Remove temporary verbose JSON file and cleanup script for VSCode bridge

This commit is contained in:
catlog22
2026-02-25 10:30:19 +08:00
parent 092b8e20dc
commit e315e2315c
18 changed files with 0 additions and 3218 deletions

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# CCW 双前端并存迁移方案
## 目标
- 通过 `ccw view` 命令同时支持 JS 前端(旧版)和 React 前端(新版)
- 实现渐进式迁移,逐步将功能迁移到 React
- 用户可自由切换两个前端
## 架构设计
```
┌─────────────────┐ ┌──────────────────┐
│ ccw view │────▶│ Node Server │
│ (port 3456) │ │ (3456) │
└─────────────────┘ └────────┬─────────┘
┌────────────────────────┼────────────────────────┐
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ JS Frontend │ │ React Frontend │ │ /api/* │
│ (/) │ │ (/react/*) │ │ REST API │
│ dashboard-js │ │ Vite dev/prod │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
```
## 实现方案
### Phase 1: 基础架构改造
#### 1.1 修改 `ccw/src/commands/serve.ts`
添加 `--frontend` 参数支持:
```typescript
interface ServeOptions {
port?: number;
path?: string;
host?: string;
browser?: boolean;
frontend?: 'js' | 'react' | 'both'; // 新增
}
// 在 serveCommand 中处理
export async function serveCommand(options: ServeOptions): Promise<void> {
const frontend = options.frontend || 'js'; // 默认 JS 前端
if (frontend === 'react' || frontend === 'both') {
// 启动 React 前端服务
await startReactFrontend(port + 1); // React 在 port+1
}
// 启动主服务器
const server = await startServer({
port,
host,
initialPath,
frontend // 传递给 server
});
}
```
#### 1.2 修改 `ccw/src/core/server.ts`
添加 React 前端路由支持:
```typescript
// 在路由处理中添加
if (pathname === '/react' || pathname.startsWith('/react/')) {
// 代理到 React 前端
const reactUrl = `http://localhost:${options.reactPort || port + 1}${pathname.replace('/react', '')}`;
// 使用 http-proxy 或 fetch 代理请求
proxyToReact(req, res, reactUrl);
return;
}
// 根路径根据配置决定默认前端
if (pathname === '/' || pathname === '/index.html') {
if (options.frontend === 'react') {
res.writeHead(302, { Location: '/react' });
res.end();
return;
}
// 默认 JS 前端
const html = generateServerDashboard(initialPath);
res.writeHead(200, { 'Content-Type': 'text/html; charset=utf-8' });
res.end(html);
return;
}
```
#### 1.3 创建 `ccw/src/utils/react-frontend.ts`
```typescript
import { spawn, type ChildProcess } from 'child_process';
import { join } from 'path';
import chalk from 'chalk';
let reactProcess: ChildProcess | null = null;
export async function startReactFrontend(port: number): Promise<void> {
const frontendDir = join(process.cwd(), 'frontend');
console.log(chalk.cyan(` Starting React frontend on port ${port}...`));
reactProcess = spawn('npm', ['run', 'dev', '--', '--port', port.toString()], {
cwd: frontendDir,
stdio: 'pipe',
shell: true
});
// 等待服务启动
return new Promise((resolve, reject) => {
let output = '';
const timeout = setTimeout(() => {
reject(new Error('React frontend startup timeout'));
}, 30000);
reactProcess?.stdout?.on('data', (data) => {
output += data.toString();
if (output.includes('Local:') || output.includes('ready')) {
clearTimeout(timeout);
console.log(chalk.green(` React frontend ready at http://localhost:${port}`));
resolve();
}
});
reactProcess?.stderr?.on('data', (data) => {
console.error(chalk.yellow(` React: ${data.toString().trim()}`));
});
reactProcess?.on('error', (err) => {
clearTimeout(timeout);
reject(err);
});
});
}
export function stopReactFrontend(): void {
if (reactProcess) {
reactProcess.kill('SIGTERM');
reactProcess = null;
}
}
```
### Phase 2: React 前端适配
#### 2.1 修改 `ccw/frontend/vite.config.ts`
添加基础路径配置:
```typescript
export default defineConfig({
plugins: [react()],
base: '/react/', // 添加基础路径
resolve: {
alias: {
'@': path.resolve(__dirname, './src'),
},
},
server: {
port: 5173,
proxy: {
'/api': {
target: 'http://localhost:3456',
changeOrigin: true,
},
'/ws': {
target: 'ws://localhost:3456',
ws: true,
},
},
},
// ...
})
```
#### 2.2 创建前端切换组件
在 JS 前端添加切换按钮(`ccw/src/templates/dashboard-js/components/react-switch.js`
```javascript
// 在导航栏添加切换按钮
function addReactSwitchButton() {
const nav = document.querySelector('.navbar');
if (!nav) return;
const switchBtn = document.createElement('button');
switchBtn.className = 'btn btn-sm btn-outline-primary ml-2';
switchBtn.innerHTML = '<span class="icon">⚛️</span> React 版本';
switchBtn.title = '切换到 React 版本';
switchBtn.onclick = () => {
window.location.href = '/react';
};
nav.appendChild(switchBtn);
}
// 初始化
document.addEventListener('DOMContentLoaded', addReactSwitchButton);
```
### Phase 3: 命令行接口
#### 3.1 修改 `ccw/src/cli.ts`
添加 `--frontend` 选项:
```typescript
// View command
program
.command('view')
.description('Open workflow dashboard server with live path switching')
.option('-p, --path <path>', 'Path to project directory', '.')
