app.py

import chainlit as cl
import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
_logger = logging.getLogger("lang-chat")
_logger.addHandler(logging.StreamHandler(stream=sys.stdout))

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.vectorstores import VectorStore

from globals import (
    DEFAULT_QUESTION1,
    DEFAULT_QUESTION2,
    gpt35_model,
    gpt4_model
)

from semantic import (
    SemanticStoreFactory,
    SemanticRAGChainFactory
)

_semantic_rag_chain = SemanticRAGChainFactory.get_semantic_rag_chain()

@cl.on_message
async def main(message: cl.Message):
    
    content = "> "
    try:
        response = _semantic_rag_chain.invoke({"question": message.content})
        content += response["response"].content
    except Exception as e:
        _logger.error(f"chat error: {e}")

    # Send a response back to the user
    await cl.Message(
        content=f"{content}",
    ).send()

@cl.on_chat_start
async def start():

    await cl.Avatar(
        name="Chatbot",
        url="https://cdn-icons-png.flaticon.com/512/8649/8649595.png"
    ).send()
    await cl.Avatar(
        name="User",
        url="https://media.architecturaldigest.com/photos/5f241de2c850b2a36b415024/master/w_1600%2Cc_limit/Luke-logo.png"
    ).send()

    content = ""
    # if _semantic_rag_chain is not None:
    #     try:
    #         response1 = _semantic_rag_chain.invoke({"question": DEFAULT_QUESTION1})    
    #         response2 = _semantic_rag_chain.invoke({"question": DEFAULT_QUESTION2})    

    #         content = (
    #             f"**Question**: {DEFAULT_QUESTION1}\n\n"
    #             f"{response1['response'].content}\n\n"
    #             f"**Question**: {DEFAULT_QUESTION2}\n\n"
    #             f"{response2['response'].content}\n\n"
    #         )
    #     except Exception as e:
    #         _logger.error(f"init error: {e}")
    
    cl.user_session.set("message_history", [{"role": "system", "content": "You are a helpful assistant. "}])
    await cl.Message(
        content=content + "\nHow can I help you with Meta's 2023 10K?"
    ).send()