Vector Database

A vector database is a specialized database designed to store, index, and retrieve unstructured data represented as high-dimensional vectors, enabling efficient semantic search, similarity search, and powering applications such as LLM long-term memory, semantic search, and recommendation systems.

About this tool

Vector Database

A vector database (also known as a vector store or vector search engine) is a specialized type of database designed to store, index, and retrieve data represented as high-dimensional vectors. These databases enable efficient semantic and similarity searches, making them essential for modern applications in AI, machine learning, and information retrieval.

Features

  • Storage of High-Dimensional Vectors: Capable of storing fixed-length lists of numbers (vectors) representing data such as text, images, audio, and more.
  • Approximate Nearest Neighbor (ANN) Search: Implements algorithms to quickly retrieve database records most similar to a given query vector.
  • Semantic and Similarity Search: Facilitates searching based on meaning or similarity rather than exact matches.
  • Support for Multi-Modal Data: Can handle vectors derived from diverse data types (text, images, audio, etc.).
  • Integration with Machine Learning: Feature vectors are often computed using machine learning methods, such as feature extraction, word embeddings, or deep learning networks.
  • Use in Retrieval-Augmented Generation (RAG): Supports methods to enhance large language model (LLM) outputs by retrieving relevant context from stored vectors.
  • Recommendation Systems: Enables building recommendation engines by finding semantically similar items.
  • Techniques for High-Dimensional Search:
    • Hierarchical Navigable Small World (HNSW) graphs
    • Locality-sensitive Hashing (LSH) and Sketching
    • Product Quantization (PQ)
    • Inverted Files
    • Combinations of these techniques
  • Scalability and Performance: Designed for efficient search and retrieval in large-scale, high-dimensional datasets.

Common Use Cases

  • Semantic search
  • Similarity search
  • Multi-modal search
  • Recommendation engines
  • Long-term memory for large language models (LLMs)
  • Object detection
  • Retrieval-augmented generation (RAG)

Implementations

Vector databases can be found as standalone products or as features in broader database systems. Examples include:

  • Milvus
  • Pinecone
  • Weaviate
  • Qdrant
  • Elasticsearch
  • OpenSearch
  • MongoDB Atlas
  • Redis Stack
  • Vespa
  • Chroma
  • PostgreSQL with pgvector extension
  • Many others (see Wikipedia article for a comprehensive list)

References

Category

  • Concepts & Definitions

Tags

  • vector-databases
  • definition
  • semantic-search
  • similarity-search

Information

PublisherFox
PublishedMay 13, 2025