mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-02-10 02:24:35 +08:00
Add comprehensive tests for schema cleanup migration and search comparison
- Implement tests for migration 005 to verify removal of deprecated fields in the database schema. - Ensure that new databases are created with a clean schema. - Validate that keywords are correctly extracted from the normalized file_keywords table. - Test symbol insertion without deprecated fields and subdir operations without direct_files. - Create a detailed search comparison test to evaluate vector search vs hybrid search performance. - Add a script for reindexing projects to extract code relationships and verify GraphAnalyzer functionality. - Include a test script to check TreeSitter parser availability and relationship extraction from sample files.
This commit is contained in:
catlog22
@@ -181,31 +181,46 @@ def search(
|
||||
limit: int = typer.Option(20, "--limit", "-n", min=1, max=500, help="Max results."),
|
||||
depth: int = typer.Option(-1, "--depth", "-d", help="Search depth (-1 = unlimited, 0 = current only)."),
|
||||
files_only: bool = typer.Option(False, "--files-only", "-f", help="Return only file paths without content snippets."),
|
||||
mode: str = typer.Option("exact", "--mode", "-m", help="Search mode: exact, fuzzy, hybrid, vector."),
|
||||
mode: str = typer.Option("exact", "--mode", "-m", help="Search mode: exact, fuzzy, hybrid, vector, pure-vector."),
|
||||
weights: Optional[str] = typer.Option(None, "--weights", help="Custom RRF weights as 'exact,fuzzy,vector' (e.g., '0.5,0.3,0.2')."),
|
||||
json_mode: bool = typer.Option(False, "--json", help="Output JSON response."),
|
||||
verbose: bool = typer.Option(False, "--verbose", "-v", help="Enable debug logging."),
|
||||
) -> None:
|
||||
"""Search indexed file contents using SQLite FTS5.
|
||||
"""Search indexed file contents using SQLite FTS5 or semantic vectors.
|
||||
|
||||
Uses chain search across directory indexes.
|
||||
Use --depth to limit search recursion (0 = current dir only).
|
||||
|
||||
Search Modes:
|
||||
- exact: Exact FTS using unicode61 tokenizer (default)
|
||||
- fuzzy: Fuzzy FTS using trigram tokenizer
|
||||
- hybrid: RRF fusion of exact + fuzzy (recommended)
|
||||
- vector: Semantic vector search (future)
|
||||
- exact: Exact FTS using unicode61 tokenizer (default) - for code identifiers
|
||||
- fuzzy: Fuzzy FTS using trigram tokenizer - for typo-tolerant search
|
||||
- hybrid: RRF fusion of exact + fuzzy + vector (recommended) - best recall
|
||||
- vector: Vector search with exact FTS fallback - semantic + keyword
|
||||
- pure-vector: Pure semantic vector search only - natural language queries
|
||||
|
||||
Vector Search Requirements:
|
||||
Vector search modes require pre-generated embeddings.
|
||||
Use 'codexlens embeddings-generate' to create embeddings first.
|
||||
|
||||
Hybrid Mode:
|
||||
Default weights: exact=0.4, fuzzy=0.3, vector=0.3
|
||||
Use --weights to customize (e.g., --weights 0.5,0.3,0.2)
|
||||
|
||||
Examples:
|
||||
# Exact code search
|
||||
codexlens search "authenticate_user" --mode exact
|
||||
|
||||
# Semantic search (requires embeddings)
|
||||
codexlens search "how to verify user credentials" --mode pure-vector
|
||||
|
||||
# Best of both worlds
|
||||
codexlens search "authentication" --mode hybrid
|
||||
"""
|
||||
_configure_logging(verbose)
|
||||
search_path = path.expanduser().resolve()
|
||||
|
||||
# Validate mode
|
||||
valid_modes = ["exact", "fuzzy", "hybrid", "vector"]
|
||||
valid_modes = ["exact", "fuzzy", "hybrid", "vector", "pure-vector"]
|
||||
if mode not in valid_modes:
|
||||
if json_mode:
|
||||
print_json(success=False, error=f"Invalid mode: {mode}. Must be one of: {', '.join(valid_modes)}")
|
||||
@@ -244,8 +259,18 @@ def search(
|
||||
engine = ChainSearchEngine(registry, mapper)
|
||||
|
||||
# Map mode to options
|
||||
hybrid_mode = mode == "hybrid"
|
||||
enable_fuzzy = mode in ["fuzzy", "hybrid"]
|
||||
if mode == "exact":
|
||||
hybrid_mode, enable_fuzzy, enable_vector, pure_vector = False, False, False, False
|
||||
elif mode == "fuzzy":
|
