mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-02-09 02:24:11 +08:00
feat: Enhance CodexLens with GPU support and semantic status improvements
- Added accelerator and providers fields to SemanticStatus interface. - Updated checkSemanticStatus function to retrieve ONNX providers and accelerator type. - Introduced detectGpuSupport function to identify available GPU modes (CUDA, DirectML). - Modified installSemantic function to support GPU acceleration modes and clean up ONNX Runtime installations. - Updated package requirements in PKG-INFO for semantic-gpu and semantic-directml extras. - Added new source files for GPU support and enrichment functionalities. - Updated tests to cover new features and ensure comprehensive testing.
This commit is contained in:
catlog22
@@ -75,6 +75,8 @@ interface ReadyStatus {
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interface SemanticStatus {
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available: boolean;
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backend?: string;
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accelerator?: string;
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providers?: string[];
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error?: string;
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}
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@@ -190,18 +192,39 @@ async function checkSemanticStatus(): Promise<SemanticStatus> {
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return { available: false, error: 'CodexLens not installed' };
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}
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// Check semantic module availability
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// Check semantic module availability and accelerator info
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return new Promise((resolve) => {
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const checkCode = `
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import sys
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import json
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try:
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from codexlens.semantic import SEMANTIC_AVAILABLE, SEMANTIC_BACKEND
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if SEMANTIC_AVAILABLE:
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print(f"available:{SEMANTIC_BACKEND}")
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else:
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print("unavailable")
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result = {"available": SEMANTIC_AVAILABLE, "backend": SEMANTIC_BACKEND if SEMANTIC_AVAILABLE else None}
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# Get ONNX providers for accelerator info
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try:
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import onnxruntime
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providers = onnxruntime.get_available_providers()
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result["providers"] = providers
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# Determine accelerator type
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if "CUDAExecutionProvider" in providers or "TensorrtExecutionProvider" in providers:
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result["accelerator"] = "CUDA"
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elif "DmlExecutionProvider" in providers:
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result["accelerator"] = "DirectML"
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elif "CoreMLExecutionProvider" in providers:
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result["accelerator"] = "CoreML"
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elif "ROCMExecutionProvider" in providers:
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result["accelerator"] = "ROCm"
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else:
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result["accelerator"] = "CPU"
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except:
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result["providers"] = []
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result["accelerator"] = "CPU"
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print(json.dumps(result))
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except Exception as e:
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print(f"error:{e}")
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print(json.dumps({"available": False, "error": str(e)}))
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`;
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const child = spawn(VENV_PYTHON, ['-c', checkCode], {
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stdio: ['ignore', 'pipe', 'pipe'],
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@@ -220,12 +243,16 @@ except Exception as e:
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child.on('close', (code) => {
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const output = stdout.trim();
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if (output.startsWith('available:')) {
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const backend = output.split(':')[1];
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resolve({ available: true, backend });
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} else if (output === 'unavailable') {
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resolve({ available: false, error: 'Semantic dependencies not installed' });
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} else {
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try {
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const result = JSON.parse(output);
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resolve({
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available: result.available || false,
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backend: result.backend,
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accelerator: result.accelerator || 'CPU',
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providers: result.providers || [],
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error: result.error
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});
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} catch {
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resolve({ available: false, error: output || stderr || 'Unknown error' });
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}
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});
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@@ -237,10 +264,66 @@ except Exception as e:
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}
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/**
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* Install semantic search dependencies (fastembed, ONNX-based, ~200MB)
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* GPU acceleration mode for semantic search
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*/
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type GpuMode = 'cpu' | 'cuda' | 'directml';
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/**
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* Detect available GPU acceleration
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* @returns Detected GPU mode and info
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*/
