How to Scrape Websites into a Vector Database for RAG (Pinecone, pgvector, Qdrant)

Building a production web-sourced RAG system requires reliable ingestion that discovers relevant URLs, extracts clean main-content Markdown rather than noisy HTML, chunks documents according to heading structure, generates embeddings, and maintains an updated vector index. The text presents Context.dev as a tool for domain mapping, crawling, page extraction, change monitoring, and webhook-based updates, while recommending metadata retention for source URLs, titles, heading paths, timestamps, and content hashes. It argues that Markdown header-based chunking preserves semantic sections, tables, and code blocks better than fixed-size chunks and can improve retrieval performance when page titles and heading breadcrumbs are included in embedded text. OpenAI’s text-embedding-3-small is used to create vectors, with Pinecone positioned for managed serverless deployments, pgvector for PostgreSQL-based transactional workflows, and Qdrant for high-throughput metadata-filtered search. To control costs and prevent duplicate or stale results, the proposed process uses content hashes to avoid re-embedding unchanged chunks, deterministic IDs to overwrite updated chunks, and cleanup of orphaned chunks after pages shrink, while semantic change detection enables updates only when meaningful content changes.

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