Use Case

YouTube Transcripts for RAG Pipelines

The Problem

Building an AI assistant that can answer questions about YouTube video content requires extracting and indexing transcripts. Manually downloading captions is slow, error-prone, and does not scale. Many videos lack captions entirely, leaving gaps in your knowledge base.

The Solution

YouTubeTranscripts.co provides a single API call to extract transcripts from any YouTube video. Our AI fallback uses Whisper to transcribe videos without captions. The clean JSON output integrates directly with LangChain, LlamaIndex, and custom RAG pipelines. Batch processing lets you index entire channels overnight.

Implementation Example

import httpx
from langchain.schema import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Fetch transcript
resp = httpx.get(
    "https://api.youtubetranscripts.co/v1/transcript",
    params={"url": "https://youtube.com/watch?v=VIDEO_ID", "format": "text"},
    headers={"x-api-key": "YOUR_API_KEY"},
)
data = resp.json()

# Convert to LangChain Document and chunk
doc = Document(page_content=data["text"], metadata={"title": data["title"]})
chunks = RecursiveCharacterTextSplitter(chunk_size=1000).split_documents([doc])

# Now embed chunks into your vector store
# vectorstore.add_documents(chunks)

Why Developers Choose YouTubeTranscripts.co

Index entire YouTube channels into your RAG system

AI fallback ensures no video is left untranscribed

Clean text output ready for chunking and embedding

Batch API processes 25 videos per request

Metadata includes title, channel, and timestamps

Works with LangChain, LlamaIndex, and custom pipelines

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