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.
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
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import chainlit as cl@cl.on_messageasync 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.