Vector Database

A Vector Database is a specialized database optimized for storing, indexing, and querying high-dimensional vectors (embeddings). These vectors are numerical representations of data like text, images, or audio that capture semantic meaning, enabling similarity-based search operations.

Vector databases are essential components of modern AI systems, particularly for RAG applications, semantic search, recommendation systems, and similarity matching. Unlike traditional databases that rely on exact matches, vector databases use algorithms like HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index) to efficiently find the most similar vectors to a query.

Popular vector databases include Pinecone, Weaviate, Milvus, Qdrant, and ChromaDB. Many traditional databases like PostgreSQL (with pgvector) and Redis now also support vector operations. Choosing the right vector database depends on factors like scale, latency requirements, filtering capabilities, and integration needs.