Implement SPLADE sparse encoder and associated database migrations

- Added `splade_encoder.py` for ONNX-optimized SPLADE encoding, including methods for encoding text and batch processing.
- Created `SPLADE_IMPLEMENTATION.md` to document the SPLADE encoder's functionality, design patterns, and integration points.
- Introduced migration script `migration_009_add_splade.py` to add SPLADE metadata and posting list tables to the database.
- Developed `splade_index.py` for managing the SPLADE inverted index, supporting efficient sparse vector retrieval.
- Added verification script `verify_watcher.py` to test FileWatcher event filtering and debouncing functionality.

ccw/src/core/routes/codexlens-routes.ts

@@ -17,6 +17,16 @@ import {
   isIndexingInProgress
 } from '../../tools/codex-lens.js';
 import type { ProgressInfo, GpuMode } from '../../tools/codex-lens.js';
+import { loadLiteLLMApiConfig } from '../../config/litellm-api-config-manager.js';
+
+// File watcher state (persisted across requests)
+let watcherProcess: any = null;
+let watcherStats = {
+  running: false,
+  root_path: '',
+  events_processed: 0,
+  start_time: null as Date | null
+};
 
 export interface RouteContext {
   pathname: string;
@@ -1052,5 +1062,478 @@ export async function handleCodexLensRoutes(ctx: RouteContext): Promise<boolean>
     return true;
   }
 
+  // ============================================================
+  // RERANKER CONFIGURATION ENDPOINTS
+  // ============================================================
+
+  // API: Get Reranker Configuration
+  if (pathname === '/api/codexlens/reranker/config' && req.method === 'GET') {
+    try {
+      const venvStatus = await checkVenvStatus();
+
+      // Default reranker config
+      const rerankerConfig = {
+        backend: 'onnx',
+        model_name: 'cross-encoder/ms-marco-MiniLM-L-6-v2',
+        api_provider: 'siliconflow',
+        api_key_set: false,
+        available_backends: ['onnx', 'api', 'litellm', 'legacy'],
+        api_providers: ['siliconflow', 'cohere', 'jina'],
+        litellm_endpoints: [] as string[],
+        config_source: 'default'
+      };
+
+      // Load LiteLLM endpoints for dropdown
+      try {
+        const litellmConfig = loadLiteLLMApiConfig(initialPath);
+        if (litellmConfig.endpoints && Array.isArray(litellmConfig.endpoints)) {
+          rerankerConfig.litellm_endpoints = litellmConfig.endpoints.map(
+            (ep: any) => ep.alias || ep.name || ep.baseUrl
+          ).filter(Boolean);
+        }
+      } catch (e) {
+        // LiteLLM config not available, continue with empty endpoints
+      }
+
+      // If CodexLens is installed, try to get actual config
+      if (venvStatus.ready) {
+        try {
+          const result = await executeCodexLens(['config', '--json']);
+          if (result.success) {
+            const config = extractJSON(result.output);
+            if (config.success && config.result) {
+              // Map config values
+              if (config.result.reranker_backend) {
+                rerankerConfig.backend = config.result.reranker_backend;
+                rerankerConfig.config_source = 'codexlens';
+              }
+              if (config.result.reranker_model) {
+                rerankerConfig.model_name = config.result.reranker_model;
+              }
+              if (config.result.reranker_api_provider) {
+                rerankerConfig.api_provider = config.result.reranker_api_provider;
+              }
+              // Check if API key is set (from env)
+              if (process.env.RERANKER_API_KEY) {
+                rerankerConfig.api_key_set = true;
+              }
+            }
+          }
+        } catch (e) {
+          console.error('[CodexLens] Failed to get reranker config:', e);
+        }
+      }
+
+      res.writeHead(200, { 'Content-Type': 'application/json' });
+      res.end(JSON.stringify({ success: true, ...rerankerConfig }));
+    } catch (err) {
+      res.writeHead(500, { 'Content-Type': 'application/json' });
+      res.end(JSON.stringify({ success: false, error: err.message }));
+    }
+    return true;
+  }
+
+  // API: Set Reranker Configuration
+  if (pathname === '/api/codexlens/reranker/config' && req.method === 'POST') {
+    handlePostRequest(req, res, async (body) => {
+      const { backend, model_name, api_provider, api_key, litellm_endpoint } = body;
+
+      // Validate backend
+      const validBackends = ['onnx', 'api', 'litellm', 'legacy'];
+      if (backend && !validBackends.includes(backend)) {
+        return { success: false, error: `Invalid backend: ${backend}. Valid options: ${validBackends.join(', ')}`, status: 400 };
+      }
+
+      // Validate api_provider
+      const validProviders = ['siliconflow', 'cohere', 'jina'];
+      if (api_provider && !validProviders.includes(api_provider)) {
+        return { success: false, error: `Invalid api_provider: ${api_provider}. Valid options: ${validProviders.join(', ')}`, status: 400 };
+      }
+
+      try {
+        const updates: string[] = [];
+
+        // Set backend
+        if (backend) {
+          const result = await executeCodexLens(['config', 'set', 'reranker_backend', backend, '--json']);
+          if (result.success) updates.push('backend');
+        }
+
+        // Set model
+        if (model_name) {
+          const result = await executeCodexLens(['config', 'set', 'reranker_model', model_name, '--json']);
+          if (result.success) updates.push('model_name');
+        }
+
+        // Set API provider
+        if (api_provider) {
+          const result = await executeCodexLens(['config', 'set', 'reranker_api_provider', api_provider, '--json']);
+          if (result.success) updates.push('api_provider');
+        }
+
+        // Set LiteLLM endpoint
+        if (litellm_endpoint) {
+          const result = await executeCodexLens(['config', 'set', 'reranker_litellm_endpoint', litellm_endpoint, '--json']);
+          if (result.success) updates.push('litellm_endpoint');
+        }
+
+        // Handle API key - write to .env file or environment
+        if (api_key) {
+          // For security, we store in process.env for the current session
+          // In production, this should be written to a secure .env file
+          process.env.RERANKER_API_KEY = api_key;
+          updates.push('api_key');
+        }
+
+        return {
+          success: true,
+          message: `Updated: ${updates.join(', ')}`,
+          updated_fields: updates
+        };
+      } catch (err) {
+        return { success: false, error: err.message, status: 500 };
+      }
+    });
+    return true;
+  }
+
+  // ============================================================
+  // FILE WATCHER CONTROL ENDPOINTS
+  // ============================================================
+
+  // API: Get File Watcher Status
+  if (pathname === '/api/codexlens/watch/status') {
+    res.writeHead(200, { 'Content-Type': 'application/json' });
+    res.end(JSON.stringify({
+      success: true,
+      running: watcherStats.running,
+      root_path: watcherStats.root_path,
+      events_processed: watcherStats.events_processed,
+      start_time: watcherStats.start_time?.toISOString() || null,
+      uptime_seconds: watcherStats.start_time
+        ? Math.floor((Date.now() - watcherStats.start_time.getTime()) / 1000)
+        : 0
+    }));
+    return true;
+  }
+
+  // API: Start File Watcher
+  if (pathname === '/api/codexlens/watch/start' && req.method === 'POST') {
+    handlePostRequest(req, res, async (body) => {
+      const { path: watchPath, debounce_ms = 1000 } = body;
+      const targetPath = watchPath || initialPath;
+
+      if (watcherStats.running) {
+        return { success: false, error: 'Watcher already running', status: 400 };
+      }
+
+      try {
+        const { spawn } = await import('child_process');
+        const { join } = await import('path');
+        const { existsSync, statSync } = await import('fs');
+
+        // Validate path exists and is a directory
+        if (!existsSync(targetPath)) {
+          return { success: false, error: `Path does not exist: ${targetPath}`, status: 400 };
+        }
+        const pathStat = statSync(targetPath);
+        if (!pathStat.isDirectory()) {
+          return { success: false, error: `Path is not a directory: ${targetPath}`, status: 400 };
+        }
+
+        // Get the codexlens CLI path
+        const venvStatus = await checkVenvStatus();
+        if (!venvStatus.ready) {
+          return { success: false, error: 'CodexLens not installed', status: 400 };
+        }
+
+        // Spawn watch process (no shell: true for security)
+        // Use process.platform to determine if we need .cmd extension on Windows
+        const isWindows = process.platform === 'win32';
+        const codexlensCmd = isWindows ? 'codexlens.exe' : 'codexlens';
+        const args = ['watch', targetPath, '--debounce', String(debounce_ms)];
+        watcherProcess = spawn(codexlensCmd, args, {
+          cwd: targetPath,
+          stdio: ['ignore', 'pipe', 'pipe'],
+          env: { ...process.env }
+        });
+
+        watcherStats = {
+          running: true,
+          root_path: targetPath,
+          events_processed: 0,
+          start_time: new Date()
+        };
+
+        // Handle process output for event counting
+        if (watcherProcess.stdout) {
+          watcherProcess.stdout.on('data', (data: Buffer) => {
+            const output = data.toString();
+            // Count processed events from output
+            const matches = output.match(/Processed \d+ events?/g);
+            if (matches) {
+              watcherStats.events_processed += matches.length;
+            }
+          });
+        }
+
+        // Handle process exit
+        watcherProcess.on('exit', (code: number) => {
+          watcherStats.running = false;
+          watcherProcess = null;
+          console.log(`[CodexLens] Watcher exited with code ${code}`);
+        });
+
+        // Broadcast watcher started
+        broadcastToClients({
+          type: 'CODEXLENS_WATCHER_STATUS',
+          payload: { running: true, path: targetPath }
+        });
+
+        return {
+          success: true,
+          message: 'Watcher started',
+          path: targetPath,
+          pid: watcherProcess.pid
+        };
+      } catch (err) {
+        return { success: false, error: err.message, status: 500 };
+      }
+    });
+    return true;
+  }
+
+  // API: Stop File Watcher
+  if (pathname === '/api/codexlens/watch/stop' && req.method === 'POST') {
+    handlePostRequest(req, res, async () => {
+      if (!watcherStats.running || !watcherProcess) {
+        return { success: false, error: 'Watcher not running', status: 400 };
+      }
+
+      try {
+        // Send SIGTERM to gracefully stop the watcher
+        watcherProcess.kill('SIGTERM');
+
+        // Wait a moment for graceful shutdown
+        await new Promise(resolve => setTimeout(resolve, 500));
+
+        // Force kill if still running
+        if (watcherProcess && !watcherProcess.killed) {
+          watcherProcess.kill('SIGKILL');
+        }
+
+        const finalStats = {
+          events_processed: watcherStats.events_processed,
+          uptime_seconds: watcherStats.start_time
+            ? Math.floor((Date.now() - watcherStats.start_time.getTime()) / 1000)
+            : 0
+        };
+
+        watcherStats = {
+          running: false,
+          root_path: '',
+          events_processed: 0,
+          start_time: null
+        };
+        watcherProcess = null;
+
+        // Broadcast watcher stopped
+        broadcastToClients({
+          type: 'CODEXLENS_WATCHER_STATUS',
+          payload: { running: false }
+        });
+
+        return {
+          success: true,
+          message: 'Watcher stopped',
+          ...finalStats
+        };
+      } catch (err) {
+        return { success: false, error: err.message, status: 500 };
+      }
+    });
+    return true;
+  }
+
+
+  // ============================================================
+  // SPLADE ENDPOINTS
+  // ============================================================
+
+  // API: SPLADE Status - Check if SPLADE is available and installed
+  if (pathname === '/api/codexlens/splade/status') {
+    try {
+      // Check if CodexLens is installed first
+      const venvStatus = await checkVenvStatus();
+      if (!venvStatus.ready) {
+        res.writeHead(200, { 'Content-Type': 'application/json' });
+        res.end(JSON.stringify({
+          available: false,
+          installed: false,
+          model: 'naver/splade-cocondenser-ensembledistil',
+          error: 'CodexLens not installed'
+        }));
+        return true;
+      }
+
+      // Check SPLADE availability using Python check
+      const result = await executeCodexLens(['python', '-c',
+        'from codexlens.semantic.splade_encoder import check_splade_available; ok, err = check_splade_available(); print("OK" if ok else err)'
+      ]);
+
+      const available = result.output.includes('OK');
+
+      res.writeHead(200, { 'Content-Type': 'application/json' });
+      res.end(JSON.stringify({
+        available,
+        installed: available,
+        model: 'naver/splade-cocondenser-ensembledistil',
+        error: available ? null : result.output.trim()
+      }));
+    } catch (err) {
+      res.writeHead(200, { 'Content-Type': 'application/json' });
+      res.end(JSON.stringify({
+        available: false,
+        installed: false,
+        model: 'naver/splade-cocondenser-ensembledistil',
+        error: err.message
+      }));
+    }
+    return true;
+  }
+
+  // API: SPLADE Install - Install SPLADE dependencies
+  if (pathname === '/api/codexlens/splade/install' && req.method === 'POST') {
+    handlePostRequest(req, res, async (body) => {
+      try {
+        const gpu = body?.gpu || false;
+        const packageName = gpu ? 'codex-lens[splade-gpu]' : 'codex-lens[splade]';
+
+        // Use pip to install the SPLADE extras
+        const { spawn } = await import('child_process');
+        const { promisify } = await import('util');
+        const execFilePromise = promisify(require('child_process').execFile);
+
+        const result = await execFilePromise('pip', ['install', packageName], {
+          timeout: 600000 // 10 minutes
+        });
+
+        return {
+          success: true,
+          message: `SPLADE installed successfully (${gpu ? 'GPU' : 'CPU'} mode)`,
+          output: result.stdout
+        };
+      } catch (err) {
+        return {
+          success: false,
+          error: err.message,
+          stderr: err.stderr,
+          status: 500
+        };
+      }
+    });
+    return true;
+  }
+
+  // API: SPLADE Index Status - Check if SPLADE index exists for a project
+  if (pathname === '/api/codexlens/splade/index-status') {
+    try {
+      const projectPath = url.searchParams.get('path');
+      if (!projectPath) {
+        res.writeHead(400, { 'Content-Type': 'application/json' });
+        res.end(JSON.stringify({ success: false, error: 'Missing path parameter' }));
+        return true;
+      }
+
+      // Check if CodexLens is installed first
+      const venvStatus = await checkVenvStatus();
+      if (!venvStatus.ready) {
+        res.writeHead(200, { 'Content-Type': 'application/json' });
+        res.end(JSON.stringify({ exists: false, error: 'CodexLens not installed' }));
+        return true;
+      }
+
+      const { join } = await import('path');
+      const indexDb = join(projectPath, '.codexlens', '_index.db');
+
+      // Use Python to check SPLADE index status
+      const pythonCode = `
+from codexlens.storage.splade_index import SpladeIndex
+from pathlib import Path
+try:
+    idx = SpladeIndex(Path("${indexDb.replace(/\\/g, '\\\\')}"))
+    if idx.has_index():
+        stats = idx.get_stats()
+        meta = idx.get_metadata()
+        model = meta.get('model_name', '') if meta else ''
+        print(f"OK|{stats['unique_chunks']}|{stats['total_postings']}|{model}")
+    else:
+        print("NO_INDEX")
+except Exception as e:
+    print(f"ERROR|{str(e)}")
+`;
+
+      const result = await executeCodexLens(['python', '-c', pythonCode]);
+
+      if (result.output.startsWith('OK|')) {
+        const parts = result.output.trim().split('|');
+        res.writeHead(200, { 'Content-Type': 'application/json' });
+        res.end(JSON.stringify({
+          exists: true,
+          chunks: parseInt(parts[1]),
+          postings: parseInt(parts[2]),
+          model: parts[3]
+        }));
+      } else if (result.output.startsWith('ERROR|')) {
+        const errorMsg = result.output.substring(6).trim();
+        res.writeHead(200, { 'Content-Type': 'application/json' });
+        res.end(JSON.stringify({ exists: false, error: errorMsg }));
+      } else {
+        res.writeHead(200, { 'Content-Type': 'application/json' });
+        res.end(JSON.stringify({ exists: false }));
+      }
+    } catch (err) {
+      res.writeHead(200, { 'Content-Type': 'application/json' });
+      res.end(JSON.stringify({ exists: false, error: err.message }));
+    }
+    return true;
+  }
+
+  // API: SPLADE Index Rebuild - Rebuild SPLADE index for a project
+  if (pathname === '/api/codexlens/splade/rebuild' && req.method === 'POST') {
+    handlePostRequest(req, res, async (body) => {
+      const { path: projectPath } = body;
+
+      if (!projectPath) {
+        return { success: false, error: 'Missing path parameter', status: 400 };
+      }
+
+      try {
+        const result = await executeCodexLens(['splade-index', projectPath, '--rebuild'], {
+          cwd: projectPath,
+          timeout: 1800000 // 30 minutes for large codebases
+        });
+
+        if (result.success) {
+          return {
+            success: true,
+            message: 'SPLADE index rebuilt successfully',
+            output: result.output
+          };
+        } else {
+          return {
+            success: false,
+            error: result.error || 'Failed to rebuild SPLADE index',
+            output: result.output,
+            status: 500
+          };
+        }
+      } catch (err) {
+        return { success: false, error: err.message, status: 500 };
+      }
+    });
+    return true;
+  }
+
   return false;
 }

ccw/src/templates/dashboard-js/i18n.js

@@ -366,6 +366,25 @@ const i18n = {
     'codexlens.depsInstalled': 'Dependencies installed successfully',
     'codexlens.depsInstallFailed': 'Failed to install dependencies',
 
