Overview
- An updated perspective argues RAG success hinges on data systems, treating embeddings as a materialized view with explicit freshness metadata, source IDs, versions, and timestamps.
- Change Data Capture enables incremental re-embedding and safe deletes, while schema-aware chunking, versioned indices, and enforceable retrieval contracts guard against silent drift.
- A rigorous Web‑to‑Vector pipeline is emphasized: headless browser scraping for SPAs, HTML‑to‑Markdown distillation to remove boilerplate, structured chunking, batched embeddings, and metadata-rich indexing.
- Operational guidance highlights retrieval precision via metadata pre-filters and hybrid dense+BM25 search, plus mitigation of embedding drift through index versioning and micro-batched throughput.
- A step-by-step tutorial shows a runnable stack for chatting with PDFs using LangChain, FAISS, and OpenAI on a FastAPI backend with a simple React UI, grounding answers in user documents.