Vector Collection

A vector collection (sometimes called an index, class, or table depending on the platform) is the logical container inside a vector database that holds a set of related embeddings along with their metadata, schema, and index configuration. Each collection typically has a fixed embedding dimension, a chosen distance metric, an index type, and an associated metadata schema, and is the unit at which most operations like search, upsert, and delete are scoped. In Pinecone they are called indexes, in Weaviate classes, in Milvus and Qdrant collections, in Chroma collections, and in pgvector regular PostgreSQL tables. Designing the right collection topology — one per use case, per tenant, per language, per content type — is an early architectural decision that affects cost, performance, and AI compliance scope downstream. AI governance frameworks treat collections as the natural unit of access control, retention policy, and audit log scoping, similar to how schemas and tables work in relational databases. Most production deployments adopt naming conventions and metadata tagging standards across collections for responsible AI operations at scale.

Vector collections governed by Centralpoint: Centralpoint can route different skills to different vector collections — one per audience, tenant, or business unit — across whatever vector database you operate. The model-agnostic platform meters tokens per skill, keeps prompts local, and deploys collection-aware chatbots through one line of JavaScript on any portal.


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