A vector database indexes and stores vector embeddings for fast retrieval and similarity search, with capabilities like CRUD operations, metadata filtering, and horizontal scaling.
What is a Vector Database? from the pinecone.io team.
Common Use cases for vector search:
- Semantic Search
- Similarity search for images, audio, video, JSON, and other forms of unstructured data
- Ranking and Recommendation Engines
- Deduplication and record matching
- Anomaly detection (eg: IT Threat detection)
Capabilities of a vector database:
- Vector Indexes for Search and Retrieval
- Single-Stage Filtering
- Data Sharding
- Hybrid Storage
- Building vector search in 200 lines of Rust
- Posts · The Data Quarry – a few articles on vector databases by Prashanth Rao at The Data Quarry
- VectorDB – see explanation of “Why use vector search and embeddings with large language models?” with example. Uses FAISS and mrpt under the hood.
See also – typesense