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What is Vector Database

A vector database is a type of database that indexes and stores vector embeddings for fast retrieval and similarity search, with capabilities like CRUD operations, metadata filtering, and horizontal scaling.

Information comes in many forms:

  • Some information is unstructured—like text documents, rich media, and audio.
  • Some information is structured—like application logs, tables, and graphs.

Innovations in artificial intelligence and machine learning (AI/ML):

  • Have allowed us to create a type of ML model—embedding models.
  • Embeddings encode all types of data into vectors that capture the meaning and context of an asset.
  • This allows us to find similar assets by searching for neighboring data points.

Vector search methods:

  • Allow unique experiences like taking a photograph with your smartphone and searching for similar images.

Vector databases provide:

  • The ability to store and retrieve vectors as high-dimensional points.
  • Additional capabilities for efficient and fast lookup of nearest-neighbors in the N-dimensional space.
  • They are typically powered by k-nearest neighbor (k-NN) indexes.
  • Built with algorithms like the Hierarchical Navigable Small World (HNSW) and Inverted File Index (IVF) algorithms.
  • Additional capabilities like data management, fault tolerance, authentication and access control, and a query engine.
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