app.py

import chainlit as cl
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import StrOutputParser
from langchain.schema.runnable import RunnableConfig, RunnablePassthrough
from langchain_openai import ChatOpenAI

import configs
from prompts import prompt
from utils import format_docs, process_documents

doc_search = process_documents(configs.DOCS_STORAGE_PATH)
model = ChatOpenAI(name=configs.CHAT_MODEL, streaming=True)


@cl.on_chat_start
async def on_chat_start():
    retriever = doc_search.as_retriever(search_kwargs={"k": 10})

    runnable = (
        {"context": retriever | format_docs, "question": RunnablePassthrough()}
        | prompt
        | model
        | StrOutputParser()
    )

    cl.user_session.set("runnable", runnable)


@cl.on_message
async def on_message(message: cl.Message):
    runnable = cl.user_session.get("runnable")
    msg = cl.Message(content="")

    class PostMessageHandler(BaseCallbackHandler):
        """
        Callback handler for handling the retriever and LLM processes.
        Used to post the sources of the retrieved documents as a Chainlit element.
        """

        def __init__(self, msg: cl.Message):
            BaseCallbackHandler.__init__(self)
            self.msg = msg
            self.sources = set()  # To store unique pairs

        def on_retriever_end(self, documents, *, run_id, parent_run_id, **kwargs):
            for d in documents:
                source_page_pair = d.metadata["source"]
                self.sources.add(source_page_pair)  # Add unique pairs to the set

        def on_llm_end(self, response, *, run_id, parent_run_id, **kwargs):
            if len(self.sources):
                sources_text = "\n".join(
                    [f"{source}#page={page}" for source, page in self.sources]
                )
                self.msg.elements.append(
                    cl.Text(name="Sources", content=sources_text, display="inline")
                )

    async with cl.Step(type="run", name="QA Assistant"):
        async for chunk in runnable.astream(
            message.content,
            config=RunnableConfig(
                callbacks=[cl.LangchainCallbackHandler(), PostMessageHandler(msg)]
            ),
        ):
            await msg.stream_token(chunk)

        with open(configs.HIISTORY_FILE, "a") as f:
            f.write(f"""{message.content}[SEP]{msg.content}[END]\n\n""")
    await msg.send()