message

Lets create a simple assistant that replies to a user message with a greeting. 
import chainlit as cl

@cl.on_message
async def on_message(message: cl.Message):
    response = f"Hello, you just sent: {message.content}!"
    await cl.Message(response).send()

Message API

Learn more about the Message API.

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Chat Context

Since LLMs are stateless, you will often have to accumulate the messages of the current conversation in a list to provide the full context to LLM with each query. You could do that manually with the user_session. However, Chainlit provides a built-in way to do this:
chat_context
import chainlit as cl

@cl.on_message
async def on_message(message: cl.Message):
    # Get all the messages in the conversation in the OpenAI format
    print(cl.chat_context.to_openai())

    # Send the response
    response = f"Hello, you just sent: {message.content}!"
    await cl.Message(response).send()
Every message sent or received will be automatically accumulated in cl.chat_context. You can then use cl.chat_context.to_openai() to get the conversation in the OpenAI format and feed it to the LLM.