Vector Database
A retrieval system optimized for storing vectors and searching nearest neighbors, usually alongside identifiers, metadata and filtering.
Retrieval Model
A vector database stores embeddings and retrieves items whose vectors are close under a configured metric. Exact search can be expensive at large scale, so systems often use approximate indexes such as HNSW.
Metadata filtering and vector search interact: filtering after ANN can reduce result quality, while pre-filtering can change index efficiency.
Operational Concerns
Index build time, memory footprint, update/delete behavior, recall, latency and persistence are part of the system design. A fast benchmark on one embedding distribution does not generalize automatically.
Embeddings should also be versioned when model changes alter the vector space.
Related Information-Retrieval Concepts
- Embedding
- HNSW
- Cosine Similarity
- Approximate Nearest Neighbor
Related technical article: Artificial Intelligence: Philosophy, Theory and Practice.