app.py

# import required dependencies
# https://docs.chainlit.io/integrations/langchain
import os
from typing import List
from langchain_groq import ChatGroq
from langchain.prompts import PromptTemplate
from langchain_community.vectorstores import Qdrant
from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
from qdrant_client import QdrantClient
from langchain_community.chat_models import ChatOllama


import chainlit as cl
from langchain.chains import RetrievalQA

# bring in our GROQ_API_KEY
from dotenv import load_dotenv
load_dotenv()

groq_api_key = os.getenv("GROQ_API_KEY")
qdrant_url = os.getenv("QDRANT_URL")
qdrant_api_key = os.getenv("QDRANT_API_KEY")

custom_prompt_template = """Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.

Context: {context}
Question: {question}

Only return the helpful answer below and nothing else.
Helpful answer:
"""

def set_custom_prompt():
    """
    Prompt template for QA retrieval for each vectorstore
    """
    prompt = PromptTemplate(template=custom_prompt_template,
                            input_variables=['context', 'question'])
    return prompt


chat_model = ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
#chat_model = ChatGroq(temperature=0, model_name="Llama2-70b-4096")
#chat_model = ChatOllama(model="llama2", request_timeout=30.0)

client = QdrantClient(api_key=qdrant_api_key, url=qdrant_url,)


def retrieval_qa_chain(llm, prompt, vectorstore):
    qa_chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=vectorstore.as_retriever(search_kwargs={'k': 2}),
        return_source_documents=True,
        chain_type_kwargs={'prompt': prompt}
    )
    return qa_chain


def qa_bot():
    embeddings = FastEmbedEmbeddings()
    vectorstore = Qdrant(client=client, embeddings=embeddings, collection_name="The_Great_Gatsby")
    llm = chat_model
    qa_prompt=set_custom_prompt()
    qa = retrieval_qa_chain(llm, qa_prompt, vectorstore)
    return qa


@cl.on_chat_start
async def start():
    """
    Initializes the bot when a new chat starts.

    This asynchronous function creates a new instance of the retrieval QA bot,
    sends a welcome message, and stores the bot instance in the user's session.
    """
    chain = qa_bot()
    welcome_message = cl.Message(content="Starting the bot...")
    await welcome_message.send()
    welcome_message.content = (
        "Hi, Welcome to The Great Gatsby GPT"
    )
    await welcome_message.update()
    cl.user_session.set("chain", chain)


@cl.on_message
async def main(message):
    """
    Processes incoming chat messages.

    This asynchronous function retrieves the QA bot instance from the user's session,
    sets up a callback handler for the bot's response, and executes the bot's
    call method with the given message and callback. The bot's answer and source
    documents are then extracted from the response.
    """
    chain = cl.user_session.get("chain")
    cb = cl.AsyncLangchainCallbackHandler()
    cb.answer_reached = True
    # res=await chain.acall(message, callbacks=[cb])
    res = await chain.acall(message.content, callbacks=[cb])
    #print(f"response: {res}")
    answer = res["result"]
    #answer = answer.replace(".", ".\n")
    source_documents = res["source_documents"]

    text_elements = []  # type: List[cl.Text]

    if source_documents:
        for source_idx, source_doc in enumerate(source_documents):
            source_name = f"source_{source_idx}"
            # Create the text element referenced in the message
            text_elements.append(
                cl.Text(content=source_doc.page_content, name=source_name)
            )
        source_names = [text_el.name for text_el in text_elements]

        if source_names:
            answer += f"\nSources: {', '.join(source_names)}"
        else:
            answer += "\nNo sources found"

    await cl.Message(content=answer, elements=text_elements).send()