Vector Database

Detailed Explanation

Vector databases are specialized database systems optimized for storing and searching high-dimensional vector representations of data (embeddings). Unlike traditional databases that excel at exact matches, vector databases are designed for similarity search—finding items that are semantically similar rather than identical. They use algorithms like Approximate Nearest Neighbor (ANN) to efficiently search through millions or billions of vectors. Vector databases are crucial components in modern AI systems, particularly for retrieval-augmented generation, recommendation systems, and semantic search applications.

Examples

• Pinecone
• Weaviate
• Milvus
• Chroma