+    // SPLADE Dependencies
+    'codexlens.spladeDeps': 'SPLADE Sparse Retrieval',
+    'codexlens.spladeInstalled': 'SPLADE Available',
+    'codexlens.spladeNotInstalled': 'SPLADE Not Installed',
+    'codexlens.spladeInstallHint': 'Install for improved synonym matching in code search',
+    'codexlens.installingSpladePackage': 'Installing SPLADE package',
+    'codexlens.spladeInstallSuccess': 'SPLADE installed successfully',
+    'codexlens.spladeInstallFailed': 'SPLADE installation failed',
+    'codexlens.spladeModel': 'Model',
+    'codexlens.spladeIndexStatus': 'SPLADE Index',
+    'codexlens.spladeIndexExists': 'Index available',
+    'codexlens.spladeIndexMissing': 'No SPLADE index',
+    'codexlens.spladeRebuild': 'Rebuild SPLADE Index',
+    'codexlens.spladeRebuilding': 'Rebuilding SPLADE index...',
+    'codexlens.spladeRebuildSuccess': 'SPLADE index rebuilt',
+    'codexlens.spladeRebuildFailed': 'SPLADE index rebuild failed',
+    'codexlens.spladeChunks': 'Chunks',
+    'codexlens.spladePostings': 'Postings',
+
     // GPU Mode Selection
     'codexlens.selectGpuMode': 'Select acceleration mode',
     'codexlens.cpuModeDesc': 'Standard CPU processing',
@@ -2288,6 +2307,25 @@ const i18n = {
     'codexlens.depsInstalled': '依赖安装成功',
     'codexlens.depsInstallFailed': '依赖安装失败',
 
+    // SPLADE 依赖
+    'codexlens.spladeDeps': 'SPLADE 稀疏检索',
+    'codexlens.spladeInstalled': 'SPLADE 已安装',
+    'codexlens.spladeNotInstalled': 'SPLADE 未安装',
+    'codexlens.spladeInstallHint': '安装后可改进代码搜索的同义词匹配',
+    'codexlens.installingSpladePackage': '正在安装 SPLADE 包',
+    'codexlens.spladeInstallSuccess': 'SPLADE 安装成功',
+    'codexlens.spladeInstallFailed': 'SPLADE 安装失败',
+    'codexlens.spladeModel': '模型',
+    'codexlens.spladeIndexStatus': 'SPLADE 索引',
+    'codexlens.spladeIndexExists': '索引可用',
+    'codexlens.spladeIndexMissing': '无 SPLADE 索引',
+    'codexlens.spladeRebuild': '重建 SPLADE 索引',
+    'codexlens.spladeRebuilding': '正在重建 SPLADE 索引...',
+    'codexlens.spladeRebuildSuccess': 'SPLADE 索引重建完成',
+    'codexlens.spladeRebuildFailed': 'SPLADE 索引重建失败',
+    'codexlens.spladeChunks': '分块数',
+    'codexlens.spladePostings': '词条数',
+
     // GPU 模式选择
     'codexlens.selectGpuMode': '选择加速模式',
     'codexlens.cpuModeDesc': '标准 CPU 处理',

ccw/src/templates/dashboard-js/views/codexlens-manager.js

@@ -120,6 +120,12 @@ function buildCodexLensConfigContent(config) {
             ? '<button class="inline-flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium rounded-md border border-primary/30 bg-primary/5 text-primary hover:bg-primary/10 transition-colors" onclick="initCodexLensIndex()">' +
                 '<i data-lucide="database" class="w-3.5 h-3.5"></i> ' + t('codexlens.initializeIndex') +
               '</button>' +
+              '<button class="inline-flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium rounded-md border border-primary/30 bg-primary/5 text-primary hover:bg-primary/10 transition-colors" onclick="showRerankerConfigModal()">' +
+                '<i data-lucide="layers" class="w-3.5 h-3.5"></i> ' + (t('codexlens.rerankerConfig') || 'Reranker Config') +
+              '</button>' +
+              '<button class="inline-flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium rounded-md border border-primary/30 bg-primary/5 text-primary hover:bg-primary/10 transition-colors" onclick="showWatcherControlModal()">' +
+                '<i data-lucide="eye" class="w-3.5 h-3.5"></i> ' + (t('codexlens.watcherControl') || 'File Watcher') +
+              '</button>' +
               '<button class="inline-flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium rounded-md border border-border bg-background hover:bg-muted/50 transition-colors" onclick="cleanCurrentWorkspaceIndex()">' +
                 '<i data-lucide="folder-x" class="w-3.5 h-3.5"></i> ' + t('codexlens.cleanCurrentWorkspace') +
               '</button>' +
@@ -145,6 +151,17 @@ function buildCodexLensConfigContent(config) {
           '</div>'
         : '') +
 
+      // SPLADE Section
+      (isInstalled
+        ? '<div class="tool-config-section">' +
+            '<h4>' + t('codexlens.spladeDeps') + '</h4>' +
+            '<div id="spladeStatus" class="space-y-2">' +
+              '<div class="text-sm text-muted-foreground">' + t('common.loading') + '</div>' +
+            '</div>' +
+          '</div>'
+        : '') +
+
+
       // Model Management Section
       (isInstalled
         ? '<div class="tool-config-section">' +
@@ -335,6 +352,9 @@ function initCodexLensConfigEvents(currentConfig) {
   // Load semantic dependencies status
   loadSemanticDepsStatus();
 
+  // Load SPLADE status
+  loadSpladeStatus();
+
   // Load model list
   loadModelList();
 }
@@ -714,6 +734,95 @@ async function installSemanticDeps() {
   await installSemanticDepsWithGpu();
 }
 
