main.py

## RAG Q&A Conversation With PDF Including Chat History
import streamlit as st
from langchain.chains import create_history_aware_retriever, create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_chroma import Chroma
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_groq import ChatGroq
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
import os
import chromadb

from dotenv import load_dotenv
load_dotenv()

@st.cache_resource(show_spinner=False)
def get_embeddings():
    os.environ['HUGGINGFACE_TOKEN'] = os.getenv("HUGGINGFACE_TOKEN", "")
    return HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")


## set up Streamlit 
st.title("Conversational RAG With PDF uplaods and chat history")
st.write("Upload Pdf's and chat with their content")

## Read GROQ API Key from environment
api_key = os.getenv("GROQ_API_KEY", "")

## Check if groq api key is provided
if api_key:
    llm = ChatGroq(groq_api_key=api_key, model_name="Gemma2-9b-It")

    ## chat interface

    session_id=st.text_input("Session ID",value="default_session")
    ## statefully manage chat history

    if 'store' not in st.session_state:
        st.session_state.store={}

    uploaded_files=st.file_uploader("Choose A PDF file",type="pdf",accept_multiple_files=True)
    ## Process uploaded  PDF's
    if uploaded_files:
        documents=[]
        for uploaded_file in uploaded_files:
            temppdf=f"./temp.pdf"
            with open(temppdf,"wb") as file:
                file.write(uploaded_file.getvalue())
                file_name=uploaded_file.name

            loader=PyPDFLoader(temppdf)
            docs=loader.load()
            documents.extend(docs)

    # Split and create embeddings for the documents
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
        splits = text_splitter.split_documents(documents)
        embeddings = get_embeddings()
        vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings, persist_directory="./chroma_db")
        retriever = vectorstore.as_retriever()    


        contextualize_q_system_prompt=(
            "Given a chat history and the latest user question"
            "which might reference context in the chat history, "
            "formulate a standalone question which can be understood "
            "without the chat history. Do NOT answer the question, "
            "just reformulate it if needed and otherwise return it as is."
        )
        contextualize_q_prompt = ChatPromptTemplate.from_messages(
                [
                    ("system", contextualize_q_system_prompt),
                    MessagesPlaceholder("chat_history"),
                    ("human", "{input}"),
                ]
            )
        
        history_aware_retriever=create_history_aware_retriever(llm,retriever,contextualize_q_prompt)

        ## Answer question

        # Answer question
        system_prompt = (
                "You are an assistant for question-answering tasks. "
                "Use the following pieces of retrieved context to answer "
                "the question. If you don't know the answer, say that you "
                "don't know. Use three sentences maximum and keep the "
                "answer concise."
                "\n\n"
                "{context}"
            )
        qa_prompt = ChatPromptTemplate.from_messages(
                [
                    ("system", system_prompt),
                    MessagesPlaceholder("chat_history"),
                    ("human", "{input}"),
                ]
            )
        
        question_answer_chain=create_stuff_documents_chain(llm,qa_prompt)
        rag_chain=create_retrieval_chain(history_aware_retriever,question_answer_chain)

        def get_session_history(session:str)->BaseChatMessageHistory:
            if session_id not in st.session_state.store:
                st.session_state.store[session_id]=ChatMessageHistory()
            return st.session_state.store[session_id]
        
        conversational_rag_chain=RunnableWithMessageHistory(
            rag_chain,get_session_history,
            input_messages_key="input",
            history_messages_key="chat_history",
            output_messages_key="answer"
        )

        user_input = st.text_input("Your question:")
        if user_input:
            session_history=get_session_history(session_id)
            response = conversational_rag_chain.invoke(
                {"input": user_input},
                config={
                    "configurable": {"session_id":session_id}
                },  # constructs a key "abc123" in store.
            )
            st.markdown(f"*You:* {user_input}")
            st.markdown(f"*Assistant:* {response['answer']}")

else:
    st.warning("Set GROQ_API_KEY in your environment to enable chat.")