What is a Vector Database?
A vector database is a type of database that stores data as high-dimensional vectors — mathematical representations of data points in a multi-dimensional space.
Why Vector Databases Matter for AI
Traditional databases store and retrieve data using exact matches or range queries. Vector databases, in contrast, are designed to find semantically similar items using approximate nearest neighbor search.
Key Features
- High-dimensional similarity search
- Efficient indexing (HNSW, IVF, PQ)
- Scalable to billions of vectors
- Integration with ML pipelines
Popular Vector Databases
Several vector databases have emerged to support AI applications:
- Pinecone: Managed, cloud-native vector database
- Weaviate: Open-source with GraphQL API
- Chroma: Simple, developer-friendly
- Qdrant: Rust-based, high performance
- pgvector: PostgreSQL extension for vectors
How Similarity Search Works
Given a query vector, the database finds the k vectors most similar to the query using distance metrics like cosine similarity, Euclidean distance, or dot product.
The challenge is efficiency: searching through millions of vectors naively is too slow. Indexing algorithms like HNSW (Hierarchical Navigable Small World) make this tractable by creating graph-based approximations.
Use Cases
Vector databases power many AI applications:
- Semantic search
- Recommendation systems
- RAG (Retrieval-Augmented Generation)
- Anomaly detection
- Image and video retrieval