LearningCircuit
653707a556
Cleanup follow-up to #3797. The check-open-encoding hook was originally scoped with exclude: ^(tests/|examples/|scripts/) because those directories had ~45 pre-existing bare open() calls and addressing them was out of scope for the core Windows bug fix. This commit: * adds encoding="utf-8" to 45 read/write call sites under examples/ and scripts/ — JSON benchmark results, config-doc generators, workflow status pages, and the datetime-timezone pre-commit hook * narrows the hook exclude to ^tests/ only, so future regressions in examples/scripts/ are blocked at commit time Windows users running the benchmark scripts and config-doc generator would previously hit silent failures or UnicodeDecodeErrors on non-ASCII content under cp1252. The package itself was already protected by #3797.
96 lines
2.6 KiB
Python
96 lines
2.6 KiB
Python
# example_optimization.py - Quick Demo Version
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"""
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Full parameter optimization example for Local Deep Research.
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This script demonstrates the full parameter optimization functionality.
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Usage:
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# Install dependencies with PDM
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cd /path/to/local-deep-research
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pdm install
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# Run the script with PDM
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pdm run python examples/optimization/example_optimization.py
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"""
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import json
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from datetime import datetime, UTC
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from pathlib import Path
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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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)
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# Loguru automatically handles logging configuration
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def main():
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# Create timestamp for unique output directory
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timestamp = datetime.now(UTC).strftime("%Y%m%d_%H%M%S")
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output_dir = str(
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Path("examples")
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/ "optimization"
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/ "results"
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/ f"optimization_results_{timestamp}"
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)
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Path(output_dir).mkdir(parents=True, exist_ok=True)
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print(
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f"Starting quick optimization demo - results will be saved to {output_dir}"
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)
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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=str(Path(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": {"parameters": balanced_params, "score": balanced_score},
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}
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with open(
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Path(output_dir) / "optimization_summary.json", "w", encoding="utf-8"
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) 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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