||||
hybrid_mode, enable_fuzzy, enable_vector, pure_vector = False, True, False, False
|
||||
elif mode == "vector":
|
||||
hybrid_mode, enable_fuzzy, enable_vector, pure_vector = True, False, True, False # Vector + exact fallback
|
||||
elif mode == "pure-vector":
|
||||
hybrid_mode, enable_fuzzy, enable_vector, pure_vector = True, False, True, True # Pure vector only
|
||||
elif mode == "hybrid":
|
||||
hybrid_mode, enable_fuzzy, enable_vector, pure_vector = True, True, True, False
|
||||
else:
|
||||
raise ValueError(f"Invalid mode: {mode}")
|
||||
|
||||
options = SearchOptions(
|
||||
depth=depth,
|
||||
@@ -253,6 +278,8 @@ def search(
|
||||
files_only=files_only,
|
||||
hybrid_mode=hybrid_mode,
|
||||
enable_fuzzy=enable_fuzzy,
|
||||
enable_vector=enable_vector,
|
||||
pure_vector=pure_vector,
|
||||
hybrid_weights=hybrid_weights,
|
||||
)
|
||||
|
||||
@@ -1573,3 +1600,483 @@ def semantic_list(
|
||||
finally:
|
||||
if registry is not None:
|
||||
registry.close()
|
||||
|
||||
|
||||
# ==================== Model Management Commands ====================
|
||||
|
||||
@app.command(name="model-list")
|
||||
def model_list(
|
||||
json_mode: bool = typer.Option(False, "--json", help="Output JSON response."),
|
||||
) -> None:
|
||||
"""List available embedding models and their installation status.
|
||||
|
||||
Shows 4 model profiles (fast, code, multilingual, balanced) with:
|
||||
- Installation status
|
||||
- Model size and dimensions
|
||||
- Use case recommendations
|
||||
"""
|
||||
try:
|
||||
from codexlens.cli.model_manager import list_models
|
||||
|
||||
result = list_models()
|
||||
|
||||
if json_mode:
|
||||
print_json(**result)
|
||||
else:
|
||||
if not result["success"]:
|
||||
console.print(f"[red]Error:[/red] {result.get('error', 'Unknown error')}")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
data = result["result"]
|
||||
models = data["models"]
|
||||
cache_dir = data["cache_dir"]
|
||||
cache_exists = data["cache_exists"]
|
||||
|
||||
console.print("[bold]Available Embedding Models:[/bold]")
|
||||
console.print(f"Cache directory: [dim]{cache_dir}[/dim] {'(exists)' if cache_exists else '(not found)'}\n")
|
||||
|
||||
table = Table(show_header=True, header_style="bold")
|
||||
table.add_column("Profile", style="cyan")
|
||||
table.add_column("Model Name", style="blue")
|
||||
table.add_column("Dims", justify="right")
|
||||
table.add_column("Size (MB)", justify="right")
|
||||
table.add_column("Status", justify="center")
|
||||
table.add_column("Use Case", style="dim")
|
||||
|
||||
for model in models:
|
||||
status_icon = "[green]✓[/green]" if model["installed"] else "[dim]—[/dim]"
|
||||
size_display = (
|
||||
f"{model['actual_size_mb']:.1f}" if model["installed"]
|
||||
else f"~{model['estimated_size_mb']}"
|
||||
)
|
||||
table.add_row(
|
||||
model["profile"],
|
||||
model["model_name"],
|
||||
str(model["dimensions"]),
|
||||
size_display,
|
||||
status_icon,
|
||||
model["use_case"][:40] + "..." if len(model["use_case"]) > 40 else model["use_case"],
|
||||
)
|
||||
|
||||
console.print(table)
|
||||
console.print("\n[dim]Use 'codexlens model-download <profile>' to download a model[/dim]")
|
||||
|
||||
except ImportError:
|
||||
if json_mode:
|
||||
print_json(success=False, error="fastembed not installed. Install with: pip install codexlens[semantic]")
|
||||
else:
|
||||
console.print("[red]Error:[/red] fastembed not installed")
|
||||
console.print("[yellow]Install with:[/yellow] pip install codexlens[semantic]")
|
||||
raise typer.Exit(code=1)
|
||||
except Exception as exc:
|
||||
if json_mode:
|
||||
print_json(success=False, error=str(exc))
|
||||
else:
|
||||
console.print(f"[red]Model-list failed:[/red] {exc}")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
|
||||
@app.command(name="model-download")
|
||||
def model_download(
|
||||
profile: str = typer.Argument(..., help="Model profile to download (fast, code, multilingual, balanced)."),
|
||||
json_mode: bool = typer.Option(False, "--json", help="Output JSON response."),
|
||||
) -> None:
|
||||
"""Download an embedding model by profile name.
