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function complex_query_example

Maturity: 42

Executes a Neo4j Cypher query to find relationships between LIMS_Parameters nodes where either the source or target node name contains a specified search term.

File:
/tf/active/vicechatdev/neo4j_schema/neo4j_python_snippets.py
Lines:
2283 - 2291
Complexity:
moderate

Purpose

This function demonstrates a complex graph database query pattern for searching bidirectional parameter references in a LIMS (Laboratory Information Management System) database. It retrieves nodes and their relationships where parameter names match a search term, useful for discovering dependencies and connections between laboratory parameters.

Source Code

def complex_query_example(search_term, limit=10):
    """Example of a more complex query combining multiple node types"""
    query = """
    MATCH (source:LIMS_Parameters)-[r:REFERENCES_PARAMETERS]->(target:LIMS_Parameters)
    WHERE source.name CONTAINS $search_term OR target.name CONTAINS $search_term
    RETURN source, r, target
    LIMIT $limit
    """
    return run_query(query, {"search_term": search_term, "limit": limit})

Parameters

Name Type Default Kind
search_term - - positional_or_keyword
limit - 10 positional_or_keyword

Parameter Details

search_term: A string value used to filter LIMS_Parameters nodes. The query searches for nodes whose 'name' property contains this term (case-sensitive substring match). Expected to be a non-empty string representing a parameter name or partial name.

limit: An integer that restricts the maximum number of results returned by the query. Defaults to 10. Should be a positive integer; higher values may impact performance on large datasets.

Return Value

Returns the result of the run_query() function call, which typically returns a list of records containing matched source nodes, relationship objects (r), and target nodes. Each record represents a REFERENCES_PARAMETERS relationship where at least one node's name contains the search term. The exact return type depends on the run_query() implementation, but is likely a list of dictionaries or Neo4j Record objects with 'source', 'r', and 'target' keys.

Dependencies

  • neo4j

Required Imports

from neo4j import GraphDatabase

Usage Example

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

# Define or import run_query 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.data() for record in result]
    driver.close()

# Use the function
results = complex_query_example('temperature', limit=5)
for record in results:
    print(f"Source: {record['source']['name']}")
    print(f"Relationship: {record['r'].type}")
    print(f"Target: {record['target']['name']}")
    print('---')

Best Practices

  • Ensure the run_query() function is properly defined with error handling and connection management
  • Consider adding input validation for search_term to prevent empty or None values
  • The CONTAINS operator is case-sensitive; consider using toLower() in Cypher for case-insensitive searches
  • Be cautious with the limit parameter on large datasets; too high values may cause performance issues
  • Ensure proper Neo4j connection pooling and session management in the run_query() implementation
  • Consider adding pagination support for better handling of large result sets
  • Add error handling for cases where the database connection fails or the query syntax is invalid
  • Document the expected schema (node labels and relationship types) for users of this function

Tags

neo4j graph-database cypher-query lims database-search parameter-references relationship-query graph-traversal laboratory-management

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