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Queries a Neo4j graph database to retrieve LIMS_Tests nodes with a configurable limit on the number of results returned.

Purpose

This function provides a simple interface to fetch Laboratory Information Management System (LIMS) test records from a Neo4j graph database. It's designed for retrieving test data for analysis, reporting, or integration with other systems. The function uses Cypher query language to match nodes labeled as LIMS_Tests and returns them through a query execution function.

Source Code

def get_all_lims_tests(limit=100):
    """Return LIMS_Tests nodes (limited to 25)"""
    query = """
    MATCH (n:LIMS_Tests)
    RETURN n
    LIMIT $limit
    """
    return run_query(query, {"limit": limit})

Parameters

Parameter Details

limit: Integer value that controls the maximum number of LIMS_Tests nodes to return from the database. Default is 100, though the docstring incorrectly states 25. Must be a positive integer. Higher values may impact performance depending on database size and network conditions.

Return Value

Returns the result of run_query() function execution. The exact return type depends on the run_query implementation, but typically would be a list of Neo4j node objects or dictionaries representing LIMS_Tests nodes, limited to the specified number. Could also return None or an empty collection if no matching nodes exist or if the query fails.

Dependencies

• neo4j

Required Imports

from neo4j import GraphDatabase

Usage Example

# Assuming run_query is defined and Neo4j connection is configured
# Example 1: Get default 100 LIMS tests
tests = get_all_lims_tests()
for test in tests:
    print(test)

# Example 2: Get only 10 LIMS tests
tests_limited = get_all_lims_tests(limit=10)

# Example 3: Get all available tests (use with caution)
tests_all = get_all_lims_tests(limit=10000)

Best Practices

• Note the discrepancy: docstring says 'limited to 25' but default parameter is 100 - update documentation for consistency
• Always specify an appropriate limit value based on your use case to avoid performance issues with large datasets
• Ensure the run_query() function is properly implemented with error handling and connection management
• Consider adding error handling around the query execution for production use
• The function depends on run_query() being available - ensure this dependency is satisfied before calling
• For large result sets, consider implementing pagination instead of increasing the limit
• Validate that the Neo4j database connection is established before calling this function

Tags

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