Vector Index

A Vector Index is the data structure inside a vector database that enables fast similarity search across millions or billions of high-dimensional embeddings. Common index algorithms include HNSW (Hierarchical Navigable Small Worlds), IVF (Inverted File), ScaNN (Google's Scalable Nearest Neighbors), and DiskANN. Each makes different tradeoffs between query speed, accuracy (recall), memory usage, and update cost. Choosing the right index matters enormously at scale — the wrong index choice can mean 100x worse latency or 50% worse recall. Major implementations live inside Pinecone, Weaviate, Milvus, Qdrant, FAISS (Facebook AI Similarity Search), and pgvector. Vector indexes are foundational infrastructure for retrieval-augmented generation, semantic search, recommendation systems, and image retrieval. AI governance, AI compliance, and AI risk management programs review vector indexes as sensitive data infrastructure — particularly because rebuilding an index can subtly change retrieval behavior in production, affecting AI accuracy and responsible AI outcomes in user-facing systems.

Centralpoint Manages Vector Indexes On-Premise: Centralpoint by Oxcyon hosts your vector indexes locally and pairs them with model-agnostic AI calls — ChatGPT, Gemini, Llama, embedded. The platform meters every LLM interaction, keeps prompts and skills on your servers, and embeds vector-search chatbots into your portals via a single JavaScript line.


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