Delete rag_chain.py

rag_chain.py

@@ -1,92 +0,0 @@
-import os
-from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain_core.prompts import PromptTemplate
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_groq import ChatGroq
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnableParallel, RunnablePassthrough, RunnableLambda
-
-VECTORSTORE_DIR = "vectorstores"
-EMBEDDING_MODEL_NAME = os.environ["EMBEDDING_MODEL"]
-
-_embedding_model = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME)
-
-
-def load_transcript(video_id: str) -> str:
-    """Fetch and flatten a YouTube video's transcript."""
-    try:
-        yt_api = YouTubeTranscriptApi()
-        transcript_list = yt_api.fetch(video_id, languages=["en"])
-        transcript = " ".join(chunk.text for chunk in transcript_list)
-        return transcript
-    except TranscriptsDisabled:
-        raise ValueError("No captions available for this video.")
-
-
-def split_transcript(transcript: str):
-    splitter = RecursiveCharacterTextSplitter(
-        chunk_size=1000,
-        chunk_overlap=200,
-    )
-    return splitter.create_documents([transcript])
-
-
-def get_or_build_vectorstore(video_id: str):
-    """Load a cached FAISS index for this video, or build + save one if missing."""
-    path = os.path.join(VECTORSTORE_DIR, video_id)
-
-    if os.path.exists(path):
-        return FAISS.load_local(
-            path,
-            _embedding_model,
-            allow_dangerous_deserialization=True,
-        )
-
-    transcript = load_transcript(video_id)
-    chunks = split_transcript(transcript)
-    vectorstore = FAISS.from_documents(chunks, _embedding_model)
-
-    os.makedirs(VECTORSTORE_DIR, exist_ok=True)
-    vectorstore.save_local(path)
-
-    return vectorstore
-
-
-def format_docs(retrieved_docs):
-    return "\n\n".join(doc.page_content for doc in retrieved_docs)
-
-
-def get_rag_chain(video_id: str):
-    """Builds and returns the full RAG chain for a given YouTube video."""
-    vectorstore = get_or_build_vectorstore(video_id)
-    retriever = vectorstore.as_retriever(search_kwargs={"k": 2})
-
-    llm = ChatGroq(
-        model="llama-3.1-8b-instant",
-        temperature=0,
-        api_key=os.environ["GROQ_API_KEY"],
-    )
-
-    prompt = PromptTemplate(
-        template="""
-          You are a helpful assistant.
-          Answer ONLY from the provided transcript context.
-          If the context is insufficient, just say you don't know.
-
-          {context}
-          Question: {question}
-        """,
-        input_variables=["context", "question"],
-    )
-
-    parser = StrOutputParser()
-
-    parallel_chain = RunnableParallel({
-        "context": retriever | RunnableLambda(format_docs),
-        "question": RunnablePassthrough(),
-    })
-
-    main_chain = parallel_chain | prompt | llm | parser
-    return main_chain