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https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-02-09 02:24:11 +08:00
feat: Add CodexLens Manager to dashboard and enhance GPU management
- Introduced a new CodexLens Manager item in the dashboard for easier access. - Implemented GPU management commands in the CLI, including listing available GPUs, selecting a specific GPU, and resetting to automatic detection. - Enhanced the embedding generation process to utilize GPU resources more effectively, including batch size optimization for better performance. - Updated the embedder to support device ID options for GPU selection, ensuring compatibility with DirectML and CUDA. - Added detailed logging and error handling for GPU detection and selection processes. - Updated package version to 6.2.9 and added comprehensive documentation for Codex Agent Execution Protocol.
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catlog22
@@ -1955,3 +1955,178 @@ def embeddings_generate(
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console.print("\n[dim]Use vector search with:[/dim]")
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console.print(" [cyan]codexlens search 'your query' --mode pure-vector[/cyan]")
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# ==================== GPU Management Commands ====================
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@app.command(name="gpu-list")
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def gpu_list(
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json_mode: bool = typer.Option(False, "--json", help="Output JSON response."),
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) -> None:
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"""List available GPU devices for embedding acceleration.
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Shows all detected GPU devices with their capabilities and selection status.
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Discrete GPUs (NVIDIA, AMD) are automatically preferred over integrated GPUs.
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Examples:
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codexlens gpu-list # List all GPUs
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codexlens gpu-list --json # JSON output for scripting
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"""
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from codexlens.semantic.gpu_support import get_gpu_devices, detect_gpu, get_selected_device_id
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gpu_info = detect_gpu()
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devices = get_gpu_devices()
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selected_id = get_selected_device_id()
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if json_mode:
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print_json(
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success=True,
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result={
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"devices": devices,
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"selected_device_id": selected_id,
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"gpu_available": gpu_info.gpu_available,
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"providers": gpu_info.onnx_providers,
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}
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)
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else:
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if not devices:
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console.print("[yellow]No GPU devices detected[/yellow]")
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console.print(f"ONNX Providers: [dim]{', '.join(gpu_info.onnx_providers)}[/dim]")
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return
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console.print("[bold]Available GPU Devices[/bold]\n")
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table = Table(show_header=True, header_style="bold")
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table.add_column("ID", justify="center")
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table.add_column("Name")
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table.add_column("Vendor", justify="center")
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table.add_column("Type", justify="center")
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table.add_column("Status", justify="center")
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for dev in devices:
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type_str = "[green]Discrete[/green]" if dev["is_discrete"] else "[dim]Integrated[/dim]"
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vendor_color = {
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"nvidia": "green",
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"amd": "red",
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"intel": "blue"
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}.get(dev["vendor"], "white")
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vendor_str = f"[{vendor_color}]{dev['vendor'].upper()}[/{vendor_color}]"
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status_parts = []
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if dev["is_preferred"]:
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status_parts.append("[cyan]Auto[/cyan]")
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if dev["is_selected"]:
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status_parts.append("[green]✓ Selected[/green]")
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status_str = " ".join(status_parts) if status_parts else "[dim]—[/dim]"
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table.add_row(
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str(dev["device_id"]),
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dev["name"],
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vendor_str,
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type_str,
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status_str,
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)
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console.print(table)
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console.print(f"\nONNX Providers: [dim]{', '.join(gpu_info.onnx_providers)}[/dim]")
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console.print("\n[dim]Select GPU with:[/dim]")
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console.print(" [cyan]codexlens gpu-select <device_id>[/cyan]")
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@app.command(name="gpu-select")
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def gpu_select(
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device_id: int = typer.Argument(
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...,
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help="GPU device ID to use for embeddings. Use 'codexlens gpu-list' to see available IDs.",
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),
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json_mode: bool = typer.Option(False, "--json", help="Output JSON response."),
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) -> None:
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"""Select a specific GPU device for embedding generation.
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By default, CodexLens automatically selects the most powerful GPU (discrete over integrated).
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Use this command to override the selection.