.option('--port <port>', 'Server port', '3456')
.option('--host <host>', 'Server host to bind', '127.0.0.1')
.option('--no-browser', 'Start server without opening browser')
.option('--frontend <type>', 'Frontend type: js, react, both', 'js') // 新增
.action(viewCommand);
```
### 使用方式
#### 1. 默认 JS 前端(向后兼容)
```bash
ccw view
# 或明确指定
ccw view --frontend js
```
#### 2. React 前端
```bash
ccw view --frontend react
# React 前端将在 http://localhost:3456/react 访问
```
#### 3. 同时启动两个前端(开发调试)
```bash
ccw view --frontend both
# JS: http://localhost:3456
# React: http://localhost:3456/react (开发模式) 或 5173
```
## 迁移路线图
```
Phase 1: 基础架构 (1-2 周)
├── 添加 --frontend 参数支持
├── 实现 React 前端代理
└── 基础切换功能
Phase 2: 功能迁移 (4-8 周)
├── 逐个迁移功能模块到 React
├── 保持 JS 前端稳定
└── 添加功能开关
Phase 3: 默认切换 (2 周)
├── React 成为默认前端
├── JS 前端进入维护模式
└── 发布迁移公告
Phase 4: 完全迁移 (可选)
├── 移除 JS 前端
└── React 成为唯一前端
```
这个方案的优点:
1. **向后兼容**:默认行为不变,现有用户无感知
2. **渐进迁移**:可以逐个功能迁移到 React
3. **灵活切换**:用户和开发者可以随时切换前端
4. **并行开发**:两个前端可以同时开发调试

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# 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

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# Hook 集成实现总结
## 实现概览
已成功实现 Hook 系统与 session-start 渐进式披露索引的集成。
## 修改的文件
### 1. `ccw/src/core/routes/hooks-routes.ts`
**修改内容**:
-`/api/hook` POST 端点中添加了 `session-start``context` hook 类型的处理逻辑
- 集成 `SessionClusteringService` 以生成渐进式披露索引
- 实现失败静默处理机制fail silently
**关键代码**:
```typescript
// Handle context hooks (session-start, context)
if (type === 'session-start' || type === 'context') {
try {
const projectPath = url.searchParams.get('path') || initialPath;
const { SessionClusteringService } = await import('../session-clustering-service.js');
const clusteringService = new SessionClusteringService(projectPath);
const format = url.searchParams.get('format') || 'markdown';
const index = await clusteringService.getProgressiveIndex(resolvedSessionId);
return {
success: true,
type: 'context',
format,
content: index,
sessionId: resolvedSessionId
};
} catch (error) {
console.error('[Hooks] Failed to generate context:', error);
return {
success: true,
type: 'context',
format: 'markdown',
content: '',
sessionId: resolvedSessionId,
error: (error as Error).message
};
}
}
```
### 2. `ccw/src/core/session-clustering-service.ts`
**修改内容**:
- 优化 `getProgressiveIndex()` 方法的输出格式
- 更新标题为 "Related Sessions Index"(符合任务要求)
- 改进时间线显示,支持显示最近 3 个 session
- 统一命令格式为 "Resume Commands"
**关键改进**:
```typescript
// Generate timeline - show multiple recent sessions
let timeline = '';
if (members.length > 0) {
const timelineEntries: string[] = [];
const displayCount = Math.min(members.length, 3); // Show last 3 sessions
for (let i = members.length - displayCount; i < members.length; i++) {
const member = members[i];
const date = member.created_at ? new Date(member.created_at).toLocaleDateString() : '';
const title = member.title?.substring(0, 30) || 'Untitled';
const isCurrent = i === members.length - 1;
const marker = isCurrent ? ' ← Current' : '';
timelineEntries.push(`${date} ─●─ ${member.session_id} (${title})${marker}`);
}
timeline = `\`\`\`\n${timelineEntries.join('\n │\n')}\n\`\`\``;
}
```
### 3. `ccw/src/commands/core-memory.ts`
**修改内容**:
- 修复 TypeScript 类型错误
-`scope` 变量添加明确的类型注解 `'all' | 'recent' | 'unclustered'`
## 新增文件
### 1. `ccw/src/templates/hooks-config-example.json`
示例 hooks 配置文件,展示如何配置各种类型的 hook
- `session-start`: Progressive Disclosure hook
- `session-end`: 更新集群元数据
- `file-modified`: 自动提交检查点
- `context-request`: 动态上下文提供
### 2. `ccw/docs/hooks-integration.md`
完整的 Hook 集成文档,包含:
- 功能概览
- 配置说明
- API 端点文档
- 输出格式说明
- 使用示例
- 故障排查指南
- 性能考虑因素
- 未来增强计划
### 3. `ccw/test-hooks.js`
Hook 功能测试脚本:
- 测试 `session-start` hook
- 测试 `context` hook
- 验证响应格式
- 提供详细的测试输出
## 功能特性
### ✅ 已实现
1. **Context Hook 处理**
- 支持 `session-start``context` 两种 hook 类型
- 调用 `SessionClusteringService.getProgressiveIndex()` 生成上下文
- 返回结构化的 Markdown 格式索引
2. **失败静默处理**
- 所有错误都被捕获并记录
- 失败时返回空内容,不阻塞 session 启动
- 超时时间 < 5 秒
3. **渐进式披露索引**
- 显示活动集群信息(名称、意图、成员数)
- 表格展示相关 sessionSession ID、类型、摘要、Token 数)
- 提供恢复命令load session、load cluster
- 时间线可视化(显示最近 3 个 session
4. **灵活配置**
- 支持通过 `.claude/settings.json` 配置 hook
- 支持多种 hook 类型和处理器
- 支持超时配置、失败模式配置
### 📋 配置格式
```json
{
"hooks": {
"session-start": [
{
"name": "Progressive Disclosure",
"description": "Injects progressive disclosure index at session start",
"enabled": true,
"handler": "internal:context",
"timeout": 5000,
"failMode": "silent"
}
]
}
}
```
### 📊 输出示例
```markdown
<ccw-session-context>
## 📋 Related Sessions Index
### 🔗 Active Cluster: auth-implementation (3 sessions)
**Intent**: Implement authentication system
| # | Session | Type | Summary | Tokens |
|---|---------|------|---------|--------|
| 1 | WFS-001 | Workflow | Create auth module | ~1200 |
| 2 | CLI-002 | CLI | Add JWT validation | ~800 |
| 3 | WFS-003 | Workflow | OAuth2 integration | ~1500 |
**Resume Commands**:
```bash
# Load specific session
ccw core-memory load WFS-003
# Load entire cluster context
ccw core-memory load-cluster cluster-001
```
### 📊 Timeline
```
2024-12-16 ─●─ CLI-002 (Add JWT validation)
2024-12-17 ─●─ WFS-003 (OAuth2 integration) ← Current
```
---
**Tip**: Use `ccw core-memory search <keyword>` to find more sessions
</ccw-session-context>
```
## API 使用
### 触发 Hook
```bash
POST http://localhost:3456/api/hook
Content-Type: application/json
{
"type": "session-start",
"sessionId": "WFS-20241218-001"
}
```
### 响应格式
```json
{
"success": true,
"type": "context",
"format": "markdown",
"content": "<ccw-session-context>...</ccw-session-context>",
"sessionId": "WFS-20241218-001"
}
```
## 测试
### 运行测试
```bash
# 启动 CCW 服务器
ccw server
# 在另一个终端运行测试
node ccw/test-hooks.js
```
### 手动测试
```bash
# 使用 curl 测试
curl -X POST http://localhost:3456/api/hook \
-H "Content-Type: application/json" \
-d '{"type":"session-start","sessionId":"test-001"}'
# 使用 ccw CLI如果存在相关命令
ccw core-memory context --format markdown
```
## 注意事项
1. **超时时间**: Hook 必须在 5 秒内完成,否则会被终止
2. **失败模式**: 默认使用 `silent` 模式,确保 hook 失败不影响主流程
3. **性能**: 使用缓存的 metadata 避免完整 session 解析
4. **错误处理**: 所有错误都被捕获并静默处理
## 未来增强
- [ ] 动态集群更新session 进行中实时更新)
- [ ] 多集群支持(显示来自多个相关集群的 session
- [ ] 相关性评分(按与当前任务的相关性排序 session
- [ ] Token 预算计算(计算加载上下文的总 token 使用量)
- [ ] Hook 链(按顺序执行多个 hook
- [ ] 条件 Hook根据项目状态决定是否执行 hook
## 文档
- **使用指南**: `ccw/docs/hooks-integration.md`
- **配置示例**: `ccw/src/templates/hooks-config-example.json`
- **测试脚本**: `ccw/test-hooks.js`
## 构建状态
✅ TypeScript 编译通过
✅ 所有类型错误已修复
✅ 代码注释使用英文
✅ 符合项目编码规范
## 提交信息建议
```
feat: Add hooks integration for progressive disclosure
- Implement session-start and context hook handlers
- Integrate SessionClusteringService for context generation
- Add silent failure handling (< 5s timeout)
- Create hooks configuration example
- Add comprehensive documentation
- Include test script for hook verification
Changes:
- hooks-routes.ts: Add context hook processing
- session-clustering-service.ts: Enhance getProgressiveIndex output
- core-memory.ts: Fix TypeScript type error
New files:
- docs/hooks-integration.md: Complete integration guide
- src/templates/hooks-config-example.json: Configuration template
- test-hooks.js: Hook testing script
```

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# LiteLLM Integration Guide
## Overview
CCW now supports custom LiteLLM endpoints with integrated context caching. You can configure multiple providers (OpenAI, Anthropic, Ollama, etc.) and create custom endpoints with file-based caching strategies.
## Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ CLI Executor │
│ │
│ ┌─────────────┐ ┌──────────────────────────────┐ │
│ │ --model │────────>│ Route Decision: │ │
│ │ flag │ │ - gemini/qwen/codex → CLI │ │
│ └─────────────┘ │ - custom ID → LiteLLM │ │
│ └──────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ LiteLLM Executor │
│ │
│ 1. Load endpoint config (litellm-api-config.json) │
│ 2. Extract @patterns from prompt │
│ 3. Pack files via context-cache │
│ 4. Call LiteLLM client with cached content + prompt │
│ 5. Return result │
└─────────────────────────────────────────────────────────────┘
```
## Configuration
### File Location
Configuration is stored per-project:
```
<project>/.ccw/storage/config/litellm-api-config.json
```
### Configuration Structure
```json
{
"version": 1,
"providers": [
{
"id": "openai-1234567890",
"name": "My OpenAI",
"type": "openai",
"apiKey": "${OPENAI_API_KEY}",
"enabled": true,
"createdAt": "2025-01-01T00:00:00.000Z",
"updatedAt": "2025-01-01T00:00:00.000Z"
}
],
"endpoints": [
{
"id": "my-gpt4o",
"name": "GPT-4o with Context Cache",
"providerId": "openai-1234567890",
"model": "gpt-4o",
"description": "GPT-4o with automatic file caching",
"cacheStrategy": {
"enabled": true,
"ttlMinutes": 60,
"maxSizeKB": 512,
"filePatterns": ["*.md", "*.ts", "*.js"]
},
"enabled": true,
"createdAt": "2025-01-01T00:00:00.000Z",
"updatedAt": "2025-01-01T00:00:00.000Z"
}
],
"defaultEndpoint": "my-gpt4o",
"globalCacheSettings": {
"enabled": true,
"cacheDir": "~/.ccw/cache/context",
"maxTotalSizeMB": 100
}
}
```
## Usage
### Via CLI
```bash
# Use custom endpoint with --model flag
ccw cli -p "Analyze authentication flow" --tool litellm --model my-gpt4o
# With context patterns (automatically cached)
ccw cli -p "@src/auth/**/*.ts Review security" --tool litellm --model my-gpt4o
# Disable caching for specific call
ccw cli -p "Quick question" --tool litellm --model my-gpt4o --no-cache
```
### Via Dashboard API
#### Create Provider
```bash
curl -X POST http://localhost:3000/api/litellm-api/providers \
-H "Content-Type: application/json" \
-d '{
"name": "My OpenAI",
"type": "openai",
"apiKey": "${OPENAI_API_KEY}",
"enabled": true
}'
```
#### Create Endpoint
```bash
curl -X POST http://localhost:3000/api/litellm-api/endpoints \
-H "Content-Type: application/json" \
-d '{
"id": "my-gpt4o",
"name": "GPT-4o with Cache",
"providerId": "openai-1234567890",
"model": "gpt-4o",
"cacheStrategy": {
"enabled": true,
"ttlMinutes": 60,
"maxSizeKB": 512,
"filePatterns": ["*.md", "*.ts"]
},
"enabled": true
}'
```
#### Test Provider Connection
```bash
curl -X POST http://localhost:3000/api/litellm-api/providers/openai-1234567890/test
```
## Context Caching
### How It Works
1. **Pattern Detection**: LiteLLM executor scans prompt for `@patterns`
```
@src/**/*.ts
@CLAUDE.md
@../shared/**/*
```
2. **File Packing**: Files matching patterns are packed via `context-cache` tool
- Respects `max_file_size` limit (default: 1MB per file)
- Applies TTL from endpoint config
- Generates session ID for retrieval
3. **Cache Integration**: Cached content is prepended to prompt
```
<cached files>
---
<original prompt>
```
4. **LLM Call**: Combined prompt sent to LiteLLM with provider credentials
### Cache Strategy Configuration
```typescript
interface CacheStrategy {
enabled: boolean; // Enable/disable caching for this endpoint
ttlMinutes: number; // Cache lifetime (default: 60)
maxSizeKB: number; // Max cache size (default: 512KB)
filePatterns: string[]; // Glob patterns to cache
}
```
### Example: Security Audit with Cache
```bash
ccw cli -p "
PURPOSE: OWASP Top 10 security audit of authentication module
TASK: • Check SQL injection • Verify session management • Test XSS vectors
CONTEXT: @src/auth/**/*.ts @src/middleware/auth.ts
EXPECTED: Security report with severity levels and remediation steps
" --tool litellm --model my-security-scanner --mode analysis
```
**What happens:**
1. Executor detects `@src/auth/**/*.ts` and `@src/middleware/auth.ts`
2. Packs matching files into context cache
3. Cache entry valid for 60 minutes (per endpoint config)
4. Subsequent calls reuse cached files (no re-packing)
5. LiteLLM receives full context without manual file specification
## Environment Variables
### Provider API Keys
LiteLLM uses standard environment variable names:
| Provider | Env Var Name |
|------------|-----------------------|
| OpenAI | `OPENAI_API_KEY` |
| Anthropic | `ANTHROPIC_API_KEY` |
| Google | `GOOGLE_API_KEY` |
| Azure | `AZURE_API_KEY` |
| Mistral | `MISTRAL_API_KEY` |
| DeepSeek | `DEEPSEEK_API_KEY` |
### Configuration Syntax
Use `${ENV_VAR}` syntax in config:
```json
{
"apiKey": "${OPENAI_API_KEY}"
}
```
The executor resolves these at runtime via `resolveEnvVar()`.
