rag_engine.py

import os
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_groq import ChatGroq
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

load_dotenv()

def get_pdf_text(pdf_file):
    text = ""
    reader = PdfReader(pdf_file)
    for page in reader.pages:
        extracted = page.extract_text()
        if extracted:
            text += extracted + "\n"
    return text.strip()

def get_chunks(text):
    splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=100)
    return splitter.split_text(text)

def get_vectorstore(chunks):
    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
    vectorstore = FAISS.from_texts(chunks, embeddings)
    return vectorstore

def get_answer(vectorstore, question):
    llm = ChatGroq(model="llama-3.3-70b-versatile", groq_api_key=os.getenv("GROQ_API_KEY"), temperature=0.3)
    prompt = PromptTemplate(template="""You are a helpful assistant. Use the context below to answer the question. Be detailed and specific in your answer. If the answer is truly not in the context, say Answer not found in the document.\n\nContext:\n{context}\n\nQuestion:\n{question}\n\nAnswer:""", input_variables=["context", "question"])
    retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
    def format_docs(docs):
        return "\n\n".join(doc.page_content for doc in docs)
    chain = ({"context": retriever | format_docs, "question": RunnablePassthrough()} | prompt | llm | StrOutputParser())
    return chain.invoke(question)

def debug_pdf(pdf_file):
    text = get_pdf_text(pdf_file)
    return f"Extracted {len(text)} characters. First 200 chars: {text[:200]}"