Why I Chose PDF RAG Chunking and Metadata for Catalog Semantic Search

Short answer: for semantic search over messy B2B catalog PDFs, I would spend the latency budget during ingestion, preserve page-level evidence, and keep the query path to one embedding plus one vector search; if a catalog must become searchable immediately after every upload, I would choose simpler deterministic chunks and defer enrichment. The decisive constraint is not the PDF parser or the language of the upload service. It is the quality-versus-latency boundary: descriptions often separate a product name, dimensions, compatibility notes, and exclusions across headings or pages, while a buyer expects one coherent result. A fast pipeline that loses those relationships produces plausible but unauditable answers. A sophisticated pipeline that blocks publication for too long fails a different operational requirement. For a Node.js RAG service, I treat the upload worker, embedding adapter, and Postgres repository as replaceable components. The durable contract is the evidence record. Each record needs a stable document version, a stable chunk identity, normalized text, page bounds, catalog identifiers, and the embedding configuration that produced its vector. That is the smallest design I trust for retries, reconciliation, and citations. How should Node.js RAG handle PDF upload chunking metadata and citations? The Node.js boundary should accept an upload, hash the original bytes, write an immutable document version, and enqueue ingestion under an idempotency key derived from the tenant, catalog, and file hash. Parsing and embedding can happen asynchronously. Search should read only a version whose ingestion status was committed as complete; otherwise a retry can expose half a catalog, which is especially awkward when two chunks describe the same SKU differently. Chunking comes after extraction, not during transport. Keep page boundaries from the parser, normalize repeated headers and whitespace without rewriting the source, then group adjacent blocks around product st