Staged Retrieval Design for Privacy-Focused Logistics Knowledge Search at Scale

A logistics knowledge manager can keep near-duplicate detection explainable and index costs under control by adopting a staged retrieval design with explicit collections, bounded queries, and traceable source context. The approach begins by defining the retrieval unit, mandatory metadata filters, and freshness requirements before choosing any endpoint. The article compares FAISS, pgvector, Qdrant, Pinecone, and managed REST vector APIs, noting that each trades off operational responsibility, database coupling, and data residency concerns. Evaluation relies on small, hand-inspectable test sets with failure cases, such as a delayed-delivery note that gets re-entered with a changed tracking number. Because cross-owner matches are treated as more serious than slightly lower top-k scores, the guidance favors a two-pass query that applies owner and document-type filters before scoring candidates.