|
||||
|
||||
Example:
|
||||
codexlens model-download code # Download code-optimized model
|
||||
"""
|
||||
try:
|
||||
from codexlens.cli.model_manager import download_model
|
||||
|
||||
if not json_mode:
|
||||
console.print(f"[bold]Downloading model:[/bold] {profile}")
|
||||
console.print("[dim]This may take a few minutes depending on your internet connection...[/dim]\n")
|
||||
|
||||
# Create progress callback for non-JSON mode
|
||||
progress_callback = None if json_mode else lambda msg: console.print(f"[cyan]{msg}[/cyan]")
|
||||
|
||||
result = download_model(profile, progress_callback=progress_callback)
|
||||
|
||||
if json_mode:
|
||||
print_json(**result)
|
||||
else:
|
||||
if not result["success"]:
|
||||
console.print(f"[red]Error:[/red] {result.get('error', 'Unknown error')}")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
data = result["result"]
|
||||
console.print(f"[green]✓[/green] Model downloaded successfully!")
|
||||
console.print(f" Profile: {data['profile']}")
|
||||
console.print(f" Model: {data['model_name']}")
|
||||
console.print(f" Cache size: {data['cache_size_mb']:.1f} MB")
|
||||
console.print(f" Location: [dim]{data['cache_path']}[/dim]")
|
||||
|
||||
except ImportError:
|
||||
if json_mode:
|
||||
print_json(success=False, error="fastembed not installed. Install with: pip install codexlens[semantic]")
|
||||
else:
|
||||
console.print("[red]Error:[/red] fastembed not installed")
|
||||
console.print("[yellow]Install with:[/yellow] pip install codexlens[semantic]")
|
||||
raise typer.Exit(code=1)
|
||||
except Exception as exc:
|
||||
if json_mode:
|
||||
print_json(success=False, error=str(exc))
|
||||
else:
|
||||
console.print(f"[red]Model-download failed:[/red] {exc}")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
|
||||
@app.command(name="model-delete")
|
||||
def model_delete(
|
||||
profile: str = typer.Argument(..., help="Model profile to delete (fast, code, multilingual, balanced)."),
|
||||
json_mode: bool = typer.Option(False, "--json", help="Output JSON response."),
|
||||
) -> None:
|
||||
"""Delete a downloaded embedding model from cache.
|
||||
|
||||
Example:
|
||||
codexlens model-delete fast # Delete fast model
|
||||
"""
|
||||
try:
|
||||
from codexlens.cli.model_manager import delete_model
|
||||
|
||||
if not json_mode:
|
||||
console.print(f"[bold yellow]Deleting model:[/bold yellow] {profile}")
|
||||
|
||||
result = delete_model(profile)
|
||||
|
||||
if json_mode:
|
||||
print_json(**result)
|
||||
else:
|
||||
if not result["success"]:
|
||||
console.print(f"[red]Error:[/red] {result.get('error', 'Unknown error')}")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
data = result["result"]
|
||||
console.print(f"[green]✓[/green] Model deleted successfully!")
|
||||
console.print(f" Profile: {data['profile']}")
|
||||
console.print(f" Model: {data['model_name']}")
|
||||
console.print(f" Freed space: {data['deleted_size_mb']:.1f} MB")
|
||||
|
||||
except Exception as exc:
|
||||
if json_mode:
|
||||
print_json(success=False, error=str(exc))
|
||||
else:
|
||||
console.print(f"[red]Model-delete failed:[/red] {exc}")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
|
||||
@app.command(name="model-info")
|
||||
def model_info(
|
||||
profile: str = typer.Argument(..., help="Model profile to get info (fast, code, multilingual, balanced)."),
|
||||
json_mode: bool = typer.Option(False, "--json", help="Output JSON response."),
|
||||
) -> None:
|
||||
"""Get detailed information about a model profile.
