Fix CodexLens embeddings generation to achieve 100% coverage

Previously, embeddings were only generated for root directory files (1.6% coverage, 5/303 files).
This fix implements recursive processing across all subdirectory indexes, achieving 100% coverage
with 2,042 semantic chunks across all 303 files in 26 index databases.

Key improvements:

1. **Recursive embeddings generation** (embedding_manager.py):
   - Add generate_embeddings_recursive() to process all _index.db files in directory tree
   - Add get_embeddings_status() for comprehensive coverage statistics
   - Add discover_all_index_dbs() helper for recursive file discovery

2. **Enhanced CLI commands** (commands.py):
   - embeddings-generate: Add --recursive flag for full project coverage
   - init: Use recursive generation by default for complete indexing
   - status: Display embeddings coverage statistics with 50% threshold

3. **Smart search routing improvements** (smart-search.ts):
   - Add 50% embeddings coverage threshold for hybrid mode routing
   - Auto-fallback to exact mode when coverage insufficient
   - Strip ANSI color codes from JSON output for correct parsing
   - Add embeddings_coverage_percent to IndexStatus and SearchMetadata
   - Provide clear warnings with actionable suggestions

4. **Documentation and analysis**:
   - Add SMART_SEARCH_ANALYSIS.md with initial investigation
   - Add SMART_SEARCH_CORRECTED_ANALYSIS.md revealing true extent of issue
   - Add EMBEDDINGS_FIX_SUMMARY.md with complete fix summary
   - Add check_embeddings.py script for coverage verification

Results:
- Coverage improved from 1.6% (5/303 files) to 100% (303/303 files) - 62.5x increase
- Semantic chunks increased from 10 to 2,042 - 204x increase
- All 26 subdirectory indexes now have embeddings vs just 1

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

ccw/EMBEDDINGS_FIX_SUMMARY.md

@@ -0,0 +1,165 @@
+# CodexLens Embeddings 修复总结
+
+## 修复成果
+
+### ✅ 已完成
+
+1. **递归 embeddings 生成功能** (`embedding_manager.py`)
+   - 添加 `generate_embeddings_recursive()` 函数
+   - 添加 `get_embeddings_status()` 函数
+   - 递归处理所有子目录的 _index.db 文件
+
+2. **CLI 命令增强** (`commands.py`)
+   - `embeddings-generate` 添加 `--recursive` 标志
+   - `init` 命令使用递归生成（自动处理所有子目录）
+   - `status` 命令显示 embeddings 覆盖率统计
+
+3. **Smart Search 智能路由** (`smart-search.ts`)
+   - 添加 50% 覆盖率阈值
+   - embeddings 不足时自动降级到 exact 模式
+   - 提供明确的警告信息
+   - Strip ANSI 颜色码以正确解析 JSON
+
+### ✅ 测试结果
+
+**CCW 项目 (d:\Claude_dms3\ccw)**:
+- 索引数据库：26 个
+- 文件总数：303
+- Embeddings 覆盖：**100%** (所有 303 个文件)
+- 生成 chunks：**2,042** (之前只有 10)
+
+**对比**:
+| 指标 | 修复前 | 修复后 | 改进 |
+|------|--------|--------|------|
+| 覆盖率 | 1.6% (5/303) | 100% (303/303) | **62.5x** |
+| Chunks | 10 | 2,042 | **204x** |
+| 有 embeddings 的索引 | 1/26 | 26/26 | **26x** |
+
+## 当前问题
+
+### ⚠️ 遗留问题
+
+1. **路径映射问题**
+   - `embeddings-generate --recursive` 需要使用索引路径而非源路径
+   - 用户应该能够使用源路径（`d:\Claude_dms3\ccw`）
+   - 当前需要使用：`C:\Users\dyw\.codexlens\indexes\D\Claude_dms3\ccw`
+
+2. **Status 命令的全局 vs 项目级别**
+   - `codexlens status` 返回全局统计（所有项目）
+   - 需要项目级别的 embeddings 状态
+   - `embeddings-status` 只检查单个 _index.db，不递归
+
+## 建议的后续修复
+
+### P1 - 路径映射修复
+
+修改 `commands.py` 中的 `embeddings_generate` 命令（line 1996-2000）：
+
+```python
+elif target_path.is_dir():
+    if recursive:
+        # Recursive mode: Map source path to index root
+        registry = RegistryStore()
+        try:
+            registry.initialize()
+            mapper = PathMapper()
+            index_db_path = mapper.source_to_index_db(target_path)
+            index_root = index_db_path.parent  # Use index directory root
+            use_recursive = True
+        finally:
+            registry.close()
+```
+
+### P2 - 项目级别 Status
+
+选项 A：扩展 `embeddings-status` 命令支持递归
+```bash
+codexlens embeddings-status . --recursive --json
+```
+
+选项 B：修改 `status` 命令接受路径参数
+```bash
+codexlens status --project . --json
+```
+
+## 使用指南
+
+### 当前工作流程
+
+**生成 embeddings（完整覆盖）**:
+```bash
+# 方法 1: 使用索引路径（当前工作方式）
+cd C:\Users\dyw\.codexlens\indexes\D\Claude_dms3\ccw
+python -m codexlens embeddings-generate . --recursive --force --model fast
+
+# 方法 2: init 命令（自动递归，推荐）
+cd d:\Claude_dms3\ccw
+python -m codexlens init . --force
+```
+
+**检查覆盖率**:
+```bash
+# 项目根目录
+cd C:\Users\dyw\.codexlens\indexes\D\Claude_dms3\ccw
+python check_embeddings.py  # 显示详细的每目录统计
+
+# 全局状态
+python -m codexlens status --json  # 所有项目的汇总
+```
+
+**Smart Search**:
+```javascript
+// MCP 工具调用
+smart_search(query="authentication patterns")
+
+// 现在会：
+// 1. 检查 embeddings 覆盖率
+// 2. 如果 >= 50%，使用 hybrid 模式
+// 3. 如果 < 50%，降级到 exact 模式
+// 4. 显示警告信息
+```
+
+### 最佳实践
+
+1. **初始化项目时自动生成 embeddings**:
+   ```bash
+   codexlens init /path/to/project --force
+   ```
+
+2. **定期重新生成以更新**:
+   ```bash
+   codexlens embeddings-generate /index/path --recursive --force
+   ```
+
+3. **使用 fast 模型快速测试**:
+   ```bash
+   codexlens embeddings-generate . --recursive --model fast
+   ```
+
+4. **使用 code 模型获得最佳质量**:
+   ```bash
+   codexlens embeddings-generate . --recursive --model code
+   ```
+
+## 技术细节
+
+### 文件修改清单
+
+**Python (CodexLens)**:
+- `codex-lens/src/codexlens/cli/embedding_manager.py` - 添加递归函数
+- `codex-lens/src/codexlens/cli/commands.py` - 更新 init, status, embeddings-generate
+
+**TypeScript (CCW)**:
+- `ccw/src/tools/smart-search.ts` - 智能路由 + ANSI stripping
+- `ccw/src/tools/codex-lens.ts` - （未修改，使用现有实现）
+
+### 依赖版本
+
+- CodexLens: 当前开发版本
+- Fastembed: 已安装（ONNX backend）
+- Models: fast (~80MB), code (~150MB)
+
+---
+
+**修复时间**: 2025-12-17  
+**验证状态**: ✅ 核心功能正常，遗留路径映射问题待修复

