0-4-3-understand-vector-database

name	0.4.3-understand-vector_database
description	[0.4.3] Specialized storage system indexing embeddings with efficient similarity search capabilities (Pinecone, Weavia

understand-vector_database

CALL NUMBER: retrieval_augmented_generation_architecture_patt.vector_database DEFINITION: Specialized storage system indexing embeddings with efficient similarity search capabilities (Pinecone, Weaviate, Chroma, Milvus, pgvector)

Invoke this skill to understand vector_database down to its primitives. The RELATIVE ROOT below is the least-fixed-point closure of everything it bundles from — the full import cone, grouped by the lib each prim comes from. Projected from a prover-typed KB (MAP/SWI-Prolog consistency gate): every reference below resolves.

THE RELATIVE ROOT (the import cone, by lib)

from retrieval_augmented_generation_architecture_patt

• approximate_nearest_neighbor (d1): Vector indexing technique trading exactness for speed: HNSW, IVF, or LSH structures enabling sub-linear retrieval at scale
• incremental_indexing (d1): Index update strategy adding or modifying vectors without rebuilding entire index structure
• index_memory_footprint (d1): Storage requirements for vector index relative to number of embedded documents and embedding dimensionality
• indexing_pipeline (d1): End-to-end process: document ingestion, preprocessing, chunking, embedding generation, and vector store population
• real_time_indexing (d1): Incremental index updates allowing newly ingested documents to be immediately searchable without full recomputation
• vector_similarity_search (d1): Core retrieval operation computing distance or similarity (cosine, dot-product, euclidean) between query and document vectors

---

Projected from the retrieval augmented generation architecture patterns KB (207 concepts / 225 relations) — consistency-typed by MAP; the facet list after the colon IS the cross-lib dependency web.

(leaf — this is an actual skill.)