|
||||
|
||||
Example:
|
||||
codexlens model-info code # Get code model details
|
||||
"""
|
||||
try:
|
||||
from codexlens.cli.model_manager import get_model_info
|
||||
|
||||
result = get_model_info(profile)
|
||||
|
||||
if json_mode:
|
||||
print_json(**result)
|
||||
else:
|
||||
if not result["success"]:
|
||||
console.print(f"[red]Error:[/red] {result.get('error', 'Unknown error')}")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
data = result["result"]
|
||||
console.print(f"[bold]Model Profile:[/bold] {data['profile']}")
|
||||
console.print(f" Model name: {data['model_name']}")
|
||||
console.print(f" Dimensions: {data['dimensions']}")
|
||||
console.print(f" Status: {'[green]Installed[/green]' if data['installed'] else '[dim]Not installed[/dim]'}")
|
||||
if data['installed'] and data['actual_size_mb']:
|
||||
console.print(f" Cache size: {data['actual_size_mb']:.1f} MB")
|
||||
console.print(f" Location: [dim]{data['cache_path']}[/dim]")
|
||||
else:
|
||||
console.print(f" Estimated size: ~{data['estimated_size_mb']} MB")
|
||||
console.print(f"\n Description: {data['description']}")
|
||||
console.print(f" Use case: {data['use_case']}")
|
||||
|
||||
except Exception as exc:
|
||||
if json_mode:
|
||||
print_json(success=False, error=str(exc))
|
||||
else:
|
||||
console.print(f"[red]Model-info failed:[/red] {exc}")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
|
||||
# ==================== Embedding Management Commands ====================
|
||||
|
||||
@app.command(name="embeddings-status")
|
||||
def embeddings_status(
|
||||
path: Optional[Path] = typer.Argument(
|
||||
None,
|
||||
exists=True,
|
||||
help="Path to specific _index.db file or directory containing indexes. If not specified, uses default index root.",
|
||||
),
|
||||
json_mode: bool = typer.Option(False, "--json", help="Output JSON response."),
|
||||
) -> None:
|
||||
"""Check embedding status for one or all indexes.
|
||||
|
||||
Shows embedding statistics including:
|
||||
- Number of chunks generated
|
||||
- File coverage percentage
|
||||
- Files missing embeddings
|
||||
|
||||
Examples:
|
||||
codexlens embeddings-status # Check all indexes
|
||||
codexlens embeddings-status ~/.codexlens/indexes/project/_index.db # Check specific index
|
||||
codexlens embeddings-status ~/projects/my-app # Check project (auto-finds index)
|
||||
"""
|
||||
try:
|
||||
from codexlens.cli.embedding_manager import check_index_embeddings, get_embedding_stats_summary
|
||||
|
||||
# Determine what to check
|
||||
if path is None:
|
||||
# Check all indexes in default root
|
||||
index_root = _get_index_root()
|
||||
result = get_embedding_stats_summary(index_root)
|
||||
|
||||
if json_mode:
|
||||
print_json(**result)
|
||||
else:
|
||||
if not result["success"]:
|
||||
console.print(f"[red]Error:[/red] {result.get('error', 'Unknown error')}")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
data = result["result"]
|
||||
total = data["total_indexes"]
|
||||
with_emb = data["indexes_with_embeddings"]
|
||||
total_chunks = data["total_chunks"]
|
||||
|
||||
console.print(f"[bold]Embedding Status Summary[/bold]")
|
||||
console.print(f"Index root: [dim]{index_root}[/dim]\n")
|
||||
console.print(f"Total indexes: {total}")
|
||||
console.print(f"Indexes with embeddings: [{'green' if with_emb > 0 else 'yellow'}]{with_emb}[/]/{total}")
|
||||
console.print(f"Total chunks: {total_chunks:,}\n")
|
||||
|
||||
if data["indexes"]:
|
||||
table = Table(show_header=True, header_style="bold")
|
||||
table.add_column("Project", style="cyan")
|
||||
table.add_column("Files", justify="right")
|
||||
table.add_column("Chunks", justify="right")
|
||||
table.add_column("Coverage", justify="right")
|
||||
table.add_column("Status", justify="center")
|
||||
|
||||
for idx_stat in data["indexes"]:
|
||||
status_icon = "[green]✓[/green]" if idx_stat["has_embeddings"] else "[dim]—[/dim]"
|
||||
coverage = f"{idx_stat['coverage_percent']:.1f}%" if idx_stat["has_embeddings"] else "—"