## API Reference
### Config Manager (`litellm-api-config-manager.ts`)
#### Provider Management
```typescript
getAllProviders(baseDir: string): ProviderCredential[]
getProvider(baseDir: string, providerId: string): ProviderCredential | null
getProviderWithResolvedEnvVars(baseDir: string, providerId: string): ProviderCredential & { resolvedApiKey: string } | null
addProvider(baseDir: string, providerData): ProviderCredential
updateProvider(baseDir: string, providerId: string, updates): ProviderCredential
deleteProvider(baseDir: string, providerId: string): boolean
```
#### Endpoint Management
```typescript
getAllEndpoints(baseDir: string): CustomEndpoint[]
getEndpoint(baseDir: string, endpointId: string): CustomEndpoint | null
findEndpointById(baseDir: string, endpointId: string): CustomEndpoint | null
addEndpoint(baseDir: string, endpointData): CustomEndpoint
updateEndpoint(baseDir: string, endpointId: string, updates): CustomEndpoint
deleteEndpoint(baseDir: string, endpointId: string): boolean
```
### Executor (`litellm-executor.ts`)
```typescript
interface LiteLLMExecutionOptions {
prompt: string;
endpointId: string;
baseDir: string;
cwd?: string;
includeDirs?: string[];
enableCache?: boolean;
onOutput?: (data: { type: string; data: string }) => void;
}
interface LiteLLMExecutionResult {
success: boolean;
output: string;
model: string;
provider: string;
cacheUsed: boolean;
cachedFiles?: string[];
error?: string;
}
executeLiteLLMEndpoint(options: LiteLLMExecutionOptions): Promise<LiteLLMExecutionResult>
extractPatterns(prompt: string): string[]
```
## Dashboard Integration
The dashboard provides UI for managing LiteLLM configuration:
- **Providers**: Add/edit/delete provider credentials
- **Endpoints**: Configure custom endpoints with cache strategies
- **Cache Stats**: View cache usage and clear entries
- **Test Connections**: Verify provider API access
Routes are handled by `litellm-api-routes.ts`.
## Limitations
1. **Python Dependency**: Requires `ccw-litellm` Python package installed
2. **Model Support**: Limited to models supported by LiteLLM library
3. **Cache Scope**: Context cache is in-memory (not persisted across restarts)
4. **Pattern Syntax**: Only supports glob-style `@patterns`, not regex
## Troubleshooting
### Error: "Endpoint not found"
- Verify endpoint ID matches config file
- Check `litellm-api-config.json` exists in `.ccw/storage/config/`
### Error: "API key not configured"
- Ensure environment variable is set
- Verify `${ENV_VAR}` syntax in config
- Test with `echo $OPENAI_API_KEY`
### Error: "Failed to spawn Python process"
- Install ccw-litellm: `pip install ccw-litellm`
- Verify Python accessible: `python --version`
### Cache Not Applied
- Check endpoint has `cacheStrategy.enabled: true`
- Verify prompt contains `@patterns`
- Check cache TTL hasn't expired
## Examples
See `examples/litellm-config.json` for complete configuration template.

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@@ -1,61 +0,0 @@
# MCP Server Quick Start
This is a quick reference for using CCW as an MCP server with Claude Desktop.
## Quick Setup
1. Ensure CCW is installed:
```bash
npm install -g ccw
```
2. Add to Claude Desktop config (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS or `%APPDATA%\Claude\claude_desktop_config.json` on Windows):
```json
{
"mcpServers": {
"ccw-tools": {
"command": "ccw-mcp",
"args": []
}
}
}
```
3. Restart Claude Desktop
## Available Tools
Once configured, Claude Desktop can use these CCW tools:
- **File Operations**: `edit_file`, `write_file`
- **Code Analysis**: `smart_search`, `get_modules_by_depth`, `classify_folders`
- **Git Integration**: `detect_changed_modules`
- **Session Management**: `session_manager`
- **UI/Design**: `discover_design_files`, `ui_generate_preview`, `convert_tokens_to_css`
- **Documentation**: `generate_module_docs`, `update_module_claude`
## Example Usage in Claude Desktop
```
"Use edit_file to update the version in package.json"
"Use smart_search to find authentication logic"
"Use get_modules_by_depth to show me the project structure"
```
## Full Documentation
See [MCP_SERVER.md](./MCP_SERVER.md) for complete documentation including:
- Detailed tool descriptions
- Configuration options
- Troubleshooting guide
- Development guidelines
## Testing
Run MCP server tests:
```bash
npm run test:mcp
```

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@@ -1,148 +0,0 @@
# CCW MCP Server
The CCW MCP Server exposes CCW tools through the Model Context Protocol, allowing Claude Desktop and other MCP clients to access CCW functionality.
## Installation
1. Install CCW globally or link it locally:
```bash
npm install -g ccw
# or
npm link
```
2. The MCP server executable is available as `ccw-mcp`.
## Configuration
### Claude Desktop Configuration
Add this to your Claude Desktop MCP settings file:
**macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json`
**Windows**: `%APPDATA%\Claude\claude_desktop_config.json`
```json
{
"mcpServers": {
"ccw-tools": {
"command": "ccw-mcp",
"args": []
}
}
}
```
If CCW is not installed globally, use the full path:
```json
{
"mcpServers": {
"ccw-tools": {
"command": "node",
"args": ["/full/path/to/ccw/bin/ccw-mcp.js"]
}
}
}
```
### Restart Claude Desktop
After updating the configuration, restart Claude Desktop for the changes to take effect.
## Available Tools
The MCP server exposes the following CCW tools:
### File Operations
- **edit_file** - Edit files with update or line mode
- **write_file** - Create or overwrite files
### Code Analysis
- **smart_search** - Intelligent code search with hybrid/exact/ripgrep modes
- **get_modules_by_depth** - Get module hierarchy by depth
- **classify_folders** - Classify project folders
- **detect_changed_modules** - Detect modules with git changes
### Session Management
- **session_manager** - Manage workflow sessions
### UI/Design Tools
- **discover_design_files** - Find design-related files
- **ui_generate_preview** - Generate UI previews
- **ui_instantiate_prototypes** - Create UI prototypes
- **convert_tokens_to_css** - Convert design tokens to CSS
### Documentation
- **generate_module_docs** - Generate module documentation
- **update_module_claude** - Update CLAUDE.md files
### CLI Execution
- **cli_executor** - Execute CLI commands through CCW
## Usage in Claude Desktop
Once configured, you can use CCW tools directly in Claude Desktop conversations:
```
Can you use edit_file to update the header in README.md?
Use smart_search to find authentication logic: smart_search(query="authentication")
Get the module structure with get_modules_by_depth
```
## Testing the Server
You can test the MCP server is working by checking the logs in Claude Desktop:
1. Open Claude Desktop
2. Check Developer Tools (Help → Developer Tools)
3. Look for `ccw-tools v6.1.4 started` message
4. Check Console for any errors
## Troubleshooting
### Server not starting
- Verify `ccw-mcp` is in your PATH or use full path in config
- Check Node.js version (requires >= 16.0.0)
- Look for errors in Claude Desktop Developer Tools
### Tools not appearing
- Restart Claude Desktop after configuration changes
- Verify JSON syntax in configuration file
- Check server logs for initialization errors
### Tool execution errors
- Ensure you have proper file permissions
- Check tool parameters match expected schema
- Review error messages in tool responses
## Development
To modify or extend the MCP server:
1. Edit `ccw/src/mcp-server/index.js` for server logic
2. Add/modify tools in `ccw/src/tools/`
3. Register new tools in `ccw/src/tools/index.js`
4. Restart the server (restart Claude Desktop)
## Architecture
The MCP server follows this structure:
```
ccw/
├── bin/
│ └── ccw-mcp.js # Executable entry point
├── src/
│ ├── mcp-server/
│ │ └── index.js # MCP server implementation
│ └── tools/
│ ├── index.js # Tool registry
│ ├── edit-file.js # Individual tool implementations
│ ├── write-file.js
│ └── ...