ccw/SMART_SEARCH_ANALYSIS.md

@@ -0,0 +1,167 @@
+# Smart Search 索引分析报告
+
+## 问题
+分析当前 `smart_search(action="init")` 是否进行了向量模型索引，还是仅进行了基础索引。
+
+## 分析结果
+
+### 1. Init 操作的默认行为
+
+从代码分析来看，`smart_search(action="init")` 的行为如下：
+
+**代码路径**：`ccw/src/tools/smart-search.ts` → `ccw/src/tools/codex-lens.ts`
+
+```typescript
+// smart-search.ts: executeInitAction (第 297-323 行)
+async function executeInitAction(params: Params): Promise<SearchResult> {
+  const { path = '.', languages } = params;
+  const args = ['init', path];
+  if (languages && languages.length > 0) {
+    args.push('--languages', languages.join(','));
+  }
+  const result = await executeCodexLens(args, { cwd: path, timeout: 300000 });
+  // ...
+}
+```
+
+**关键发现**：
+- `smart_search(action="init")` 调用 `codexlens init` 命令
+- **不传递** `--no-embeddings` 参数
+- **不传递** `--embedding-model` 参数
+
+### 2. CodexLens Init 的默认行为
+
+根据 `codexlens init --help` 的输出：
+
+> If semantic search dependencies are installed, **automatically generates embeddings** after indexing completes. Use --no-embeddings to skip this step.
+
+**结论**：
+- ✅ `init` 命令**默认会**生成 embeddings（如果安装了语义搜索依赖）
+- ❌ 当前实现**未生成**所有文件的 embeddings
+
+### 3. 实际测试结果
+
+#### 第一次 Init（未生成 embeddings）
+```bash
+$ smart_search(action="init", path="d:\\Claude_dms3\\ccw")
+# 结果：索引了 303 个文件，但 vector_search: false
+```
+
+**原因分析**：
+虽然语义搜索依赖（fastembed）已安装，但 init 过程中遇到警告：
+```
+Warning: Embedding generation failed: Index already has 10 chunks. Use --force to regenerate.
+```
+
+#### 手动生成 Embeddings 后
+```bash
+$ python -m codexlens embeddings-generate . --force --verbose
+
+Processing 5 files...
+- D:\Claude_dms3\ccw\MCP_QUICKSTART.md: 1 chunks
+- D:\Claude_dms3\ccw\MCP_SERVER.md: 2 chunks
+- D:\Claude_dms3\ccw\README.md: 2 chunks
+- D:\Claude_dms3\ccw\tailwind.config.js: 3 chunks
+- D:\Claude_dms3\ccw\WRITE_FILE_FIX_SUMMARY.md: 2 chunks
+
+Total: 10 chunks, 5 files
+Model: jinaai/jina-embeddings-v2-base-code (768 dimensions)
+```
+
+**关键发现**：
+- ⚠️ 只为 **5 个文档/配置文件**生成了 embeddings
+- ⚠️ **未为 298 个代码文件**（.ts, .js 等）生成 embeddings
+- ✅ Embeddings 状态显示 `coverage_percent: 100.0`（但这是针对"应该生成 embeddings 的文件"而言）
+
+#### Hybrid Search 测试
+```bash
+$ smart_search(query="authentication and authorization patterns", mode="hybrid")
+# ✅ 成功返回 5 个结果，带有相似度分数
+# ✅ 证明向量搜索功能可用
+```
+
+## 4. 索引类型对比
+
+| 索引类型 | 当前状态 | 支持的文件 | 说明 |
+|---------|---------|-----------|------|
+| **Exact FTS** | ✅ 启用 | 所有 303 个文件 | 基于 SQLite FTS5 的全文搜索 |
+| **Fuzzy FTS** | ❌ 未启用 | - | 模糊匹配搜索 |
+| **Vector Search** | ⚠️ 部分启用 | 仅 5 个文档文件 | 基于 fastembed 的语义搜索 |
+| **Hybrid Search** | ⚠️ 部分启用 | 仅 5 个文档文件 | RRF 融合（exact + fuzzy + vector） |
+
+## 5. 为什么只有 5 个文件有 Embeddings？
+
+**可能的原因**：
+
+1. **文件类型过滤**：CodexLens 可能只为文档文件（.md）和配置文件生成 embeddings
+2. **代码文件使用符号索引**：代码文件（.ts, .js）可能依赖于符号提取而非文本 embeddings
+3. **性能考虑**：生成 300+ 文件的 embeddings 需要大量时间和存储空间
+
+## 6. 结论
+
+### 当前 `smart_search(action="init")` 的行为：
+
+✅ **会尝试**生成向量索引（如果语义依赖已安装）  
+⚠️ **实际只**为文档/配置文件生成 embeddings（5/303 文件）  
+✅ **支持** hybrid 模式搜索（对于有 embeddings 的文件）  
+✅ **支持** exact 模式搜索（对于所有 303 个文件）  
+
+### 搜索模式智能路由：
+
+```
+用户查询 → auto 模式 → 决策树：
+  ├─ 自然语言查询 + 有 embeddings → hybrid 模式（RRF 融合）
+  ├─ 简单查询 + 有索引 → exact 模式（FTS）
+  └─ 无索引 → ripgrep 模式（字面匹配）
+```
+
+## 7. 建议
+
+### 如果需要完整的语义搜索支持：
+
+```bash
+# 方案 1：检查是否所有代码文件都应该有 embeddings
+python -m codexlens embeddings-status . --verbose
+
+# 方案 2：明确为代码文件生成 embeddings（如果支持）
+# 需要查看 CodexLens 文档确认代码文件的语义索引策略
+
+# 方案 3：使用 hybrid 模式进行文档搜索，exact 模式进行代码搜索
+smart_search(query="架构设计", mode="hybrid")  # 文档语义搜索
+smart_search(query="function_name", mode="exact")  # 代码精确搜索
+```
+
+### 当前最佳实践：
+
+```javascript
+// 1. 初始化索引（一次性）
+smart_search(action="init", path=".")
+
+// 2. 智能搜索（推荐使用 auto 模式）
+smart_search(query="your query")  // 自动选择最佳模式
+
+// 3. 特定模式搜索
+smart_search(query="natural language query", mode="hybrid")  // 语义搜索
+smart_search(query="exact_identifier", mode="exact")         // 精确匹配
+smart_search(query="quick literal", mode="ripgrep")          // 快速字面搜索
+```
+
+## 8. 技术细节
+
+### Embeddings 模型
+- **模型**：jinaai/jina-embeddings-v2-base-code
+- **维度**：768
+- **大小**：~150MB
+- **后端**：fastembed (ONNX-based)
+
+### 索引存储
+- **位置**：`C:\Users\dyw\.codexlens\indexes\D\Claude_dms3\ccw\_index.db`
+- **大小**：122.57 MB
+- **Schema 版本**：5
+- **文件数**：303
+- **目录数**：26
+
+---
+
+**生成时间**：2025-12-17  
+**CodexLens 版本**：从当前安装中检测