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async function detectGpuSupport(): Promise<{ mode: GpuMode; available: GpuMode[]; info: string }> {
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const available: GpuMode[] = ['cpu'];
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let detectedInfo = 'CPU only';
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// Check for NVIDIA GPU (CUDA)
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try {
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if (process.platform === 'win32') {
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execSync('nvidia-smi', { stdio: 'pipe' });
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available.push('cuda');
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detectedInfo = 'NVIDIA GPU detected (CUDA available)';
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} else {
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execSync('which nvidia-smi', { stdio: 'pipe' });
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available.push('cuda');
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detectedInfo = 'NVIDIA GPU detected (CUDA available)';
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}
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} catch {
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// NVIDIA not available
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}
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// On Windows, DirectML is always available if DirectX 12 is supported
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if (process.platform === 'win32') {
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try {
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// Check for DirectX 12 support via dxdiag or registry
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// DirectML works on most modern Windows 10/11 systems
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available.push('directml');
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if (available.includes('cuda')) {
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detectedInfo = 'NVIDIA GPU detected (CUDA & DirectML available)';
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} else {
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detectedInfo = 'DirectML available (Windows GPU acceleration)';
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}
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} catch {
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// DirectML check failed
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}
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}
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// Recommend best available mode
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let recommendedMode: GpuMode = 'cpu';
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if (process.platform === 'win32' && available.includes('directml')) {
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recommendedMode = 'directml'; // DirectML is easier on Windows (no CUDA toolkit needed)
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} else if (available.includes('cuda')) {
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recommendedMode = 'cuda';
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}
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return { mode: recommendedMode, available, info: detectedInfo };
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}
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/**
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* Install semantic search dependencies with optional GPU acceleration
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* @param gpuMode - GPU acceleration mode: 'cpu', 'cuda', or 'directml'
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* @returns Bootstrap result
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*/
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async function installSemantic(): Promise<BootstrapResult> {
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async function installSemantic(gpuMode: GpuMode = 'cpu'): Promise<BootstrapResult> {
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// First ensure CodexLens is installed
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const venvStatus = await checkVenvStatus();
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if (!venvStatus.ready) {
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@@ -252,42 +335,106 @@ async function installSemantic(): Promise<BootstrapResult> {
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? join(CODEXLENS_VENV, 'Scripts', 'pip.exe')
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: join(CODEXLENS_VENV, 'bin', 'pip');
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return new Promise((resolve) => {
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console.log('[CodexLens] Installing semantic search dependencies (fastembed)...');
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console.log('[CodexLens] Using ONNX-based fastembed backend (~200MB)');
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// IMPORTANT: Uninstall all onnxruntime variants first to prevent conflicts
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// Having multiple onnxruntime packages causes provider detection issues
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const onnxVariants = ['onnxruntime', 'onnxruntime-gpu', 'onnxruntime-directml'];
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console.log(`[CodexLens] Cleaning up existing ONNX Runtime packages...`);
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const child = spawn(pipPath, ['install', 'numpy>=1.24', 'fastembed>=0.2'], {
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for (const pkg of onnxVariants) {
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try {
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execSync(`"${pipPath}" uninstall ${pkg} -y`, { stdio: 'pipe' });
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console.log(`[CodexLens] Removed ${pkg}`);
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} catch {
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// Package not installed, ignore
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}
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}
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// Build package list based on GPU mode
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const packages = ['numpy>=1.24', 'fastembed>=0.5', 'hnswlib>=0.8.0'];
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let modeDescription = 'CPU (ONNX Runtime)';
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let onnxPackage = 'onnxruntime>=1.18.0'; // Default CPU
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if (gpuMode === 'cuda') {
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onnxPackage = 'onnxruntime-gpu>=1.18.0';
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modeDescription = 'NVIDIA CUDA GPU acceleration';
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} else if (gpuMode === 'directml') {
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onnxPackage = 'onnxruntime-directml>=1.18.0';
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modeDescription = 'Windows DirectML GPU acceleration';
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}
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return new Promise((resolve) => {
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console.log(`[CodexLens] Installing semantic search dependencies...`);
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console.log(`[CodexLens] Mode: ${modeDescription}`);