|
||||
|
||||
table.add_row(
|
||||
idx_stat["project"],
|
||||
str(idx_stat["total_files"]),
|
||||
f"{idx_stat['total_chunks']:,}" if idx_stat["has_embeddings"] else "0",
|
||||
coverage,
|
||||
status_icon,
|
||||
)
|
||||
|
||||
console.print(table)
|
||||
|
||||
else:
|
||||
# Check specific index or find index for project
|
||||
target_path = path.expanduser().resolve()
|
||||
|
||||
if target_path.is_file() and target_path.name == "_index.db":
|
||||
# Direct index file
|
||||
index_path = target_path
|
||||
elif target_path.is_dir():
|
||||
# Try to find index for this project
|
||||
registry = RegistryStore()
|
||||
try:
|
||||
registry.initialize()
|
||||
mapper = PathMapper()
|
||||
index_path = mapper.source_to_index_db(target_path)
|
||||
|
||||
if not index_path.exists():
|
||||
console.print(f"[red]Error:[/red] No index found for {target_path}")
|
||||
console.print("Run 'codexlens init' first to create an index")
|
||||
raise typer.Exit(code=1)
|
||||
finally:
|
||||
registry.close()
|
||||
else:
|
||||
console.print(f"[red]Error:[/red] Path must be _index.db file or directory")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
result = check_index_embeddings(index_path)
|
||||
|
||||
if json_mode:
|
||||
print_json(**result)
|
||||
else:
|
||||
if not result["success"]:
|
||||
console.print(f"[red]Error:[/red] {result.get('error', 'Unknown error')}")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
data = result["result"]
|
||||
has_emb = data["has_embeddings"]
|
||||
|
||||
console.print(f"[bold]Embedding Status[/bold]")
|
||||
console.print(f"Index: [dim]{data['index_path']}[/dim]\n")
|
||||
|
||||
if has_emb:
|
||||
console.print(f"[green]✓[/green] Embeddings available")
|
||||
console.print(f" Total chunks: {data['total_chunks']:,}")
|
||||
console.print(f" Total files: {data['total_files']:,}")
|
||||
console.print(f" Files with embeddings: {data['files_with_chunks']:,}/{data['total_files']}")
|
||||
console.print(f" Coverage: {data['coverage_percent']:.1f}%")
|
||||
|
||||
if data["files_without_chunks"] > 0:
|
||||
console.print(f"\n[yellow]Warning:[/yellow] {data['files_without_chunks']} files missing embeddings")
|
||||
if data["missing_files_sample"]:
|
||||
console.print(" Sample missing files:")
|
||||
for file in data["missing_files_sample"]:
|
||||
console.print(f" [dim]{file}[/dim]")
|
||||
else:
|
||||
console.print(f"[yellow]—[/yellow] No embeddings found")
|
||||
console.print(f" Total files indexed: {data['total_files']:,}")
|
||||
console.print("\n[dim]Generate embeddings with:[/dim]")
|
||||
console.print(f" [cyan]codexlens embeddings-generate {index_path}[/cyan]")
|
||||
|
||||
except Exception as exc:
|
||||
if json_mode:
|
||||
print_json(success=False, error=str(exc))
|
||||
else:
|
||||
console.print(f"[red]Embeddings-status failed:[/red] {exc}")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
|
||||
@app.command(name="embeddings-generate")
|
||||
def embeddings_generate(
|
||||
path: Path = typer.Argument(
|
||||
...,
|
||||
exists=True,
|
||||
help="Path to _index.db file or project directory.",
|
||||
),
|
||||
model: str = typer.Option(
|
||||
"code",
|
||||
"--model",
|
||||
"-m",
|
||||
help="Model profile: fast, code, multilingual, balanced.",
|
||||
),
|
||||
force: bool = typer.Option(
|
||||
False,
|
||||
"--force",
|
||||
"-f",
|
||||
help="Force regeneration even if embeddings exist.",
|
||||
),
|
||||
chunk_size: int = typer.Option(
|
||||
2000,
|
||||
"--chunk-size",
|
||||
help="Maximum chunk size in characters.",
|
||||
),
|
||||
json_mode: bool = typer.Option(False, "--json", help="Output JSON response."),
|
||||
verbose: bool = typer.Option(False, "--verbose", "-v", help="Enable verbose output."),
|
||||
) -> None:
|
||||
"""Generate semantic embeddings for code search.
|
||||
|
||||
Creates vector embeddings for all files in an index to enable
|
||||
semantic search capabilities. Embeddings are stored in the same
|
||||
database as the FTS index.
