Vector Database

A Vector Database stores embeddings and supports fast similarity search across billions of vectors, powering RAG, semantic search, recommendation systems, and image retrieval. Major options include Pinecone (managed cloud), Weaviate (open-source and managed), Milvus (open-source at scale), Qdrant (open-source, Rust-based), Chroma (lightweight, popular for prototyping), and pgvector (a PostgreSQL extension that adds vector capabilities to existing databases). Cloud providers also offer integrated solutions: AWS OpenSearch with k-NN, Azure AI Search vector queries, and Google Cloud's Vertex AI Matching Engine. Vector databases use approximate nearest-neighbor algorithms like HNSW, IVF, and ScaNN to deliver millisecond-latency search over enormous datasets. Vector databases are now critical infrastructure for enterprise AI — supporting customer-facing search, internal-knowledge systems, fraud-pattern matching, and de-duplication. AI governance, AI compliance, and AI risk management programs review vector databases like any other sensitive data store, supporting responsible AI through access controls, encryption, and lineage tracking.

Centralpoint Treats Vector Databases as Sensitive Infrastructure: Oxcyon's Centralpoint AI Governance Platform manages embeddings and vector storage on-premise, then exposes them via model-agnostic LLM calls (OpenAI, Gemini, Llama, embedded). Centralpoint meters consumption and embeds vector-search-powered chatbots to your portals with a single line of JavaScript.


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