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150 lines
3.3 KiB
Markdown
150 lines
3.3 KiB
Markdown
---
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title: "Graph Store Module"
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description: "Unified interface for Neo4j, FalkorDB, Apache AGE, and Amazon Neptune graph databases."
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icon: "server"
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---
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> Unified interface for property graph databases.
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---
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## Overview
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The **Graph Store Module** provides a single API for persisting and querying knowledge graphs in production graph databases.
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Backends: **Neo4j**, **FalkorDB**, **Apache AGE** (PostgreSQL), **Amazon Neptune**, and in-memory **NetworkX** for development.
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---
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## Basic Usage
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```python
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from semantica.graph_store import GraphStore
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store = GraphStore(backend="neo4j", uri="bolt://localhost:7687", user="neo4j", password="password")
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store.add_nodes(entities)
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store.add_edges(relationships)
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results = store.query("MATCH (n)-[r]->(m) RETURN n, r, m LIMIT 10")
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```
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---
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## Backends
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<Tabs>
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<Tab title="Neo4j">
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```python
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store = GraphStore(
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backend="neo4j",
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uri="bolt://localhost:7687",
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user="neo4j",
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password="password",
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database="neo4j" # optional, default database
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)
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```
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</Tab>
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<Tab title="FalkorDB">
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```python
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store = GraphStore(
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backend="falkordb",
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host="localhost",
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port=6379,
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graph_name="semantica"
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)
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```
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</Tab>
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<Tab title="Apache AGE">
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```python
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store = GraphStore(
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backend="apache_age",
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connection_string="postgresql://user:pass@localhost/graphdb",
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graph_name="semantica"
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)
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```
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See the [Apache AGE Guide](../graph_stores/apache_age) for setup.
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</Tab>
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<Tab title="In-Memory">
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```python
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store = GraphStore(backend="networkx")
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```
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For development and testing only — data is not persisted.
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</Tab>
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</Tabs>
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---
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## Querying
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```python
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# Cypher (Neo4j, FalkorDB)
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results = store.query(
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"MATCH (p:Person)-[:WORKS_FOR]->(o:Organization) WHERE o.name = $org RETURN p",
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parameters={"org": "Apple Inc."}
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)
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# Path traversal
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paths = store.find_paths(
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start_node="steve_jobs",
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end_node="apple_inc",
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max_hops=3,
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relationship_types=["FOUNDED", "WORKED_AT"]
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)
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```
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---
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## Graph Operations
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```python
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# Add a single node
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store.add_node("apple_inc", node_type="Organization", properties={"founded": 1976})
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# Add a relationship
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store.add_edge("steve_jobs", "apple_inc", "FOUNDED", properties={"year": 1976})
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# Bulk operations
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store.add_nodes_bulk(entities, batch_size=1000)
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store.add_edges_bulk(relationships, batch_size=1000)
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# Delete
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store.delete_node("node_id")
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store.delete_edge("edge_id")
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# Get neighbors
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neighbors = store.get_neighbors("apple_inc", relationship_type="HAS_EMPLOYEE", direction="in")
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```
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---
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## Schema Management
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```python
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# Create indexes for performance
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store.create_index(label="Person", property="name")
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store.create_constraint(label="Organization", property="id", constraint_type="unique")
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# Get schema
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schema = store.get_schema()
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```
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---
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## See Also
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<CardGroup cols={2}>
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<Card title="KG Module" icon="diagram-project" href="kg">
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Build the graph before persisting it.
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</Card>
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<Card title="Apache AGE Guide" icon="database" href="../graph_stores/apache_age">
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PostgreSQL-based graph storage setup.
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</Card>
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<Card title="Triplet Store" icon="table" href="triplet_store">
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RDF triple store for semantic web.
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</Card>
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<Card title="Visualization" icon="chart-bar" href="visualization">
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Visualize stored graphs.
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</Card>
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</CardGroup>
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