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console.log(`[CodexLens] ONNX Runtime: ${onnxPackage}`);
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console.log(`[CodexLens] Packages: ${packages.join(', ')}`);
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// Install ONNX Runtime first with force-reinstall to ensure clean state
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const installOnnx = spawn(pipPath, ['install', '--force-reinstall', onnxPackage], {
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stdio: ['ignore', 'pipe', 'pipe'],
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timeout: 600000, // 10 minutes for potential model download
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timeout: 600000, // 10 minutes for GPU packages
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});
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let stdout = '';
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let stderr = '';
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let onnxStdout = '';
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let onnxStderr = '';
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child.stdout.on('data', (data) => {
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stdout += data.toString();
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// Log progress
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installOnnx.stdout.on('data', (data) => {
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onnxStdout += data.toString();
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const line = data.toString().trim();
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if (line.includes('Downloading') || line.includes('Installing') || line.includes('Collecting')) {
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if (line.includes('Downloading') || line.includes('Installing')) {
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console.log(`[CodexLens] ${line}`);
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}
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});
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child.stderr.on('data', (data) => {
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stderr += data.toString();
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installOnnx.stderr.on('data', (data) => {
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onnxStderr += data.toString();
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});
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child.on('close', (code) => {
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if (code === 0) {
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console.log('[CodexLens] Semantic dependencies installed successfully');
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resolve({ success: true });
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} else {
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resolve({ success: false, error: `Installation failed: ${stderr || stdout}` });
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installOnnx.on('close', (onnxCode) => {
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if (onnxCode !== 0) {
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resolve({ success: false, error: `Failed to install ${onnxPackage}: ${onnxStderr || onnxStdout}` });
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return;
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}
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console.log(`[CodexLens] ${onnxPackage} installed successfully`);
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// Now install remaining packages
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const child = spawn(pipPath, ['install', ...packages], {
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stdio: ['ignore', 'pipe', 'pipe'],
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timeout: 600000,
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});
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let stdout = '';
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let stderr = '';
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child.stdout.on('data', (data) => {
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stdout += data.toString();
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const line = data.toString().trim();
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if (line.includes('Downloading') || line.includes('Installing') || line.includes('Collecting')) {
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console.log(`[CodexLens] ${line}`);
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}
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});
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child.stderr.on('data', (data) => {
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stderr += data.toString();
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});
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child.on('close', (code) => {
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if (code === 0) {
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console.log(`[CodexLens] Semantic dependencies installed successfully (${gpuMode} mode)`);
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resolve({ success: true, message: `Installed with ${modeDescription}` });
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} else {
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resolve({ success: false, error: `Installation failed: ${stderr || stdout}` });
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}
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});
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child.on('error', (err) => {
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resolve({ success: false, error: `Failed to run pip: ${err.message}` });
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});
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});
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child.on('error', (err) => {
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resolve({ success: false, error: `Failed to run pip: ${err.message}` });
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installOnnx.on('error', (err) => {
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resolve({ success: false, error: `Failed to install ONNX Runtime: ${err.message}` });
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});
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});
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}
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@@ -1126,7 +1273,8 @@ function isIndexingInProgress(): boolean {
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export type { ProgressInfo, ExecuteOptions };
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// Export for direct usage
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export { ensureReady, executeCodexLens, checkVenvStatus, bootstrapVenv, checkSemanticStatus, installSemantic, uninstallCodexLens, cancelIndexing, isIndexingInProgress };
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export { ensureReady, executeCodexLens, checkVenvStatus, bootstrapVenv, checkSemanticStatus, installSemantic, detectGpuSupport, uninstallCodexLens, cancelIndexing, isIndexingInProgress };
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export type { GpuMode };
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// Backward-compatible export for tests
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export const codexLensTool = {
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