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feat: Add code analysis and LLM action templates with detailed configurations and examples
- Introduced a comprehensive code analysis action template for integrating code exploration and analysis capabilities. - Added LLM action template for seamless integration of LLM calls with customizable prompts and tools. - Implemented a benchmark search script to compare multiple search methods across various dimensions including speed, result quality, ranking stability, and coverage. - Provided preset configurations for common analysis tasks and LLM actions, enhancing usability and flexibility.
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catlog22
@@ -432,75 +432,55 @@ def search(
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limit: int = typer.Option(20, "--limit", "-n", min=1, max=500, help="Max results."),
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depth: int = typer.Option(-1, "--depth", "-d", help="Search depth (-1 = unlimited, 0 = current only)."),
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files_only: bool = typer.Option(False, "--files-only", "-f", help="Return only file paths without content snippets."),
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method: str = typer.Option("hybrid", "--method", "-m", help="Search method: fts, vector, splade, hybrid, cascade."),
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method: str = typer.Option("dense_rerank", "--method", "-m", help="Search method: 'dense_rerank' (semantic, default), 'fts' (exact keyword)."),
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use_fuzzy: bool = typer.Option(False, "--use-fuzzy", help="Enable fuzzy matching in FTS method."),
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# Hidden advanced options for backward compatibility
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weights: Optional[str] = typer.Option(
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None,
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"--weights", "-w",
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help="RRF weights as key=value pairs (e.g., 'splade=0.4,vector=0.6' or 'fts=0.4,vector=0.6'). Default: auto-detect based on available backends."
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hidden=True,
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help="[Advanced] RRF weights as key=value pairs."
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),
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cascade_strategy: Optional[str] = typer.Option(
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None,
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"--cascade-strategy",
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help="Cascade search strategy: 'binary' (fast binary+dense) or 'hybrid' (FTS+cross-encoder). Only used with --method cascade."
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hidden=True,
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help="[Advanced] Cascade strategy for --method cascade."
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),
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# Hidden deprecated parameter for backward compatibility
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mode: Optional[str] = typer.Option(None, "--mode", hidden=True, help="[DEPRECATED] Use --method instead."),
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json_mode: bool = typer.Option(False, "--json", help="Output JSON response."),
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verbose: bool = typer.Option(False, "--verbose", "-v", help="Enable debug logging."),
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) -> None:
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"""Search indexed file contents using hybrid retrieval.
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"""Search indexed file contents.
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Uses chain search across directory indexes.
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Use --depth to limit search recursion (0 = current dir only).
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Search Methods:
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- fts: Full-text search using FTS5 (unicode61 tokenizer). Use --use-fuzzy for typo tolerance.
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- vector: Pure semantic vector search - for natural language queries.
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- splade: SPLADE sparse neural search - semantic term expansion.
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- hybrid: RRF fusion of sparse + dense search (default) - best recall.
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- cascade: Two-stage retrieval (binary coarse + dense rerank) - fast + accurate.
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- dense_rerank (default): Semantic search using Dense embedding coarse retrieval +
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Cross-encoder reranking. Best for natural language queries and code understanding.
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- fts: Full-text search using FTS5 (unicode61 tokenizer). Best for exact code
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identifiers like function/class names. Use --use-fuzzy for typo tolerance.
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Method Selection Guide:
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- Code identifiers (function/class names): fts
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- Natural language queries: vector or hybrid
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- Natural language queries: dense_rerank (default)
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- Typo-tolerant search: fts --use-fuzzy
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- Best overall quality: hybrid (default)
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- Large codebase performance: cascade
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Vector Search Requirements:
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Vector, hybrid, and cascade methods require pre-generated embeddings.
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Requirements:
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The dense_rerank method requires pre-generated embeddings.
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Use 'codexlens embeddings-generate' to create embeddings first.
