0-4-1-understand-vector-database

name	0.4.1-understand-vector_database
description	[0.4.1] A specialized database system optimized for storing, indexing, and searching high-dimensional vector embedding

understand-vector_database

CALL NUMBER: vector_databases_and_embeddings.vector_database DEFINITION: A specialized database system optimized for storing, indexing, and searching high-dimensional vector embeddings by similarity.

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 ?

• integrates_with_embedding_model (d2): The integration relationship where a vector database connects to embedding models to ingest, store, and search generated vector representations.
• manages_vector_database (d2): The management relationship where a service (such as Pinecone) orchestrates vector storage, indexing, and querying infrastructure.

from vector_databases_and_embeddings

• chroma (d1): An open-source embedded vector database written in Python; designed for developer simplicity with in-memory or persistence mode, commonly used in LLM and RAG applications.
• embedding_cache (d1): A key-value store (Redis, memcached, disk) that persists computed embedding vectors to avoid redundant model inference for identical or duplicate inputs.
• milvus (d1): An open-source cloud-native vector database developed by Zilliz; supports multiple ANN indexes (HNSW, IVF, DiskANN), GPU acceleration, and distributed shard/replica deployments.
• pinecone (d1): A managed cloud vector database service that provides serverless ANN indexing, real-time upserts, filtering, and multi-tenancy with SLA-backed availability.
• qdrant (d1): An open-source vector similarity search engine with a Rust core; provides HNSW and SCAN indexing, payload filtering, quantization, and a gRPC-first API.
• weaviate (d1): An open-source vector database with built-in module support for embedding models (transformers, CLIP), hybrid BM25-plus-vector search, and GraphQL API.

CONSUMERS (what needs this)

deletion_marking, image_embedding, metadata_filtering, shard_partitioning, vector_embedding

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Projected from the vector databases and embeddings KB (262 concepts / 150 relations) — consistency-typed by MAP; the facet list after the colon IS the cross-lib dependency web.

(leaf — this is an actual skill.)