Vector Database

A vector database is a specialized data store designed to index, search, and manage high-dimensional numerical vectors — the embeddings produced by neural networks — at scale. Unlike traditional relational databases that excel at exact-match queries on structured rows, vector databases optimize for approximate nearest neighbor (ANN) search over millions or billions of vectors using algorithms like HNSW, IVF, and product quantization. They form the storage backbone of RAG pipelines, recommendation engines, image and audio similarity search, anomaly detection, and any AI application that compares meanings rather than literal strings. Leading vector databases include Pinecone, Weaviate, Milvus, Qdrant, Chroma, pgvector, and LanceDB, each balancing trade-offs between cost, latency, recall accuracy, and operational complexity. AI governance frameworks treat vector databases as governed data assets because the embeddings can leak training data, encode bias, or expose sensitive content under certain attack patterns. Enterprise responsible AI programs apply the same access controls, encryption, audit logging, and retention policies to vector stores as to traditional databases.

Vector databases in the Centralpoint AI Governance Platform: Centralpoint connects to any vector database your team uses — Pinecone, Weaviate, Milvus, Qdrant, pgvector — under one model-agnostic governance layer. The platform keeps prompts and skills local, meters tokens across every retrieval-plus-generation call, and lets you deploy chatbots backed by vector search with a single line of JavaScript on any portal.


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