Build a RAG Pipeline with ContentAPI in 5 Minutes

Learn to build a RAG pipeline with ContentAPI in 5 minutes. Extract, chunk, embed, and query content with Python — complete working code included.

What is RAG?

Retrieval-Augmented Generation (RAG) is the pattern of giving LLMs access to external knowledge by retrieving relevant documents before generating a response. Instead of relying solely on training data, a RAG pipeline:

Extracts content from sources (web pages, PDFs, videos)
Chunks content into smaller pieces
Embeds chunks into vector representations
Stores vectors in a database
Retrieves relevant chunks at query time
Generates an answer using the retrieved context

The biggest bottleneck? Step 1 and 2. Getting clean, structured content from arbitrary sources and chunking it properly. That's exactly what ContentAPI solves.