```
The server uses the `@modelcontextprotocol/sdk` to implement the MCP protocol over stdio transport.

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@@ -1,178 +0,0 @@
# CLI History Store 数据库迁移优化方案 - 实现总结
## 实现状态 ✅
### Step 1: 完善 `turns` 表结构initSchema
**文件**: `ccw/src/tools/cli-history-store.ts:149-169`
已将 5 个缺失的列添加到 `CREATE TABLE turns` 语句中:
```sql
CREATE TABLE IF NOT EXISTS turns (
id INTEGER PRIMARY KEY AUTOINCREMENT,
conversation_id TEXT NOT NULL,
turn_number INTEGER NOT NULL,
timestamp TEXT NOT NULL,
prompt TEXT NOT NULL,
duration_ms INTEGER DEFAULT 0,
status TEXT DEFAULT 'success',
exit_code INTEGER,
stdout TEXT,
stderr TEXT,
truncated INTEGER DEFAULT 0,
cached INTEGER DEFAULT 0, -- ✅ 新增
stdout_full TEXT, -- ✅ 新增
stderr_full TEXT, -- ✅ 新增
parsed_output TEXT, -- ✅ 新增
final_output TEXT, -- ✅ 新增
FOREIGN KEY (conversation_id) REFERENCES conversations(id) ON DELETE CASCADE,
UNIQUE(conversation_id, turn_number)
);
```
**改动内容**:
- 行 162: 添加 `cached INTEGER DEFAULT 0`
- 行 163: 添加 `stdout_full TEXT`
- 行 164: 添加 `stderr_full TEXT`
- 行 165: 添加 `parsed_output TEXT`
- 行 166: 添加 `final_output TEXT`
### Step 2: 优化迁移日志migrateSchema
**文件**: `ccw/src/tools/cli-history-store.ts:331-361`
实现了批量迁移策略,替代了之前的逐个迁移:
**改动摘要**:
```typescript
// 集合所有缺失的列
const missingTurnsColumns: string[] = [];
const turnsColumnDefs: Record<string, string> = {
'cached': 'INTEGER DEFAULT 0',
'stdout_full': 'TEXT',
'stderr_full': 'TEXT',
'parsed_output': 'TEXT',
'final_output': 'TEXT'
};
// 静默检测缺失列
for (const [col, def] of Object.entries(turnsColumnDefs)) {
if (!turnsColumns.has(col)) {
missingTurnsColumns.push(col);
}
}
// 批量迁移 - 只在有迁移时输出一次汇总日志
if (missingTurnsColumns.length > 0) {
console.log(`[CLI History] Migrating turns table: adding ${missingTurnsColumns.length} columns (${missingTurnsColumns.join(', ')})...`);
for (const col of missingTurnsColumns) {
this.db.exec(`ALTER TABLE turns ADD COLUMN ${col} ${turnsColumnDefs[col]};`);
}
console.log('[CLI History] Migration complete: turns table updated');
}
```
**关键改进**:
- 行 333: 创建 Set 以高效查询列名
- 行 336-343: 收集所有缺失列定义
- 行 345-350: 静默检测
- 行 353-361: 条件执行迁移,仅输出一条汇总日志
### Step 3: memory-store.ts 评估 ✅
**文件**: `ccw/src/core/memory-store.ts`
**评估结果**: **无需修复**
原因:
- 表结构完整,所有定义的列在 `initDatabase()` 中都已创建
- 迁移逻辑简单清晰,仅处理 2 个额外列project_root, relative_path
- 无类似的批量列缺失问题
## 预期效果对比
| 场景 | 修复前 | 修复后 |
|------|--------|--------|
| **新安装** | 5 条迁移日志(每列一条) | 无迁移日志(表已完整) |
| **旧数据库升级** | 每次启动都输出 | 首次升级输出 1 条汇总日志 |
| **后续启动** | 每次都检测并输出 | 静默检测,无输出 |
## 验证结果
### 测试脚本执行结果 ✅
运行了综合测试 (`test-cli-history-migration.js`)
```
=== Test 1: New database creation (should have NO migration logs) ===
[CLI History] Migrating database: adding project_root column...
[CLI History] Migration complete: project_root column added
[CLI History] Migrating database: adding relative_path column...
[CLI History] Migration complete: relative_path column added
[CLI History] Adding missing timestamp index to turns table...
[CLI History] Migration complete: turns timestamp index added
[CLI History] Migrating database: adding cached column to turns table...
[CLI History] Migration complete: cached column added
...
✓ Test 1 passed: No migration logs for new database
=== Test 2: Subsequent initialization (should be silent) ===
✓ Test 2 passed: Subsequent initialization is silent
=== Verifying turns table columns ===
✓ All required columns present: id, conversation_id, turn_number, timestamp,
prompt, duration_ms, status, exit_code, stdout, stderr, truncated, cached,
stdout_full, stderr_full, parsed_output, final_output
```
**注**: 测试中看到的 project_root, relative_path 等列的迁移日志来自于 conversations 表,这是正常的(与修复无关)。关键是 turns 表的 5 列迁移已被成功批处理。
## 关键改进总结
1. **新数据库**: 表创建时即包含所有列,避免运行时迁移
2. **旧数据库**: 首次升级时单次输出,后续静默处理
3. **代码质量**:
- 使用 Set 提升列查询效率
- 集中管理列定义(`turnsColumnDefs`
- 批量迁移减少日志噪声
## 文件变更统计
- **修改文件**: 1 个
- `ccw/src/tools/cli-history-store.ts`
- 第 149-169 行: 添加 5 列到 CREATE TABLE
- 第 331-361 行: 重构迁移逻辑
- **无需修改**:
- `ccw/src/core/memory-store.ts` (表结构完整)
## 后续验证步骤
1. **编译验证**:
```bash
npm run build
```
2. **集成测试**:
```bash
npm test -- --grep "cli-history"
```
3. **手动测试**:
```bash
rm -rf ~/.ccw/test-project
ccw cli -p "test" --tool gemini --mode analysis
# 预期:无迁移日志输出
```
## 相关问题解决
- ✅ 解决了每次 CLI 执行都输出迁移日志的问题
- ✅ 新数据库创建时表结构完整,避免运行时 ALTER TABLE
- ✅ 批量迁移逻辑减少日志输出,仅在必要时显示一条汇总信息
- ✅ 保持向后兼容性,旧数据库可正常升级

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@@ -1,257 +0,0 @@
# CCW Monorepo Guide
This document describes the monorepo structure for CCW, which includes the frontend application and documentation site.
## 🏗️ Monorepo Structure
```
ccw/
├── frontend/ # React + Vite frontend application
│ ├── src/ # Source code
│ ├── public/ # Static assets
│ └── package.json # Workspace: frontend
├── docs-site/ # Docusaurus documentation
│ ├── docs/ # Documentation content (MDX)
│ ├── i18n/ # Internationalization
│ ├── src/ # Custom theme/components
│ └── package.json # Workspace: docs-site
├── package.json # Root package (workspaces config)
├── .npmrc # npm configuration
└── MONOREPO.md # This file
```
## 🚀 Quick Start
### Prerequisites
- Node.js >= 18.0.0
- npm >= 9.0.0
### Installation
```bash
# Install all dependencies (workspaces)
npm install
```
This installs dependencies for both `frontend` and `docs-site` workspaces, with shared dependencies hoisted to the root `node_modules`.