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Hybrid Mode Weights:
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Use --weights to adjust RRF fusion weights:
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- SPLADE mode: 'splade=0.4,vector=0.6' (default)
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- FTS mode: 'fts=0.4,vector=0.6' (default)
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Examples:
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# Default hybrid search
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codexlens search "authentication"
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# Default semantic search (dense_rerank)
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codexlens search "authentication logic"
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# Exact code identifier search
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codexlens search "authenticate_user" --method fts
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# Typo-tolerant fuzzy search
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codexlens search "authentcate" --method fts --use-fuzzy
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# Pure semantic search
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codexlens search "how to verify user credentials" --method vector
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# SPLADE sparse neural search
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codexlens search "user login flow" --method splade
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# Fast cascade retrieval for large codebases (binary strategy)
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codexlens search "authentication" --method cascade
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# Cascade with cross-encoder reranking (hybrid strategy)
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codexlens search "authentication" --method cascade --cascade-strategy hybrid
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# Hybrid with custom weights
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codexlens search "authentication" --method hybrid --weights splade=0.5,vector=0.5
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"""
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_configure_logging(verbose, json_mode)
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search_path = path.expanduser().resolve()
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@@ -538,29 +518,33 @@ def search(
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# Configure search (load settings from file)
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config = Config.load()
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# Validate method
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valid_methods = ["fts", "vector", "splade", "hybrid", "cascade"]
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# Validate method - simplified interface exposes only dense_rerank and fts
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# Other methods (vector, splade, hybrid, cascade) are hidden but still work for backward compatibility
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valid_methods = ["fts", "dense_rerank", "vector", "splade", "hybrid", "cascade"]
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if actual_method not in valid_methods:
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if json_mode:
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print_json(success=False, error=f"Invalid method: {actual_method}. Must be one of: {', '.join(valid_methods)}")
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print_json(success=False, error=f"Invalid method: {actual_method}. Use 'dense_rerank' (semantic) or 'fts' (exact keyword).")
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else:
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console.print(f"[red]Invalid method:[/red] {actual_method}")
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console.print(f"[dim]Valid methods: {', '.join(valid_methods)}[/dim]")
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console.print("[dim]Use 'dense_rerank' (semantic, default) or 'fts' (exact keyword)[/dim]")
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raise typer.Exit(code=1)
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# Validate cascade_strategy if provided
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if cascade_strategy is not None:
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valid_strategies = ["binary", "hybrid"]
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if cascade_strategy not in valid_strategies:
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# Map dense_rerank to cascade method internally
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internal_cascade_strategy = cascade_strategy
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if actual_method == "dense_rerank":
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actual_method = "cascade"
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internal_cascade_strategy = "dense_rerank"
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# Validate cascade_strategy if provided (for advanced users)
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if internal_cascade_strategy is not None:
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valid_strategies = ["binary", "hybrid", "binary_rerank", "dense_rerank"]
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if internal_cascade_strategy not in valid_strategies:
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if json_mode:
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print_json(success=False, error=f"Invalid cascade strategy: {cascade_strategy}. Must be one of: {', '.join(valid_strategies)}")
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print_json(success=False, error=f"Invalid cascade strategy: {internal_cascade_strategy}. Must be one of: {', '.join(valid_strategies)}")
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else:
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console.print(f"[red]Invalid cascade strategy:[/red] {cascade_strategy}")
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console.print(f"[red]Invalid cascade strategy:[/red] {internal_cascade_strategy}")
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console.print(f"[dim]Valid strategies: {', '.join(valid_strategies)}[/dim]")
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raise typer.Exit(code=1)
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# Warn if using cascade_strategy with non-cascade method
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if actual_method != "cascade" and not json_mode:
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console.print(f"[yellow]Warning: --cascade-strategy is only effective with --method cascade[/yellow]")
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# Parse custom weights if provided
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hybrid_weights = None
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@@ -693,7 +677,7 @@ def search(
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else:
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# Dispatch to cascade_search for cascade method
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if actual_method == "cascade":
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result = engine.cascade_search(query, search_path, k=limit, options=options, strategy=cascade_strategy)
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result = engine.cascade_search(query, search_path, k=limit, options=options, strategy=internal_cascade_strategy)
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else:
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result = engine.search(query, search_path, options)
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results_list = [
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