Build a Token-Efficient RAG Pipeline with pgvector & Markdown

TL;DR

Converting scraped web content directly into Markdown reduces token consumption by up to 90% while preserving the semantic structure needed by LLMs. Combining Markdown extraction with PostgreSQL and the pgvector extension creates a highly efficient, production-ready Retrieval-Augmented Generation (RAG) pipeline without the operational overhead of a dedicated vector database.

The Token Problem in Web-Based RAG

Retrieval-Augmented Generation (RAG) systems are only as good as the context you feed them. When building RAG applications that ingest public documentation, technical blogs, or market reports, the default approach is often to scrape raw HTML, strip the tags, and dump the text into an embedding model.

This approach is fundamentally flawed.

TL;DR

The Token Problem in Web-Based RAG

This approach is fundamentally flawed.

Build a Token-Efficient RAG Pipeline with pgvector & Markdown

Build a Token-Efficient RAG Pipeline with pgvector & Markdown

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Related reading

RAG with OpenAI Embeddings, pgvector and LangChain

PixelRAG outperforms text parsers, reduces AI agent token costs by 10x

RAG and Vector Search with pgvector and Amazon Bedrock (Part 4)

RAG with Postgres pgvector in 2026: the full TypeScript pipeline.

Securing the Retrieval-Augmented Generation (RAG)

What I Learned Building a Local RAG Agent