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Capabilities

Product features at a glance — schema abstraction, inference, connectors, and ingestion patterns.

Schema and abstraction

  • Declarative LPG schema — Schema defines vertices, edges, identity rules, and edge properties in YAML or Python; the single source of truth for graph structure. Transforms/resources are defined in IngestionModel.
  • Database abstraction — one logical schema, multiple backends; each target uses its own Connection type behind ConnectionManager / DBWriter, with DB-specific behavior applied in DB-aware projection (Schema.resolve_db_aware(...), VertexConfigDBAware, EdgeConfigDBAware).
  • Resource abstraction — each Resource is a reusable actor pipeline that maps raw records to graph elements, decoupled from data retrieval.
  • DataSourceRegistry — pluggable AbstractDataSource adapters (FILE, SQL, API, SPARQL, IN_MEMORY) bound to Resources by name.

Schema features

  • Flexible identity and indexing — logical identity plus DB-specific secondary indexes (schema.db_profile.vertex_indexes, edge_specs, …). See Vertex identity modes.
  • Typed properties — optional type information on vertex and edge properties (INT, FLOAT, STRING, DATETIME, BOOL).
  • Hierarchical edge definition — define edges at any level of nested documents (via resource edge steps and actors).
  • Relationship payload — logical edges declare properties; additional payload from vertices or row shape is wired in edge actors (vertex_weights, maps, etc.) with optional types.
  • Blank vertices — create intermediate vertices for complex relationships.
  • Actor pipeline — process documents through a sequence of specialised actors (descend, transform, vertex, edge).
  • Reusable transforms — define and reference transformations by name across Resources. See Transforms.
  • Vertex filtering — filter vertices based on custom conditions.
  • PostgreSQL schema inference — infer schemas from normalised PostgreSQL databases (3NF) with PK/FK constraints.
  • Graph export and migration — introspect Neo4j or ArangoDB (or a file backend) and migrate_graph() to any supported target (graph→graph, graph→PostgreSQL); optional file backend for large exports. See Graph export and migration and Graph DB migration guide.
  • RDF / OWL schema inference — infer schemas from OWL/RDFS ontologies: owl:Class → vertices, owl:ObjectProperty → edges, owl:DatatypeProperty → vertex properties.
  • SelectSpec — declarative SQL view on top of TableConnector (view field): kind="type_lookup" for polymorphic relation rows joined to type lookup table(s), or kind="select" for full from / joins / select / where. See Table connector views and SelectSpec.
  • Bindings SQL filters — TableConnector.filters and view.where use the same YAML logical-operator shorthand as vertex filters (OR:, AND:, NOT:, IF_THEN:), validated when Bindings load and rendered to SQL WHERE (including IF_THEN as (NOT … OR …)). See Bindings filter cookbook.