rag_utility.py

import os
from dotenv import load_dotenv
from langchain_community.document_loaders import UnstructuredPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_chroma import Chroma
from langchain_groq import ChatGroq
from langchain.chains import RetrievalQA

# Load environment variables
load_dotenv()
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
os.environ["GROQ_API_KEY"] = GROQ_API_KEY

working_dir = os.path.dirname(os.path.abspath(__file__))

# Initialize the embedding model
embedding = HuggingFaceEmbeddings()

# Initialize the DeepSeek-R1 70B model
deepseek_llm = ChatGroq(
    model="deepseek-r1-distill-llama-70b",
    temperature=0
)

# Initialize the Llama-3 70B model
llama3_llm = ChatGroq(
    model="llama-3.3-70b-versatile",
    temperature=0
)

def process_document_to_chromadb(file_name):
    """Processes a PDF document and stores embeddings in ChromaDB."""
    loader = UnstructuredPDFLoader(os.path.join(working_dir, file_name))
    documents = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=200)
    texts = text_splitter.split_documents(documents)
    vectordb = Chroma.from_documents(
        documents=texts,
        embedding=embedding,
        persist_directory=os.path.join(working_dir, "doc_vectorstore")
    )
    return "Document successfully processed and stored."

def answer_question(user_question):
    """Retrieves answers from stored documents using DeepSeek-R1 and Llama-3."""
    vectordb = Chroma(
        persist_directory=os.path.join(working_dir, "doc_vectorstore"),
        embedding_function=embedding
    )
    retriever = vectordb.as_retriever()
    
    # DeepSeek-R1 response
    qa_chain_deepseek = RetrievalQA.from_chain_type(
        llm=deepseek_llm,
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=True
    )
    response_deepseek = qa_chain_deepseek.invoke({"query": user_question})
    answer_deepseek = response_deepseek["result"]
    
    # Llama-3 response
    qa_chain_llama3 = RetrievalQA.from_chain_type(
        llm=llama3_llm,
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=True
    )
    response_llama3 = qa_chain_llama3.invoke({"query": user_question})
    answer_llama3 = response_llama3["result"]
    
    return {"answer_deepseek": answer_deepseek, "answer_llama3": answer_llama3}