Python Code Samples - Analyzers & Scoring¶

This directory contains Python implementations for Azure AI Search analyzer and scoring profile examples.

Prerequisites¶

pip install azure-search-documents azure-identity python-dotenv

Configuration¶

Create a .env file or update config.py with your Azure AI Search service details:

# config.py
SEARCH_SERVICE_NAME = "your-search-service"
SEARCH_ADMIN_KEY = "your-admin-key"
SEARCH_QUERY_KEY = "your-query-key"
SEARCH_INDEX_NAME = "analyzer-test-index"

Sample Files¶

Core Samples¶

01_builtin_analyzers.py - Compare built-in analyzers
02_custom_analyzers.py - Create and test custom analyzers
03_analyzer_testing.py - Comprehensive testing framework
04_ngram_autocomplete.py - N-gram analyzers for autocomplete
05_basic_scoring.py - Field weights and basic scoring
06_advanced_scoring_performance.py - Complex scoring with multiple functions
07_location_scoring.py - Geographic distance-based scoring
08_performance_optimization.py - Performance testing and optimization

Utility Files¶

config.py - Configuration settings
utils.py - Common utility functions
run_all_samples.py - Execute all samples in sequence

Running Samples¶

Individual Sample¶

python 01_builtin_analyzers.py

All Samples¶

python run_all_samples.py

Sample Output¶

Each sample provides detailed output showing: - Configuration steps - API responses - Analysis results - Performance metrics - Validation results

Error Handling¶

All samples include comprehensive error handling for: - Service connectivity issues - Authentication problems - Index creation failures - Query execution errors - Performance measurement issues

Best Practices Demonstrated¶

Proper SDK initialization and configuration
Error handling and logging
Performance measurement
Resource cleanup
Configuration validation