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function get_dbo_concepthouses_with_references_concepts_dbo_concepts

Maturity: 42

Queries a Neo4j graph database to retrieve dbo_Concepts nodes that are connected to a specific dbo_ConceptHouses node through a REFERENCES_CONCEPTS relationship.

File:
/tf/active/vicechatdev/neo4j_schema/neo4j_python_snippets.py
Lines:
1673 - 1680
Complexity:
simple

Purpose

This function is designed to traverse a knowledge graph stored in Neo4j, specifically to find concept nodes referenced by a concept house. It's useful for exploring semantic relationships in a database where concept houses organize or categorize multiple concepts. The function supports pagination through a limit parameter to control result set size.

Source Code

def get_dbo_concepthouses_with_references_concepts_dbo_concepts(source_id, limit=100):
    """Get dbo_Concepts nodes connected to a dbo_ConceptHouses via REFERENCES_CONCEPTS"""
    query = """
    MATCH (source:dbo_ConceptHouses {id: $source_id})-[r:REFERENCES_CONCEPTS]->(target:dbo_Concepts)
    RETURN target
    LIMIT $limit
    """
    return run_query(query, {"source_id": source_id, "limit": limit})

Parameters

Name Type Default Kind
source_id - - positional_or_keyword
limit - 100 positional_or_keyword

Parameter Details

source_id: The unique identifier (id property) of the dbo_ConceptHouses node from which to start the traversal. This should be a string or value that matches the 'id' property of a ConceptHouses node in the Neo4j database.

limit: Maximum number of target dbo_Concepts nodes to return. Defaults to 100. This parameter controls pagination and prevents overwhelming result sets. Must be a positive integer.

Return Value

Returns the result of the run_query function, which typically contains a list or collection of dbo_Concepts nodes that match the query criteria. The exact return type depends on the run_query implementation, but it likely returns a list of dictionaries or Neo4j Record objects containing the properties of the target concept nodes. Returns an empty collection if no matching relationships are found.

Dependencies

  • neo4j

Required Imports

from neo4j import GraphDatabase

Usage Example

# Assuming run_query is defined and Neo4j connection is configured
from neo4j import GraphDatabase

# Define or import the run_query helper function
def run_query(query, params):
    driver = GraphDatabase.driver('bolt://localhost:7687', auth=('neo4j', 'password'))
    with driver.session() as session:
        result = session.run(query, params)
        return [record['target'] for record in result]
    driver.close()

# Use the function to get concepts referenced by a concept house
concept_house_id = 'CH_12345'
concepts = get_dbo_concepthouses_with_references_concepts_dbo_concepts(concept_house_id, limit=50)

# Process the results
for concept in concepts:
    print(f"Concept ID: {concept['id']}, Name: {concept.get('name', 'N/A')}")

Best Practices

  • Ensure the source_id parameter matches an existing dbo_ConceptHouses node in the database to avoid empty results
  • Use appropriate limit values to balance between performance and completeness of results
  • Handle cases where no relationships exist (empty result set) in calling code
  • Consider implementing error handling for database connection failures
  • Validate that the run_query function properly manages Neo4j driver sessions and connections
  • For large result sets, consider implementing pagination by calling the function multiple times with different offsets
  • Ensure proper indexing on the 'id' property of dbo_ConceptHouses nodes for optimal query performance
  • Close Neo4j driver connections properly to avoid resource leaks

Tags

neo4j graph-database cypher-query knowledge-graph relationship-traversal database-query concept-retrieval graph-traversal semantic-relationships

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