How to Extract YouTube Transcripts for LangChain RAG Pipelines

Retrieval-augmented generation is only as good as its knowledge source. YouTube contains millions of hours of expert knowledge — lectures, tutorials, interviews — that most RAG systems never tap. This guide shows you exactly how to change that.

Why YouTube for RAG?

Most RAG implementations pull from PDFs, web pages, or internal documents. But YouTube has something those sources lack: spoken expert knowledgethat has never been written down anywhere. Conference talks, podcast interviews, tutorial series — this is high-signal content that's been completely underutilized for knowledge bases.

The problem has always been extraction. YouTube's own Data API only gives you auto-generated captions for a subset of videos, and even those aren't available in a clean, timestamped JSON format you can actually work with.

Step 1: Get your SupaCrawlX API key

Sign up and you get 100 free credits instantly — no credit card. Set your key as an environment variable so you never hardcode it:

export SUPACRAWLX_API_KEY=your_api_key_here

Step 2: Extract the transcript

Install the SDK and pull the first transcript:

# pip install supacrawlx langchain openai

import os
from supacrawlx import Client

client = Client(api_key=os.environ["SUPACRAWLX_API_KEY"])

result = client.youtube.transcript(video_id="dQw4w9WgXcQ")
# result.content is a list of {text, offset, duration} objects
transcript_text = " ".join(seg["text"] for seg in result.content)

Step 3: Chunk with LangChain

LangChain's RecursiveCharacterTextSplitter is ideal for transcript content because it respects sentence boundaries:

from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=50,
    separators=[". ", "? ", "! ", "\n"],
)

chunks = splitter.create_documents(
    texts=[transcript_text],
    metadatas=[{"video_id": "dQw4w9WgXcQ", "source": "youtube"}],
)

Step 4: Embed and index

Use OpenAI embeddings and push to your vector store of choice:

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma

vectorstore = Chroma.from_documents(
    documents=chunks,
    embedding=OpenAIEmbeddings(),
    collection_name="youtube-knowledge-base",
)

Step 5: Query the pipeline

from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

qa = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    retriever=vectorstore.as_retriever(search_kwargs={"k": 4}),
)

print(qa.run("What does the speaker say about distributed systems?"))

Performance tips

Use chunk_overlap=50 to preserve context across chunk boundaries.
Store the offset timestamp as metadata to deep-link back to the source moment.
For playlists, use the SupaCrawlX batch endpoint to process up to 50 videos in one request.
Cache transcripts locally — they don't change, so there's no reason to re-fetch.

The timestamp metadata is the hidden gem here. You can surface not just the answer but the exact moment in the video where it was said — dramatically better UX for your users.

What's next

From here, you can extend this pipeline to include TikTok and Instagram content using the same SupaCrawlX SDK. One API key, every platform — the knowledge base your RAG system deserves.