|
||||
|
||||
Model Profiles:
|
||||
- fast: BAAI/bge-small-en-v1.5 (384 dims, ~80MB)
|
||||
- code: jinaai/jina-embeddings-v2-base-code (768 dims, ~150MB) [recommended]
|
||||
- multilingual: intfloat/multilingual-e5-large (1024 dims, ~1GB)
|
||||
- balanced: mixedbread-ai/mxbai-embed-large-v1 (1024 dims, ~600MB)
|
||||
|
||||
Examples:
|
||||
codexlens embeddings-generate ~/projects/my-app # Auto-find index for project
|
||||
codexlens embeddings-generate ~/.codexlens/indexes/project/_index.db # Specific index
|
||||
codexlens embeddings-generate ~/projects/my-app --model fast --force # Regenerate with fast model
|
||||
"""
|
||||
_configure_logging(verbose)
|
||||
|
||||
try:
|
||||
from codexlens.cli.embedding_manager import generate_embeddings
|
||||
|
||||
# Resolve path
|
||||
target_path = path.expanduser().resolve()
|
||||
|
||||
if target_path.is_file() and target_path.name == "_index.db":
|
||||
# Direct index file
|
||||
index_path = target_path
|
||||
elif target_path.is_dir():
|
||||
# Try to find index for this project
|
||||
registry = RegistryStore()
|
||||
try:
|
||||
registry.initialize()
|
||||
mapper = PathMapper()
|
||||
index_path = mapper.source_to_index_db(target_path)
|
||||
|
||||
if not index_path.exists():
|
||||
console.print(f"[red]Error:[/red] No index found for {target_path}")
|
||||
console.print("Run 'codexlens init' first to create an index")
|
||||
raise typer.Exit(code=1)
|
||||
finally:
|
||||
registry.close()
|
||||
else:
|
||||
console.print(f"[red]Error:[/red] Path must be _index.db file or directory")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
# Progress callback
|
||||
def progress_update(msg: str):
|
||||
if not json_mode and verbose:
|
||||
console.print(f" {msg}")
|
||||
|
||||
console.print(f"[bold]Generating embeddings[/bold]")
|
||||
console.print(f"Index: [dim]{index_path}[/dim]")
|
||||
console.print(f"Model: [cyan]{model}[/cyan]\n")
|
||||
|
||||
result = generate_embeddings(
|
||||
index_path,
|
||||
model_profile=model,
|
||||
force=force,
|
||||
chunk_size=chunk_size,
|
||||
progress_callback=progress_update,
|
||||
)
|
||||
|
||||
if json_mode:
|
||||
print_json(**result)
|
||||
else:
|
||||
if not result["success"]:
|
||||
error_msg = result.get("error", "Unknown error")
|
||||
console.print(f"[red]Error:[/red] {error_msg}")
|
||||
|
||||
# Provide helpful hints
|
||||
if "already has" in error_msg:
|
||||
console.print("\n[dim]Use --force to regenerate existing embeddings[/dim]")
|
||||
elif "Semantic search not available" in error_msg:
|
||||
console.print("\n[dim]Install semantic dependencies:[/dim]")
|
||||
console.print(" [cyan]pip install codexlens[semantic][/cyan]")
|
||||
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
data = result["result"]
|
||||
elapsed = data["elapsed_time"]
|
||||
|
||||
console.print(f"[green]✓[/green] Embeddings generated successfully!")
|
||||
console.print(f" Model: {data['model_name']}")
|
||||
console.print(f" Chunks created: {data['chunks_created']:,}")
|
||||
console.print(f" Files processed: {data['files_processed']}")
|
||||
|
||||
if data["files_failed"] > 0:
|
||||
console.print(f" [yellow]Files failed: {data['files_failed']}[/yellow]")
|
||||
if data["failed_files"]:
|
||||
console.print(" [dim]First failures:[/dim]")
|
||||
for file_path, error in data["failed_files"]:
|
||||
console.print(f" [dim]{file_path}: {error}[/dim]")
|
||||
|
||||
console.print(f" Time: {elapsed:.1f}s")
|
||||
|
||||
console.print("\n[dim]Use vector search with:[/dim]")
|
||||
console.print(" [cyan]codexlens search 'your query' --mode pure-vector[/cyan]")
|
||||
|
||||
except Exception as exc:
|
||||
if json_mode:
|
||||
print_json(success=False, error=str(exc))
|
||||
else:
|
||||
console.print(f"[red]Embeddings-generate failed:[/red] {exc}")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
Reference in New Issue
Block a user