ccw/SMART_SEARCH_CORRECTED_ANALYSIS.md

@@ -0,0 +1,330 @@
+# Smart Search 索引分析报告（修正版）
+
+## 用户质疑
+
+1. ❓ 为什么不为代码文件生成向量 embeddings？
+2. ❓ Exact FTS 和 Vector 索引内容应该一样才对
+3. ❓ init 应该返回 FTS 和 vector 索引概况
+
+**结论：用户的质疑 100% 正确！这是 CodexLens 的设计缺陷。**
+
+---
+
+## 真实情况
+
+### 1. 分层索引架构
+
+CodexLens 使用**分层目录索引**：
+
+```
+D:\Claude_dms3\ccw\
+├── _index.db             ← 根目录索引（5个文件）
+├── src/
+│   ├── _index.db         ← src目录索引（2个文件）
+│   ├── tools/
+│   │   └── _index.db     ← tools子目录索引（25个文件）
+│   └── ...
+└── ... （总共 26 个 _index.db）
+```
+
+### 2. 索引覆盖情况
+
+| 目录 | 文件数 | FTS索引 | Embeddings |
+|------|--------|---------|------------|
+| **根目录** | 5 | ✅ | ✅ (10 chunks) |
+| bin/ | 2 | ✅ | ❌ 无semantic_chunks表 |
+| dist/ | 4 | ✅ | ❌ 无semantic_chunks表 |
+| dist/commands/ | 24 | ✅ | ❌ 无semantic_chunks表 |
+| dist/tools/ | 50 | ✅ | ❌ 无semantic_chunks表 |
+| src/tools/ | 25 | ✅ | ❌ 无semantic_chunks表 |
+| src/commands/ | 12 | ✅ | ❌ 无semantic_chunks表 |
+| ... | ... | ... | ... |
+| **总计** | **303** | **✅ 100%** | **❌ 1.6%** (5/303) |
+
+### 3. 关键发现
+
+```python
+# 运行检查脚本的结果
+Total index databases: 26
+Directories with embeddings: 1        # ❌ 只有根目录！
+Total files indexed: 303              # ✅ FTS索引完整
+Total semantic chunks: 10             # ❌ 只有根目录的5个文件
+```
+
+**问题**：
+- ✅ **所有303个文件**都有 FTS 索引（分布在26个_index.db中）
+- ❌ **只有5个文件**（1.6%）有 vector embeddings
+- ❌ **25个子目录**的_index.db根本没有`semantic_chunks`表结构
+
+---
+
+## 为什么会这样？
+
+### 原因分析
+
+1. **`init` 操作**：
+   ```bash
+   codexlens init .
+   ```
+   - ✅ 为所有303个文件创建 FTS 索引（分布式）
+   - ⚠️ 尝试生成 embeddings，但遇到"Index already has 10 chunks"警告
+   - ❌ 只为根目录生成了 embeddings
+
+2. **`embeddings-generate` 操作**：
+   ```bash
+   codexlens embeddings-generate . --force
+   ```
+   - ❌ 只处理了根目录的 _index.db
+   - ❌ **未递归处理子目录的索引**
+   - 结果：只有5个文档文件有 embeddings
+
+### 设计问题
+
+**CodexLens 的 embeddings 架构有缺陷**：
+
+```python
+# 期望行为
+for each _index.db in project:
+    generate_embeddings(index_db)
+
+# 实际行为  
+generate_embeddings(root_index_db_only)
+```
+
+---
+
+## Init 返回信息缺陷
+
+### 当前 `init` 的返回
+
+```json
+{
+  "success": true,
+  "message": "CodexLens index created successfully for d:\\Claude_dms3\\ccw"
+}
+```
+
+**问题**：
+- ❌ 没有说明索引了多少文件
+- ❌ 没有说明是否生成了 embeddings
+- ❌ 没有说明 embeddings 覆盖率
+
+### 应该返回的信息
+
+```json
+{
+  "success": true,
+  "message": "Index created successfully",
+  "stats": {
+    "total_files": 303,
+    "total_directories": 26,
+    "index_databases": 26,
+    "fts_coverage": {
+      "files": 303,
+      "percentage": 100.0
+    },
+    "embeddings_coverage": {
+      "files": 5,
+      "chunks": 10,
+      "percentage": 1.6,
+      "warning": "Embeddings only generated for root directory. Run embeddings-generate on each subdir for full coverage."
+    },
+    "features": {
+      "exact_fts": true,
+      "fuzzy_fts": false,
+      "vector_search": "partial"
+    }
+  }
+}
+```
+
+---
+
+## 解决方案
+
+### 方案 1：递归生成 Embeddings（推荐）
+
+```bash
+# 为所有子目录生成 embeddings
+find .codexlens/indexes -name "_index.db" -exec \
+  python -m codexlens embeddings-generate {} --force \;
+```
+
+### 方案 2：改进 Init 命令
+
+```python
+# codexlens/cli.py
+def init_with_embeddings(project_root):
+    """Initialize with recursive embeddings generation"""
+    # 1. Build FTS indexes (current behavior)
+    build_indexes(project_root)
+    
+    # 2. Generate embeddings for ALL subdirs
+    for index_db in find_all_index_dbs(project_root):
+        if has_semantic_deps():
+            generate_embeddings(index_db)
+    
+    # 3. Return comprehensive stats
+    return {
+        "fts_coverage": get_fts_stats(),
+        "embeddings_coverage": get_embeddings_stats(),
+        "features": detect_features()
+    }
+```
+
+### 方案 3：Smart Search 路由改进
+
+```python
+# 当前逻辑
+def classify_intent(query, hasIndex):
+    if not hasIndex:
+        return "ripgrep"
+    elif is_natural_language(query):
+        return "hybrid"  # ❌ 但只有5个文件有embeddings！
+    else:
+        return "exact"
+
+# 改进逻辑
+def classify_intent(query, indexStatus):
+    embeddings_coverage = indexStatus.embeddings_coverage_percent
+    
+    if embeddings_coverage < 50:
+        # 如果覆盖率<50%，即使是自然语言也降级到exact
+        return "exact" if indexStatus.indexed else "ripgrep"
+    elif is_natural_language(query):
+        return "hybrid"
+    else:
+        return "exact"
+```
+
+---
+
+## 验证用户质疑
+
+### ❓ 为什么不为代码文件生成 embeddings？
+
+**答**：不是"不为代码文件生成"，而是：
+- ✅ 代码文件都有 FTS 索引
+- ❌ `embeddings-generate` 命令有BUG，**只处理根目录**
+- ❌ 子目录的索引数据库甚至**没有创建 semantic_chunks 表**
+
+### ❓ FTS 和 Vector 应该索引相同内容
+
+**答**：**完全正确！** 当前实际情况：
+- FTS: 303/303 (100%)
+- Vector: 5/303 (1.6%)
+
+**这是严重的不一致性，违背了设计原则。**
+
+### ❓ Init 应该返回索引概况
+
+**答**：**完全正确！** 当前 init 只返回简单成功消息，应该返回：
+- FTS 索引统计
+- Embeddings 覆盖率
+- 功能特性状态
+- 警告信息（如果覆盖不完整）
+
+---
+
+## 测试验证
+
+### Hybrid Search 的实际效果
+
+```javascript
+// 当前查询
+smart_search(query="authentication patterns", mode="hybrid")
+
+// 实际搜索范围：
+// ✅ 可搜索的文件：5个（根目录的.md文件）
+// ❌ 不可搜索的文件：298个代码文件
+// 结果：返回的都是文档文件，代码文件被忽略
+```
+
+### 修复后的效果（理想状态）
+
+```javascript
+// 修复后
+smart_search(query="authentication patterns", mode="hybrid")
+
+// 实际搜索范围：
+// ✅ 可搜索的文件：303个（所有文件）
+// 结果：包含代码文件和文档文件的综合结果
+```
+
+---
+
+## 建议的修复优先级
+
+### P0 - 紧急修复
+
+1. **修复 `embeddings-generate` 命令**
+   - 递归处理所有子目录的 _index.db
+   - 为每个 _index.db 创建 semantic_chunks 表
+
+2. **改进 `init` 返回信息**
+   - 返回详细的索引统计
+   - 显示 embeddings 覆盖率
+   - 如果覆盖不完整，给出警告
+
+### P1 - 重要改进
+
+3. **Smart Search 自适应路由**
+   - 检查 embeddings 覆盖率
+   - 如果覆盖率低，自动降级到 exact 模式
+
+4. **Status 命令增强**
+   - 显示每个子目录的索引状态
+   - 显示 embeddings 分布情况
+
+---
+
+## 临时解决方案
+
+### 当前推荐使用方式
+
+```javascript
+// 1. 文档搜索 - 使用 hybrid（有embeddings）
+smart_search(query="architecture design patterns", mode="hybrid")
+
+// 2. 代码搜索 - 使用 exact（无embeddings，但有FTS）
+smart_search(query="function executeQuery", mode="exact")
+
+// 3. 快速搜索 - 使用 ripgrep（跨所有文件）
+smart_search(query="TODO", mode="ripgrep")
+```
+
+### 完整覆盖的变通方案
+
+```bash
+# 手动为所有子目录生成 embeddings（如果CodexLens支持）
+cd D:\Claude_dms3\ccw
+
+# 为每个子目录分别运行
+python -m codexlens embeddings-generate ./src/tools --force
+python -m codexlens embeddings-generate ./src/commands --force
+# ... 重复26次
+
+# 或使用脚本自动化
+python check_embeddings.py --generate-all
+```
+
+---
+
+## 总结
+
+| 用户质疑 | 状态 | 结论 |
+|---------|------|------|
+| 为什么不对代码生成embeddings？ | ✅ 正确 | 是BUG，不是设计 |
+| FTS和Vector应该内容一致 | ✅ 正确 | 当前严重不一致 |
+| Init应返回详细概况 | ✅ 正确 | 当前信息不足 |
+
+**用户的所有质疑都是正确的，揭示了 CodexLens 的三个核心问题：**
+
+1. **Embeddings 生成不完整**（只有1.6%覆盖率）
+2. **索引一致性问题**（FTS vs Vector）
+3. **返回信息不透明**（缺少统计数据）
+
+---
+
+**生成时间**：2025-12-17  
+**验证方法**：`python check_embeddings.py`

ccw/check_embeddings.py

@@ -0,0 +1,47 @@
+import sqlite3
+import os
+
+# Find all _index.db files
+root_dir = r'C:\Users\dyw\.codexlens\indexes\D\Claude_dms3\ccw'
+index_files = []
+for dirpath, dirnames, filenames in os.walk(root_dir):
+    if '_index.db' in filenames:
+        index_files.append(os.path.join(dirpath, '_index.db'))
+
+print(f'Found {len(index_files)} index databases\n')
+
+total_files = 0
+total_chunks = 0
+dirs_with_chunks = 0
+
+for db_path in sorted(index_files):
+    rel_path = db_path.replace(r'C:\Users\dyw\.codexlens\indexes\D\Claude_dms3\ccw\\', '')
+    conn = sqlite3.connect(db_path)
+    
+    try:
+        cursor = conn.execute('SELECT COUNT(*) FROM files')
+        file_count = cursor.fetchone()[0]
+        total_files += file_count
+        
+        try:
+            cursor = conn.execute('SELECT COUNT(*) FROM semantic_chunks')
+            chunk_count = cursor.fetchone()[0]
+            total_chunks += chunk_count
+            
+            if chunk_count > 0:
+                dirs_with_chunks += 1
+                print(f'[+] {rel_path:<40} Files: {file_count:3d}  Chunks: {chunk_count:3d}')
+            else:
+                print(f'[ ] {rel_path:<40} Files: {file_count:3d}  (no chunks)')
+        except sqlite3.OperationalError:
+            print(f'[ ] {rel_path:<40} Files: {file_count:3d}  (no semantic_chunks table)')
+    except Exception as e:
+        print(f'[!] {rel_path:<40} Error: {e}')
+    finally:
+        conn.close()
+
+print(f'\n=== Summary ===')
+print(f'Total index databases: {len(index_files)}')
+print(f'Directories with embeddings: {dirs_with_chunks}')
+print(f'Total files indexed: {total_files}')
+print(f'Total semantic chunks: {total_chunks}')

ccw/src/tools/smart-search.ts

@@ -1,12 +1,17 @@
 /**
- * Smart Search Tool - Unified search with mode-based execution
+ * Smart Search Tool - Unified intelligent search with CodexLens integration
- * Modes: auto, exact, fuzzy, semantic, graph
  *
  * Features:
- * - Intent classification (auto mode)
+ * - Intent classification with automatic mode selection
- * - Multi-backend search routing
+ * - CodexLens integration (init, hybrid, vector, semantic)
- * - Result fusion with RRF ranking
+ * - Ripgrep fallback for exact mode
- * - Configurable search parameters
+ * - Index status checking and warnings
+ * - Multi-backend search routing with RRF ranking
+ *
+ * Actions:
+ * - init: Initialize CodexLens index
+ * - search: Intelligent search with auto mode selection
+ * - status: Check index status
  */
 
 import { z } from 'zod';
@@ -19,19 +24,23 @@ import {
 
 // Define Zod schema for validation
 const ParamsSchema = z.object({
-  query: z.string().min(1, 'Query is required'),
+  action: z.enum(['init', 'search', 'search_files', 'status']).default('search'),
-  mode: z.enum(['auto', 'exact', 'fuzzy', 'semantic', 'graph']).default('auto'),
+  query: z.string().optional(),
+  mode: z.enum(['auto', 'hybrid', 'exact', 'ripgrep']).default('auto'),
   output_mode: z.enum(['full', 'files_only', 'count']).default('full'),
+  path: z.string().optional(),
   paths: z.array(z.string()).default([]),
   contextLines: z.number().default(0),
   maxResults: z.number().default(100),
   includeHidden: z.boolean().default(false),
+  languages: z.array(z.string()).optional(),
+  limit: z.number().default(100),
 });
 
 type Params = z.infer<typeof ParamsSchema>;
 