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Examples:
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codexlens gpu-select 1 # Use GPU device 1
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codexlens gpu-select 0 --json # Select GPU 0 with JSON output
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"""
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from codexlens.semantic.gpu_support import set_selected_device_id, get_gpu_devices
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from codexlens.semantic.embedder import clear_embedder_cache
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devices = get_gpu_devices()
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valid_ids = [dev["device_id"] for dev in devices]
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if device_id not in valid_ids:
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if json_mode:
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print_json(success=False, error=f"Invalid device_id {device_id}. Valid IDs: {valid_ids}")
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else:
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console.print(f"[red]Error:[/red] Invalid device_id {device_id}")
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console.print(f"Valid IDs: {valid_ids}")
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console.print("\n[dim]Use 'codexlens gpu-list' to see available devices[/dim]")
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raise typer.Exit(code=1)
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success = set_selected_device_id(device_id)
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if success:
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# Clear embedder cache to force reload with new GPU
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clear_embedder_cache()
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device_name = next((dev["name"] for dev in devices if dev["device_id"] == device_id), "Unknown")
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if json_mode:
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print_json(
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success=True,
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result={
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"device_id": device_id,
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"device_name": device_name,
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"message": f"GPU selection set to device {device_id}: {device_name}",
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}
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)
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else:
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console.print(f"[green]✓[/green] GPU selection updated")
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console.print(f" Device ID: {device_id}")
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console.print(f" Device: [cyan]{device_name}[/cyan]")
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console.print("\n[dim]New embeddings will use this GPU[/dim]")
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else:
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if json_mode:
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print_json(success=False, error="Failed to set GPU selection")
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else:
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console.print("[red]Error:[/red] Failed to set GPU selection")
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raise typer.Exit(code=1)
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@app.command(name="gpu-reset")
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def gpu_reset(
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json_mode: bool = typer.Option(False, "--json", help="Output JSON response."),
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) -> None:
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"""Reset GPU selection to automatic detection.
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Clears any manual GPU selection and returns to automatic selection
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(discrete GPU preferred over integrated).
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Examples:
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codexlens gpu-reset # Reset to auto-detection
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"""
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from codexlens.semantic.gpu_support import set_selected_device_id, detect_gpu
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from codexlens.semantic.embedder import clear_embedder_cache
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set_selected_device_id(None)
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clear_embedder_cache()
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gpu_info = detect_gpu(force_refresh=True)
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if json_mode:
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print_json(
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success=True,
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result={
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"message": "GPU selection reset to auto-detection",
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"preferred_device_id": gpu_info.preferred_device_id,
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"preferred_device_name": gpu_info.gpu_name,
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}
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)
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else:
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console.print("[green]✓[/green] GPU selection reset to auto-detection")
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if gpu_info.preferred_device_id is not None:
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console.print(f" Auto-selected device: {gpu_info.preferred_device_id}")
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console.print(f" Device: [cyan]{gpu_info.gpu_name}[/cyan]")
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@@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
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# Embedding batch size - larger values improve throughput on modern hardware
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# Benchmark: 256 gives ~2.35x speedup over 64 with DirectML GPU acceleration
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EMBEDDING_BATCH_SIZE = 256 # Optimized from 64 based on batch size benchmarks
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EMBEDDING_BATCH_SIZE = 256
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def _generate_chunks_from_cursor(
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@@ -337,7 +337,8 @@ def generate_embeddings(
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# Generate embeddings directly to numpy (no tolist() conversion)
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try:
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batch_contents = [chunk.content for chunk, _ in chunk_batch]
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embeddings_numpy = embedder.embed_to_numpy(batch_contents)
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# Pass batch_size to fastembed for optimal GPU utilization
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embeddings_numpy = embedder.embed_to_numpy(batch_contents, batch_size=EMBEDDING_BATCH_SIZE)
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# Use add_chunks_batch_numpy to avoid numpy->list->numpy roundtrip
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vector_store.add_chunks_batch_numpy(chunk_batch, embeddings_numpy)
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