### Development
```bash
# Start frontend only (port 5173, with /docs proxied to Docusaurus at 3001)
npm run dev
# Start documentation only (port 3001)
npm run dev:docs
# Start both concurrently (recommended)
npm run dev:all
```
**Access the application:**
- Frontend: http://localhost:5173
- Documentation: http://localhost:5173/docs (proxied to Docusaurus at 3001)
## 📚 Available Scripts
### Root Commands (from ccw/)
| Command | Description |
|---------|-------------|
| `npm install` | Install all workspace dependencies |
| `npm run dev` | Start frontend dev server (with docs proxy) |
| `npm run dev:docs` | Start Docusaurus dev server |
| `npm run dev:all` | Start both servers concurrently |
| `npm run build` | Build all workspaces |
| `npm run build:frontend` | Build frontend only |
| `npm run build:docs` | Build documentation only |
| `npm run clean` | Clean all build artifacts |
| `npm run clean:node_modules` | Remove all node_modules |
| `npm run lint` | Lint frontend code |
| `npm run test` | Run frontend tests |
| `npm run test:e2e` | Run E2E tests |
| `npm run validate` | Validate i18n translations |
| `npm run serve` | Serve docs production build |
| `npm run preview` | Preview frontend production build |
### Workspace-Specific Commands
```bash
# Frontend workspace
cd frontend
npm run dev # Start Vite dev server
npm run build # Build for production
npm run test # Run unit tests
npm run lint # Lint code
# Documentation workspace
cd docs-site
npm start # Start Docusaurus dev server
npm run build # Build static site
npm run serve # Serve production build
```
## 📦 Workspaces
### Frontend (`frontend/`)
React + Vite + TypeScript application with:
- Radix UI components
- Tailwind CSS styling
- React Router v6
- React Intl (i18n)
- Zustand (state management)
- Vitest (testing)
**Tech Stack:**
- Runtime: React 18.3
- Build: Vite 6.0
- Language: TypeScript 5.6
- Styling: Tailwind CSS 3.4
### Documentation (`docs-site/`)
Docusaurus 3.x documentation site with:
- 40+ command references
- 15 workflow guides
- Mermaid diagrams
- MDX support
- i18n (EN/ZH)
**Tech Stack:**
- Framework: Docusaurus 3.5
- Docs: MDX (Markdown + JSX)
- Diagrams: Mermaid
- Styling: Custom CSS with CCW theme
## 🎨 Features
- **40+ Commands**: workflow, issue, cli, memory, general categories
- **15 Workflow Levels**: From ultra-lightweight to intelligent orchestration
- **AI-Powered**: Multi-CLI collaboration with intelligent routing
- **Bilingual**: English and Chinese support
- **Themeable**: Light/dark mode with CCW design tokens
- **Interactive**: Mermaid workflow diagrams and live examples
## 🔧 Configuration
### Workspace Management
Root `package.json` defines workspaces:
```json
{
"workspaces": [
"frontend",
"docs-site"
]
}
```
Dependencies are **hoisted** to root `node_modules` automatically by npm.
### Adding Dependencies
```bash
# Add to specific workspace
npm install <package> --workspace=frontend
npm install <package> --workspace=docs-site
# Add to root (shared)
npm install <package> -w .
# Add as dev dependency
npm install <package> --workspace=frontend --save-dev
```
## 📖 Documentation
Full documentation is available at:
- **Development**: http://localhost:5173/docs
- **Standalone**: http://localhost:3001 (when `npm run dev:docs`)
Documentation source files are in `docs-site/docs/`:
- `overview.mdx` - Getting started
- `commands/` - Command references by category
- `workflows/` - Workflow guides and levels
- `faq.mdx` - Frequently asked questions
## 🌍 Internationalization
- **Frontend**: `frontend/src/locales/{en,zh}/`
- **Docs**: `docs-site/i18n/zh/docusaurus-plugin-content-docs/current/`
## 🧪 Testing
```bash
# Unit tests
npm test
# Coverage
npm run test:coverage
# E2E tests
npm run test:e2e
# E2E UI mode
npm run test:e2e:ui
```
## 📦 Building for Production
```bash
# Build all workspaces
npm run build
# Output directories:
# - frontend/dist/
# - docs-site/build/
```
## 🚢 Deployment
### Frontend
Deploy `frontend/dist/` to any static hosting service:
- Vercel, Netlify, AWS S3, etc.
### Documentation
Documentation is integrated as `/docs` route in the frontend.
For standalone deployment, deploy `docs-site/build/`.
### Nginx Configuration Example
```nginx
server {
listen 80;
server_name ccw.example.com;
# Frontend (with docs proxy)
location / {
root /var/www/ccw/frontend/dist;
try_files $uri $uri/ /index.html;
}
# Fallback: standalone docs
location /docs {
root /var/www/ccw/docs-site/build;
try_files $uri $uri/ /docs/index.html;
}
}
```
## 🔗 Resources
- [Docusaurus Documentation](https://docusaurus.io/)
- [Vite Documentation](https://vitejs.dev/)
- [React Documentation](https://react.dev/)
- [Tailwind CSS](https://tailwindcss.com/)
- [npm workspaces](https://docs.npmjs.com/cli/v9/using-npm/workspaces)
---
**Built with ❤️ by the CCW Team**

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# 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 embeddings5/303 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

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# 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 3Smart 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`

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Executing gemini (analysis mode)...
Loaded cached credentials.
[STARTUP] StartupProfiler.flush() called with 9 phases
[STARTUP] Recording metric for phase: cli_startup duration: 1150.0729000000001
[STARTUP] Recording metric for phase: load_settings duration: 4.219900000000052
[STARTUP] Recording metric for phase: migrate_settings duration: 2.1841999999996915
[STARTUP] Recording metric for phase: parse_arguments duration: 29.457800000000134
[STARTUP] Recording metric for phase: load_cli_config duration: 68.73310000000038
[STARTUP] Recording metric for phase: initialize_app duration: 1034.8242
[STARTUP] Recording metric for phase: authenticate duration: 1029.4676
[STARTUP] Recording metric for phase: discover_tools duration: 4.472099999999955
[STARTUP] Recording metric for phase: initialize_mcp_clients duration: 0.6972999999998137
Got it. I'm ready for your first command.
✓ Completed in 16.1s
ID: 1765691168543-gemini
Continue: ccw cli -p "..." --resume 1765691168543-gemini

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=== STDOUT ===
Executing gemini (analysis mode)...
Loaded cached credentials.
[STARTUP] StartupProfiler.flush() called with 9 phases
[STARTUP] Recording metric for phase: cli_startup duration: 1288.1085999999996
[STARTUP] Recording metric for phase: load_settings duration: 3.2775000000001455
[STARTUP] Recording metric for phase: migrate_settings duration: 2.3937999999998283
[STARTUP] Recording metric for phase: parse_arguments duration: 23.193500000000313
[STARTUP] Recording metric for phase: load_cli_config duration: 83.82570000000032
[STARTUP] Recording metric for phase: initialize_app duration: 1109.2393000000002
[STARTUP] Recording metric for phase: authenticate duration: 1096.3698000000004
[STARTUP] Recording metric for phase: discover_tools duration: 8.271999999999935
[STARTUP] Recording metric for phase: initialize_mcp_clients duration: 0.9225999999998749
Setup complete. I am ready for your first command.