 // Search mode constants
-const SEARCH_MODES = ['auto', 'exact', 'fuzzy', 'semantic', 'graph'] as const;
+const SEARCH_MODES = ['auto', 'hybrid', 'exact', 'ripgrep'] as const;
 
 // Classification confidence threshold
 const CONFIDENCE_THRESHOLD = 0.7;
@@ -70,16 +79,89 @@ interface SearchMetadata {
   classified_as?: string;
   confidence?: number;
   reasoning?: string;
+  embeddings_coverage_percent?: number;
   warning?: string;
   note?: string;
+  index_status?: 'indexed' | 'not_indexed' | 'partial';
 }
 
 interface SearchResult {
   success: boolean;
-  results?: ExactMatch[] | SemanticMatch[] | GraphMatch[];
+  results?: ExactMatch[] | SemanticMatch[] | GraphMatch[] | unknown;
   output?: string;
   metadata?: SearchMetadata;
   error?: string;
+  status?: unknown;
+  message?: string;
+}
+
+interface IndexStatus {
+  indexed: boolean;
+  has_embeddings: boolean;
+  file_count?: number;
+  embeddings_coverage_percent?: number;
+  warning?: string;
+}
+
+/**
+ * Check if CodexLens index exists for current directory
+ * @param path - Directory path to check
+ * @returns Index status
+ */
+async function checkIndexStatus(path: string = '.'): Promise<IndexStatus> {
+  try {
+    const result = await executeCodexLens(['status', '--json'], { cwd: path });
+
+    if (!result.success) {
+      return {
+        indexed: false,
+        has_embeddings: false,
+        warning: 'No CodexLens index found. Run smart_search(action="init") to create index for better search results.',
+      };
+    }
+
+    // Parse status output
+    try {
+      // Strip ANSI color codes from JSON output
+      const cleanOutput = (result.output || '{}').replace(/\x1b\[[0-9;]*m/g, '');
+      const status = JSON.parse(cleanOutput);
+      const indexed = status.indexed === true || status.file_count > 0;
+      
+      // Get embeddings coverage from comprehensive status
+      const embeddingsData = status.embeddings || {};
+      const embeddingsCoverage = embeddingsData.coverage_percent || 0;
+      const has_embeddings = embeddingsCoverage >= 50; // Threshold: 50%
+
+      let warning: string | undefined;
+      if (!indexed) {
+        warning = 'No CodexLens index found. Run smart_search(action="init") to create index for better search results.';
+      } else if (embeddingsCoverage === 0) {
+        warning = 'Index exists but no embeddings generated. Run: codexlens embeddings-generate --recursive';
+      } else if (embeddingsCoverage < 50) {
+        warning = `Embeddings coverage is ${embeddingsCoverage.toFixed(1)}% (below 50%). Hybrid search will use exact mode. Run: codexlens embeddings-generate --recursive`;
+      }
+
+      return {
+        indexed,
+        has_embeddings,
+        file_count: status.file_count,
+        embeddings_coverage_percent: embeddingsCoverage,
+        warning,
+      };
+    } catch {
+      return {
+        indexed: false,
+        has_embeddings: false,
+        warning: 'Failed to parse index status',
+      };
+    }
+  } catch {
+    return {
+      indexed: false,
+      has_embeddings: false,
+      warning: 'CodexLens not available',
+    };
+  }
 }
 
 /**
@@ -123,43 +205,34 @@ function detectRelationship(query: string): boolean {
 
 /**
  * Classify query intent and recommend search mode
+ * Simple mapping: hybrid (NL + index + embeddings) | exact (index or insufficient embeddings) | ripgrep (no index)
  * @param query - Search query string
+ * @param hasIndex - Whether CodexLens index exists
+ * @param hasSufficientEmbeddings - Whether embeddings coverage >= 50%
  * @returns Classification result
  */
-function classifyIntent(query: string): Classification {
+function classifyIntent(query: string, hasIndex: boolean = false, hasSufficientEmbeddings: boolean = false): Classification {
-  // Initialize mode scores
+  // Detect query patterns
-  const scores: Record<string, number> = {
+  const isNaturalLanguage = detectNaturalLanguage(query);
-    exact: 0,
-    fuzzy: 0,
-    semantic: 0,
-    graph: 0,
-  };
 
-  // Apply detection heuristics with weighted scoring
+  // Simple decision tree
-  if (detectLiteral(query)) {
+  let mode: string;
-    scores.exact += 0.8;
+  let confidence: number;
+
+  if (!hasIndex) {
+    // No index: use ripgrep
+    mode = 'ripgrep';
+    confidence = 1.0;
+  } else if (isNaturalLanguage && hasSufficientEmbeddings) {
+    // Natural language + sufficient embeddings: use hybrid
+    mode = 'hybrid';
+    confidence = 0.9;
+  } else {
+    // Simple query OR insufficient embeddings: use exact
+    mode = 'exact';
+    confidence = 0.8;
   }
 
-  if (detectRegex(query)) {
-    scores.fuzzy += 0.7;
-  }
-
-  if (detectNaturalLanguage(query)) {
-    scores.semantic += 0.9;
-  }
-
-  if (detectFilePath(query)) {
-    scores.exact += 0.6;
-  }
-
-  if (detectRelationship(query)) {
-    scores.graph += 0.85;
-  }
-
-  // Find mode with highest confidence score
-  const mode = Object.keys(scores).reduce((a, b) => (scores[a] > scores[b] ? a : b));
-  const confidence = scores[mode];
-
   // Build reasoning string
   const detectedPatterns: string[] = [];
   if (detectLiteral(query)) detectedPatterns.push('literal');
@@ -168,7 +241,7 @@ function classifyIntent(query: string): Classification {
   if (detectFilePath(query)) detectedPatterns.push('file path');
   if (detectRelationship(query)) detectedPatterns.push('relationship');
 
-  const reasoning = `Query classified as ${mode} (confidence: ${confidence.toFixed(2)}, detected: ${detectedPatterns.join(', ')})`;
+  const reasoning = `Query classified as ${mode} (confidence: ${confidence.toFixed(2)}, detected: ${detectedPatterns.join(', ')}, index: ${hasIndex ? 'available' : 'not available'}, embeddings: ${hasSufficientEmbeddings ? 'sufficient' : 'insufficient'})`;
 
   return { mode, confidence, reasoning };
 }
@@ -234,105 +307,192 @@ function buildRipgrepCommand(params: {
 }
 
 /**
- * Mode: auto - Intent classification and mode selection
+ * Action: init - Initialize CodexLens index
- * Analyzes query to determine optimal search mode
  */
-async function executeAutoMode(params: Params): Promise<SearchResult> {
+async function executeInitAction(params: Params): Promise<SearchResult> {
-  const { query } = params;
+  const { path = '.', languages } = params;
 
-  // Classify intent
+  // Check CodexLens availability
-  const classification = classifyIntent(query);
+  const readyStatus = await ensureCodexLensReady();
-
+  if (!readyStatus.ready) {
-  // Route to appropriate mode based on classification
-  switch (classification.mode) {
-    case 'exact': {
-      const exactResult = await executeExactMode(params);
-      return {
-        ...exactResult,
-        metadata: {
-          ...exactResult.metadata!,
-          classified_as: classification.mode,
-          confidence: classification.confidence,
-          reasoning: classification.reasoning,
-        },
-      };
-    }
-
-    case 'fuzzy':
     return {
       success: false,
-        error: 'Fuzzy mode not yet implemented',
+      error: `CodexLens not available: ${readyStatus.error}. CodexLens will be auto-installed on first use.`,
-        metadata: {
-          mode: 'fuzzy',
-          backend: '',
-          count: 0,
-          query,
-          classified_as: classification.mode,
-          confidence: classification.confidence,
-          reasoning: classification.reasoning,
-        },
-      };
-
-    case 'semantic': {
-      const semanticResult = await executeSemanticMode(params);
-      return {
-        ...semanticResult,
-        metadata: {
-          ...semanticResult.metadata!,
-          classified_as: classification.mode,
-          confidence: classification.confidence,
-          reasoning: classification.reasoning,
-        },
     };
   }
 
-    case 'graph': {
+  const args = ['init', path];
-      const graphResult = await executeGraphMode(params);
+  if (languages && languages.length > 0) {
-      return {
+    args.push('--languages', languages.join(','));
-        ...graphResult,
-        metadata: {
-          ...graphResult.metadata!,
-          classified_as: classification.mode,
-          confidence: classification.confidence,
-          reasoning: classification.reasoning,
-        },
-      };
   }
 
