protologue

Vector Database

Also called vector store, vector index, vector search.

A vector database stores embeddings alongside their source content and metadata and answers queries by finding the stored vectors nearest to a query vector, usually with approximate nearest-neighbor search.

Description

Exact nearest-neighbor search becomes slow at millions of vectors, so vector databases use index structures such as HNSW graphs or quantized inverted indexes that trade a little recall for large speedups. Most also support metadata filtering and, increasingly, keyword search for hybrid retrieval. A vector index library such as FAISS provides the search without the database features.

Sources

  1. Malkov and Yashunin (2016). Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs.
  2. Johnson et al. (2017). Billion-scale similarity search with GPUs.

Cite this entry

Protologue. (2026). Vector Database. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0, PTL-0124). https://protologue.com/t/vector-database/

BibTeX
@misc{protologue_vector_database,
  title = {Vector Database},
  author = {{Protologue}},
  year = {2026},
  howpublished = {Protologue: A Taxonomy of Prompting and LLM Techniques, v1.4.0},
  note = {Entry PTL-0124},
  url = {https://protologue.com/t/vector-database/}
}

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