rag_chain.py

# rag_chain.py

import os
from dotenv import load_dotenv
from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound, VideoUnavailable
import requests

from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate

load_dotenv()

# Already handled by Hugging Face via secret env vars
hf_token = os.getenv("HUGGINGFACEHUB_ACCESS_TOKEN")
openai_token = os.getenv("OPENAI_API_KEY")

os.environ["HUGGINGFACEHUB_API_TOKEN"] = hf_token
os.environ["OPENAI_API_KEY"] = openai_token

# Hugging Face Embeddings
os.environ['HF_HOME'] = 'Embedding Models'

embedding = HuggingFaceEmbeddings(
    model_name="Embedding Models/hub/models--sentence-transformers--all-MiniLM-L6-v2/snapshots/c9745ed1d9f207416be6d2e6f8de32d1f16199bf",
    model_kwargs={"local_files_only": True}
)

# OpenRouter LLM (Meta LLaMA 3.3)
llm = ChatOpenAI(
    openai_api_base="https://openrouter.ai/api/v1",
    model="meta-llama/llama-3.3-70b-instruct:free",
)

# Custom prompt for RAG
qa_prompt = PromptTemplate(
    template="""
You are a helpful assistant answering questions based on YouTube video content.

Context:
{context}

Question:
{question}

Answer:""",
    input_variables=["context", "question"],
)

def fetch_transcript(video_id: str) -> str:
    try:
        # ✅ Define your proxy here
        proxies = {
            "http": "http://219.65.73.81:80",
            "https": "http://219.65.73.81:80"
        }

        # Patch requests session with proxy
        session = requests.Session()
        session.proxies.update(proxies)
        YouTubeTranscriptApi._requests = session  # monkey patch

        transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=["en", "hi"])
        return " ".join([t["text"] for t in transcript])

    except (TranscriptsDisabled, NoTranscriptFound, VideoUnavailable) as e:
        raise Exception(f"Transcript not available for video_id {video_id}: {str(e)}")
    except Exception as e:
        raise Exception(f"Error fetching transcript: {str(e)}")


# Build RAG chain from transcript
def build_chain(video_id: str) -> RetrievalQA:
    text = fetch_transcript(video_id)

    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    docs = splitter.create_documents([text])

    vectorstore = FAISS.from_documents(docs, embedding)
    retriever = vectorstore.as_retriever()

    memory = ConversationBufferMemory(
        memory_key="chat_history",
        return_messages=True,
        output_key="result"
    )

    qa_chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=retriever,
        memory=memory,
        return_source_documents=True,
        output_key="result",
        chain_type_kwargs={"prompt": qa_prompt}
    )

    return qa_chain