+// ============================================================
+// SPLADE MANAGEMENT
+// ============================================================
+
+/**
+ * Load SPLADE status
+ */
+async function loadSpladeStatus() {
+  var container = document.getElementById('spladeStatus');
+  if (!container) return;
+
+  try {
+    var response = await fetch('/api/codexlens/splade/status');
+    var status = await response.json();
+
+    if (status.available) {
+      container.innerHTML =
+        '<div class="flex items-center justify-between p-3 border border-success/30 rounded-lg bg-success/5">' +
+          '<div class="flex items-center gap-3">' +
+            '<i data-lucide="check-circle" class="w-5 h-5 text-success"></i>' +
+            '<div>' +
+              '<span class="font-medium">' + t('codexlens.spladeInstalled') + '</span>' +
+              '<div class="text-xs text-muted-foreground">' + status.model + '</div>' +
+            '</div>' +
+          '</div>' +
+        '</div>';
+    } else {
+      container.innerHTML =
+        '<div class="flex items-center justify-between p-3 border border-border rounded-lg">' +
+          '<div class="flex items-center gap-3">' +
+            '<i data-lucide="alert-circle" class="w-5 h-5 text-muted-foreground"></i>' +
+            '<div>' +
+              '<span class="font-medium">' + t('codexlens.spladeNotInstalled') + '</span>' +
+              '<div class="text-xs text-muted-foreground">' + (status.error || t('codexlens.spladeInstallHint')) + '</div>' +
+            '</div>' +
+          '</div>' +
+          '<div class="flex gap-2">' +
+            '<button class="btn-sm btn-outline" onclick="installSplade(false)">' +
+              '<i data-lucide="download" class="w-3.5 h-3.5 mr-1"></i>CPU' +
+            '</button>' +
+            '<button class="btn-sm btn-primary" onclick="installSplade(true)">' +
+              '<i data-lucide="zap" class="w-3.5 h-3.5 mr-1"></i>GPU' +
+            '</button>' +
+          '</div>' +
+        '</div>';
+    }
+
+    if (window.lucide) lucide.createIcons();
+  } catch (err) {
+    container.innerHTML = '<div class="text-sm text-error">' + err.message + '</div>';
+  }
+}
+
+/**
+ * Install SPLADE package
+ */
+async function installSplade(gpu) {
+  var container = document.getElementById('spladeStatus');
+  if (!container) return;
+
+  container.innerHTML =
+    '<div class="flex items-center gap-3 p-3 border border-primary/30 rounded-lg">' +
+      '<div class="animate-spin"><i data-lucide="loader-2" class="w-5 h-5 text-primary"></i></div>' +
+      '<span>' + t('codexlens.installingSpladePackage') + (gpu ? ' (GPU)' : ' (CPU)') + '...</span>' +
+    '</div>';
+  if (window.lucide) lucide.createIcons();
+
+  try {
+    var response = await fetch('/api/codexlens/splade/install', {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({ gpu: gpu })
+    });
+    var result = await response.json();
+
+    if (result.success) {
+      showRefreshToast(t('codexlens.spladeInstallSuccess'), 'success');
+      loadSpladeStatus();
+    } else {
+      showRefreshToast(t('codexlens.spladeInstallFailed') + ': ' + result.error, 'error');
+      loadSpladeStatus();
+    }
+  } catch (err) {
+    showRefreshToast(t('common.error') + ': ' + err.message, 'error');
+    loadSpladeStatus();
+  }
+}
+
+
 // ============================================================
 // MODEL MANAGEMENT
 // ============================================================
@@ -2975,3 +3084,546 @@ async function saveRotationConfig() {
     showRefreshToast(t('common.error') + ': ' + err.message, 'error');
   }
 }
+
+// ============================================================
+// RERANKER CONFIGURATION MODAL
+// ============================================================
+
+/**
+ * Show Reranker configuration modal
+ */
+async function showRerankerConfigModal() {
+  try {
+    showRefreshToast(t('codexlens.loadingRerankerConfig') || 'Loading reranker configuration...', 'info');
+
+    // Fetch current reranker config
+    const response = await fetch('/api/codexlens/reranker/config');
+    const config = await response.json();
+
+    if (!config.success) {
+      showRefreshToast(t('common.error') + ': ' + (config.error || 'Failed to load config'), 'error');
+      return;
+    }
+
+    const modalHtml = buildRerankerConfigContent(config);
+
+    // Create and show modal
+    const tempContainer = document.createElement('div');
+    tempContainer.innerHTML = modalHtml;
+    const modal = tempContainer.firstElementChild;
+    document.body.appendChild(modal);
+
+    // Initialize icons
+    if (window.lucide) lucide.createIcons();
+
+    // Initialize event handlers
+    initRerankerConfigEvents(config);
+  } catch (err) {
+    showRefreshToast(t('common.error') + ': ' + err.message, 'error');
+  }
+}
+
+/**
+ * Build Reranker configuration modal content
+ */
+function buildRerankerConfigContent(config) {
+  const backend = config.backend || 'onnx';
+  const modelName = config.model_name || '';
+  const apiProvider = config.api_provider || 'siliconflow';
+  const apiKeySet = config.api_key_set || false;
+  const availableBackends = config.available_backends || ['onnx', 'api', 'litellm', 'legacy'];
+  const apiProviders = config.api_providers || ['siliconflow', 'cohere', 'jina'];
+  const litellmEndpoints = config.litellm_endpoints || [];
+
+  // ONNX models
+  const onnxModels = [
+    'cross-encoder/ms-marco-MiniLM-L-6-v2',
+    'cross-encoder/ms-marco-TinyBERT-L-2-v2',
+    'BAAI/bge-reranker-base',
+    'BAAI/bge-reranker-large'
+  ];
+
+  // Build backend options
+  const backendOptions = availableBackends.map(function(b) {
+    const labels = {
+      'onnx': 'ONNX (Local, Optimum)',
+      'api': 'API (SiliconFlow/Cohere/Jina)',
+      'litellm': 'LiteLLM (Custom Endpoint)',
+      'legacy': 'Legacy (SentenceTransformers)'
+    };
+    return '<option value="' + b + '" ' + (backend === b ? 'selected' : '') + '>' + (labels[b] || b) + '</option>';
+  }).join('');
+
+  // Build API provider options
+  const providerOptions = apiProviders.map(function(p) {
+    return '<option value="' + p + '" ' + (apiProvider === p ? 'selected' : '') + '>' + p.charAt(0).toUpperCase() + p.slice(1) + '</option>';
+  }).join('');
+
+  // Build ONNX model options
+  const onnxModelOptions = onnxModels.map(function(m) {
+    return '<option value="' + m + '" ' + (modelName === m ? 'selected' : '') + '>' + m + '</option>';
+  }).join('');
+
+  // Build LiteLLM endpoint options
+  const litellmOptions = litellmEndpoints.length > 0
+    ? litellmEndpoints.map(function(ep) {
+        return '<option value="' + ep + '">' + ep + '</option>';
+      }).join('')
+    : '<option value="" disabled>No endpoints configured</option>';
+
+  return '<div class="modal-backdrop" id="rerankerConfigModal">' +
+    '<div class="modal-container max-w-xl">' +
+      '<div class="modal-header">' +
+        '<div class="flex items-center gap-3">' +
+          '<div class="modal-icon">' +
+            '<i data-lucide="layers" class="w-5 h-5"></i>' +
+          '</div>' +
+          '<div>' +
+            '<h2 class="text-lg font-bold">' + (t('codexlens.rerankerConfig') || 'Reranker Configuration') + '</h2>' +
+            '<p class="text-xs text-muted-foreground">' + (t('codexlens.rerankerConfigDesc') || 'Configure cross-encoder reranking for semantic search') + '</p>' +
+          '</div>' +
+        '</div>' +
+        '<button onclick="closeRerankerModal()" class="text-muted-foreground hover:text-foreground">' +
+          '<i data-lucide="x" class="w-5 h-5"></i>' +
+        '</button>' +
+      '</div>' +
+
+      '<div class="modal-body space-y-4">' +
+        // Backend Selection
+        '<div class="tool-config-section">' +
+          '<h4>' + (t('codexlens.rerankerBackend') || 'Backend') + '</h4>' +
+          '<select id="rerankerBackend" class="w-full px-3 py-2 border border-border rounded-lg bg-background text-sm" onchange="toggleRerankerSections()">' +
+            backendOptions +
+          '</select>' +
+          '<p class="text-xs text-muted-foreground mt-1">' + (t('codexlens.rerankerBackendHint') || 'Select reranking backend based on your needs') + '</p>' +
+        '</div>' +
+
+        // ONNX Section (visible when backend=onnx)
+        '<div id="rerankerOnnxSection" class="tool-config-section" style="display:' + (backend === 'onnx' ? 'block' : 'none') + '">' +
+          '<h4>' + (t('codexlens.onnxModel') || 'ONNX Model') + '</h4>' +
+          '<select id="rerankerOnnxModel" class="w-full px-3 py-2 border border-border rounded-lg bg-background text-sm">' +
+            onnxModelOptions +
+            '<option value="custom">Custom model...</option>' +
+          '</select>' +
+          '<input type="text" id="rerankerCustomModel" value="' + (onnxModels.includes(modelName) ? '' : modelName) + '" ' +
+            'placeholder="Enter custom model name" ' +
+            'class="w-full mt-2 px-3 py-2 border border-border rounded-lg bg-background text-sm" style="display:' + (onnxModels.includes(modelName) ? 'none' : 'block') + '" />' +
+        '</div>' +
+
+        // API Section (visible when backend=api)
+        '<div id="rerankerApiSection" class="tool-config-section" style="display:' + (backend === 'api' ? 'block' : 'none') + '">' +
+          '<h4>' + (t('codexlens.apiConfig') || 'API Configuration') + '</h4>' +
+          '<div class="space-y-3">' +
+            '<div>' +
+              '<label class="block text-sm font-medium mb-1.5">' + (t('codexlens.apiProvider') || 'Provider') + '</label>' +
+              '<select id="rerankerApiProvider" class="w-full px-3 py-2 border border-border rounded-lg bg-background text-sm">' +
+                providerOptions +
+              '</select>' +
+            '</div>' +
+            '<div>' +
+              '<label class="block text-sm font-medium mb-1.5">' + (t('codexlens.apiKey') || 'API Key') + '</label>' +
+              '<div class="flex items-center gap-2">' +
+                '<input type="password" id="rerankerApiKey" placeholder="' + (apiKeySet ? '••••••••' : 'Enter API key') + '" ' +
+                  'class="flex-1 px-3 py-2 border border-border rounded-lg bg-background text-sm" />' +
+                (apiKeySet ? '<span class="inline-flex items-center gap-1 px-2 py-1 rounded text-xs bg-success/10 text-success border border-success/20"><i data-lucide="check" class="w-3 h-3"></i>Set</span>' : '') +
+              '</div>' +
+            '</div>' +
+            '<div>' +
+              '<label class="block text-sm font-medium mb-1.5">' + (t('codexlens.modelName') || 'Model Name') + '</label>' +
+              '<input type="text" id="rerankerApiModel" value="' + modelName + '" ' +
+                'placeholder="e.g., BAAI/bge-reranker-v2-m3" ' +
+                'class="w-full px-3 py-2 border border-border rounded-lg bg-background text-sm" />' +
+            '</div>' +
+          '</div>' +
+        '</div>' +
+
+        // LiteLLM Section (visible when backend=litellm)
+        '<div id="rerankerLitellmSection" class="tool-config-section" style="display:' + (backend === 'litellm' ? 'block' : 'none') + '">' +
+          '<h4>' + (t('codexlens.litellmEndpoint') || 'LiteLLM Endpoint') + '</h4>' +
+          '<select id="rerankerLitellmEndpoint" class="w-full px-3 py-2 border border-border rounded-lg bg-background text-sm">' +
+            litellmOptions +
+          '</select>' +
+          (litellmEndpoints.length === 0
+            ? '<p class="text-xs text-warning mt-1">' + (t('codexlens.noEndpointsHint') || 'Configure LiteLLM endpoints in API Settings first') + '</p>'
+            : '') +
+        '</div>' +
+
+        // Legacy Section (visible when backend=legacy)
+        '<div id="rerankerLegacySection" class="tool-config-section" style="display:' + (backend === 'legacy' ? 'block' : 'none') + '">' +
+          '<div class="flex items-start gap-2 bg-warning/10 border border-warning/30 rounded-lg p-3">' +
+            '<i data-lucide="alert-triangle" class="w-4 h-4 text-warning mt-0.5"></i>' +
+            '<div class="text-sm">' +
+              '<p class="font-medium text-warning">' + (t('codexlens.legacyWarning') || 'Legacy Backend') + '</p>' +
+              '<p class="text-muted-foreground mt-1">' + (t('codexlens.legacyWarningDesc') || 'Uses SentenceTransformers CrossEncoder. Consider using ONNX for better performance.') + '</p>' +
+            '</div>' +
+          '</div>' +
+        '</div>' +
+      '</div>' +
+
+      '<div class="modal-footer">' +
+        '<button onclick="resetRerankerConfig()" class="btn btn-outline">' +
+          '<i data-lucide="rotate-ccw" class="w-4 h-4"></i> ' + (t('common.reset') || 'Reset') +
+        '</button>' +
+        '<button onclick="closeRerankerModal()" class="btn btn-outline">' + t('common.cancel') + '</button>' +
+        '<button onclick="saveRerankerConfig()" class="btn btn-primary">' +
+          '<i data-lucide="save" class="w-4 h-4"></i> ' + t('common.save') +
+        '</button>' +
+      '</div>' +
+    '</div>' +
+  '</div>';
+}
+
+/**
+ * Toggle reranker configuration sections based on selected backend
+ */
+function toggleRerankerSections() {
+  var backend = document.getElementById('rerankerBackend').value;
+
+  document.getElementById('rerankerOnnxSection').style.display = backend === 'onnx' ? 'block' : 'none';
+  document.getElementById('rerankerApiSection').style.display = backend === 'api' ? 'block' : 'none';
+  document.getElementById('rerankerLitellmSection').style.display = backend === 'litellm' ? 'block' : 'none';
+  document.getElementById('rerankerLegacySection').style.display = backend === 'legacy' ? 'block' : 'none';
+}
+
+/**
+ * Initialize reranker config modal events
+ */
+function initRerankerConfigEvents(config) {
+  // Handle ONNX model custom input toggle
+  var onnxModelSelect = document.getElementById('rerankerOnnxModel');
+  var customModelInput = document.getElementById('rerankerCustomModel');
+
+  if (onnxModelSelect && customModelInput) {
+    onnxModelSelect.addEventListener('change', function() {
+      customModelInput.style.display = this.value === 'custom' ? 'block' : 'none';
+    });
+  }
+
+  // Store original config for reset
+  window._rerankerOriginalConfig = config;
+}
+
+/**
+ * Close the reranker config modal
+ */
+function closeRerankerModal() {
+  var modal = document.getElementById('rerankerConfigModal');
+  if (modal) modal.remove();
+}
+
+/**
+ * Reset reranker config to original values
+ */
+function resetRerankerConfig() {
+  var config = window._rerankerOriginalConfig;
+  if (!config) return;
+
+  document.getElementById('rerankerBackend').value = config.backend || 'onnx';
+  toggleRerankerSections();
+
+  // Reset ONNX section
+  var onnxModels = [
+    'cross-encoder/ms-marco-MiniLM-L-6-v2',
+    'cross-encoder/ms-marco-TinyBERT-L-2-v2',
+    'BAAI/bge-reranker-base',
+    'BAAI/bge-reranker-large'
+  ];
+  if (onnxModels.includes(config.model_name)) {
+    document.getElementById('rerankerOnnxModel').value = config.model_name;
+    document.getElementById('rerankerCustomModel').style.display = 'none';
+  } else {
+    document.getElementById('rerankerOnnxModel').value = 'custom';
+    document.getElementById('rerankerCustomModel').value = config.model_name || '';
+    document.getElementById('rerankerCustomModel').style.display = 'block';
+  }
+
+  // Reset API section
+  document.getElementById('rerankerApiProvider').value = config.api_provider || 'siliconflow';
+  document.getElementById('rerankerApiKey').value = '';
+  document.getElementById('rerankerApiModel').value = config.model_name || '';
+
+  showRefreshToast(t('common.reset') || 'Reset to original values', 'info');
+}
+
+/**
+ * Save reranker configuration
+ */
+async function saveRerankerConfig() {
+  try {
+    var backend = document.getElementById('rerankerBackend').value;
+    var payload = { backend: backend };
+
+    // Collect model name based on backend
+    if (backend === 'onnx') {
+      var onnxModel = document.getElementById('rerankerOnnxModel').value;
+      if (onnxModel === 'custom') {
+        payload.model_name = document.getElementById('rerankerCustomModel').value.trim();
+      } else {
+        payload.model_name = onnxModel;
+      }
+    } else if (backend === 'api') {
+      payload.api_provider = document.getElementById('rerankerApiProvider').value;
+      payload.model_name = document.getElementById('rerankerApiModel').value.trim();
+      var apiKey = document.getElementById('rerankerApiKey').value.trim();
+      if (apiKey) {
+        payload.api_key = apiKey;
+      }
+    } else if (backend === 'litellm') {
+      payload.litellm_endpoint = document.getElementById('rerankerLitellmEndpoint').value;
+    }
+
+    var response = await fetch('/api/codexlens/reranker/config', {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify(payload)
+    });
+
+    var result = await response.json();
+
+    if (result.success) {
+      showRefreshToast((t('codexlens.rerankerConfigSaved') || 'Reranker configuration saved') + ': ' + result.message, 'success');
+      closeRerankerModal();
+    } else {
+      showRefreshToast(t('common.saveFailed') + ': ' + result.error, 'error');
+    }
+  } catch (err) {
+    showRefreshToast(t('common.error') + ': ' + err.message, 'error');
+  }
+}
+
+// ============================================================
+// FILE WATCHER CONTROL
+// ============================================================
+
+/**
+ * Show File Watcher control modal
+ */
+async function showWatcherControlModal() {
+  try {
+    showRefreshToast(t('codexlens.loadingWatcherStatus') || 'Loading watcher status...', 'info');
+
+    // Fetch current watcher status
+    const response = await fetch('/api/codexlens/watch/status');
+    const status = await response.json();
+
+    const modalHtml = buildWatcherControlContent(status);
+
+    // Create and show modal
+    const tempContainer = document.createElement('div');
+    tempContainer.innerHTML = modalHtml;
+    const modal = tempContainer.firstElementChild;
+    document.body.appendChild(modal);
+
+    // Initialize icons
+    if (window.lucide) lucide.createIcons();
+
+    // Start polling if watcher is running
+    if (status.running) {
+      startWatcherStatusPolling();
+    }
+  } catch (err) {
+    showRefreshToast(t('common.error') + ': ' + err.message, 'error');
+  }
+}
+
+/**
+ * Build File Watcher control modal content
+ */
+function buildWatcherControlContent(status) {
+  const running = status.running || false;
+  const rootPath = status.root_path || '';
+  const eventsProcessed = status.events_processed || 0;
+  const uptimeSeconds = status.uptime_seconds || 0;
+
+  // Format uptime
+  const formatUptime = function(seconds) {
+    if (seconds < 60) return seconds + 's';
+    if (seconds < 3600) return Math.floor(seconds / 60) + 'm ' + (seconds % 60) + 's';
+    return Math.floor(seconds / 3600) + 'h ' + Math.floor((seconds % 3600) / 60) + 'm';
+  };
+
+  return '<div class="modal-backdrop" id="watcherControlModal">' +
+    '<div class="modal-container max-w-lg">' +
+      '<div class="modal-header">' +
+        '<div class="flex items-center gap-3">' +
+          '<div class="modal-icon">' +
+            '<i data-lucide="eye" class="w-5 h-5"></i>' +
+          '</div>' +
+          '<div>' +
+            '<h2 class="text-lg font-bold">' + (t('codexlens.watcherControl') || 'File Watcher') + '</h2>' +
+            '<p class="text-xs text-muted-foreground">' + (t('codexlens.watcherControlDesc') || 'Real-time incremental index updates') + '</p>' +
+          '</div>' +
+        '</div>' +
+        '<button onclick="closeWatcherModal()" class="text-muted-foreground hover:text-foreground">' +
+          '<i data-lucide="x" class="w-5 h-5"></i>' +
+        '</button>' +
+      '</div>' +
+
+      '<div class="modal-body space-y-4">' +
+        // Status and Toggle
+        '<div class="flex items-center justify-between p-4 bg-muted/30 rounded-lg">' +
+          '<div class="flex items-center gap-3">' +
+            '<div class="w-3 h-3 rounded-full ' + (running ? 'bg-success animate-pulse' : 'bg-muted-foreground') + '"></div>' +
+            '<div>' +
+              '<span class="font-medium">' + (running ? (t('codexlens.watcherRunning') || 'Watcher Running') : (t('codexlens.watcherStopped') || 'Watcher Stopped')) + '</span>' +
+              (running ? '<p class="text-xs text-muted-foreground">' + rootPath + '</p>' : '') +
+            '</div>' +
+          '</div>' +
+          '<label class="relative inline-flex items-center cursor-pointer">' +
+            '<input type="checkbox" id="watcherToggle" ' + (running ? 'checked' : '') + ' onchange="toggleWatcher()" class="sr-only peer" />' +
+            '<div class="w-11 h-6 bg-muted peer-focus:outline-none rounded-full peer peer-checked:after:translate-x-full peer-checked:after:border-white after:content-[\'\'] after:absolute after:top-[2px] after:left-[2px] after:bg-white after:border-gray-300 after:border after:rounded-full after:h-5 after:w-5 after:transition-all peer-checked:bg-success"></div>' +
+          '</label>' +
+        '</div>' +
+
+        // Statistics (shown when running)
+        '<div id="watcherStats" class="tool-config-section" style="display:' + (running ? 'block' : 'none') + '">' +
+          '<h4>' + (t('codexlens.watcherStats') || 'Statistics') + '</h4>' +
+          '<div class="grid grid-cols-2 gap-4">' +
+            '<div class="p-3 bg-muted/20 rounded-lg">' +
+              '<div class="text-2xl font-bold text-primary" id="watcherEventsCount">' + eventsProcessed + '</div>' +
+              '<div class="text-xs text-muted-foreground">' + (t('codexlens.eventsProcessed') || 'Events Processed') + '</div>' +
+            '</div>' +
+            '<div class="p-3 bg-muted/20 rounded-lg">' +
+              '<div class="text-2xl font-bold text-primary" id="watcherUptime">' + formatUptime(uptimeSeconds) + '</div>' +
+              '<div class="text-xs text-muted-foreground">' + (t('codexlens.uptime') || 'Uptime') + '</div>' +
+            '</div>' +
+          '</div>' +
+        '</div>' +
+
+        // Start Configuration (shown when not running)
+        '<div id="watcherStartConfig" class="tool-config-section" style="display:' + (running ? 'none' : 'block') + '">' +
+          '<h4>' + (t('codexlens.watcherConfig') || 'Configuration') + '</h4>' +
+          '<div class="space-y-3">' +
+            '<div>' +
+              '<label class="block text-sm font-medium mb-1.5">' + (t('codexlens.watchPath') || 'Watch Path') + '</label>' +
+              '<input type="text" id="watcherPath" value="" placeholder="Leave empty for current workspace" ' +
+                'class="w-full px-3 py-2 border border-border rounded-lg bg-background text-sm" />' +
+            '</div>' +
+            '<div>' +
+              '<label class="block text-sm font-medium mb-1.5">' + (t('codexlens.debounceMs') || 'Debounce (ms)') + '</label>' +
+              '<input type="number" id="watcherDebounce" value="1000" min="100" max="10000" step="100" ' +
+                'class="w-full px-3 py-2 border border-border rounded-lg bg-background text-sm" />' +
+              '<p class="text-xs text-muted-foreground mt-1">' + (t('codexlens.debounceHint') || 'Time to wait before processing file changes') + '</p>' +
+            '</div>' +
+          '</div>' +
+        '</div>' +
+
+        // Info box
+        '<div class="flex items-start gap-2 bg-primary/10 border border-primary/30 rounded-lg p-3">' +
+          '<i data-lucide="info" class="w-4 h-4 text-primary mt-0.5"></i>' +
+          '<div class="text-sm text-muted-foreground">' +
+            (t('codexlens.watcherInfo') || 'The file watcher monitors your codebase for changes and automatically updates the search index in real-time.') +
+          '</div>' +
+        '</div>' +
+      '</div>' +
+
+      '<div class="modal-footer">' +
+        '<button onclick="closeWatcherModal()" class="btn btn-outline">' + t('common.close') + '</button>' +
+      '</div>' +
+    '</div>' +
+  '</div>';
+}
+
+/**
+ * Toggle file watcher on/off
+ */
+async function toggleWatcher() {
+  var toggle = document.getElementById('watcherToggle');
+  var shouldRun = toggle.checked;
+
+  try {
+    if (shouldRun) {
+      // Start watcher
+      var watchPath = document.getElementById('watcherPath').value.trim();
+      var debounceMs = parseInt(document.getElementById('watcherDebounce').value, 10) || 1000;
+
+      var response = await fetch('/api/codexlens/watch/start', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ path: watchPath || undefined, debounce_ms: debounceMs })
+      });
+
+      var result = await response.json();
+
+      if (result.success) {
+        showRefreshToast((t('codexlens.watcherStarted') || 'Watcher started') + ': ' + result.path, 'success');
+        document.getElementById('watcherStats').style.display = 'block';
+        document.getElementById('watcherStartConfig').style.display = 'none';
+        startWatcherStatusPolling();
+      } else {
+        toggle.checked = false;
+        showRefreshToast(t('common.error') + ': ' + result.error, 'error');
+      }
+    } else {
+      // Stop watcher
+      var response = await fetch('/api/codexlens/watch/stop', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' }
+      });
+
+      var result = await response.json();
+
+      if (result.success) {
+        showRefreshToast((t('codexlens.watcherStopped') || 'Watcher stopped') + ': ' + result.events_processed + ' events processed', 'success');
+        document.getElementById('watcherStats').style.display = 'none';
+        document.getElementById('watcherStartConfig').style.display = 'block';
+        stopWatcherStatusPolling();
+      } else {
+        toggle.checked = true;
+        showRefreshToast(t('common.error') + ': ' + result.error, 'error');
+      }
+    }
+  } catch (err) {
+    toggle.checked = !shouldRun;
+    showRefreshToast(t('common.error') + ': ' + err.message, 'error');
+  }
+}
+
+// Watcher status polling
+var watcherPollingInterval = null;
+
+function startWatcherStatusPolling() {
+  if (watcherPollingInterval) return;
+
+  watcherPollingInterval = setInterval(async function() {
+    try {
+      var response = await fetch('/api/codexlens/watch/status');
+      var status = await response.json();
+
+      if (status.running) {
+        document.getElementById('watcherEventsCount').textContent = status.events_processed || 0;
+
+        // Format uptime
+        var seconds = status.uptime_seconds || 0;
+        var formatted = seconds < 60 ? seconds + 's' :
+          seconds < 3600 ? Math.floor(seconds / 60) + 'm ' + (seconds % 60) + 's' :
+          Math.floor(seconds / 3600) + 'h ' + Math.floor((seconds % 3600) / 60) + 'm';
+        document.getElementById('watcherUptime').textContent = formatted;
+      } else {
+        // Watcher stopped externally
+        stopWatcherStatusPolling();
+        document.getElementById('watcherToggle').checked = false;
+        document.getElementById('watcherStats').style.display = 'none';
+        document.getElementById('watcherStartConfig').style.display = 'block';
+      }
+    } catch (err) {
+      console.error('Failed to poll watcher status:', err);
+    }
+  }, 2000);
+}
+
+function stopWatcherStatusPolling() {
+  if (watcherPollingInterval) {
+    clearInterval(watcherPollingInterval);
+    watcherPollingInterval = null;
+  }
+}
+
+/**
+ * Close the watcher control modal
+ */
+function closeWatcherModal() {
+  stopWatcherStatusPolling();
+  var modal = document.getElementById('watcherControlModal');
+  if (modal) modal.remove();
+}

