Vector Schema

A vector schema is the formal definition of the vector dimension, distance metric, metadata fields, and index parameters that govern how vectors are stored, validated, and queried in a vector collection. Schemas vary in formality across platforms — Weaviate enforces strict typed schemas with a defined class structure, Pinecone uses lighter metadata typing, and Chroma takes a schema-on-read approach where any JSON metadata is acceptable. Schema decisions are largely irreversible without migration: changing the embedding dimension or distance metric typically requires re-embedding the entire corpus and rebuilding the index, which can take hours or days at scale. AI governance teams treat schema design as a foundational architecture decision, documenting the chosen embedding model, dimension, similarity metric, and metadata semantics in the same way data dictionaries document relational databases. Schema versioning becomes important when embedding models are upgraded, since new and old vectors live in different semantic spaces and cannot be directly compared. Most enterprise platforms maintain a schema registry alongside the vector database for AI compliance documentation.

Vector schemas in Centralpoint: Centralpoint coordinates vector schemas across whatever backend you use — Weaviate, Pinecone, Milvus, pgvector — letting you swap embedding models like MiniLM, BGE, or OpenAI embeddings without rewriting downstream skills. The platform meters tokens per skill, keeps prompts local, and deploys schema-aware chatbots through one line of JavaScript.


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