-    default: {
+  const result = await executeCodexLens(args, { cwd: path, timeout: 300000 });
-      const fallbackResult = await executeExactMode(params);
+
   return {
-        ...fallbackResult,
+    success: result.success,
-        metadata: {
+    error: result.error,
-          ...fallbackResult.metadata!,
+    message: result.success
-          classified_as: 'exact',
+      ? `CodexLens index created successfully for ${path}`
-          confidence: 0.5,
+      : undefined,
-          reasoning: 'Fallback to exact mode due to unknown classification',
-        },
   };
-    }
-  }
 }
 
 /**
- * Mode: exact - Precise file path and content matching
+ * Action: status - Check CodexLens index status
- * Uses ripgrep for literal string matching
  */
-async function executeExactMode(params: Params): Promise<SearchResult> {
+async function executeStatusAction(params: Params): Promise<SearchResult> {
-  const { query, paths = [], contextLines = 0, maxResults = 100, includeHidden = false } = params;
+  const { path = '.' } = params;
 
-  // Check ripgrep availability
+  const indexStatus = await checkIndexStatus(path);
-  if (!checkToolAvailability('rg')) {
+
+  return {
+    success: true,
+    status: indexStatus,
+    message: indexStatus.warning || `Index status: ${indexStatus.indexed ? 'indexed' : 'not indexed'}, embeddings: ${indexStatus.has_embeddings ? 'available' : 'not available'}`,
+  };
+}
+
+/**
+ * Mode: auto - Intent classification and mode selection
+ * Routes to: hybrid (NL + index) | exact (index) | ripgrep (no index)
+ */
+async function executeAutoMode(params: Params): Promise<SearchResult> {
+  const { query, path = '.' } = params;
+
+  if (!query) {
     return {
       success: false,
-      error: 'ripgrep not available - please install ripgrep (rg) to use exact search mode',
+      error: 'Query is required for search action',
     };
   }
 
-  // Build ripgrep command
+  // Check index status
+  const indexStatus = await checkIndexStatus(path);
+
+  // Classify intent with index and embeddings awareness
+  const classification = classifyIntent(
+    query, 
+    indexStatus.indexed, 
+    indexStatus.has_embeddings  // This now considers 50% threshold
+  );
+
+  // Route to appropriate mode based on classification
+  let result: SearchResult;
+
+  switch (classification.mode) {
+    case 'hybrid':
+      result = await executeHybridMode(params);
+      break;
+
+    case 'exact':
+      result = await executeCodexLensExactMode(params);
+      break;
+
+    case 'ripgrep':
+      result = await executeRipgrepMode(params);
+      break;
+
+    default:
+      // Fallback to ripgrep
+      result = await executeRipgrepMode(params);
+      break;
+  }
+
+  // Add classification metadata
+  if (result.metadata) {
+    result.metadata.classified_as = classification.mode;
+    result.metadata.confidence = classification.confidence;
+    result.metadata.reasoning = classification.reasoning;
+    result.metadata.embeddings_coverage_percent = indexStatus.embeddings_coverage_percent;
+    result.metadata.index_status = indexStatus.indexed
+      ? (indexStatus.has_embeddings ? 'indexed' : 'partial')
+      : 'not_indexed';
+
+    // Add warning if needed
+    if (indexStatus.warning) {
+      result.metadata.warning = indexStatus.warning;
+    }
+  }
+
+  return result;
+}
+
+/**
+ * Mode: ripgrep - Fast literal string matching using ripgrep
+ * No index required, fallback to CodexLens if ripgrep unavailable
+ */
+async function executeRipgrepMode(params: Params): Promise<SearchResult> {
+  const { query, paths = [], contextLines = 0, maxResults = 100, includeHidden = false, path = '.' } = params;
+
+  if (!query) {
+    return {
+      success: false,
+      error: 'Query is required for search',
+    };
+  }
+
+  // Check if ripgrep is available
+  const hasRipgrep = checkToolAvailability('rg');
+
+  // If ripgrep not available, fall back to CodexLens exact mode
+  if (!hasRipgrep) {
+    const readyStatus = await ensureCodexLensReady();
+    if (!readyStatus.ready) {
+      return {
+        success: false,
+        error: 'Neither ripgrep nor CodexLens available. Install ripgrep (rg) or CodexLens for search functionality.',
+      };
+    }
+
+    // Use CodexLens exact mode as fallback
+    const args = ['search', query, '--limit', maxResults.toString(), '--mode', 'exact', '--json'];
+    const result = await executeCodexLens(args, { cwd: path });
+
+    if (!result.success) {
+      return {
+        success: false,
+        error: result.error,
+        metadata: {
+          mode: 'ripgrep',
+          backend: 'codexlens-fallback',
+          count: 0,
+          query,
+        },
+      };
+    }
+
+    // Parse results
+    let results: SemanticMatch[] = [];
+    try {
+      const parsed = JSON.parse(result.output || '{}');
+      const data = parsed.results || parsed;
+      results = (Array.isArray(data) ? data : []).map((item: any) => ({
+        file: item.path || item.file,
+        score: item.score || 0,
+        content: item.excerpt || item.content || '',
+        symbol: item.symbol || null,
+      }));
+    } catch {
+      // Keep empty results
+    }
+
+    return {
+      success: true,
+      results,
+      metadata: {
+        mode: 'ripgrep',
+        backend: 'codexlens-fallback',
+        count: results.length,
+        query,
+        note: 'Using CodexLens exact mode (ripgrep not available)',
+      },
+    };
+  }
+
+  // Use ripgrep
   const { command, args } = buildRipgrepCommand({
     query,
-    paths: paths.length > 0 ? paths : ['.'],
+    paths: paths.length > 0 ? paths : [path],
     contextLines,
     maxResults,
     includeHidden,
@@ -340,7 +500,7 @@ async function executeExactMode(params: Params): Promise<SearchResult> {
 
   return new Promise((resolve) => {
     const child = spawn(command, args, {
-      cwd: process.cwd(),
+      cwd: path || process.cwd(),
       stdio: ['ignore', 'pipe', 'pipe'],
     });
 
@@ -386,7 +546,7 @@ async function executeExactMode(params: Params): Promise<SearchResult> {
           success: true,
           results,
           metadata: {
-            mode: 'exact',
+            mode: 'ripgrep',
             backend: 'ripgrep',
             count: results.length,
             query,
@@ -412,60 +572,126 @@ async function executeExactMode(params: Params): Promise<SearchResult> {
 }
 
 /**
- * Mode: fuzzy - Approximate matching with tolerance
+ * Mode: exact - CodexLens exact/FTS search
- * Uses fuzzy matching algorithms for typo-tolerant search
+ * Requires index
  */
-async function executeFuzzyMode(params: Params): Promise<SearchResult> {
+async function executeCodexLensExactMode(params: Params): Promise<SearchResult> {
+  const { query, path = '.', limit = 100 } = params;
+
+  if (!query) {
     return {
       success: false,
-    error: 'Fuzzy mode not implemented - fuzzy matching engine pending',
+      error: 'Query is required for search',
     };
-}
+  }
-
-/**
- * Mode: semantic - Natural language understanding search
- * Uses CodexLens embeddings for semantic similarity
- */
-async function executeSemanticMode(params: Params): Promise<SearchResult> {
-  const { query, paths = [], maxResults = 100 } = params;
 
   // Check CodexLens availability
   const readyStatus = await ensureCodexLensReady();
   if (!readyStatus.ready) {
     return {
       success: false,
-      error: `CodexLens not available: ${readyStatus.error}. Run 'ccw tool exec codex_lens {"action":"bootstrap"}' to install.`,
+      error: `CodexLens not available: ${readyStatus.error}`,
     };
   }
 
-  // Determine search path
+  // Check index status
-  const searchPath = paths.length > 0 ? paths[0] : '.';
+  const indexStatus = await checkIndexStatus(path);
 
-  // Execute CodexLens semantic search
+  const args = ['search', query, '--limit', limit.toString(), '--mode', 'exact', '--json'];
-  const result = await executeCodexLens(['search', query, '--limit', maxResults.toString(), '--json'], {
+  const result = await executeCodexLens(args, { cwd: path });
-    cwd: searchPath,
-  });
 
   if (!result.success) {
     return {
       success: false,
       error: result.error,
       metadata: {
-        mode: 'semantic',
+        mode: 'exact',
         backend: 'codexlens',
         count: 0,
         query,
+        warning: indexStatus.warning,
       },
     };
   }
 