codex-lens/pyproject.toml

@@ -80,6 +80,18 @@ reranker = [
     "transformers>=4.36",
 ]
 
+# SPLADE sparse retrieval
+splade = [
+    "transformers>=4.36",
+    "optimum[onnxruntime]>=1.16",
+]
+
+# SPLADE with GPU acceleration (CUDA)
+splade-gpu = [
+    "transformers>=4.36",
+    "optimum[onnxruntime-gpu]>=1.16",
+]
+
 # Encoding detection for non-UTF8 files
 encoding = [
     "chardet>=5.0",

codex-lens/src/codexlens/cli/commands.py

@@ -415,11 +415,20 @@ def search(
     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("auto", "--mode", "-m", help="Search mode: auto, 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')."),
+    weights: Optional[str] = typer.Option(
+        None,
+        "--weights", "-w",
+        help="RRF weights as key=value pairs (e.g., 'splade=0.4,vector=0.6' or 'exact=0.3,fuzzy=0.1,vector=0.6'). Default: auto-detect based on available backends."
+    ),
+    use_fts: bool = typer.Option(
+        False,
+        "--use-fts",
+        help="Use FTS (exact+fuzzy) instead of SPLADE for sparse retrieval"
+    ),
     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 or semantic vectors.
+    """Search indexed file contents using hybrid retrieval.
 
     Uses chain search across directory indexes.
     Use --depth to limit search recursion (0 = current dir only).
@@ -428,17 +437,27 @@ def search(
       - auto: Auto-detect (hybrid if embeddings exist, exact otherwise) [default]
       - exact: Exact FTS using unicode61 tokenizer - 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
+      - hybrid: RRF fusion of sparse + dense search (recommended) - best recall
+      - vector: Vector search with sparse fallback - semantic + keyword
       - pure-vector: Pure semantic vector search only - natural language queries
 
+    SPLADE Mode:
+      When SPLADE is available (pip install codex-lens[splade]), it automatically
+      replaces FTS (exact+fuzzy) as the sparse retrieval backend. SPLADE provides
+      semantic term expansion for better synonym handling.
+      
+      Use --use-fts to force FTS mode instead of SPLADE.
+
     Vector Search Requirements:
       Vector search modes require pre-generated embeddings.
       Use 'codexlens embeddings-generate' to create embeddings first.
 
-    Hybrid Mode:
-      Default weights: exact=0.3, fuzzy=0.1, vector=0.6
-      Use --weights to customize (e.g., --weights 0.5,0.3,0.2)
+    Hybrid Mode Weights:
+      Use --weights to adjust RRF fusion weights:
+      - SPLADE mode: 'splade=0.4,vector=0.6' (default)
+      - FTS mode: 'exact=0.3,fuzzy=0.1,vector=0.6' (default)
+      
+      Legacy format also supported: '0.3,0.1,0.6' (exact,fuzzy,vector)
 
     Examples:
       # Auto-detect mode (uses hybrid if embeddings available)
@@ -450,11 +469,19 @@ def search(
       # Semantic search (requires embeddings)
       codexlens search "how to verify user credentials" --mode pure-vector
 
-      # Force hybrid mode
-      codexlens search "authentication" --mode hybrid
+      # Force hybrid mode with custom weights
+      codexlens search "authentication" --mode hybrid --weights splade=0.5,vector=0.5
+      
+      # Force FTS instead of SPLADE
+      codexlens search "authentication" --use-fts
     """
     _configure_logging(verbose, json_mode)
     search_path = path.expanduser().resolve()
+    
+    # Configure search with FTS fallback if requested
+    config = Config()
+    if use_fts:
+        config.use_fts_fallback = True
 
     # Validate mode
     valid_modes = ["auto", "exact", "fuzzy", "hybrid", "vector", "pure-vector"]
@@ -470,22 +497,56 @@ def search(
     hybrid_weights = None
     if weights:
         try:
-            weight_parts = [float(w.strip()) for w in weights.split(",")]
-            if len(weight_parts) == 3:
-                weight_sum = sum(weight_parts)
+            # Check if using key=value format (new) or legacy comma-separated format
+            if "=" in weights:
+                # New format: splade=0.4,vector=0.6 or exact=0.3,fuzzy=0.1,vector=0.6
+                weight_dict = {}
+                for pair in weights.split(","):
+                    if "=" in pair:
+                        key, val = pair.split("=", 1)
+                        weight_dict[key.strip()] = float(val.strip())
+                    else:
+                        raise ValueError("Mixed format not supported - use all key=value pairs")
+                
+                # Validate and normalize weights
+                weight_sum = sum(weight_dict.values())
                 if abs(weight_sum - 1.0) > 0.01:
-                    console.print(f"[yellow]Warning: Weights sum to {weight_sum:.2f}, should sum to 1.0. Normalizing...[/yellow]")
-                    # Normalize weights
-                    weight_parts = [w / weight_sum for w in weight_parts]
-                hybrid_weights = {
-                    "exact": weight_parts[0],
-                    "fuzzy": weight_parts[1],
-                    "vector": weight_parts[2],
-                }
+                    if not json_mode:
+                        console.print(f"[yellow]Warning: Weights sum to {weight_sum:.2f}, should sum to 1.0. Normalizing...[/yellow]")
+                    weight_dict = {k: v / weight_sum for k, v in weight_dict.items()}
+                
+                hybrid_weights = weight_dict
             else:
-                console.print("[yellow]Warning: Invalid weights format (need 3 values). Using defaults.[/yellow]")
-        except ValueError:
-            console.print("[yellow]Warning: Invalid weights format. Using defaults.[/yellow]")
+                # Legacy format: 0.3,0.1,0.6 (exact,fuzzy,vector)
+                weight_parts = [float(w.strip()) for w in weights.split(",")]
+                if len(weight_parts) == 3:
+                    weight_sum = sum(weight_parts)
+                    if abs(weight_sum - 1.0) > 0.01:
+                        if not json_mode:
+                            console.print(f"[yellow]Warning: Weights sum to {weight_sum:.2f}, should sum to 1.0. Normalizing...[/yellow]")
+                        weight_parts = [w / weight_sum for w in weight_parts]
+                    hybrid_weights = {
+                        "exact": weight_parts[0],
+                        "fuzzy": weight_parts[1],
+                        "vector": weight_parts[2],
+                    }
+                elif len(weight_parts) == 2:
+                    # Two values: assume splade,vector
+                    weight_sum = sum(weight_parts)
+                    if abs(weight_sum - 1.0) > 0.01:
+                        if not json_mode:
+                            console.print(f"[yellow]Warning: Weights sum to {weight_sum:.2f}, should sum to 1.0. Normalizing...[/yellow]")
+                        weight_parts = [w / weight_sum for w in weight_parts]
+                    hybrid_weights = {
+                        "splade": weight_parts[0],
+                        "vector": weight_parts[1],
+                    }
+                else:
+                    if not json_mode:
+                        console.print("[yellow]Warning: Invalid weights format. Using defaults.[/yellow]")
+        except ValueError as e:
+            if not json_mode:
+                console.print(f"[yellow]Warning: Invalid weights format ({e}). Using defaults.[/yellow]")
 
     registry: RegistryStore | None = None
     try:
@@ -2381,6 +2442,188 @@ def gpu_reset(
             console.print(f"  Device: [cyan]{gpu_info.gpu_name}[/cyan]")
 
 
+
+# ==================== SPLADE Commands ====================
+
+@app.command("splade-index")
+def splade_index_command(
+    path: Path = typer.Argument(..., help="Project path to index"),
+    rebuild: bool = typer.Option(False, "--rebuild", "-r", help="Force rebuild SPLADE index"),
+    verbose: bool = typer.Option(False, "--verbose", "-v", help="Enable verbose output."),
+) -> None:
+    """Generate SPLADE sparse index for existing codebase.
+
+    Encodes all semantic chunks with SPLADE model and builds inverted index
+    for efficient sparse retrieval.
+
+    Examples:
+        codexlens splade-index ~/projects/my-app
+        codexlens splade-index . --rebuild
+    """
+    _configure_logging(verbose)
+
+    from codexlens.semantic.splade_encoder import get_splade_encoder, check_splade_available
+    from codexlens.storage.splade_index import SpladeIndex
+    from codexlens.semantic.vector_store import VectorStore
+
+    # Check SPLADE availability
+    ok, err = check_splade_available()
+    if not ok:
+        console.print(f"[red]SPLADE not available: {err}[/red]")
+        console.print("[dim]Install with: pip install transformers torch[/dim]")
+        raise typer.Exit(1)
+
+    # Find index database
+    target_path = path.expanduser().resolve()
+
+    # Try to find _index.db
+    if target_path.is_file() and target_path.name == "_index.db":
+        index_db = target_path
+    elif target_path.is_dir():
+        # Check for local .codexlens/_index.db
+        local_index = target_path / ".codexlens" / "_index.db"
+        if local_index.exists():
+            index_db = local_index
+        else:
+            # Try to find via registry
+            registry = RegistryStore()
+            try:
+                registry.initialize()
+                mapper = PathMapper()
+                index_db = mapper.source_to_index_db(target_path)
+                if not index_db.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(1)
+            finally:
+                registry.close()
+    else:
+        console.print(f"[red]Error:[/red] Path must be _index.db file or indexed directory")
+        raise typer.Exit(1)
+
+    splade_db = index_db.parent / "_splade.db"
+
+    if splade_db.exists() and not rebuild:
+        console.print("[yellow]SPLADE index exists. Use --rebuild to regenerate.[/yellow]")
+        return
+
+    # Load chunks from vector store
+    console.print(f"[blue]Loading chunks from {index_db.name}...[/blue]")
+    vector_store = VectorStore(index_db)
+    chunks = vector_store.get_all_chunks()
+
+    if not chunks:
+        console.print("[yellow]No chunks found in vector store[/yellow]")
+        console.print("[dim]Generate embeddings first with 'codexlens embeddings-generate'[/dim]")
+        raise typer.Exit(1)
+
+    console.print(f"[blue]Encoding {len(chunks)} chunks with SPLADE...[/blue]")
+
+    # Initialize SPLADE
+    encoder = get_splade_encoder()
+    splade_index = SpladeIndex(splade_db)
+    splade_index.create_tables()
+
+    # Encode in batches with progress bar
+    with Progress(
+        SpinnerColumn(),
+        TextColumn("[progress.description]{task.description}"),
+        BarColumn(),
+        TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
+        TimeElapsedColumn(),
+        console=console,
+    ) as progress:
+        task = progress.add_task("Encoding...", total=len(chunks))
+        for chunk in chunks:
+            sparse_vec = encoder.encode_text(chunk.content)
+            splade_index.add_posting(chunk.id, sparse_vec)
+            progress.advance(task)
+
+    # Set metadata
+    splade_index.set_metadata(
+        model_name=encoder.model_name,
+        vocab_size=encoder.vocab_size
+    )
+
+    stats = splade_index.get_stats()
+    console.print(f"[green]✓[/green] SPLADE index built: {stats['unique_chunks']} chunks, {stats['total_postings']} postings")
+    console.print(f"  Database: [dim]{splade_db}[/dim]")
+
+
+@app.command("splade-status")
+def splade_status_command(
+    path: Path = typer.Argument(..., help="Project path"),
+    verbose: bool = typer.Option(False, "--verbose", "-v", help="Enable verbose output."),
+) -> None:
+    """Show SPLADE index status and statistics.
+
+    Examples:
+        codexlens splade-status ~/projects/my-app
+        codexlens splade-status .
+    """
+    _configure_logging(verbose)
+
+    from codexlens.storage.splade_index import SpladeIndex
+    from codexlens.semantic.splade_encoder import check_splade_available
+
+    # Find index database
+    target_path = path.expanduser().resolve()
+
+    if target_path.is_file() and target_path.name == "_index.db":
+        splade_db = target_path.parent / "_splade.db"
+    elif target_path.is_dir():
+        # Check for local .codexlens/_splade.db
+        local_splade = target_path / ".codexlens" / "_splade.db"
+        if local_splade.exists():
+            splade_db = local_splade
+        else:
+            # Try to find via registry
+            registry = RegistryStore()
+            try:
+                registry.initialize()
+                mapper = PathMapper()
+                index_db = mapper.source_to_index_db(target_path)
+                splade_db = index_db.parent / "_splade.db"
+            finally:
+                registry.close()
+    else:
+        console.print(f"[red]Error:[/red] Path must be _index.db file or indexed directory")
+        raise typer.Exit(1)
+
+    if not splade_db.exists():
+        console.print("[yellow]No SPLADE index found[/yellow]")
+        console.print(f"[dim]Run 'codexlens splade-index {path}' to create one[/dim]")
+        return
+
+    splade_index = SpladeIndex(splade_db)
+
+    if not splade_index.has_index():
+        console.print("[yellow]SPLADE tables not initialized[/yellow]")
+        return
+
+    metadata = splade_index.get_metadata()
+    stats = splade_index.get_stats()
+
+    # Create status table
+    table = Table(title="SPLADE Index Status", show_header=False)
+    table.add_column("Property", style="cyan")
+    table.add_column("Value")
+
+    table.add_row("Database", str(splade_db))
+    if metadata:
+        table.add_row("Model", metadata['model_name'])
+        table.add_row("Vocab Size", str(metadata['vocab_size']))
+    table.add_row("Chunks", str(stats['unique_chunks']))
+    table.add_row("Unique Tokens", str(stats['unique_tokens']))
+    table.add_row("Total Postings", str(stats['total_postings']))
+
+    ok, err = check_splade_available()
+    status_text = "[green]Yes[/green]" if ok else f"[red]No[/red] - {err}"
+    table.add_row("SPLADE Available", status_text)
+
+    console.print(table)
+
+
 # ==================== Watch Command ====================
 