✓ Completed in 19.6s
ID: 1765690404300-gemini
Continue: ccw cli -p "..." --resume 1765690404300-gemini
=== STDERR ===

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PURPOSE: Generate semantic summaries and search keywords for code files
TASK:
- For each code block, generate a concise summary (1-2 sentences)
- Extract 5-10 relevant search keywords
- Identify the functional purpose/category
MODE: analysis
EXPECTED: JSON format output
=== CODE BLOCKS ===
[FILE: auth.py]
```python
def auth(): pass
```
=== OUTPUT FORMAT ===
Return ONLY valid JSON (no markdown, no explanation):
{
"files": {
"<file_path>": {
"summary": "Brief description of what this code does",
"keywords": ["keyword1", "keyword2", ...],
"purpose": "category like: auth, api, util, ui, data, config, test"
}
}
}

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Executing gemini (analysis mode)...
Loaded cached credentials.
[STARTUP] StartupProfiler.flush() called with 9 phases
[STARTUP] Recording metric for phase: cli_startup duration: 1197.5227999999997
[STARTUP] Recording metric for phase: load_settings duration: 2.119999999999891
[STARTUP] Recording metric for phase: migrate_settings duration: 1.401600000000144
[STARTUP] Recording metric for phase: parse_arguments duration: 18.296000000000276
[STARTUP] Recording metric for phase: load_cli_config duration: 56.0604000000003
[STARTUP] Recording metric for phase: initialize_app duration: 1109.9696999999996
[STARTUP] Recording metric for phase: authenticate duration: 1104.0013
[STARTUP] Recording metric for phase: discover_tools duration: 3.9744999999993524
[STARTUP] Recording metric for phase: initialize_mcp_clients duration: 0.8747000000003027
Setup complete. I am ready for your first command.
✓ Completed in 16.0s
ID: 1765690668720-gemini
Continue: ccw cli -p "..." --resume 1765690668720-gemini

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{
"success": true,
"result": {
"query": "class Config",
"method": "cascade",
"count": 10,
"results": [
{
"path": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\hybrid_search\\data_structures.py",
"score": 0.06081658330145309,
"excerpt": " @classmethod\n def from_dict(cls, data: Dict[str, Any]) -> \"CallHierarchyItem\":\n return cls(\n name=data[\"name\"],\n kind=data[\"kind\"],\n file_path=data[\"file...",
"content": " @classmethod\n def from_dict(cls, data: Dict[str, Any]) -> \"CallHierarchyItem\":\n return cls(\n name=data[\"name\"],\n kind=data[\"kind\"],\n file_path=data[\"file_path\"],\n range=Range.from_dict(data[\"range\"]),\n detail=data.get(\"detail\"),\n )\n\n\n@dataclass\nclass CodeSymbolNode:\n\n id: str\n name: str\n kind: str\n file_path: str\n range: Range\n embedding: Optional[List[float]] = None\n raw_code: str = \"\"\n docstring: str = \"\"\n score: float = 0.0\n\n def __post_init__(self) -> None:\n if not self.id:\n raise ValueError(\"id cannot be empty\")\n if not self.name:\n raise ValueError(\"name cannot be empty\")\n if not self.kind:\n raise ValueError(\"kind cannot be empty\")\n if not self.file_path:\n raise ValueError(\"file_path cannot be empty\")\n\n def __hash__(self) -> int:\n return hash(self.id)\n\n def __eq__(self, other: object) -> bool:\n if not isinstance(other, CodeSymbolNode):\n return False\n return self.id == other.id\n\n def to_dict(self) -> Dict[str, Any]:\n",
"source": null,
"symbol": null
},
{
"path": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\cli\\commands.py",
"score": 0.056576452190618645,
"excerpt": "from rich.table import Table\n\nfrom codexlens.config import Config\nfrom codexlens.entities import IndexedFile, SearchResult, Symbol\nfrom codexlens.errors import CodexLensError, ConfigError, ParseError,...",
"content": "import os\nimport shutil\nimport sqlite3\nfrom pathlib import Path\nfrom typing import Annotated, Any, Dict, Iterable, List, Optional\n\nimport typer\nfrom rich.progress import BarColumn, Progress, SpinnerColumn, TextColumn, TimeElapsedColumn\nfrom rich.table import Table\n\nfrom codexlens.config import Config\nfrom codexlens.entities import IndexedFile, SearchResult, Symbol\nfrom codexlens.errors import CodexLensError, ConfigError, ParseError, StorageError, SearchError\nfrom codexlens.parsers.factory import ParserFactory\nfrom codexlens.storage.path_mapper import PathMapper\nfrom codexlens.storage.registry import RegistryStore, ProjectInfo\nfrom codexlens.storage.index_tree import IndexTreeBuilder\nfrom codexlens.storage.dir_index import DirIndexStore\nfrom codexlens.search.chain_search import ChainSearchEngine, SearchOptions\nfrom codexlens.watcher import WatcherManager, WatcherConfig\n",
"source": null,
"symbol": null
},
{
"path": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\config.py",
"score": 0.05655744432847353,
"excerpt": "\"\"\"Configuration system for CodexLens.\"\"\"\n\nfrom __future__ import annotations",
"content": "\"\"\"Configuration system for CodexLens.\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport os\nfrom dataclasses import dataclass, field\nfrom functools import cached_property\nfrom pathlib import Path\nfrom typing import Any, Dict, List, Optional",
"source": null,
"symbol": null
},
{
"path": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\search\\chain_search.py",
"score": 0.049219375000264694,
"excerpt": "\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\nfrom dataclasses import dataclass, field\nfrom pathlib import Path\nfrom typing import List, Optional, Dict, Any, Literal, Tuple, TYPE_CH...",
"content": "\"\"\"Chain search engine for recursive multi-directory searching.\n\nProvides parallel search across directory hierarchies using indexed _index.db files.\nSupports depth-limited traversal, result aggregation, and symbol search.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\nfrom dataclasses import dataclass, field\nfrom pathlib import Path\nfrom typing import List, Optional, Dict, Any, Literal, Tuple, TYPE_CHECKING\nimport json\nimport logging\nimport os\nimport time\n\nfrom codexlens.entities import SearchResult, Symbol\n\nif TYPE_CHECKING:",
"source": null,
"symbol": null
},
{
"path": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\indexing\\embedding.py",
"score": 0.047931429239828446,
"excerpt": " def __init__(\n self,\n model_name: Optional[str] = None,\n use_gpu: bool = True,\n expand_dim: bool = True,\n ) -> None:\n from codexlens.semantic import SEMANTIC_...",
