Document Intake to Answer-Ready Knowledge: An Automated RAG Indexing Pipeline
Drop a file into a watched folder, inbox, or upload endpoint — minutes later it's answerable by your AI assistant. This automation handles the unglamorous half of RAG: classification, extraction, chunking with metadata, embedding, versioning, and a quarantine lane for everything that fails. Because retrieval quality is decided at write time, not query time.
Key Takeaways
- RAG answer quality is mostly determined at ingestion, before any query arrives.
- Every chunk carries provenance metadata — source, version, section anchor — so answers can cite and updates can supersede.
- New document versions defeat old ones explicitly; stale chunks are removed from retrieval, not left to compete.
- Failures route to a quarantine queue with reasons, never silently dropped.
- The same pipeline pattern works for contracts, support docs, specs, and research PDFs.