Graph Store Interface

OntoEnv can route all graph reads and writes through a caller-provided Python object. This is useful when you already manage graph storage — an in-memory dict, a database, or a custom triplestore — and want OntoEnv to slot in without touching the filesystem.

Protocol

Pass a graph_store= object to OntoEnv(). It must implement the following methods:

Required

• add_graph(iri: str, graph: Graph, overwrite: bool = False) → None — store a graph under the given IRI.
• get_graph(iri: str) → Graph — retrieve a previously stored graph by IRI.
• remove_graph(iri: str) → None — delete a graph from the store.
• graph_ids() → list[str] — return all currently stored IRIs.

Optional

• size() → dict[str, int] — return {"num_graphs": …, "num_triples": …} for diagnostic use.

⚠️

Constraint: graph_store cannot be combined with recreate=True or create_or_use_cached. Graphs are always passed as rdflib.Graph instances.

Example

A minimal in-memory store and how to register it:

from rdflib import Graph
from ontoenv import OntoEnv


class DictGraphStore:
    def __init__(self) -> None:
        self.graphs: dict[str, Graph] = {}

    def add_graph(self, iri: str, graph: Graph, overwrite: bool = False) -> None:
        if not overwrite and iri in self.graphs:
            return
        self.graphs[iri] = graph

    def get_graph(self, iri: str) -> Graph:
        return self.graphs[iri]

    def remove_graph(self, iri: str) -> None:
        del self.graphs[iri]

    def graph_ids(self) -> list[str]:
        return list(self.graphs.keys())

    def size(self) -> dict[str, int]:
        return {
            "num_graphs": len(self.graphs),
            "num_triples": sum(len(g) for g in self.graphs.values()),
        }


store = DictGraphStore()
env = OntoEnv(graph_store=store, temporary=True)

env.add("./ontologies/my.ttl")
print(store.graph_ids())   # ['https://example.com/myOntology']