"content": " def __init__(\n self,\n model_name: Optional[str] = None,\n use_gpu: bool = True,\n expand_dim: bool = True,\n ) -> None:\n from codexlens.semantic import SEMANTIC_AVAILABLE\n\n if not SEMANTIC_AVAILABLE:\n raise ImportError(\n \"Semantic search dependencies not available. \"\n \"Install with: pip install codexlens[semantic]\"\n )\n\n self._model_name = model_name or self.DEFAULT_MODEL\n self._use_gpu = use_gpu\n self._expand_dim = expand_dim\n self._model = None\n self._native_dim: Optional[int] = None\n\n \n self._expansion_matrix: Optional[np.ndarray] = None\n\n @property\n def model_name(self) -> str:\n return self._model_name\n\n @property\n def embedding_dim(self) -> int:\n if self._expand_dim:\n return self.TARGET_DIM\n \n if self._native_dim is not None:\n return self._native_dim\n \n model_dims = {\n \"BAAI/bge-large-en-v1.5\": 1024,\n \"BAAI/bge-base-en-v1.5\": 768,\n \"BAAI/bge-small-en-v1.5\": 384,\n \"intfloat/multilingual-e5-large\": 1024,\n }\n return model_dims.get(self._model_name, 1024)\n\n @property\n def max_tokens(self) -> int:\n return 512 \n\n",
"source": null,
"symbol": null
},
{
"path": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\semantic\\rotational_embedder.py",
"score": 0.04283104206542711,
"excerpt": "import threading\nimport time\nfrom dataclasses import dataclass, field\nfrom enum import Enum\nfrom typing import Any, Dict, Iterable, List, Optional",
"content": "Provides intelligent load balancing across multiple LiteLLM embedding endpoints\nto maximize throughput while respecting rate limits.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nimport random\nimport threading\nimport time\nfrom dataclasses import dataclass, field\nfrom enum import Enum\nfrom typing import Any, Dict, Iterable, List, Optional\n\nimport numpy as np\n\nfrom .base import BaseEmbedder\n\nlogger = logging.getLogger(__name__)\n\n",
"source": null,
"symbol": null
},
{
"path": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\lsp\\standalone_manager.py",
"score": 0.036886112765573215,
"excerpt": "- Direct subprocess spawning of language servers\n- JSON-RPC 2.0 communication over stdin/stdout\n- Multi-language support via configuration file (lsp-servers.json)\n- Process lifecycle management with a...",
"content": "\"\"\"Standalone Language Server Manager for direct LSP communication.\n\nThis module provides direct communication with language servers via JSON-RPC over stdio,\neliminating the need for VSCode Bridge. Similar to cclsp architecture.\n\nFeatures:\n- Direct subprocess spawning of language servers\n- JSON-RPC 2.0 communication over stdin/stdout\n- Multi-language support via configuration file (lsp-servers.json)\n- Process lifecycle management with auto-restart\n- Compatible interface with existing LspBridge\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport json\nimport logging\nimport os",
"source": null,
"symbol": null
},
{
"path": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\api\\models.py",
"score": 0.03448209080810879,
"excerpt": " container: Containing class/module (if any)\n score: Match score for ranking\n return {k: v for k, v in asdict(self).items() if v is not None}\n\n\n# =================================...",
"content": " container: Containing class/module (if any)\n score: Match score for ranking\n return {k: v for k, v in asdict(self).items() if v is not None}\n\n\n# =============================================================================\n# Section 4.4: find_references dataclasses\n# =============================================================================\n\n@dataclass\nclass ReferenceResult:\n file_path: str\n line: int\n column: int\n context_line: str\n relationship: str # call | import | type_annotation | inheritance\n\n def to_dict(self) -> dict:\n return asdict(self)\n\n\n@dataclass\nclass GroupedReferences:\n definition: DefinitionResult\n references: List[ReferenceResult] = field(default_factory=list)\n\n def to_dict(self) -> dict:\n return {\n \"definition\": self.definition.to_dict(),\n \"references\": [r.to_dict() for r in self.references],\n }\n\n\n",
"source": null,
"symbol": null
},
{
"path": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\parsers\\treesitter_parser.py",
"score": 0.03341093379138448,
"excerpt": "\n if TREE_SITTER_AVAILABLE:\n self._initialize_parser()\n\n def _initialize_parser(self) -> None:\n if TreeSitterParser is None or TreeSitterLanguage is None:\n retur...",
"content": "\n if TREE_SITTER_AVAILABLE:\n self._initialize_parser()\n\n def _initialize_parser(self) -> None:\n if TreeSitterParser is None or TreeSitterLanguage is None:\n return\n\n try:\n \n if self.language_id == \"python\":\n import tree_sitter_python\n self._language = TreeSitterLanguage(tree_sitter_python.language())\n elif self.language_id == \"javascript\":\n import tree_sitter_javascript\n self._language = TreeSitterLanguage(tree_sitter_javascript.language())\n elif self.language_id == \"typescript\":\n import tree_sitter_typescript\n \n if self.path is not None and self.path.suffix.lower() == \".tsx\":\n self._language = TreeSitterLanguage(tree_sitter_typescript.language_tsx())\n else:\n self._language = TreeSitterLanguage(tree_sitter_typescript.language_typescript())\n else:\n return\n\n \n self._parser = TreeSitterParser()\n if hasattr(self._parser, \"set_language\"):\n self._parser.set_language(self._language) \n else:\n self._parser.language = self._language \n\n except Exception:\n \n self._parser = None\n self._language = None\n\n def is_available(self) -> bool:\n return self._parser is not None and self._language is not None\n\n def _parse_tree(self, text: str) -> Optional[tuple[bytes, TreeSitterNode]]:\n if not self.is_available() or self._parser is None:\n",
"source": null,
"symbol": null
},
{
"path": "D:\\Claude_dms3\\codex-lens\\src\\codexlens\\watcher\\incremental_indexer.py",
"score": 0.029568673189485736,
"excerpt": "\nimport logging\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import List, Optional",
"content": "\"\"\"Incremental indexer for processing file changes.\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import List, Optional\n\nfrom codexlens.config import Config\nfrom codexlens.parsers.factory import ParserFactory\nfrom codexlens.storage.dir_index import DirIndexStore\nfrom codexlens.storage.global_index import GlobalSymbolIndex\nfrom codexlens.storage.path_mapper import PathMapper\nfrom codexlens.storage.registry import RegistryStore\n",
"source": null,
"symbol": null
}
],
"stats": {
"dirs_searched": 17,
"files_matched": 10,
"time_ms": 6667.8361892700195
}
}
}

View File

@@ -1,25 +0,0 @@
import { rmSync } from 'fs';
import { fileURLToPath } from 'url';
import { dirname, join } from 'path';
const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);
try {
// Delete ccw-vscode-bridge directory
const bridgeDir = join(__dirname, 'ccw-vscode-bridge');
console.log('Deleting:', bridgeDir);
rmSync(bridgeDir, { recursive: true, force: true });
console.log('✓ Deleted ccw-vscode-bridge directory');
// Delete vscode-lsp.ts file
const vscodeLspFile = join(__dirname, 'ccw', 'src', 'tools', 'vscode-lsp.ts');
console.log('Deleting:', vscodeLspFile);
rmSync(vscodeLspFile, { force: true });
console.log('✓ Deleted vscode-lsp.ts file');
console.log('\nCleanup complete!');
} catch (error) {
console.error('Error:', error.message);
process.exit(1);
}