mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
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261c98549d
- Add `association_tree` module with components for building and processing call association trees using LSP call hierarchy capabilities. - Introduce `AssociationTreeBuilder` for constructing call trees from seed locations with depth-first expansion. - Create data structures: `TreeNode`, `CallTree`, and `UniqueNode` for representing nodes and relationships in the call tree. - Implement `ResultDeduplicator` to extract unique nodes from call trees and assign relevance scores based on depth, frequency, and kind. - Add unit tests for `AssociationTreeBuilder` and `ResultDeduplicator` to ensure functionality and correctness.
111 lines
3.0 KiB
Python
111 lines
3.0 KiB
Python
"""Simple search method comparison using CLI commands.
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Compares:
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1. FTS (Full-Text Search)
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2. Semantic (Dense + Rerank)
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3. Hybrid (Future: FTS + Semantic fusion)
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Usage:
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python examples/simple_search_comparison.py
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"""
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import subprocess
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import time
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import json
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from pathlib import Path
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def run_search(query: str, method: str, limit: int = 20) -> tuple[list, float]:
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"""Run search via CLI and measure time."""
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cmd = [
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"python", "-m", "codexlens", "search",
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query,
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"--method", method,
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"--limit", str(limit),
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"--json",
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"-p", "."
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]
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start = time.perf_counter()
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result = subprocess.run(
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cmd,
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cwd=str(Path("D:/Claude_dms3/codex-lens/src")),
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capture_output=True,
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text=True,
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)
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elapsed = time.perf_counter() - start
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if result.returncode != 0:
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print(f"Error running {method} search:")
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print(result.stderr)
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return [], elapsed
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try:
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data = json.loads(result.stdout)
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return data.get("results", []), elapsed
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except json.JSONDecodeError:
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print(f"Failed to parse JSON output for {method}")
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return [], elapsed
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def print_comparison(query: str):
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"""Print comparison for a single query."""
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print(f"\n{'='*80}")
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print(f"Query: {query}")
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print(f"{'='*80}\n")
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# Method 1: FTS
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print("Method 1: FTS (Full-Text Search)")
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results_fts, time_fts = run_search(query, "fts", 20)
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print(f" Time: {time_fts*1000:.2f}ms")
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print(f" Results: {len(results_fts)}")
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if results_fts:
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print(f" Top 3:")
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for i, r in enumerate(results_fts[:3], 1):
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path = r.get("path", "").replace("D:\\Claude_dms3\\codex-lens\\src\\", "")
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score = r.get("score", 0)
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print(f" {i}. [{score:.4f}] {path}")
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print()
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# Method 2: Semantic (Dense + Rerank)
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print("Method 2: Semantic (Dense + Rerank)")
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results_semantic, time_semantic = run_search(query, "dense_rerank", 20)
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print(f" Time: {time_semantic*1000:.2f}ms")
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print(f" Results: {len(results_semantic)}")
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if results_semantic:
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print(f" Top 3:")
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for i, r in enumerate(results_semantic[:3], 1):
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path = r.get("path", "").replace("D:\\Claude_dms3\\codex-lens\\src\\", "")
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score = r.get("score", 0)
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print(f" {i}. [{score:.4f}] {path}")
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print()
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# Summary
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print(f"Summary:")
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print(f" FTS: {time_fts*1000:8.2f}ms {len(results_fts):3d} results")
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print(f" Semantic: {time_semantic*1000:8.2f}ms {len(results_semantic):3d} results")
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print(f" Speedup: {time_semantic/time_fts:6.2f}x (FTS faster)")
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def main():
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"""Main comparison entry point."""
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queries = [
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"vector search",
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"LSP call hierarchy",
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"search ranking",
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"index building",
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]
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print("Search Method Comparison")
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print("=" * 80)
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for query in queries:
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print_comparison(query)
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print(f"\n{'='*80}")
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print("Comparison complete")
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print(f"{'='*80}")
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if __name__ == "__main__":
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main()
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