 @app.command()

codex-lens/src/codexlens/cli/embedding_manager.py

@@ -33,6 +33,15 @@ def _cleanup_fastembed_resources() -> None:
         pass
 
 
+def _cleanup_splade_resources() -> None:
+    """Release SPLADE encoder ONNX resources."""
+    try:
+        from codexlens.semantic.splade_encoder import clear_splade_cache
+        clear_splade_cache()
+    except Exception:
+        pass
+
+
 def _generate_chunks_from_cursor(
     cursor,
     chunker,
@@ -675,10 +684,96 @@ def generate_embeddings(
                 if progress_callback:
                     progress_callback(f"Finalizing index... Building ANN index for {total_chunks_created} chunks")
 
+            # --- SPLADE SPARSE ENCODING (after dense embeddings) ---
+            # Add SPLADE encoding if enabled in config
+            splade_success = False
+            splade_error = None
+
+            try:
+                from codexlens.config import Config
+                config = Config.load()
+
+                if config.enable_splade:
+                    from codexlens.semantic.splade_encoder import check_splade_available, get_splade_encoder
+                    from codexlens.storage.splade_index import SpladeIndex
+
+                    ok, err = check_splade_available()
+                    if ok:
+                        if progress_callback:
+                            progress_callback(f"Generating SPLADE sparse vectors for {total_chunks_created} chunks...")
+
+                        # Initialize SPLADE encoder and index
+                        splade_encoder = get_splade_encoder(use_gpu=use_gpu)
+                        # Use main index database for SPLADE (not separate _splade.db)
+                        splade_index = SpladeIndex(index_path)
+                        splade_index.create_tables()
+
+                        # Retrieve all chunks from database for SPLADE encoding
+                        with sqlite3.connect(index_path) as conn:
+                            conn.row_factory = sqlite3.Row
+                            cursor = conn.execute("SELECT id, content FROM semantic_chunks ORDER BY id")
+
+                            # Batch encode for efficiency
+                            SPLADE_BATCH_SIZE = 32
+                            batch_postings = []
+                            chunk_batch = []
+                            chunk_ids = []
+
+                            for row in cursor:
+                                chunk_id = row["id"]
+                                content = row["content"]
+
+                                chunk_ids.append(chunk_id)
+                                chunk_batch.append(content)
+
+                                # Process batch when full
+                                if len(chunk_batch) >= SPLADE_BATCH_SIZE:
+                                    sparse_vecs = splade_encoder.encode_batch(chunk_batch, batch_size=SPLADE_BATCH_SIZE)
+                                    for cid, sparse_vec in zip(chunk_ids, sparse_vecs):
+                                        batch_postings.append((cid, sparse_vec))
+
+                                    chunk_batch = []
+                                    chunk_ids = []
+
+                            # Process remaining chunks
+                            if chunk_batch:
+                                sparse_vecs = splade_encoder.encode_batch(chunk_batch, batch_size=SPLADE_BATCH_SIZE)
+                                for cid, sparse_vec in zip(chunk_ids, sparse_vecs):
+                                    batch_postings.append((cid, sparse_vec))
+
+                            # Batch insert all postings
+                            if batch_postings:
+                                splade_index.add_postings_batch(batch_postings)
+
+                                # Set metadata
+                                splade_index.set_metadata(
+                                    model_name=splade_encoder.model_name,
+                                    vocab_size=splade_encoder.vocab_size
+                                )
+
+                                splade_success = True
+                                if progress_callback:
+                                    stats = splade_index.get_stats()
+                                    progress_callback(
+                                        f"SPLADE index created: {stats['total_postings']} postings, "
+                                        f"{stats['unique_tokens']} unique tokens"
+                                    )
+                    else:
+                        logger.debug("SPLADE not available: %s", err)
+                        splade_error = f"SPLADE not available: {err}"
+            except Exception as e:
+                splade_error = str(e)
+                logger.warning("SPLADE encoding failed: %s", e)
+
+            # Report SPLADE status after processing
+            if progress_callback and not splade_success and splade_error:
+                progress_callback(f"SPLADE index: FAILED - {splade_error}")
+
     except Exception as e:
         # Cleanup on error to prevent process hanging
         try:
             _cleanup_fastembed_resources()
+            _cleanup_splade_resources()
             gc.collect()
         except Exception:
             pass
@@ -690,6 +785,7 @@ def generate_embeddings(
     # This is critical - without it, ONNX Runtime threads prevent Python from exiting
     try:
         _cleanup_fastembed_resources()
+        _cleanup_splade_resources()
         gc.collect()
     except Exception:
         pass
@@ -874,6 +970,7 @@ def generate_embeddings_recursive(
     # Each generate_embeddings() call does its own cleanup, but do a final one to be safe
     try:
         _cleanup_fastembed_resources()
+        _cleanup_splade_resources()
         gc.collect()
     except Exception:
         pass

codex-lens/src/codexlens/config.py

@@ -103,6 +103,15 @@ class Config:
                                    # For litellm: model name from config (e.g., "qwen3-embedding")
     embedding_use_gpu: bool = True  # For fastembed: whether to use GPU acceleration
 
+    # SPLADE sparse retrieval configuration
+    enable_splade: bool = True  # Enable SPLADE as default sparse backend
+    splade_model: str = "naver/splade-cocondenser-ensembledistil"
+    splade_threshold: float = 0.01  # Min weight to store in index
+    splade_onnx_path: Optional[str] = None  # Custom ONNX model path
+
+    # FTS fallback (disabled by default, available via --use-fts)
+    use_fts_fallback: bool = False  # Use FTS instead of SPLADE
+
     # Indexing/search optimizations
     global_symbol_index_enabled: bool = True  # Enable project-wide symbol index fast path
     enable_merkle_detection: bool = True  # Enable content-hash based incremental indexing

codex-lens/src/codexlens/search/hybrid_search.py

@@ -8,7 +8,7 @@ from __future__ import annotations
 
 import logging
 import time
-from concurrent.futures import ThreadPoolExecutor, as_completed
+from concurrent.futures import ThreadPoolExecutor, TimeoutError as FuturesTimeoutError, as_completed
 from contextlib import contextmanager
 from pathlib import Path
 from typing import Any, Dict, List, Optional
@@ -33,6 +33,8 @@ def timer(name: str, logger: logging.Logger, level: int = logging.DEBUG):
 from codexlens.config import Config
 from codexlens.entities import SearchResult
 from codexlens.search.ranking import (
+    DEFAULT_WEIGHTS,
+    FTS_FALLBACK_WEIGHTS,
     apply_symbol_boost,
     cross_encoder_rerank,
     get_rrf_weights,
@@ -54,12 +56,9 @@ class HybridSearchEngine:
         default_weights: Default RRF weights for each source
     """
 
-    # Default RRF weights (vector: 60%, exact: 30%, fuzzy: 10%)
-    DEFAULT_WEIGHTS = {
-        "exact": 0.3,
-        "fuzzy": 0.1,
-        "vector": 0.6,
-    }
+    # NOTE: DEFAULT_WEIGHTS imported from ranking.py - single source of truth
+    # Default RRF weights: SPLADE-based hybrid (splade: 0.4, vector: 0.6)
+    # FTS fallback mode uses FTS_FALLBACK_WEIGHTS (exact: 0.3, fuzzy: 0.1, vector: 0.6)
 
     def __init__(
         self,
@@ -75,10 +74,11 @@ class HybridSearchEngine:
             embedder: Optional embedder instance for embedding-based reranking
         """
         self.logger = logging.getLogger(__name__)
-        self.weights = weights or self.DEFAULT_WEIGHTS.copy()
+        self.weights = weights or DEFAULT_WEIGHTS.copy()
         self._config = config
         self.embedder = embedder
         self.reranker: Any = None
+        self._use_gpu = config.embedding_use_gpu if config else True
 
     def search(
         self,
@@ -124,6 +124,26 @@ class HybridSearchEngine:
 
         # Determine which backends to use
         backends = {}
+        
+        # Check if SPLADE is available
+        splade_available = False
+        # Respect config.enable_splade flag and use_fts_fallback flag
+        if self._config and getattr(self._config, 'use_fts_fallback', False):
+            # Config explicitly requests FTS fallback - disable SPLADE
+            splade_available = False
+        elif self._config and not getattr(self._config, 'enable_splade', True):
+            # Config explicitly disabled SPLADE
+            splade_available = False
+        else:
+            # Check if SPLADE dependencies are available
+            try:
+                from codexlens.semantic.splade_encoder import check_splade_available
+                ok, _ = check_splade_available()
+                if ok:
+                    # SPLADE tables are in main index database, will check table existence in _search_splade
+                    splade_available = True
+            except Exception:
+                pass
 
         if pure_vector:
             # Pure vector mode: only use vector search, no FTS fallback
@@ -138,12 +158,19 @@ class HybridSearchEngine:
                 )
                 backends["exact"] = True
         else:
-            # Hybrid mode: always include exact search as baseline
-            backends["exact"] = True
-            if enable_fuzzy:
-                backends["fuzzy"] = True
-            if enable_vector:
-                backends["vector"] = True
+            # Hybrid mode: default to SPLADE if available, otherwise use FTS
+            if splade_available:
+                # Default: enable SPLADE, disable exact and fuzzy
+                backends["splade"] = True
+                if enable_vector:
+                    backends["vector"] = True
+            else:
+                # Fallback mode: enable exact+fuzzy when SPLADE unavailable
+                backends["exact"] = True
+                if enable_fuzzy:
+                    backends["fuzzy"] = True
+                if enable_vector:
+                    backends["vector"] = True
 
         # Execute parallel searches
         with timer("parallel_search_total", self.logger):
@@ -354,23 +381,40 @@ class HybridSearchEngine:
                 )
                 future_to_source[future] = "vector"
 
-            # Collect results as they complete
-            for future in as_completed(future_to_source):
-                source = future_to_source[future]
-                elapsed_ms = (time.perf_counter() - submit_times[source]) * 1000
-                timing_data[source] = elapsed_ms
-                try:
-                    results = future.result()
-                    # Tag results with source for debugging
-                    tagged_results = tag_search_source(results, source)
-                    results_map[source] = tagged_results
-                    self.logger.debug(
-                        "[TIMING] %s_search: %.2fms (%d results)",
-                        source, elapsed_ms, len(results)
-                    )
-                except Exception as exc:
-                    self.logger.error("Search failed for %s: %s", source, exc)
-                    results_map[source] = []
+            if backends.get("splade"):
+                submit_times["splade"] = time.perf_counter()
+                future = executor.submit(
+                    self._search_splade, index_path, query, limit
+                )
+                future_to_source[future] = "splade"
+
+            # Collect results as they complete with timeout protection
+            try:
+                for future in as_completed(future_to_source, timeout=30.0):
+                    source = future_to_source[future]
+                    elapsed_ms = (time.perf_counter() - submit_times[source]) * 1000
+                    timing_data[source] = elapsed_ms
+                    try:
+                        results = future.result(timeout=10.0)
+                        # Tag results with source for debugging
+                        tagged_results = tag_search_source(results, source)
+                        results_map[source] = tagged_results
+                        self.logger.debug(
+                            "[TIMING] %s_search: %.2fms (%d results)",
+                            source, elapsed_ms, len(results)
+                        )
+                    except (Exception, FuturesTimeoutError) as exc:
+                        self.logger.error("Search failed for %s: %s", source, exc)
+                        results_map[source] = []
+            except FuturesTimeoutError:
+                self.logger.warning("Search timeout: some backends did not respond in time")
+                # Cancel remaining futures
+                for future in future_to_source:
+                    future.cancel()
+                # Set empty results for sources that didn't complete
+                for source in backends:
+                    if source not in results_map:
+                        results_map[source] = []
 
         # Log timing summary
         if timing_data:
@@ -564,3 +608,113 @@ class HybridSearchEngine:
         except Exception as exc:
             self.logger.error("Vector search error: %s", exc)
             return []
+
+    def _search_splade(
+        self, index_path: Path, query: str, limit: int
+    ) -> List[SearchResult]:
+        """SPLADE sparse retrieval via inverted index.
+        
+        Args:
+            index_path: Path to _index.db file
+            query: Natural language query string
+            limit: Maximum results
+        
+        Returns:
+            List of SearchResult ordered by SPLADE score
+        """
+        try:
+            from codexlens.semantic.splade_encoder import get_splade_encoder, check_splade_available
+            from codexlens.storage.splade_index import SpladeIndex
+            import sqlite3
+            import json
+            
+            # Check dependencies
+            ok, err = check_splade_available()
+            if not ok:
+                self.logger.debug("SPLADE not available: %s", err)
+                return []
+            
+            # Use main index database (SPLADE tables are in _index.db, not separate _splade.db)
+            splade_index = SpladeIndex(index_path)
+            if not splade_index.has_index():
+                self.logger.debug("SPLADE index not initialized")
+                return []
+            
+            # Encode query to sparse vector
+            encoder = get_splade_encoder(use_gpu=self._use_gpu)
+            query_sparse = encoder.encode_text(query)
+            
+            # Search inverted index for top matches
+            raw_results = splade_index.search(query_sparse, limit=limit, min_score=0.0)
+            
+            if not raw_results:
+                return []
+            
+            # Fetch chunk details from main index database
+            chunk_ids = [chunk_id for chunk_id, _ in raw_results]
+            score_map = {chunk_id: score for chunk_id, score in raw_results}
+            
+            # Query semantic_chunks table for full details
+            placeholders = ",".join("?" * len(chunk_ids))
+            with sqlite3.connect(index_path) as conn:
+                conn.row_factory = sqlite3.Row
+                rows = conn.execute(
+                    f"""
+                    SELECT id, file_path, content, metadata
+                    FROM semantic_chunks
+                    WHERE id IN ({placeholders})
+                    """,
+                    chunk_ids
+                ).fetchall()
+            
+            # Build SearchResult objects
+            results = []
+            for row in rows:
+                chunk_id = row["id"]
+                file_path = row["file_path"]
+                content = row["content"]
+                metadata_json = row["metadata"]
+                metadata = json.loads(metadata_json) if metadata_json else {}
+                
+                score = score_map.get(chunk_id, 0.0)
+                
+                # Build excerpt (short preview)
+                excerpt = content[:200] + "..." if len(content) > 200 else content
+                
+                # Extract symbol information from metadata
+                symbol_name = metadata.get("symbol_name")
+                symbol_kind = metadata.get("symbol_kind")
+                start_line = metadata.get("start_line")
+                end_line = metadata.get("end_line")
+                
+                # Build Symbol object if we have symbol info
+                symbol = None
+                if symbol_name and symbol_kind and start_line and end_line:
+                    try:
+                        from codexlens.entities import Symbol
+                        symbol = Symbol(
+                            name=symbol_name,
+                            kind=symbol_kind,
+                            range=(start_line, end_line)
+                        )
+                    except Exception:
+                        pass
+                
+                results.append(SearchResult(
+                    path=file_path,
+                    score=score,
+                    excerpt=excerpt,
+                    content=content,
+                    symbol=symbol,
+                    metadata=metadata,
+                    start_line=start_line,
+                    end_line=end_line,
+                    symbol_name=symbol_name,
+                    symbol_kind=symbol_kind,
+                ))
+            
+            return results
+            
+        except Exception as exc:
+            self.logger.debug("SPLADE search error: %s", exc)
+            return []

codex-lens/src/codexlens/search/ranking.py

@@ -1,7 +1,7 @@
 """Ranking algorithms for hybrid search result fusion.
 
