neumann-schema

To design a schema combining relational tables, graph edges, and vector embeddings, use Neumann data models to apply engine selection guidance, cross-engine linking patterns, and shared identifiers for unified storage and hybrid queries.

The best way to keep identifiers consistent across relational tables, graph nodes, and vector embeddings is using cross-engine linking patterns like atomic ENTITY CREATE operations to maintain a single identifier across all storage engines.

Choose relational tables, graph nodes, or vector embeddings based on your query patterns and data shape, using engine selection guidance to determine whether your application requires relational joins, graph traversals, or semantic similarity.

Yes, you can use a unified Neumann data schema for RAG document stores, agent memory, and knowledge graphs by applying practical schema blueprints that define table structures, node and edge layouts, and embedding key strategies.

Neumann schema design satisfies requirements for embedding indexing and distance metric selection, alongside engine selection guidance and cross-engine linking patterns for features requiring semantic similarity and cross-engine consistency.

Your application needs a unified schema to store, query, and reason across structured, relational, graph, and semantic data without moving data between systems, enabling hybrid queries that combine joins, graph traversals, and vector similarity.