chore: import upstream snapshot with attribution

examples/optimization/browsecomp_optimization.py

@@ -0,0 +1,115 @@
+#!/usr/bin/env python
+"""
+Parameter Optimization Using BrowseComp Benchmark for Local Deep Research.
+
+This script demonstrates optimizing research parameters using the BrowseComp benchmark
+for higher quality evaluation.
+
+Usage:
+    # Install dependencies with PDM
+    cd /path/to/local-deep-research
+    pdm install
+
+    # Run the script with PDM
+    pdm run python examples/optimization/browsecomp_optimization.py
+"""
+
+import json
+import sys
+from datetime import datetime
+from pathlib import Path
+
+
+from local_deep_research.benchmarks.optimization import optimize_parameters
+
+# Add the src directory to the Python path
+project_root = str(Path(__file__).parent.parent.parent.resolve())
+sys.path.insert(0, str(Path(project_root) / "src"))
+
+
+def main():
+    # Create timestamp for unique output directory
+    from datetime import timezone
+
+    timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
+    output_dir = str(
+        Path("examples")
+        / "optimization"
+        / "results"
+        / f"browsecomp_opt_{timestamp}"
+    )
+    Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+    print(
+        f"Starting BrowseComp optimization - results will be saved to {output_dir}"
+    )
+
+    # Define a simple parameter space for demonstration
+    param_space = {
+        "iterations": {
+            "type": "int",
+            "low": 1,
+            "high": 3,
+            "step": 1,
+        },
+        "questions_per_iteration": {
+            "type": "int",
+            "low": 1,
+            "high": 3,
+            "step": 1,
+        },
+        "search_strategy": {
+            "type": "categorical",
+            "choices": ["rapid", "standard", "parallel"],
+        },
+    }
+
+    # Run optimization with BrowseComp benchmark
+    # Using a small number of trials and examples for demonstration
+    print("\n=== Running balanced optimization with BrowseComp benchmark ===")
+    balanced_params, balanced_score = optimize_parameters(
+        query="Climate change effects on biodiversity",
+        param_space=param_space,
+        output_dir=output_dir,
+        n_trials=3,  # Small number for demo purposes
+        search_tool="searxng",
+        benchmark_weights={
+            "browsecomp": 1.0
+        },  # Specify BrowseComp benchmark only
+    )
+
+    print(f"Best balanced parameters: {balanced_params}")
+    print(f"Best balanced score: {balanced_score:.4f}")
+
+    # Save optimization results
+    summary = {
+        "timestamp": timestamp,
+        "benchmark_weights": {"browsecomp": 1.0},
+        "balanced": {
+            "parameters": balanced_params,
+            "score": float(balanced_score),
+        },
+    }
+
+    with open(
+        Path(output_dir) / "browsecomp_optimization_summary.json",
+        "w",
+        encoding="utf-8",
+    ) as f:
+        json.dump(summary, f, indent=2)
+
+    print(
+        f"\nDemo complete! Results saved to {output_dir}/browsecomp_optimization_summary.json"
+    )
+    print(f"Recommended parameters for BrowseComp: {balanced_params}")
+
+    print(
+        "\nNote: For actual optimizations, we recommend increasing n_trials to at least 20."
+    )
+    print(
+        "This demo runs with minimal trials to demonstrate the functionality quickly."
+    )
+
+
+if __name__ == "__main__":
+    main()