-  // Parse and transform results
+  // Parse results
   let results: SemanticMatch[] = [];
   try {
-    const cleanOutput = result.output!.replace(/\r\n/g, '\n');
+    const parsed = JSON.parse(result.output || '{}');
-    const parsed = JSON.parse(cleanOutput);
+    const data = parsed.results || parsed;
-    const data = parsed.result || parsed;
+    results = (Array.isArray(data) ? data : []).map((item: any) => ({
-    results = (data.results || []).map((item: any) => ({
+      file: item.path || item.file,
+      score: item.score || 0,
+      content: item.excerpt || item.content || '',
+      symbol: item.symbol || null,
+    }));
+  } catch {
+    // Keep empty results
+  }
+
+  return {
+    success: true,
+    results,
+    metadata: {
+      mode: 'exact',
+      backend: 'codexlens',
+      count: results.length,
+      query,
+      warning: indexStatus.warning,
+    },
+  };
+}
+
+/**
+ * Mode: hybrid - Best quality search with RRF fusion
+ * Uses CodexLens hybrid mode (exact + fuzzy + vector)
+ * Requires index with embeddings
+ */
+async function executeHybridMode(params: Params): Promise<SearchResult> {
+  const { query, path = '.', limit = 100 } = params;
+
+  if (!query) {
+    return {
+      success: false,
+      error: 'Query is required for search',
+    };
+  }
+
+  // Check CodexLens availability
+  const readyStatus = await ensureCodexLensReady();
+  if (!readyStatus.ready) {
+    return {
+      success: false,
+      error: `CodexLens not available: ${readyStatus.error}`,
+    };
+  }
+
+  // Check index status
+  const indexStatus = await checkIndexStatus(path);
+
+  const args = ['search', query, '--limit', limit.toString(), '--mode', 'hybrid', '--json'];
+  const result = await executeCodexLens(args, { cwd: path });
+
+  if (!result.success) {
+    return {
+      success: false,
+      error: result.error,
+      metadata: {
+        mode: 'hybrid',
+        backend: 'codexlens',
+        count: 0,
+        query,
+        warning: indexStatus.warning,
+      },
+    };
+  }
+
+  // Parse results
+  let results: SemanticMatch[] = [];
+  try {
+    const parsed = JSON.parse(result.output || '{}');
+    const data = parsed.results || parsed;
+    results = (Array.isArray(data) ? data : []).map((item: any) => ({
       file: item.path || item.file,
       score: item.score || 0,
       content: item.excerpt || item.content || '',
@@ -477,11 +703,11 @@ async function executeSemanticMode(params: Params): Promise<SearchResult> {
       results: [],
       output: result.output,
       metadata: {
-        mode: 'semantic',
+        mode: 'hybrid',
         backend: 'codexlens',
         count: 0,
         query,
-        warning: 'Failed to parse JSON output',
+        warning: indexStatus.warning || 'Failed to parse JSON output',
       },
     };
   }
@@ -490,105 +716,12 @@ async function executeSemanticMode(params: Params): Promise<SearchResult> {
     success: true,
     results,
     metadata: {
-      mode: 'semantic',
+      mode: 'hybrid',
       backend: 'codexlens',
       count: results.length,
       query,
-    },
+      note: 'Hybrid mode uses RRF fusion (exact + fuzzy + vector) for best results',
-  };
+      warning: indexStatus.warning,
-}
-
-/**
- * Mode: graph - Dependency and relationship traversal
- * Uses CodexLens symbol extraction for code analysis
- */
-async function executeGraphMode(params: Params): Promise<SearchResult> {
-  const { query, paths = [], maxResults = 100 } = params;
-
-  // Check CodexLens availability
-  const readyStatus = await ensureCodexLensReady();
-  if (!readyStatus.ready) {
-    return {
-      success: false,
-      error: `CodexLens not available: ${readyStatus.error}. Run 'ccw tool exec codex_lens {"action":"bootstrap"}' to install.`,
-    };
-  }
-
-  // First, search for relevant files using text search
-  const searchPath = paths.length > 0 ? paths[0] : '.';
-
-  const textResult = await executeCodexLens(['search', query, '--limit', maxResults.toString(), '--json'], {
-    cwd: searchPath,
-  });
-
-  if (!textResult.success) {
-    return {
-      success: false,
-      error: textResult.error,
-      metadata: {
-        mode: 'graph',
-        backend: 'codexlens',
-        count: 0,
-        query,
-      },
-    };
-  }
-
-  // Parse results and extract symbols from top files
-  let results: GraphMatch[] = [];
-  try {
-    const parsed = JSON.parse(textResult.output!);
-    const files = [...new Set((parsed.results || parsed).map((item: any) => item.path || item.file))].slice(
-      0,
-      10
-    );
-
-    // Extract symbols from files in parallel
-    const symbolPromises = files.map((file) =>
-      executeCodexLens(['symbol', file as string, '--json'], { cwd: searchPath }).then((result) => ({
-        file,
-        result,
-      }))
-    );
-
-    const symbolResults = await Promise.all(symbolPromises);
-
-    for (const { file, result } of symbolResults) {
-      if (result.success) {
-        try {
-          const symbols = JSON.parse(result.output!);
-          results.push({
-            file: file as string,
-            symbols: symbols.symbols || symbols,
-            relationships: [],
-          });
-        } catch {
-          // Skip files with parse errors
-        }
-      }
-    }
-  } catch {
-    return {
-      success: false,
-      error: 'Failed to parse search results',
-      metadata: {
-        mode: 'graph',
-        backend: 'codexlens',
-        count: 0,
-        query,
-      },
-    };
-  }
-
-  return {
-    success: true,
-    results,
-    metadata: {
-      mode: 'graph',
-      backend: 'codexlens',
-      count: results.length,
-      query,
-      note: 'Graph mode provides symbol extraction; full dependency graph analysis pending',
     },
   };
 }
@@ -596,36 +729,73 @@ async function executeGraphMode(params: Params): Promise<SearchResult> {
 // Tool schema for MCP
 export const schema: ToolSchema = {
   name: 'smart_search',
-  description: `Intelligent code search with multiple modes.
+  description: `Intelligent code search with three optimized modes: hybrid, exact, ripgrep.
 
-Usage:
+**Quick Start:**
-  smart_search(query="function main", path=".")           # Auto-select mode
+  smart_search(query="authentication logic")           # Auto mode (intelligent routing)
-  smart_search(query="def init", mode="exact")            # Exact match
+  smart_search(action="init", path=".")                # Initialize index (required for hybrid)
-  smart_search(query="authentication logic", mode="semantic")  # NL search
+  smart_search(action="status")                        # Check index status
 
-Modes: auto (default), exact, fuzzy, semantic, graph`,
+**Three Core Modes:**
+  1. auto (default): Intelligent routing based on query and index
+     - Natural language + index → hybrid
+     - Simple query + index → exact
+     - No index → ripgrep
+
+  2. hybrid: CodexLens RRF fusion (exact + fuzzy + vector)
+     - Best quality, semantic understanding
+     - Requires index with embeddings
+
+  3. exact: CodexLens FTS (full-text search)
+     - Precise keyword matching
+     - Requires index
+
+  4. ripgrep: Direct ripgrep execution
+     - Fast, no index required
+     - Literal string matching
+
+**Actions:**
+  - search (default): Intelligent search with auto routing
+  - init: Create CodexLens index (required for hybrid/exact)
+  - status: Check index and embedding availability
+  - search_files: Return file paths only
+
+**Workflow:**
+  1. Run action="init" to create index
+  2. Use auto mode - it routes to hybrid for NL queries, exact for simple queries
+  3. Use ripgrep mode for fast searches without index`,
   inputSchema: {
     type: 'object',
     properties: {
+      action: {
+        type: 'string',
+        enum: ['init', 'search', 'search_files', 'status'],
+        description: 'Action to perform: init (create index), search (default), search_files (paths only), status (check index)',
+        default: 'search',
+      },
       query: {
         type: 'string',
-        description: 'Search query (file pattern, text content, or natural language)',
+        description: 'Search query (required for search/search_files actions)',
       },
       mode: {
         type: 'string',
         enum: SEARCH_MODES,
-        description: 'Search mode (default: auto)',
+        description: 'Search mode: auto (default), hybrid (best quality), exact (CodexLens FTS), ripgrep (fast, no index)',
         default: 'auto',
       },
       output_mode: {
         type: 'string',
         enum: ['full', 'files_only', 'count'],
-        description: 'Output mode: full (default), files_only (paths only), count (per-file counts)',
+        description: 'Output format: full (default), files_only (paths only), count (per-file counts)',
         default: 'full',
       },
+      path: {
+        type: 'string',
+        description: 'Directory path for init/search actions (default: current directory)',
+      },
       paths: {
         type: 'array',
-        description: 'Paths to search within (default: current directory)',
+        description: 'Multiple paths to search within (for search action)',
         items: {
           type: 'string',
         },
@@ -633,21 +803,31 @@ Modes: auto (default), exact, fuzzy, semantic, graph`,
       },
       contextLines: {
         type: 'number',
-        description: 'Number of context lines around matches (default: 0)',
+        description: 'Number of context lines around matches (exact mode only)',
         default: 0,
       },
       maxResults: {
         type: 'number',
-        description: 'Maximum number of results to return (default: 100)',
+        description: 'Maximum number of results (default: 100)',
+        default: 100,
+      },
+      limit: {
+        type: 'number',
+        description: 'Alias for maxResults',
         default: 100,
       },
       includeHidden: {
         type: 'boolean',
-        description: 'Include hidden files/directories (default: false)',
+        description: 'Include hidden files/directories',
         default: false,
       },
+      languages: {
+        type: 'array',
+        items: { type: 'string' },
+        description: 'Languages to index (for init action). Example: ["javascript", "typescript"]',
       },
-    required: ['query'],
+    },
+    required: [],
   },
 };
 
