relational-schema-analyzer 0.1.0

Analyze a relational database schema into a conceptual model (OWL-capable) with mapping back to the source schema.

relational-schema-analyzer

Analyze a relational database schema and produce a canonical conceptual model (entities / relationships / properties), a conceptual → physical mapping back to the source relational schema, and metadata (confidence, fingerprints, patterns). Optional exports include OWL (Turtle / JSON-LD) for ontology pipelines.

This library is the relational analogue of arangodb-schema-analyzer and emits the same tool-contract bundle shape so that downstream consumers (arango-ontoextract, transpilers, and ETL tools such as r2g) can treat relational and ArangoDB sources interchangeably.

PostgreSQL / MySQL / SQL Server / Snowflake / CSV
        │
        ▼  introspect (live catalog views, not DDL parsing)
   Physical Schema  (tables, columns, PKs, FKs, types)
        │
        ▼  infer (deterministic baseline + optional LLM refinement)
   { conceptualSchema, physicalMapping, metadata }   ← canonical JSON bundle
        │
        ├──► OWL Turtle / JSON-LD       (arango-ontoextract, ontology tooling)
        ├──► relational physical view   (SQL-native query tooling, future)
        └──► consumed by r2g            (drives ArangoDB MappingConfig generation)

Status

Early development. Phases 0–3 implemented: the physical core (connectors, types, FK inference) is extracted from r2g; the deterministic conceptual baseline emits a contract-valid {conceptualSchema, physicalMapping, metadata} bundle with no LLM; and OWL (Turtle / JSON-LD) exports + a CLI are in place. Next: optional LLM refinement (Phase 4) and ecosystem integration (Phase 5). See:

• docs/DESIGN.md — architecture, data model, tool contract, OWL mapping
• docs/IMPLEMENTATION-PLAN.md — phased delivery plan & extraction inventory

from relational_schema_analyzer import (
    create_connector, RelationalSchemaAnalyzer, export_owl_turtle,
)

physical = create_connector("postgresql", url, schema_name="public").get_schema()
analysis = RelationalSchemaAnalyzer().analyze(physical)   # baseline, no LLM
bundle = analysis.to_bundle()    # {conceptualSchema, physicalMapping, metadata}
ttl = export_owl_turtle(analysis)

# Optional LLM refinement (additive; falls back to baseline on any error):
refined = RelationalSchemaAnalyzer(
    llm_provider="openai",           # or "anthropic" / "openrouter" / a provider object
).analyze(physical)                  # better names + embed/n-ary hints

relational-schema-analyzer snapshot --source postgresql --url "$DSN" -o physical.json
relational-schema-analyzer analyze  --from-snapshot physical.json --pretty
relational-schema-analyzer owl      --from-snapshot physical.json --format turtle -o schema.ttl

Sources: postgresql, mysql, sqlserver, snowflake, duckdb, databricks, csv.

MCP server (optional, pip install 'relational-schema-analyzer[mcp]') exposes the same snapshot / analyze / owl operations over the v1 tool contract:

relational-schema-analyzer-mcp                                   # stdio (local IDE)
relational-schema-analyzer-mcp --transport sse --host 0.0.0.0 --port 8000   # remote (set RSA_MCP_TOKEN)

Why this exists

Most of the relational introspection layer already exists and is battle-tested inside the r2g (relational-to-graph) project, but it is welded to ArangoDB ETL and cannot be reused elsewhere. This repo extracts that core into a paradigm-neutral library and adds the conceptual / OWL layer that r2g never had, conforming to the contract the ArangoDB analyzer already publishes.

License

Apache-2.0 — matching the surrounding Arango ecosystem libraries (arangodb-schema-analyzer, r2g). See LICENSE.