neumann-schema

name: neumann-schema description: Design Neumann data models using relational tables, graph nodes/edges, and vector embeddings. Use when planning a database schema or deciding which Neumann engines to use for a feature.

Neumann Schema Design

When to Use Which Engine

Neumann has multiple engines. Choose based on data shape and query patterns.

Decision Guide

• Need columns and types? Use relational tables (CREATE TABLE).
• Need to traverse connections? Use graph nodes and edges.
• Need "find similar" or "nearest neighbor"? Use vector embeddings.
• Need all three for one entity? Use unified entities or link by key.
• Need encryption at rest? Use vault for secrets.
• Need to cache expensive LLM calls? Use cache engine.

Most real applications use 2-3 engines together. A user profile might be a relational row (structured fields), a graph node (connections), and a vector embedding (semantic search) -- all linked by the same user ID.

Schema Design Rules

Relational Tables

Use for structured data with known columns and types. Tables support indexes, constraints, joins, and aggregations.

CREATE TABLE users (name STRING, email STRING, age INT, active BOOL)
CREATE INDEX idx_email ON users (email)

Graph Nodes and Edges

Use for entities with dynamic properties and typed relationships. Edges are always directed (from -> to). Use labels to categorize nodes and type edges.

NODE CREATE user { name: 'Alice', role: 'engineer' }
NODE CREATE user { name: 'Bob', role: 'manager' }
EDGE CREATE 1 -> 2 : reports_to

Vector Embeddings

Use for data that needs similarity search. Key embeddings by entity ID for cross-engine linking. Choose the right distance metric:

• cosine -- text embeddings, normalized vectors (most common)
• euclidean -- spatial data, coordinate distances
• dot -- when vectors are already normalized and you want speed

EMBED STORE 'user:1' [0.12, -0.34, 0.56, ...]
SIMILAR TO [0.12, -0.34, 0.56, ...] LIMIT 10

Vault, Cache, Blob

• Vault: Store secrets with identity-based access control. VAULT STORE 'db-password' 'secret123' AS admin
• Cache: Cache LLM responses by exact query or semantic similarity. CACHE PUT 'prompt-hash' 'response-text'
• Blob: Store files with content-addressable deduplication. ARTIFACT UPLOAD 'report.pdf' FROM '/path/to/file'

Cross-Engine Linking Patterns

The key principle: use the same identifier across engines.

Pattern 1: Shared Key

Store the same ID in a table column, as a node property, and as the embedding key.

-- Relational: row with id
INSERT INTO users (id, name, email) VALUES (1, 'Alice', 'alice@co.com')

-- Graph: node with same id
NODE CREATE user { user_id: '1', name: 'Alice' }

-- Vector: embedding keyed by same id
EMBED STORE 'user:1' [0.12, -0.34, ...]

Pattern 2: Unified Entity

ENTITY CREATE writes to all relevant engines atomically.

ENTITY CREATE user {
  name: 'Alice',
  email: 'alice@co.com',
  embedding: [0.12, -0.34, ...]
}

Pattern 3: Combined Queries

Chain vector search with graph traversal.

SIMILAR TO [0.12, ...] LIMIT 10
-- Then use those keys to traverse the graph
NEIGHBORS 42 OUTGOING DEPTH 2

Anti-Patterns

Do not store graphs in relational tables. Using foreign keys and self-joins to model a graph is slow and awkward. Use NODE CREATE and EDGE CREATE instead -- the graph engine handles traversals efficiently.

Do not store structured records as node properties. If you have 20 typed columns with constraints and indexes, that is a table. Node properties are schemaless key-value pairs.

Do not embed everything. Vector embeddings cost storage and compute. Only embed data that needs similarity search or semantic matching. Exact lookups should use relational SELECT or graph GET.

Do not use vector search for exact lookups. If you know the exact key, use SELECT ... WHERE id = X or NODE GET id. Vector search is approximate and slower for exact matches.

Do not duplicate data across engines without a linking key. If a user exists in the table and in the graph, both must share an identifier. Otherwise updates diverge silently.

Example Schemas

See the examples/ directory for complete schema designs:

• examples/rag-app.md -- RAG application with documents, chunks, and semantic retrieval
• examples/agent-memory.md -- AI agent memory with structured recall, graph associations, and semantic search
• examples/knowledge-graph.md -- Knowledge graph with entity resolution, typed relationships, and similarity

Reference

• See neumann-query skill for complete query syntax
• See neumann-vector skill for HNSW index configuration
• See neumann-graph skill for traversal patterns
• See neumann-client skill for SDK integration