@@ -655,20 +835,27 @@ Modes: auto (default), exact, fuzzy, semantic, graph`,
  * Transform results based on output_mode
  */
 function transformOutput(
-  results: ExactMatch[] | SemanticMatch[] | GraphMatch[],
+  results: ExactMatch[] | SemanticMatch[] | GraphMatch[] | unknown[],
   outputMode: 'full' | 'files_only' | 'count'
 ): unknown {
+  if (!Array.isArray(results)) {
+    return results;
+  }
+
   switch (outputMode) {
     case 'files_only': {
       // Extract unique file paths
-      const files = [...new Set(results.map((r) => r.file))];
+      const files = [...new Set(results.map((r: any) => r.file))].filter(Boolean);
       return { files, count: files.length };
     }
     case 'count': {
       // Count matches per file
       const counts: Record<string, number> = {};
       for (const r of results) {
-        counts[r.file] = (counts[r.file] || 0) + 1;
+        const file = (r as any).file;
+        if (file) {
+          counts[file] = (counts[file] || 0) + 1;
+        }
       }
       return {
         files: Object.entries(counts).map(([file, count]) => ({ file, count })),
@@ -688,34 +875,58 @@ export async function handler(params: Record<string, unknown>): Promise<ToolResu
     return { success: false, error: `Invalid params: ${parsed.error.message}` };
   }
 
-  const { mode, output_mode } = parsed.data;
+  const { action, mode, output_mode, limit, maxResults } = parsed.data;
+
+  // Use limit if maxResults not provided
+  if (limit && !maxResults) {
+    parsed.data.maxResults = limit;
+  }
 
   try {
     let result: SearchResult;
 
+    // Handle actions
+    switch (action) {
+      case 'init':
+        result = await executeInitAction(parsed.data);
+        break;
+
+      case 'status':
+        result = await executeStatusAction(parsed.data);
+        break;
+
+      case 'search_files':
+        // For search_files, use search mode but force files_only output
+        parsed.data.output_mode = 'files_only';
+        // Fall through to search
+
+      case 'search':
+      default:
+        // Handle search modes: auto | hybrid | exact | ripgrep
         switch (mode) {
           case 'auto':
             result = await executeAutoMode(parsed.data);
             break;
+          case 'hybrid':
+            result = await executeHybridMode(parsed.data);
+            break;
           case 'exact':
-        result = await executeExactMode(parsed.data);
+            result = await executeCodexLensExactMode(parsed.data);
             break;
-      case 'fuzzy':
+          case 'ripgrep':
-        result = await executeFuzzyMode(parsed.data);
+            result = await executeRipgrepMode(parsed.data);
-        break;
-      case 'semantic':
-        result = await executeSemanticMode(parsed.data);
-        break;
-      case 'graph':
-        result = await executeGraphMode(parsed.data);
             break;
           default:
-        throw new Error(`Unsupported mode: ${mode}`);
+            throw new Error(`Unsupported mode: ${mode}. Use: auto, hybrid, exact, or ripgrep`);
+        }
+        break;
     }
 
-    // Transform output based on output_mode
+    // Transform output based on output_mode (for search actions only)
+    if (action === 'search' || action === 'search_files') {
       if (result.success && result.results && output_mode !== 'full') {
-      result.results = transformOutput(result.results, output_mode) as typeof result.results;
+        result.results = transformOutput(result.results as any[], output_mode);
+      }
     }
 
     return result.success ? { success: true, result } : { success: false, error: result.error };

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

@@ -142,11 +142,11 @@ def init(
         if not no_embeddings:
             try:
                 from codexlens.semantic import SEMANTIC_AVAILABLE
-                from codexlens.cli.embedding_manager import generate_embeddings
+                from codexlens.cli.embedding_manager import generate_embeddings_recursive, get_embeddings_status
 
                 if SEMANTIC_AVAILABLE:
-                    # Find the index file
+                    # Use the index root directory (not the _index.db file)
-                    index_path = Path(build_result.index_root) / "_index.db"
+                    index_root = Path(build_result.index_root)
 
                     if not json_mode:
                         console.print("\n[bold]Generating embeddings...[/bold]")
@@ -157,8 +157,8 @@ def init(
                         if not json_mode and verbose:
                             console.print(f"  {msg}")
 
-                    embed_result = generate_embeddings(
+                    embed_result = generate_embeddings_recursive(
-                        index_path,
+                        index_root,
                         model_profile=embedding_model,
                         force=False,  # Don't force regenerate during init
                         chunk_size=2000,
@@ -167,29 +167,56 @@ def init(
 
                     if embed_result["success"]:
                         embed_data = embed_result["result"]
-                        result["embeddings_generated"] = True
+                        
-                        result["embeddings_count"] = embed_data["chunks_embedded"]
+                        # Get comprehensive coverage statistics
+                        status_result = get_embeddings_status(index_root)
+                        if status_result["success"]:
+                            coverage = status_result["result"]
+                            result["embeddings"] = {
+                                "generated": True,
+                                "total_indexes": coverage["total_indexes"],
+                                "total_files": coverage["total_files"],
+                                "files_with_embeddings": coverage["files_with_embeddings"],
+                                "coverage_percent": coverage["coverage_percent"],
+                                "total_chunks": coverage["total_chunks"],
+                            }
+                        else:
+                            result["embeddings"] = {
+                                "generated": True,
+                                "total_chunks": embed_data["total_chunks_created"],
+                                "files_processed": embed_data["total_files_processed"],
+                            }
 
                         if not json_mode:
-                            console.print(f"[green]✓[/green] Generated [bold]{embed_data['chunks_embedded']}[/bold] embeddings in {embed_data['elapsed_time']:.1f}s")
+                            console.print(f"[green]✓[/green] Generated embeddings for [bold]{embed_data['total_files_processed']}[/bold] files")
+                            console.print(f"  Total chunks: [bold]{embed_data['total_chunks_created']}[/bold]")
+                            console.print(f"  Indexes processed: [bold]{embed_data['indexes_successful']}/{embed_data['indexes_processed']}[/bold]")
                     else:
                         if not json_mode:
                             console.print(f"[yellow]Warning:[/yellow] Embedding generation failed: {embed_result.get('error', 'Unknown error')}")
-                        result["embeddings_generated"] = False
+                        result["embeddings"] = {
-                        result["embeddings_error"] = embed_result.get("error")
+                            "generated": False,
+                            "error": embed_result.get("error"),
+                        }
                 else:
                     if not json_mode and verbose:
                         console.print("[dim]Semantic search not available. Skipping embeddings.[/dim]")
-                    result["embeddings_generated"] = False
+                    result["embeddings"] = {
-                    result["embeddings_error"] = "Semantic dependencies not installed"
+                        "generated": False,
+                        "error": "Semantic dependencies not installed",
+                    }
             except Exception as e:
                 if not json_mode and verbose:
                     console.print(f"[yellow]Warning:[/yellow] Could not generate embeddings: {e}")
-                result["embeddings_generated"] = False
+                result["embeddings"] = {
-                result["embeddings_error"] = str(e)
+                    "generated": False,
+                    "error": str(e),
+                }
         else:
-            result["embeddings_generated"] = False
+            result["embeddings"] = {
-            result["embeddings_error"] = "Skipped (--no-embeddings)"
+                "generated": False,
+                "error": "Skipped (--no-embeddings)",
+            }
 
     except StorageError as exc:
         if json_mode:
@@ -611,6 +638,24 @@ def status(
                 except Exception:
                     pass
 
+        # Check embeddings coverage
+        embeddings_info = None
+        has_vector_search = False
+        try:
+            from codexlens.cli.embedding_manager import get_embeddings_status
+            
+            if index_root.exists():
+                embed_status = get_embeddings_status(index_root)
+                if embed_status["success"]:
+                    embeddings_info = embed_status["result"]
+                    # Enable vector search if coverage >= 50%
+                    has_vector_search = embeddings_info["coverage_percent"] >= 50.0
+        except ImportError:
+            # Embedding manager not available
+            pass
+        except Exception as e:
+            logger.debug(f"Failed to get embeddings status: {e}")
+
         stats = {
             "index_root": str(index_root),
             "registry_path": str(_get_registry_path()),
@@ -624,10 +669,14 @@ def status(
                 "exact_fts": True,  # Always available
                 "fuzzy_fts": has_dual_fts,
                 "hybrid_search": has_dual_fts,
-                "vector_search": False,  # Not yet implemented
+                "vector_search": has_vector_search,
             },
         }
         
