semantica/docs/reference/graph_store.md

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Graph Store Module	Unified interface for Neo4j, FalkorDB, Apache AGE, and Amazon Neptune graph databases.	server

Unified interface for property graph databases.

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Overview

The Graph Store Module provides a single API for persisting and querying knowledge graphs in production graph databases.

Backends: Neo4j, FalkorDB, Apache AGE (PostgreSQL), Amazon Neptune, and in-memory NetworkX for development.

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Basic Usage

from semantica.graph_store import GraphStore

store = GraphStore(backend="neo4j", uri="bolt://localhost:7687", user="neo4j", password="password")

store.add_nodes(entities)
store.add_edges(relationships)

results = store.query("MATCH (n)-[r]->(m) RETURN n, r, m LIMIT 10")

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Backends

```python store = GraphStore( backend="neo4j", uri="bolt://localhost:7687", user="neo4j", password="password", database="neo4j" # optional, default database ) ``` ```python store = GraphStore( backend="falkordb", host="localhost", port=6379, graph_name="semantica" ) ``` ```python store = GraphStore( backend="apache_age", connection_string="postgresql://user:pass@localhost/graphdb", graph_name="semantica" ) ``` See the [Apache AGE Guide](../graph_stores/apache_age) for setup. ```python store = GraphStore(backend="networkx") ``` For development and testing only — data is not persisted.

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Querying

# Cypher (Neo4j, FalkorDB)
results = store.query(
    "MATCH (p:Person)-[:WORKS_FOR]->(o:Organization) WHERE o.name = $org RETURN p",
    parameters={"org": "Apple Inc."}
)

# Path traversal
paths = store.find_paths(
    start_node="steve_jobs",
    end_node="apple_inc",
    max_hops=3,
    relationship_types=["FOUNDED", "WORKED_AT"]
)

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Graph Operations

# Add a single node
store.add_node("apple_inc", node_type="Organization", properties={"founded": 1976})

# Add a relationship
store.add_edge("steve_jobs", "apple_inc", "FOUNDED", properties={"year": 1976})

# Bulk operations
store.add_nodes_bulk(entities, batch_size=1000)
store.add_edges_bulk(relationships, batch_size=1000)

# Delete
store.delete_node("node_id")
store.delete_edge("edge_id")

# Get neighbors
neighbors = store.get_neighbors("apple_inc", relationship_type="HAS_EMPLOYEE", direction="in")

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Schema Management

# Create indexes for performance
store.create_index(label="Person", property="name")
store.create_constraint(label="Organization", property="id", constraint_type="unique")

# Get schema
schema = store.get_schema()

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See Also

Build the graph before persisting it. PostgreSQL-based graph storage setup. RDF triple store for semantic web. Visualize stored graphs.