 Implements Reciprocal Rank Fusion (RRF) and score normalization utilities
-for combining results from heterogeneous search backends (exact FTS, fuzzy FTS, vector search).
+for combining results from heterogeneous search backends (SPLADE, exact FTS, fuzzy FTS, vector search).
 """
 
 from __future__ import annotations
@@ -14,6 +14,20 @@ from typing import Any, Dict, List
 from codexlens.entities import SearchResult, AdditionalLocation
 
 
+# Default RRF weights for SPLADE-based hybrid search
+DEFAULT_WEIGHTS = {
+    "splade": 0.4,  # Replaces exact(0.3) + fuzzy(0.1)
+    "vector": 0.6,
+}
+
+# Legacy weights for FTS fallback mode (when SPLADE unavailable)
+FTS_FALLBACK_WEIGHTS = {
+    "exact": 0.3,
+    "fuzzy": 0.1,
+    "vector": 0.6,
+}
+
+
 class QueryIntent(str, Enum):
     """Query intent for adaptive RRF weights (Python/TypeScript parity)."""
 
@@ -87,15 +101,24 @@ def adjust_weights_by_intent(
     intent: QueryIntent,
     base_weights: Dict[str, float],
 ) -> Dict[str, float]:
-    """Map intent → weights (kept aligned with TypeScript mapping)."""
+    """Adjust RRF weights based on query intent."""
+    # Check if using SPLADE or FTS mode
+    use_splade = "splade" in base_weights
+    
     if intent == QueryIntent.KEYWORD:
-        target = {"exact": 0.5, "fuzzy": 0.1, "vector": 0.4}
+        if use_splade:
+            target = {"splade": 0.6, "vector": 0.4}
+        else:
+            target = {"exact": 0.5, "fuzzy": 0.1, "vector": 0.4}
     elif intent == QueryIntent.SEMANTIC:
-        target = {"exact": 0.2, "fuzzy": 0.1, "vector": 0.7}
+        if use_splade:
+            target = {"splade": 0.3, "vector": 0.7}
+        else:
+            target = {"exact": 0.2, "fuzzy": 0.1, "vector": 0.7}
     else:
         target = dict(base_weights)
-
-    # Preserve only keys that are present in base_weights (active backends).
+    
+    # Filter to active backends
     keys = list(base_weights.keys())
     filtered = {k: float(target.get(k, 0.0)) for k in keys}
     return normalize_weights(filtered)

codex-lens/src/codexlens/semantic/SPLADE_IMPLEMENTATION.md

@@ -0,0 +1,225 @@
+# SPLADE Encoder Implementation
+
+## Overview
+
+Created `splade_encoder.py` - A complete ONNX-optimized SPLADE sparse encoder for code search.
+
+## File Location
+
+`src/codexlens/semantic/splade_encoder.py` (474 lines)
+
+## Key Components
+
+### 1. Dependency Checking
+
+**Function**: `check_splade_available() -> Tuple[bool, Optional[str]]`
+- Validates numpy, onnxruntime, optimum, transformers availability
+- Returns (True, None) if all dependencies present
+- Returns (False, error_message) with install instructions if missing
+
+### 2. Caching System
+
+**Global Cache**: Thread-safe singleton pattern
+- `_splade_cache: Dict[str, SpladeEncoder]` - Global encoder cache
+- `_cache_lock: threading.RLock()` - Thread safety lock
+
+**Factory Function**: `get_splade_encoder(...) -> SpladeEncoder`
+- Cache key includes: model_name, gpu/cpu, max_length, sparsity_threshold
+- Pre-loads model on first access
+- Returns cached instance on subsequent calls
+
+**Cleanup Function**: `clear_splade_cache() -> None`
+- Releases ONNX resources
+- Clears model and tokenizer references
+- Prevents memory leaks
+
+### 3. SpladeEncoder Class
+
+#### Initialization Parameters
+- `model_name: str` - Default: "naver/splade-cocondenser-ensembledistil"
+- `use_gpu: bool` - Enable GPU acceleration (default: True)
+- `max_length: int` - Max sequence length (default: 512)
+- `sparsity_threshold: float` - Min weight threshold (default: 0.01)
+- `providers: Optional[List]` - Explicit ONNX providers (overrides use_gpu)
+
+#### Core Methods
+
+**`_load_model()`**: Lazy loading with GPU support
+- Uses `optimum.onnxruntime.ORTModelForMaskedLM`
+- Falls back to CPU if GPU unavailable
+- Integrates with `gpu_support.get_optimal_providers()`
+- Handles device_id options for DirectML/CUDA
+
+**`_splade_activation(logits, attention_mask)`**: Static method
+- Formula: `log(1 + ReLU(logits)) * attention_mask`
+- Input: (batch, seq_len, vocab_size)
+- Output: (batch, seq_len, vocab_size)
+
+**`_max_pooling(splade_repr)`**: Static method
+- Max pooling over sequence dimension
+- Input: (batch, seq_len, vocab_size)
+- Output: (batch, vocab_size)
+
+**`_to_sparse_dict(dense_vec)`**: Conversion helper
+- Filters by sparsity_threshold
+- Returns: `Dict[int, float]` mapping token_id to weight
+
+**`encode_text(text: str) -> Dict[int, float]`**: Single text encoding
+- Tokenizes input with truncation/padding
+- Forward pass through ONNX model
+- Applies SPLADE activation + max pooling
+- Returns sparse vector
+
+**`encode_batch(texts: List[str], batch_size: int = 32) -> List[Dict[int, float]]`**: Batch encoding
+- Processes in batches for memory efficiency
+- Returns list of sparse vectors
+
+#### Properties
+
+**`vocab_size: int`**: Vocabulary size (~30k for BERT)
+- Cached after first model load
+- Returns tokenizer length
+
+#### Debugging Methods
+
+**`get_token(token_id: int) -> str`**
+- Converts token_id to human-readable string
+- Uses tokenizer.decode()
+
+**`get_top_tokens(sparse_vec: Dict[int, float], top_k: int = 10) -> List[Tuple[str, float]]`**
+- Extracts top-k highest-weight tokens
+- Returns (token_string, weight) pairs
+- Useful for understanding model focus
+
+## Design Patterns Followed
+
+### 1. From `onnx_reranker.py`
+✓ ONNX loading with provider detection
+✓ Lazy model initialization
+✓ Thread-safe loading with RLock
+✓ Signature inspection for backward compatibility
+✓ Fallback for older Optimum versions
+✓ Static helper methods for numerical operations
+
+### 2. From `embedder.py`
+✓ Global cache with thread safety
+✓ Factory function pattern (get_splade_encoder)
+✓ Cache cleanup function (clear_splade_cache)
+✓ GPU provider configuration
+✓ Batch processing support
+
+### 3. From `gpu_support.py`
+✓ `get_optimal_providers(use_gpu, with_device_options=True)`
+✓ Device ID options for DirectML/CUDA
+✓ Provider tuple format: (provider_name, options_dict)
+
+## SPLADE Algorithm
+
+### Activation Formula
+```python
+# Step 1: ReLU activation
+relu_logits = max(0, logits)
+
+# Step 2: Log(1 + x) transformation
+log_relu = log(1 + relu_logits)
+
+# Step 3: Apply attention mask
+splade_repr = log_relu * attention_mask
+
+# Step 4: Max pooling over sequence
+splade_vec = max(splade_repr, axis=sequence_length)
+
+# Step 5: Sparsification by threshold
+sparse_dict = {token_id: weight for token_id, weight in enumerate(splade_vec) if weight > threshold}
+```
+
+### Output Format
+- Sparse dictionary: `{token_id: weight}`
+- Token IDs: 0 to vocab_size-1 (typically ~30,000)
+- Weights: Float values > sparsity_threshold
+- Interpretable: Can decode token_ids to strings
+
+## Integration Points
+
+### With `splade_index.py`
+- `SpladeIndex.add_posting(chunk_id, sparse_vec: Dict[int, float])`
+- `SpladeIndex.search(query_sparse: Dict[int, float])`
+- Encoder produces the sparse vectors consumed by index
+
+### With Indexing Pipeline
+```python
+encoder = get_splade_encoder(use_gpu=True)
+
+# Single document
+sparse_vec = encoder.encode_text("def main():\n    print('hello')")
+index.add_posting(chunk_id=1, sparse_vec=sparse_vec)
+
+# Batch indexing
+texts = ["code chunk 1", "code chunk 2", ...]
+sparse_vecs = encoder.encode_batch(texts, batch_size=64)
+postings = [(chunk_id, vec) for chunk_id, vec in enumerate(sparse_vecs)]
+index.add_postings_batch(postings)
+```
+
+### With Search Pipeline
+```python
+encoder = get_splade_encoder(use_gpu=True)
+query_sparse = encoder.encode_text("authentication function")
+results = index.search(query_sparse, limit=50, min_score=0.5)
+```
+
+## Dependencies
+
+Required packages:
+- `numpy` - Numerical operations
+- `onnxruntime` - ONNX model execution (CPU)
+- `onnxruntime-gpu` - ONNX with GPU support (optional)
+- `optimum[onnxruntime]` - Hugging Face ONNX optimization
+- `transformers` - Tokenizer and model loading
+
+Install command:
+```bash
+# CPU only
+pip install numpy onnxruntime optimum[onnxruntime] transformers
+
+# With GPU support
+pip install numpy onnxruntime-gpu optimum[onnxruntime-gpu] transformers
+```
+
+## Testing Status
+
+✓ Python syntax validation passed
+✓ Module import successful
+✓ Dependency checking works correctly
+✗ Full functional test pending (requires optimum installation)
+
+## Next Steps
+
+1. Install dependencies for functional testing
+2. Create unit tests in `tests/semantic/test_splade_encoder.py`
+3. Benchmark encoding performance (CPU vs GPU)
+4. Integrate with codex-lens indexing pipeline
+5. Add SPLADE option to semantic search configuration
+
+## Performance Considerations
+
+### Memory Usage
+- Model size: ~100MB (ONNX optimized)
+- Sparse vectors: ~100-500 non-zero entries per document
+- Batch size: 32 recommended (adjust based on GPU memory)
+
+### Speed Benchmarks (Expected)
+- CPU encoding: ~10-20 docs/sec
+- GPU encoding (CUDA): ~100-200 docs/sec
+- GPU encoding (DirectML): ~50-100 docs/sec
+
+### Sparsity Analysis
+- Threshold 0.01: ~200-400 tokens per document
+- Threshold 0.05: ~100-200 tokens per document
+- Threshold 0.10: ~50-100 tokens per document
+
+## References
+
+- SPLADE paper: https://arxiv.org/abs/2107.05720
+- SPLADE v2: https://arxiv.org/abs/2109.10086
+- Naver model: https://huggingface.co/naver/splade-cocondenser-ensembledistil

