🔍 Code Extractor

function test_adjusted_topk

Maturity: 42

A test function that validates the adjusted top_k calculation by testing multiple base values against the number of supported languages and logging the results.

File:
/tf/active/vicechatdev/docchat/test_multilanguage.py
Lines:
78 - 93
Complexity:
simple

Purpose

This function serves as a unit test to verify that the config.get_adjusted_top_k() method correctly scales the top_k parameter based on the number of supported languages. It tests multiple base values (5, 10, 15, 20) and logs both the adjusted values and the multiplication factor for verification purposes. This is useful for ensuring that multi-language RAG systems properly adjust their retrieval parameters.

Source Code

def test_adjusted_topk():
    """Test adjusted top_k calculation"""
    logger.info("")
    logger.info("=" * 80)
    logger.info("Testing Adjusted top_k Calculation")
    logger.info("=" * 80)
    
    num_languages = len(config.SUPPORTED_LANGUAGES)
    logger.info(f"Number of supported languages: {num_languages}")
    
    test_values = [5, 10, 15, 20]
    for base_k in test_values:
        adjusted = config.get_adjusted_top_k(base_k, num_languages)
        logger.info(f"  Base top_k={base_k} -> Adjusted top_k={adjusted} (x{adjusted/base_k:.1f})")
    
    logger.info("=" * 80)

Return Value

This function does not return any value (implicitly returns None). It performs logging operations to output test results to the configured logger.

Dependencies

  • logging
  • config

Required Imports

import logging
import config

Usage Example

import logging
import config

# Setup logger
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO, format='%(message)s')

# Ensure config has required attributes
# config.SUPPORTED_LANGUAGES = ['en', 'es', 'fr', 'de']
# config.get_adjusted_top_k = lambda base_k, num_langs: base_k * num_langs

# Run the test
test_adjusted_topk()

# Expected output:
# ================================================================================
# Testing Adjusted top_k Calculation
# ================================================================================
# Number of supported languages: 4
#   Base top_k=5 -> Adjusted top_k=20 (x4.0)
#   Base top_k=10 -> Adjusted top_k=40 (x4.0)
#   Base top_k=15 -> Adjusted top_k=60 (x4.0)
#   Base top_k=20 -> Adjusted top_k=80 (x4.0)
# ================================================================================

Best Practices

  • Ensure the logger is properly configured before calling this function to see the test output
  • The config module must have both SUPPORTED_LANGUAGES and get_adjusted_top_k() defined before calling
  • This function is designed for testing/debugging purposes and should not be used in production code paths
  • The function assumes logger is available in the module scope - ensure it's defined as 'logger = logging.getLogger(__name__)' or similar
  • Consider wrapping this in a proper test framework (pytest, unittest) for automated testing
  • The test values [5, 10, 15, 20] are hardcoded - modify them if testing different ranges is needed

Similar Components

AI-powered semantic similarity - components with related functionality:

  • function get_adjusted_top_k 76.3% similar

    Calculates an adjusted top_k value for multi-language search operations by multiplying the base value by the number of languages to ensure sufficient results per language.

    From: /tf/active/vicechatdev/docchat/config.py
  • function test_config 52.9% similar

    A test function that validates the presence and correctness of all required configuration settings for a multi-model RAG (Retrieval-Augmented Generation) system.

    From: /tf/active/vicechatdev/docchat/test_model_selection.py
  • function test_language_detection_and_translation 52.6% similar

    A test function that validates multi-language query processing capabilities including language detection, translation, and query expansion across multiple supported languages.

    From: /tf/active/vicechatdev/docchat/test_multilanguage.py
  • function test_rag_engine 44.2% similar

    A test function that validates the RAG engine's ability to correctly instantiate different LLM models (OpenAI, Anthropic, Gemini) based on configuration settings.

    From: /tf/active/vicechatdev/docchat/test_model_selection.py
  • function test_incremental_indexing 43.3% similar

    Comprehensive test function that validates incremental indexing functionality of a document indexing system, including initial indexing, change detection, re-indexing, and force re-indexing scenarios.

    From: /tf/active/vicechatdev/docchat/test_incremental_indexing.py
← Back to Browse