Vector Database

A vector database stores embeddings and provides fast similarity search over millions or billions of vectors. Traditional databases search by exact matches or keywords. Vector databases search by meaning — finding the most semantically similar items to a given query.

Popular vector databases include Pinecone, Weaviate, Chroma, Qdrant, and Milvus. Some traditional databases like PostgreSQL (with pgvector) and MongoDB also offer vector search capabilities. The choice depends on scale, latency requirements, and integration needs.

Vector databases are the backbone of RAG systems. When an AI agent needs to answer a question about your business, it converts the question to an embedding, searches the vector database for the most similar document chunks, and uses those as context. At Agentik {OS}, vector databases give our agents institutional memory — they remember your codebase, documentation, brand guidelines, and previous decisions, making each interaction more informed than the last.