codex-lens/src/codexlens/semantic/splade_encoder.py

@@ -0,0 +1,474 @@
+"""ONNX-optimized SPLADE sparse encoder for code search.
+
+This module provides SPLADE (Sparse Lexical and Expansion) encoding using ONNX Runtime
+for efficient sparse vector generation. SPLADE produces vocabulary-aligned sparse vectors
+that combine the interpretability of BM25 with neural relevance modeling.
+
+Install (CPU):
+    pip install onnxruntime optimum[onnxruntime] transformers
+
+Install (GPU):
+    pip install onnxruntime-gpu optimum[onnxruntime-gpu] transformers
+"""
+
+from __future__ import annotations
+
+import logging
+import threading
+from typing import Any, Dict, List, Optional, Tuple
+
+logger = logging.getLogger(__name__)
+
+
+def check_splade_available() -> Tuple[bool, Optional[str]]:
+    """Check whether SPLADE dependencies are available.
+
+    Returns:
+        Tuple of (available: bool, error_message: Optional[str])
+    """
+    try:
+        import numpy  # noqa: F401
+    except ImportError as exc:
+        return False, f"numpy not available: {exc}. Install with: pip install numpy"
+
+    try:
+        import onnxruntime  # noqa: F401
+    except ImportError as exc:
+        return (
+            False,
+            f"onnxruntime not available: {exc}. Install with: pip install onnxruntime",
+        )
+
+    try:
+        from optimum.onnxruntime import ORTModelForMaskedLM  # noqa: F401
+    except ImportError as exc:
+        return (
+            False,
+            f"optimum[onnxruntime] not available: {exc}. Install with: pip install optimum[onnxruntime]",
+        )
+
+    try:
+        from transformers import AutoTokenizer  # noqa: F401
+    except ImportError as exc:
+        return (
+            False,
+            f"transformers not available: {exc}. Install with: pip install transformers",
+        )
+
+    return True, None
+
+
+# Global cache for SPLADE encoders (singleton pattern)
+_splade_cache: Dict[str, "SpladeEncoder"] = {}
+_cache_lock = threading.RLock()
+
+
+def get_splade_encoder(
+    model_name: str = "naver/splade-cocondenser-ensembledistil",
+    use_gpu: bool = True,
+    max_length: int = 512,
+    sparsity_threshold: float = 0.01,
+) -> "SpladeEncoder":
+    """Get or create cached SPLADE encoder (thread-safe singleton).
+
+    This function provides significant performance improvement by reusing
+    SpladeEncoder instances across multiple searches, avoiding repeated model
+    loading overhead.
+
+    Args:
+        model_name: SPLADE model name (default: naver/splade-cocondenser-ensembledistil)
+        use_gpu: If True, use GPU acceleration when available
+        max_length: Maximum sequence length for tokenization
+        sparsity_threshold: Minimum weight to include in sparse vector
+
+    Returns:
+        Cached SpladeEncoder instance for the given configuration
+    """
+    global _splade_cache
+
+    # Cache key includes all configuration parameters
+    cache_key = f"{model_name}:{'gpu' if use_gpu else 'cpu'}:{max_length}:{sparsity_threshold}"
+
+    with _cache_lock:
+        encoder = _splade_cache.get(cache_key)
+        if encoder is not None:
+            return encoder
+
+        # Create new encoder and cache it
+        encoder = SpladeEncoder(
+            model_name=model_name,
+            use_gpu=use_gpu,
+            max_length=max_length,
+            sparsity_threshold=sparsity_threshold,
+        )
+        # Pre-load model to ensure it's ready
+        encoder._load_model()
+        _splade_cache[cache_key] = encoder
+
+        return encoder
+
+
+def clear_splade_cache() -> None:
+    """Clear the SPLADE encoder cache and release ONNX resources.
+
+    This method ensures proper cleanup of ONNX model resources to prevent
+    memory leaks when encoders are no longer needed.
+    """
+    global _splade_cache
+    with _cache_lock:
+        # Release ONNX resources before clearing cache
+        for encoder in _splade_cache.values():
+            if encoder._model is not None:
+                del encoder._model
+                encoder._model = None
+            if encoder._tokenizer is not None:
+                del encoder._tokenizer
+                encoder._tokenizer = None
+        _splade_cache.clear()
+
+
+class SpladeEncoder:
+    """ONNX-optimized SPLADE sparse encoder.
+
+    Produces sparse vectors with vocabulary-aligned dimensions.
+    Output: Dict[int, float] mapping token_id to weight.
+
+    SPLADE activation formula:
+        splade_repr = log(1 + ReLU(logits)) * attention_mask
+        splade_vec = max_pooling(splade_repr, axis=sequence_length)
+
+    References:
+        - SPLADE: https://arxiv.org/abs/2107.05720
+        - SPLADE v2: https://arxiv.org/abs/2109.10086
+    """
+
+    DEFAULT_MODEL = "naver/splade-cocondenser-ensembledistil"
+
+    def __init__(
+        self,
+        model_name: str = DEFAULT_MODEL,
+        use_gpu: bool = True,
+        max_length: int = 512,
+        sparsity_threshold: float = 0.01,
+        providers: Optional[List[Any]] = None,
+    ) -> None:
+        """Initialize SPLADE encoder.
+
+        Args:
+            model_name: SPLADE model name (default: naver/splade-cocondenser-ensembledistil)
+            use_gpu: If True, use GPU acceleration when available
+            max_length: Maximum sequence length for tokenization
+            sparsity_threshold: Minimum weight to include in sparse vector
+            providers: Explicit ONNX providers list (overrides use_gpu)
+        """
+        self.model_name = (model_name or self.DEFAULT_MODEL).strip()
+        if not self.model_name:
+            raise ValueError("model_name cannot be blank")
+
+        self.use_gpu = bool(use_gpu)
+        self.max_length = int(max_length) if max_length > 0 else 512
+        self.sparsity_threshold = float(sparsity_threshold)
+        self.providers = providers
+
+        self._tokenizer: Any | None = None
+        self._model: Any | None = None
+        self._vocab_size: int | None = None
+        self._lock = threading.RLock()
+
+    def _load_model(self) -> None:
+        """Lazy load ONNX model and tokenizer."""
+        if self._model is not None and self._tokenizer is not None:
+            return
+
+        ok, err = check_splade_available()
+        if not ok:
+            raise ImportError(err)
+
+        with self._lock:
+            if self._model is not None and self._tokenizer is not None:
+                return
+
+            from inspect import signature
+
+            from optimum.onnxruntime import ORTModelForMaskedLM
+            from transformers import AutoTokenizer
+
+            if self.providers is None:
+                from .gpu_support import get_optimal_providers
+
+                # Include device_id options for DirectML/CUDA selection when available
+                self.providers = get_optimal_providers(
+                    use_gpu=self.use_gpu, with_device_options=True
+                )
+
+            # Some Optimum versions accept `providers`, others accept a single `provider`
+            # Prefer passing the full providers list, with a conservative fallback
+            model_kwargs: dict[str, Any] = {}
+            try:
+                params = signature(ORTModelForMaskedLM.from_pretrained).parameters
+                if "providers" in params:
+                    model_kwargs["providers"] = self.providers
+                elif "provider" in params:
+                    provider_name = "CPUExecutionProvider"
+                    if self.providers:
+                        first = self.providers[0]
+                        provider_name = first[0] if isinstance(first, tuple) else str(first)
+                    model_kwargs["provider"] = provider_name
+            except Exception:
+                model_kwargs = {}
+
+            try:
+                self._model = ORTModelForMaskedLM.from_pretrained(
+                    self.model_name,
+                    **model_kwargs,
+                )
+                logger.debug(f"SPLADE model loaded: {self.model_name}")
+            except TypeError:
+                # Fallback for older Optimum versions: retry without provider arguments
+                self._model = ORTModelForMaskedLM.from_pretrained(self.model_name)
+                logger.warning(
+                    "Optimum version doesn't support provider parameters. "
+                    "Upgrade optimum for GPU acceleration: pip install --upgrade optimum"
+                )
+
+            self._tokenizer = AutoTokenizer.from_pretrained(self.model_name, use_fast=True)
+
+            # Cache vocabulary size
+            self._vocab_size = len(self._tokenizer)
+            logger.debug(f"SPLADE tokenizer loaded: vocab_size={self._vocab_size}")
+
+    @staticmethod
+    def _splade_activation(logits: Any, attention_mask: Any) -> Any:
+        """Apply SPLADE activation function to model outputs.
+
+        Formula: log(1 + ReLU(logits)) * attention_mask
+
+        Args:
+            logits: Model output logits (batch, seq_len, vocab_size)
+            attention_mask: Attention mask (batch, seq_len)
+
+        Returns:
+            SPLADE representations (batch, seq_len, vocab_size)
+        """
+        import numpy as np
+
+        # ReLU activation
+        relu_logits = np.maximum(0, logits)
+
+        # Log(1 + x) transformation
+        log_relu = np.log1p(relu_logits)
+
+        # Apply attention mask (expand to match vocab dimension)
+        # attention_mask: (batch, seq_len) -> (batch, seq_len, 1)
+        mask_expanded = np.expand_dims(attention_mask, axis=-1)
+
+        # Element-wise multiplication
+        splade_repr = log_relu * mask_expanded
+
+        return splade_repr
+
+    @staticmethod
+    def _max_pooling(splade_repr: Any) -> Any:
+        """Max pooling over sequence length dimension.
+
+        Args:
+            splade_repr: SPLADE representations (batch, seq_len, vocab_size)
+
+        Returns:
+            Pooled sparse vectors (batch, vocab_size)
+        """
+        import numpy as np
+
+        # Max pooling over sequence dimension (axis=1)
+        return np.max(splade_repr, axis=1)
+
+    def _to_sparse_dict(self, dense_vec: Any) -> Dict[int, float]:
+        """Convert dense vector to sparse dictionary.
+
+        Args:
+            dense_vec: Dense vector (vocab_size,)
+
+        Returns:
+            Sparse dictionary {token_id: weight} with weights above threshold
+        """
+        import numpy as np
+
+        # Find non-zero indices above threshold
+        nonzero_indices = np.where(dense_vec > self.sparsity_threshold)[0]
+
+        # Create sparse dictionary
+        sparse_dict = {
+            int(idx): float(dense_vec[idx])
+            for idx in nonzero_indices
+        }
+
+        return sparse_dict
+
+    def encode_text(self, text: str) -> Dict[int, float]:
+        """Encode text to sparse vector {token_id: weight}.
+
+        Args:
+            text: Input text to encode
+
+        Returns:
+            Sparse vector as dictionary mapping token_id to weight
+        """
+        self._load_model()
+
+        if self._model is None or self._tokenizer is None:
+            raise RuntimeError("Model not loaded")
+
+        import numpy as np
+
+        # Tokenize input
+        encoded = self._tokenizer(
+            text,
+            padding=True,
+            truncation=True,
+            max_length=self.max_length,
+            return_tensors="np",
+        )
+
+        # Forward pass through model
+        outputs = self._model(**encoded)
+
+        # Extract logits
+        if hasattr(outputs, "logits"):
+            logits = outputs.logits
+        elif isinstance(outputs, dict) and "logits" in outputs:
+            logits = outputs["logits"]
+        elif isinstance(outputs, (list, tuple)) and outputs:
+            logits = outputs[0]
+        else:
+            raise RuntimeError("Unexpected model output format")
+
+        # Apply SPLADE activation
+        attention_mask = encoded["attention_mask"]
+        splade_repr = self._splade_activation(logits, attention_mask)
+
+        # Max pooling over sequence length
+        splade_vec = self._max_pooling(splade_repr)
+
+        # Convert to sparse dictionary (single item batch)
+        sparse_dict = self._to_sparse_dict(splade_vec[0])
+
+        return sparse_dict
+
+    def encode_batch(self, texts: List[str], batch_size: int = 32) -> List[Dict[int, float]]:
+        """Batch encode texts to sparse vectors.
+
+        Args:
+            texts: List of input texts to encode
+            batch_size: Batch size for encoding (default: 32)
+
+        Returns:
+            List of sparse vectors as dictionaries
+        """
+        if not texts:
+            return []
+
+        self._load_model()
+
+        if self._model is None or self._tokenizer is None:
+            raise RuntimeError("Model not loaded")
+
+        import numpy as np
+
+        results: List[Dict[int, float]] = []
+
+        # Process in batches
+        for i in range(0, len(texts), batch_size):
+            batch_texts = texts[i:i + batch_size]
+
+            # Tokenize batch
+            encoded = self._tokenizer(
+                batch_texts,
+                padding=True,
+                truncation=True,
+                max_length=self.max_length,
+                return_tensors="np",
+            )
+
+            # Forward pass through model
+            outputs = self._model(**encoded)
+
+            # Extract logits
+            if hasattr(outputs, "logits"):
+                logits = outputs.logits
+            elif isinstance(outputs, dict) and "logits" in outputs:
+                logits = outputs["logits"]
+            elif isinstance(outputs, (list, tuple)) and outputs:
+                logits = outputs[0]
+            else:
+                raise RuntimeError("Unexpected model output format")
+
+            # Apply SPLADE activation
+            attention_mask = encoded["attention_mask"]
+            splade_repr = self._splade_activation(logits, attention_mask)
+
+            # Max pooling over sequence length
+            splade_vecs = self._max_pooling(splade_repr)
+
+            # Convert each vector to sparse dictionary
+            for vec in splade_vecs:
+                sparse_dict = self._to_sparse_dict(vec)
+                results.append(sparse_dict)
+
+        return results
+
+    @property
+    def vocab_size(self) -> int:
+        """Return vocabulary size (~30k for BERT-based models).
+
+        Returns:
+            Vocabulary size (number of tokens in tokenizer)
+        """
+        if self._vocab_size is not None:
+            return self._vocab_size
+
+        self._load_model()
+        return self._vocab_size or 0
+
+    def get_token(self, token_id: int) -> str:
+        """Convert token_id to string (for debugging).
+
+        Args:
+            token_id: Token ID to convert
+
+        Returns:
+            Token string
+        """
+        self._load_model()
+
+        if self._tokenizer is None:
+            raise RuntimeError("Tokenizer not loaded")
+
+        return self._tokenizer.decode([token_id])
+
+    def get_top_tokens(self, sparse_vec: Dict[int, float], top_k: int = 10) -> List[Tuple[str, float]]:
+        """Get top-k tokens with highest weights from sparse vector.
+
+        Useful for debugging and understanding what the model is focusing on.
+
+        Args:
+            sparse_vec: Sparse vector as {token_id: weight}
+            top_k: Number of top tokens to return
+
+        Returns:
+            List of (token_string, weight) tuples, sorted by weight descending
+        """
+        self._load_model()
+
+        if not sparse_vec:
+            return []
+
+        # Sort by weight descending
+        sorted_items = sorted(sparse_vec.items(), key=lambda x: x[1], reverse=True)
+
+        # Take top-k and convert token_ids to strings
+        top_items = sorted_items[:top_k]
+
+        return [
+            (self.get_token(token_id), weight)
+            for token_id, weight in top_items
+        ]

codex-lens/src/codexlens/storage/migrations/migration_009_add_splade.py

@@ -0,0 +1,103 @@
+"""
+Migration 009: Add SPLADE sparse retrieval tables.
+
+This migration introduces SPLADE (Sparse Lexical AnD Expansion) support:
+- splade_metadata: Model configuration (model name, vocab size, ONNX path)
+- splade_posting_list: Inverted index mapping token_id -> (chunk_id, weight)
+
+The SPLADE tables are designed for efficient sparse vector retrieval:
+- Token-based lookup for query expansion
+- Chunk-based deletion for index maintenance
+- Maintains backward compatibility with existing FTS tables
+"""
+
+import logging
+from sqlite3 import Connection
+
+log = logging.getLogger(__name__)
+
+
+def upgrade(db_conn: Connection) -> None:
+    """
+    Adds SPLADE tables for sparse retrieval.
+
+    Creates:
+    - splade_metadata: Stores model configuration and ONNX path
+    - splade_posting_list: Inverted index with token_id -> (chunk_id, weight) mappings
+    - Indexes for efficient token-based and chunk-based lookups
+
+    Args:
+        db_conn: The SQLite database connection.
+    """
+    cursor = db_conn.cursor()
+
+    log.info("Creating splade_metadata table...")
+    cursor.execute(
+        """
+        CREATE TABLE IF NOT EXISTS splade_metadata (
+            id INTEGER PRIMARY KEY DEFAULT 1,
+            model_name TEXT NOT NULL,
+            vocab_size INTEGER NOT NULL,
+            onnx_path TEXT,
+            created_at REAL
+        )
+        """
+    )
+
+    log.info("Creating splade_posting_list table...")
+    cursor.execute(
+        """
+        CREATE TABLE IF NOT EXISTS splade_posting_list (
+            token_id INTEGER NOT NULL,
+            chunk_id INTEGER NOT NULL,
+            weight REAL NOT NULL,
+            PRIMARY KEY (token_id, chunk_id),
+            FOREIGN KEY (chunk_id) REFERENCES semantic_chunks(id) ON DELETE CASCADE
+        )
+        """
+    )
+
+    log.info("Creating indexes for splade_posting_list...")
+    # Index for efficient chunk-based lookups (deletion, updates)
+    cursor.execute(
+        """
+        CREATE INDEX IF NOT EXISTS idx_splade_by_chunk
+        ON splade_posting_list(chunk_id)
+        """
+    )
+
+    # Index for efficient term-based retrieval
+    cursor.execute(
+        """
+        CREATE INDEX IF NOT EXISTS idx_splade_by_token
+        ON splade_posting_list(token_id)
+        """
+    )
+
+    log.info("Migration 009 completed successfully")
+
+
+def downgrade(db_conn: Connection) -> None:
+    """
+    Removes SPLADE tables.
+
+    Drops:
+    - splade_posting_list (and associated indexes)
+    - splade_metadata
+
+    Args:
+        db_conn: The SQLite database connection.
+    """
+    cursor = db_conn.cursor()
+
+    log.info("Dropping SPLADE indexes...")
+    cursor.execute("DROP INDEX IF EXISTS idx_splade_by_chunk")
+    cursor.execute("DROP INDEX IF EXISTS idx_splade_by_token")
+
+    log.info("Dropping splade_posting_list table...")
+    cursor.execute("DROP TABLE IF EXISTS splade_posting_list")
+
+    log.info("Dropping splade_metadata table...")
+    cursor.execute("DROP TABLE IF EXISTS splade_metadata")
+
+    log.info("Migration 009 downgrade completed successfully")

