mirror of
https://github.com/LearningCircuit/local-deep-research.git
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aa5531a29a
- Add benchmark CLI module with parameter optimization, comparison and profiling functionality - Add efficiency module for speed and resource monitoring - Add comparison module for evaluating different configurations - Add example scripts for benchmarks and optimization - Updated import references from 'benchmarking' to 'benchmarks' module
82 lines
2.4 KiB
Python
82 lines
2.4 KiB
Python
# example_optimization.py - Quick Demo Version
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import os
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import logging
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import json
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from datetime import datetime
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# Import the optimization functionality
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from local_deep_research.benchmarks.optimization import (
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optimize_parameters,
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optimize_for_speed,
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optimize_for_quality
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)
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from local_deep_research.benchmarks.optimization.metrics import calculate_combined_score
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# Configure logging to see progress
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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def main():
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# Create timestamp for unique output directory
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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output_dir = f"optimization_results_{timestamp}"
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os.makedirs(output_dir, exist_ok=True)
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print(f"Starting quick optimization demo - results will be saved to {output_dir}")
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# Demo with just a single simple optimization
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print("\n=== Running quick demo optimization ===")
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# Create a very simple parameter set to test
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param_space = {
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"iterations": {
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"type": "int",
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"low": 1,
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"high": 2,
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"step": 1,
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},
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"questions_per_iteration": {
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"type": "int",
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"low": 1,
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"high": 2,
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"step": 1,
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},
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"search_strategy": {
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"type": "categorical",
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"choices": ["rapid"], # Just use the fastest strategy
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},
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}
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balanced_params, balanced_score = optimize_parameters(
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query="SimpleQA quick demo", # Task descriptor
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search_tool="searxng", # Using SearXNG
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n_trials=2, # Just 2 trials for quick demo
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output_dir=os.path.join(output_dir, "demo"),
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param_space=param_space, # Limited parameter space
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metric_weights={"quality": 0.5, "speed": 0.5}
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)
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print(f"Best parameters: {balanced_params}")
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print(f"Best score: {balanced_score:.4f}")
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# Save demo results to a summary file
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summary = {
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"timestamp": timestamp,
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"demo": {
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"parameters": balanced_params,
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"score": balanced_score
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}
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}
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with open(os.path.join(output_dir, "optimization_summary.json"), "w") as f:
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json.dump(summary, f, indent=2)
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print(f"\nDemo complete! Results saved to {output_dir}")
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print(f"Recommended parameters: {balanced_params}")
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if __name__ == "__main__":
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main()
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