RAG Pipeline
| Build a RAG (retrieval-augmented generation) pipeline or a custom search engine on top of Bright Data's Discover API — using intent-ranked web results + parsed page content as the retrieval/ingestion layer for an LLM or vector store. Use when the user wants to "build a RAG pipeline", "add web search to my LLM/agent", "ground my model in live web data", "build a search engine over the web", "ingest web content into a vector DB / knowledge base", or "give my chatbot retrieval". Covers both live retrieval (Discover at query time as a web-grounded retriever) and ingestion (Discover → chunk → embed → vector store → retrieve). Built on the discover-api skill. For a one-off written report use live-research; for raw markdown of specific known URLs use scrape.
Works with: Claude Code (native) · Cursor, Codex CLI (manual)
native: this artifact type is that client's own format
Category: Search — see all ranked ›
Install (Claude Code):
cp -r rag-pipeline ~/.claude/skills/- Adoption: 1 repos
- Upkeep: 93.0
- Freshness: 86.0
- Evidence coverage: 84% of the inputs this score can use — the rest are unknown, and the score is discounted for it
- Health: active
- Contributors: 5
- License: MIT
Security audit
Not scanned yet. We audit npm-published capabilities for known advisories, install-time scripts and permission surface; this one has no npm package we can resolve, or has not reached the queue.
source ↗ · skill:brightdata/rag-pipeline
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Measured 2026-08-05 · scorer s5 · how · something wrong here?