document_chat.py

import os
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.document_loaders import PyMuPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.llms import HuggingFaceHub

# Constants
CHROMA_DB_PATH = "chroma_db"
SENTENCE_TRANSFORMER_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
LLM_MODEL = "HuggingFaceH4/zephyr-7b-beta"

# Initialize vector store
def initialize_vector_store():
    embeddings = HuggingFaceEmbeddings(model_name=SENTENCE_TRANSFORMER_MODEL)
    return Chroma(persist_directory=CHROMA_DB_PATH, embedding_function=embeddings)

vector_store = initialize_vector_store()

def ingest_pdf(pdf_path):
    """Loads, splits, and stores PDF content in a vector database."""
    loader = PyMuPDFLoader(pdf_path)
    documents = loader.load()

    # Optimized text splitting: Smaller chunks, no overlap to prevent redundancy
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=0)
    split_docs = text_splitter.split_documents(documents)

    # Add documents to vector store and persist
    vector_store.add_documents(split_docs)
    vector_store.persist()

def process_query_with_memory(query, chat_memory):
    """Processes user queries while maintaining conversational memory."""
    retriever = vector_store.as_retriever(search_kwargs={"k": 3})  # Optimized retrieval

    # Debug: Print retrieved documents
    retrieved_docs = retriever.get_relevant_documents(query)
    print("Retrieved Docs:\n", [doc.page_content for doc in retrieved_docs])  

    # Initialize LLM
    llm = HuggingFaceHub(repo_id=LLM_MODEL, model_kwargs={"max_new_tokens": 500})

    # Create conversational retrieval chain
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever=retriever,
        memory=chat_memory
    )

    # Debug: Print chat history to detect repetition
    chat_history = chat_memory.load_memory_variables({}).get("chat_history", [])
    print("Chat History:\n", chat_history)  

    # Ensure no duplicate chat history
    chat_history = list(set(chat_history))

    return conversation_chain.run({"question": query, "chat_history": chat_history})

# Initialize chat memory
chat_memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

# Example Usage
if __name__ == "__main__":
    pdf_path = "CV_Data_Science.pdf"
    ingest_pdf(pdf_path)

    user_query = "What are my skills in CV?"
    response = process_query_with_memory(user_query, chat_memory)
    print("\nChatbot Response:", response)