codex-lens/src/codexlens/storage/splade_index.py

@@ -0,0 +1,432 @@
+"""SPLADE inverted index storage for sparse vector retrieval.
+
+This module implements SQLite-based inverted index for SPLADE sparse vectors,
+enabling efficient sparse retrieval using dot-product scoring.
+"""
+
+from __future__ import annotations
+
+import logging
+import sqlite3
+import threading
+import time
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+from codexlens.entities import SearchResult
+from codexlens.errors import StorageError
+
+logger = logging.getLogger(__name__)
+
+
+class SpladeIndex:
+    """SQLite-based inverted index for SPLADE sparse vectors.
+    
+    Stores sparse vectors as posting lists mapping token_id -> (chunk_id, weight).
+    Supports efficient dot-product retrieval using SQL joins.
+    """
+
+    def __init__(self, db_path: Path | str) -> None:
+        """Initialize SPLADE index.
+        
+        Args:
+            db_path: Path to SQLite database file.
+        """
+        self.db_path = Path(db_path)
+        self.db_path.parent.mkdir(parents=True, exist_ok=True)
+        
+        # Thread-safe connection management
+        self._lock = threading.RLock()
+        self._local = threading.local()
+        
+    def _get_connection(self) -> sqlite3.Connection:
+        """Get or create a thread-local database connection."""
+        conn = getattr(self._local, "conn", None)
+        if conn is None:
+            conn = sqlite3.connect(self.db_path, check_same_thread=False)
+            conn.row_factory = sqlite3.Row
+            conn.execute("PRAGMA journal_mode=WAL")
+            conn.execute("PRAGMA synchronous=NORMAL")
+            conn.execute("PRAGMA foreign_keys=ON")
+            conn.execute("PRAGMA mmap_size=30000000000")  # 30GB limit
+            self._local.conn = conn
+        return conn
+    
+    def close(self) -> None:
+        """Close thread-local database connection."""
+        with self._lock:
+            conn = getattr(self._local, "conn", None)
+            if conn is not None:
+                conn.close()
+                self._local.conn = None
+    
+    def __enter__(self) -> SpladeIndex:
+        """Context manager entry."""
+        self.create_tables()
+        return self
+    
+    def __exit__(self, exc_type, exc, tb) -> None:
+        """Context manager exit."""
+        self.close()
+    
+    def has_index(self) -> bool:
+        """Check if SPLADE tables exist in database.
+        
+        Returns:
+            True if tables exist, False otherwise.
+        """
+        with self._lock:
+            conn = self._get_connection()
+            try:
+                cursor = conn.execute(
+                    """
+                    SELECT name FROM sqlite_master 
+                    WHERE type='table' AND name='splade_posting_list'
+                    """
+                )
+                return cursor.fetchone() is not None
+            except sqlite3.Error as e:
+                logger.error("Failed to check index existence: %s", e)
+                return False
+    
+    def create_tables(self) -> None:
+        """Create SPLADE schema if not exists.
+        
+        Note: The splade_posting_list table has a FOREIGN KEY constraint
+        referencing semantic_chunks(id). Ensure VectorStore.create_tables()
+        is called first to create the semantic_chunks table.
+        """
+        with self._lock:
+            conn = self._get_connection()
+            try:
+                # Inverted index for sparse vectors
+                conn.execute("""
+                    CREATE TABLE IF NOT EXISTS splade_posting_list (
+                        token_id INTEGER NOT NULL,
+                        chunk_id INTEGER NOT NULL,
+                        weight REAL NOT NULL,
+                        PRIMARY KEY (token_id, chunk_id),
+                        FOREIGN KEY (chunk_id) REFERENCES semantic_chunks(id) ON DELETE CASCADE
+                    )
+                """)
+                
+                # Indexes for efficient lookups
+                conn.execute("""
+                    CREATE INDEX IF NOT EXISTS idx_splade_by_chunk 
+                    ON splade_posting_list(chunk_id)
+                """)
+                conn.execute("""
+                    CREATE INDEX IF NOT EXISTS idx_splade_by_token 
+                    ON splade_posting_list(token_id)
+                """)
+                
+                # Model metadata
+                conn.execute("""
+                    CREATE TABLE IF NOT EXISTS splade_metadata (
+                        id INTEGER PRIMARY KEY DEFAULT 1,
+                        model_name TEXT NOT NULL,
+                        vocab_size INTEGER NOT NULL,
+                        onnx_path TEXT,
+                        created_at REAL
+                    )
+                """)
+                
+                conn.commit()
+                logger.debug("SPLADE schema created successfully")
+            except sqlite3.Error as e:
+                raise StorageError(
+                    f"Failed to create SPLADE schema: {e}",
+                    db_path=str(self.db_path),
+                    operation="create_tables"
+                ) from e
+    
+    def add_posting(self, chunk_id: int, sparse_vec: Dict[int, float]) -> None:
+        """Add a single document to inverted index.
+        
+        Args:
+            chunk_id: Chunk ID (foreign key to semantic_chunks.id).
+            sparse_vec: Sparse vector as {token_id: weight} mapping.
+        """
+        if not sparse_vec:
+            logger.warning("Empty sparse vector for chunk_id=%d, skipping", chunk_id)
+            return
+        
+        with self._lock:
+            conn = self._get_connection()
+            try:
+                # Insert all non-zero weights for this chunk
+                postings = [
+                    (token_id, chunk_id, weight)
+                    for token_id, weight in sparse_vec.items()
+                    if weight > 0  # Only store non-zero weights
+                ]
+                
+                if postings:
+                    conn.executemany(
+                        """
+                        INSERT OR REPLACE INTO splade_posting_list 
+                        (token_id, chunk_id, weight)
+                        VALUES (?, ?, ?)
+                        """,
+                        postings
+                    )
+                    conn.commit()
+                    logger.debug(
+                        "Added %d postings for chunk_id=%d", len(postings), chunk_id
+                    )
+            except sqlite3.Error as e:
+                raise StorageError(
+                    f"Failed to add posting for chunk_id={chunk_id}: {e}",
+                    db_path=str(self.db_path),
+                    operation="add_posting"
+                ) from e
+    
+    def add_postings_batch(
+        self, postings: List[Tuple[int, Dict[int, float]]]
+    ) -> None:
+        """Batch insert postings for multiple chunks.
+        
+        Args:
+            postings: List of (chunk_id, sparse_vec) tuples.
+        """
+        if not postings:
+            return
+        
+        with self._lock:
+            conn = self._get_connection()
+            try:
+                # Flatten all postings into single batch
+                batch_data = []
+                for chunk_id, sparse_vec in postings:
+                    for token_id, weight in sparse_vec.items():
+                        if weight > 0:  # Only store non-zero weights
+                            batch_data.append((token_id, chunk_id, weight))
+                
+                if batch_data:
+                    conn.executemany(
+                        """
+                        INSERT OR REPLACE INTO splade_posting_list 
+                        (token_id, chunk_id, weight)
+                        VALUES (?, ?, ?)
+                        """,
+                        batch_data
+                    )
+                    conn.commit()
+                    logger.debug(
+                        "Batch inserted %d postings for %d chunks",
+                        len(batch_data),
+                        len(postings)
+                    )
+            except sqlite3.Error as e:
+                raise StorageError(
+                    f"Failed to batch insert postings: {e}",
+                    db_path=str(self.db_path),
+                    operation="add_postings_batch"
+                ) from e
+    
+    def remove_chunk(self, chunk_id: int) -> int:
+        """Remove all postings for a chunk.
+        
+        Args:
+            chunk_id: Chunk ID to remove.
+            
+        Returns:
+            Number of deleted postings.
+        """
+        with self._lock:
+            conn = self._get_connection()
+            try:
+                cursor = conn.execute(
+                    "DELETE FROM splade_posting_list WHERE chunk_id = ?",
+                    (chunk_id,)
+                )
+                conn.commit()
+                deleted = cursor.rowcount
+                logger.debug("Removed %d postings for chunk_id=%d", deleted, chunk_id)
+                return deleted
+            except sqlite3.Error as e:
+                raise StorageError(
+                    f"Failed to remove chunk_id={chunk_id}: {e}",
+                    db_path=str(self.db_path),
+                    operation="remove_chunk"
+                ) from e
+    
+    def search(
+        self,
+        query_sparse: Dict[int, float],
+        limit: int = 50,
+        min_score: float = 0.0
+    ) -> List[Tuple[int, float]]:
+        """Search for similar chunks using dot-product scoring.
+        
+        Implements efficient sparse dot-product via SQL JOIN:
+        score(q, d) = sum(q[t] * d[t]) for all tokens t
+        
+        Args:
+            query_sparse: Query sparse vector as {token_id: weight}.
+            limit: Maximum number of results.
+            min_score: Minimum score threshold.
+            
+        Returns:
+            List of (chunk_id, score) tuples, ordered by score descending.
+        """
+        if not query_sparse:
+            logger.warning("Empty query sparse vector")
+            return []
+        
+        with self._lock:
+            conn = self._get_connection()
+            try:
+                # Build VALUES clause for query terms
+                # Each term: (token_id, weight)
+                query_terms = [
+                    (token_id, weight)
+                    for token_id, weight in query_sparse.items()
+                    if weight > 0
+                ]
+                
+                if not query_terms:
+                    logger.warning("No non-zero query terms")
+                    return []
+                
+                # Create CTE for query terms using parameterized VALUES
+                # Build placeholders and params to prevent SQL injection
+                params = []
+                placeholders = []
+                for token_id, weight in query_terms:
+                    placeholders.append("(?, ?)")
+                    params.extend([token_id, weight])
+
+                values_placeholders = ", ".join(placeholders)
+
+                sql = f"""
+                    WITH query_terms(token_id, weight) AS (
+                        VALUES {values_placeholders}
+                    )
+                    SELECT
+                        p.chunk_id,
+                        SUM(p.weight * q.weight) as score
+                    FROM splade_posting_list p
+                    INNER JOIN query_terms q ON p.token_id = q.token_id
+                    GROUP BY p.chunk_id
+                    HAVING score >= ?
+                    ORDER BY score DESC
+                    LIMIT ?
+                """
+
+                # Append min_score and limit to params
+                params.extend([min_score, limit])
+                rows = conn.execute(sql, params).fetchall()
+                
+                results = [(row["chunk_id"], float(row["score"])) for row in rows]
+                logger.debug(
+                    "SPLADE search: %d query terms, %d results", 
+                    len(query_terms), 
+                    len(results)
+                )
+                return results
+                
+            except sqlite3.Error as e:
+                raise StorageError(
+                    f"SPLADE search failed: {e}",
+                    db_path=str(self.db_path),
+                    operation="search"
+                ) from e
+    
+    def get_metadata(self) -> Optional[Dict]:
+        """Get SPLADE model metadata.
+        
+        Returns:
+            Dictionary with model_name, vocab_size, onnx_path, created_at,
+            or None if not set.
+        """
+        with self._lock:
+            conn = self._get_connection()
+            try:
+                row = conn.execute(
+                    """
+                    SELECT model_name, vocab_size, onnx_path, created_at
+                    FROM splade_metadata
+                    WHERE id = 1
+                    """
+                ).fetchone()
+                
+                if row:
+                    return {
+                        "model_name": row["model_name"],
+                        "vocab_size": row["vocab_size"],
+                        "onnx_path": row["onnx_path"],
+                        "created_at": row["created_at"]
+                    }
+                return None
+            except sqlite3.Error as e:
+                logger.error("Failed to get metadata: %s", e)
+                return None
+    
+    def set_metadata(
+        self,
+        model_name: str,
+        vocab_size: int,
+        onnx_path: Optional[str] = None
+    ) -> None:
+        """Set SPLADE model metadata.
+        
+        Args:
+            model_name: SPLADE model name.
+            vocab_size: Vocabulary size (typically ~30k for BERT vocab).
+            onnx_path: Optional path to ONNX model file.
+        """
+        with self._lock:
+            conn = self._get_connection()
+            try:
+                current_time = time.time()
+                conn.execute(
+                    """
+                    INSERT OR REPLACE INTO splade_metadata
+                    (id, model_name, vocab_size, onnx_path, created_at)
+                    VALUES (1, ?, ?, ?, ?)
+                    """,
+                    (model_name, vocab_size, onnx_path, current_time)
+                )
+                conn.commit()
+                logger.info(
+                    "Set SPLADE metadata: model=%s, vocab_size=%d",
+                    model_name,
+                    vocab_size
+                )
+            except sqlite3.Error as e:
+                raise StorageError(
+                    f"Failed to set metadata: {e}",
+                    db_path=str(self.db_path),
+                    operation="set_metadata"
+                ) from e
+    
+    def get_stats(self) -> Dict:
+        """Get index statistics.
+        
+        Returns:
+            Dictionary with total_postings, unique_tokens, unique_chunks.
+        """
+        with self._lock:
+            conn = self._get_connection()
+            try:
+                row = conn.execute("""
+                    SELECT
+                        COUNT(*) as total_postings,
+                        COUNT(DISTINCT token_id) as unique_tokens,
+                        COUNT(DISTINCT chunk_id) as unique_chunks
+                    FROM splade_posting_list
+                """).fetchone()
+                
+                return {
+                    "total_postings": row["total_postings"],
+                    "unique_tokens": row["unique_tokens"],
+                    "unique_chunks": row["unique_chunks"]
+                }
+            except sqlite3.Error as e:
+                logger.error("Failed to get stats: %s", e)
+                return {
+                    "total_postings": 0,
+                    "unique_tokens": 0,
+                    "unique_chunks": 0
+                }

codex-lens/verify_watcher.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+"""Verification script for FileWatcher event filtering and debouncing."""
+
+import time
+from pathlib import Path
+from codexlens.watcher.file_watcher import FileWatcher
+from codexlens.watcher.events import WatcherConfig, FileEvent
+
+def test_should_index_file():
+    """Test _should_index_file filtering logic."""
+    print("Testing _should_index_file filtering...")
+
+    # Create watcher instance
+    config = WatcherConfig()
+    watcher = FileWatcher(
+        root_path=Path("."),
+        config=config,
+        on_changes=lambda events: None,
+    )
+
+    # Test cases
+    test_cases = [
+        # (path, expected_result, description)
+        (Path("test.py"), True, "Python file should be indexed"),
+        (Path("test.txt"), True, "Text file should be indexed"),
+        (Path("test.js"), True, "JavaScript file should be indexed"),
+        (Path("test.ts"), True, "TypeScript file should be indexed"),
+        (Path("src/test.py"), True, "Python file in subdirectory should be indexed"),
+        (Path(".git/config"), False, ".git files should be filtered"),
+        (Path("node_modules/pkg/index.js"), False, "node_modules should be filtered"),
+        (Path("__pycache__/test.pyc"), False, "__pycache__ should be filtered"),
+        (Path(".venv/lib/test.py"), False, ".venv should be filtered"),
+        (Path("test.unknown"), False, "Unknown extension should be filtered"),
+        (Path("README.md"), True, "Markdown file should be indexed"),
+    ]
+
+    passed = 0
+    failed = 0
+
+    for path, expected, description in test_cases:
+        result = watcher._should_index_file(path)
+        status = "✓" if result == expected else "✗"
+
+        if result == expected:
+            passed += 1
+        else:
+            failed += 1
+
+        print(f"  {status} {description}")
+        print(f"    Path: {path}, Expected: {expected}, Got: {result}")
+
+    print(f"\nResults: {passed} passed, {failed} failed")
+    return failed == 0
+
+def test_debounce_and_dedup():
+    """Test event debouncing and deduplication."""
+    print("\nTesting event debouncing and deduplication...")
+
+    received_events = []
+
+    def on_changes(events):
+        received_events.append(events)
+        print(f"  Received batch: {len(events)} events")
+
+    # Create watcher with short debounce time for testing
+    config = WatcherConfig(debounce_ms=500)
+    watcher = FileWatcher(
+        root_path=Path("."),
+        config=config,
+        on_changes=on_changes,
+    )
+
+    # Simulate rapid events to same file (should be deduplicated)
+    from codexlens.watcher.events import ChangeType
+
+    test_path = Path("test_file.py")
+    for i in range(5):
+        event = FileEvent(
+            path=test_path,
+            change_type=ChangeType.MODIFIED,
+            timestamp=time.time(),
+        )
+        watcher._on_raw_event(event)
+
+    # Wait for debounce
+    time.sleep(0.6)
+
+    # Force flush to ensure we get the events
+    watcher._flush_events()
+
+    if received_events:
+        batch = received_events[0]
+        # Should deduplicate 5 events to 1
+        if len(batch) == 1:
+            print("  ✓ Deduplication working: 5 events reduced to 1")
+            return True
+        else:
+            print(f"  ✗ Deduplication failed: expected 1 event, got {len(batch)}")
+            return False
+    else:
+        print("  ✗ No events received")
+        return False
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("FileWatcher Verification")
+    print("=" * 60)
+
+    test1 = test_should_index_file()
+    test2 = test_debounce_and_dedup()
+
+    print("\n" + "=" * 60)
+    if test1 and test2:
+        print("✓ All tests passed!")
+    else:
+        print("✗ Some tests failed")
+    print("=" * 60)