+        # Add embeddings info if available
+        if embeddings_info:
+            stats["embeddings"] = embeddings_info
+
         if json_mode:
             print_json(success=True, result=stats)
         else:
@@ -648,7 +697,20 @@ def status(
             else:
                 console.print(f"  Fuzzy FTS: ✗ (run 'migrate' to enable)")
                 console.print(f"  Hybrid Search: ✗ (run 'migrate' to enable)")
-            console.print(f"  Vector Search: ✗ (future)")
+            
+            if has_vector_search:
+                console.print(f"  Vector Search: ✓ (embeddings available)")
+            else:
+                console.print(f"  Vector Search: ✗ (no embeddings or coverage < 50%)")
+            
+            # Display embeddings statistics if available
+            if embeddings_info:
+                console.print("\n[bold]Embeddings Coverage:[/bold]")
+                console.print(f"  Total Indexes: {embeddings_info['total_indexes']}")
+                console.print(f"  Total Files: {embeddings_info['total_files']}")
+                console.print(f"  Files with Embeddings: {embeddings_info['files_with_embeddings']}")
+                console.print(f"  Coverage: {embeddings_info['coverage_percent']:.1f}%")
+                console.print(f"  Total Chunks: {embeddings_info['total_chunks']}")
 
     except StorageError as exc:
         if json_mode:
@@ -1885,6 +1947,12 @@ def embeddings_generate(
         "--chunk-size",
         help="Maximum chunk size in characters.",
     ),
+    recursive: bool = typer.Option(
+        False,
+        "--recursive",
+        "-r",
+        help="Recursively process all _index.db files in directory tree.",
+    ),
     json_mode: bool = typer.Option(False, "--json", help="Output JSON response."),
     verbose: bool = typer.Option(False, "--verbose", "-v", help="Enable verbose output."),
 ) -> None:
@@ -1908,16 +1976,30 @@ def embeddings_generate(
     _configure_logging(verbose)
 
     try:
-        from codexlens.cli.embedding_manager import generate_embeddings
+        from codexlens.cli.embedding_manager import generate_embeddings, generate_embeddings_recursive
 
         # Resolve path
         target_path = path.expanduser().resolve()
 
+        # Determine if we should use recursive mode
+        use_recursive = False
+        index_path = None
+        index_root = None
+
         if target_path.is_file() and target_path.name == "_index.db":
             # Direct index file
             index_path = target_path
+            if recursive:
+                # Use parent directory for recursive processing
+                use_recursive = True
+                index_root = target_path.parent
         elif target_path.is_dir():
-            # Try to find index for this project
+            if recursive:
+                # Recursive mode: process all _index.db files in directory tree
+                use_recursive = True
+                index_root = target_path
+            else:
+                # Non-recursive: Try to find index for this project
                 registry = RegistryStore()
                 try:
                     registry.initialize()
@@ -1940,9 +2022,22 @@ def embeddings_generate(
                 console.print(f"  {msg}")
 
         console.print(f"[bold]Generating embeddings[/bold]")
+        if use_recursive:
+            console.print(f"Index root: [dim]{index_root}[/dim]")
+            console.print(f"Mode: [yellow]Recursive[/yellow]")
+        else:
             console.print(f"Index: [dim]{index_path}[/dim]")
         console.print(f"Model: [cyan]{model}[/cyan]\n")
 
+        if use_recursive:
+            result = generate_embeddings_recursive(
+                index_root,
+                model_profile=model,
+                force=force,
+                chunk_size=chunk_size,
+                progress_callback=progress_update,
+            )
+        else:
             result = generate_embeddings(
                 index_path,
                 model_profile=model,
@@ -1968,6 +2063,30 @@ def embeddings_generate(
                 raise typer.Exit(code=1)
 
             data = result["result"]
+
+            if use_recursive:
+                # Recursive mode output
+                console.print(f"[green]✓[/green] Recursive embeddings generation complete!")
+                console.print(f"  Indexes processed: {data['indexes_processed']}")
+                console.print(f"  Indexes successful: {data['indexes_successful']}")
+                if data['indexes_failed'] > 0:
+                    console.print(f"  [yellow]Indexes failed: {data['indexes_failed']}[/yellow]")
+                console.print(f"  Total chunks created: {data['total_chunks_created']:,}")
+                console.print(f"  Total files processed: {data['total_files_processed']}")
+                if data['total_files_failed'] > 0:
+                    console.print(f"  [yellow]Total files failed: {data['total_files_failed']}[/yellow]")
+                console.print(f"  Model profile: {data['model_profile']}")
+
+                # Show details if verbose
+                if verbose and data.get('details'):
+                    console.print("\n[dim]Index details:[/dim]")
+                    for detail in data['details']:
+                        status_icon = "[green]✓[/green]" if detail['success'] else "[red]✗[/red]"
+                        console.print(f"  {status_icon} {detail['path']}")
+                        if not detail['success'] and detail.get('error'):
+                            console.print(f"    [dim]Error: {detail['error']}[/dim]")
+            else:
+                # Single index mode output
                 elapsed = data["elapsed_time"]
 
                 console.print(f"[green]✓[/green] Embeddings generated successfully!")

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

@@ -255,6 +255,21 @@ def generate_embeddings(
     }
 
 
+def discover_all_index_dbs(index_root: Path) -> List[Path]:
+    """Recursively find all _index.db files in an index tree.
+
+    Args:
+        index_root: Root directory to scan for _index.db files
+
+    Returns:
+        Sorted list of paths to _index.db files
+    """
+    if not index_root.exists():
+        return []
+
+    return sorted(index_root.rglob("_index.db"))
+
+
 def find_all_indexes(scan_dir: Path) -> List[Path]:
     """Find all _index.db files in directory tree.
 
@@ -270,6 +285,146 @@ def find_all_indexes(scan_dir: Path) -> List[Path]:
     return list(scan_dir.rglob("_index.db"))
 
 
+
+def generate_embeddings_recursive(
+    index_root: Path,
+    model_profile: str = "code",
+    force: bool = False,
+    chunk_size: int = 2000,
+    progress_callback: Optional[callable] = None,
+) -> Dict[str, any]:
+    """Generate embeddings for all index databases in a project recursively.
+
+    Args:
+        index_root: Root index directory containing _index.db files
+        model_profile: Model profile (fast, code, multilingual, balanced)
+        force: If True, regenerate even if embeddings exist
+        chunk_size: Maximum chunk size in characters
+        progress_callback: Optional callback for progress updates
+
+    Returns:
+        Aggregated result dictionary with generation statistics
+    """
+    # Discover all _index.db files
+    index_files = discover_all_index_dbs(index_root)
+
+    if not index_files:
+        return {
+            "success": False,
+            "error": f"No index databases found in {index_root}",
+        }
+
+    if progress_callback:
+        progress_callback(f"Found {len(index_files)} index databases to process")
+
+    # Process each index database
+    all_results = []
+    total_chunks = 0
+    total_files_processed = 0
+    total_files_failed = 0
+
+    for idx, index_path in enumerate(index_files, 1):
+        if progress_callback:
+            try:
+                rel_path = index_path.relative_to(index_root)
+            except ValueError:
+                rel_path = index_path
+            progress_callback(f"[{idx}/{len(index_files)}] Processing {rel_path}")
+
+        result = generate_embeddings(
+            index_path,
+            model_profile=model_profile,
+            force=force,
+            chunk_size=chunk_size,
+            progress_callback=None,  # Don't cascade callbacks
+        )
+
+        all_results.append({
+            "path": str(index_path),
+            "success": result["success"],
+            "result": result.get("result"),
+            "error": result.get("error"),
+        })
+
+        if result["success"]:
+            data = result["result"]
+            total_chunks += data["chunks_created"]
+            total_files_processed += data["files_processed"]
+            total_files_failed += data["files_failed"]
+
+    successful = sum(1 for r in all_results if r["success"])
+
+    return {
+        "success": successful > 0,
+        "result": {
+            "indexes_processed": len(index_files),
+            "indexes_successful": successful,
+            "indexes_failed": len(index_files) - successful,
+            "total_chunks_created": total_chunks,
+            "total_files_processed": total_files_processed,
+            "total_files_failed": total_files_failed,
+            "model_profile": model_profile,
+            "details": all_results,
+        },
+    }
+
+
+def get_embeddings_status(index_root: Path) -> Dict[str, any]:
+    """Get comprehensive embeddings coverage status for all indexes.
+
+    Args:
+        index_root: Root index directory
+
+    Returns:
+        Aggregated status with coverage statistics
+    """
+    index_files = discover_all_index_dbs(index_root)
+
+    if not index_files:
+        return {
+            "success": True,
+            "result": {
+                "total_indexes": 0,
+                "total_files": 0,
+                "files_with_embeddings": 0,
+                "files_without_embeddings": 0,
+                "total_chunks": 0,
+                "coverage_percent": 0.0,
+                "indexes_with_embeddings": 0,
+                "indexes_without_embeddings": 0,
+            },
+        }
+
+    total_files = 0
+    files_with_embeddings = 0
+    total_chunks = 0
+    indexes_with_embeddings = 0
+
+    for index_path in index_files:
+        status = check_index_embeddings(index_path)
+        if status["success"]:
+            result = status["result"]
+            total_files += result["total_files"]
+            files_with_embeddings += result["files_with_chunks"]
+            total_chunks += result["total_chunks"]
+            if result["has_embeddings"]:
+                indexes_with_embeddings += 1
+
+    return {
+        "success": True,
+        "result": {
+            "total_indexes": len(index_files),
+            "total_files": total_files,
+            "files_with_embeddings": files_with_embeddings,
+            "files_without_embeddings": total_files - files_with_embeddings,
+            "total_chunks": total_chunks,
+            "coverage_percent": round((files_with_embeddings / total_files * 100) if total_files > 0 else 0, 1),
+            "indexes_with_embeddings": indexes_with_embeddings,
+            "indexes_without_embeddings": len(index_files) - indexes_with_embeddings,
+        },
+    }
+
+
 def get_embedding_stats_summary(index_root: Path) -> Dict[str, any]:
     """Get summary statistics for all indexes in root directory.