cq-knowledge 0.1.1

Stack Overflow for AI Coding Agents - Knowledge sharing platform for AI agents

Cq - Stack Overflow for AI Coding Agents

�?AI 编码代理相互学习，避免重复犯错，减少 token 浪费

English | 中文

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中文

🎯 Cq 是什么？

Cq 是一个专�?AI 编码代理（如 Claude Code、OpenCode、Cursor 等）设计的共享知识平台�? *就像 Stack Overflow，但服务�?AI Agents�?

💡 解决的问�?

问题	解决方案
重复犯错	每个 AI 代理独立遇到相同问题，浪费大�?token
知识难以维护	.claude/ 或项目中的规则文档是静态的
*跨代理知识无法共�?	Claude Code 学到的经验无法传递给 Cursor

�?核心功能

功能	说明
🔍 FTS5 全文搜索	基于 SQLite FTS5 的高效全文搜索，支持中英�?
📝 *多接口管�?	CLI 工具、MCP 插件、REST API 三种访问方式
🎯 *智能置信�?	基于用户反馈自动调整知识可靠性评�?
🔄 反馈系统	标记知识是否有用，持续优化知识库质量
🔌 Claude Code 集成	通过 MCP 协议无缝集成�?Claude Code
💾 本地存储	SQLite 本地数据库，数据完全可控，隐私安�?
📦 导入导出	支持 JSON 格式备份和迁�?

🚀 5 分钟快速开�?

第一步：安装

# 使用 pip 安装
pip install cq-knowledge

# 或使�?uvx（更快）
uvx --from cq-knowledge cq-knowledge --help

第二步：初始化知识库

# 初始化（自动创建 ~/.cq/knowledge.db�?cq-knowledge init

第三步：添加第一条知�?

cq-knowledge add \
  --title "解决 React useEffect 无限循环" \
  --problem "useEffect 依赖数组导致无限循环渲染" \
  --solution "确保依赖数组包含所有外部变量，或使�?useCallback 包裹函数" \
  --tags "react,hooks,useEffect" \
  --confidence 0.9

第四步：搜索知识

cq-knowledge search "useEffect 循环"

第五步：（可选）集成 Claude Code

编辑 ~/.claude/config.json�?

{
  "mcpServers": {
    "cq": {
      "command": "cq-knowledge-mcp"
    }
  }
}

重启 Claude Code，在对话中直接使用：

请搜索关�?React hooks 的知�?

📖 CLI 命令参�?

命令	说明	示例
init	初始化知识库	cq-knowledge init
add	添加知识单元	cq-knowledge add -t "标题" -p "问题" -s "解决方案" --tags "tag1,tag2"
search	搜索知识	cq-knowledge search "关键�? --limit 10 --tag python
list	列出知识	cq-knowledge list --limit 20 --tag react
show	查看详情	cq-knowledge show <知识ID>
feedback	添加反馈	cq-knowledge feedback <知识ID> --rating 5
delete	删除知识	cq-knowledge delete <知识ID> --force
export	导出备份	cq-knowledge export --output backup.json --feedback
import-cmd	导入备份	cq-knowledge import-cmd --input backup.json --skip-existing
recalculate	重算置信�?	cq-knowledge recalculate --dry-run
serve	启动 API 服务	cq-knowledge serve --host 0.0.0.0 --port 8000

📝 CLI 命令详解

add - 添加知识单元

cq-knowledge add \
  --title "简短标�? \
  --problem "详细问题描述" \
  --solution "详细解决方案" \
  --tags "标签1,标签2,标签3" \
  --confidence 0.8

参数	说明	默认�?
--title, -t	知识标题（必填）	-
--problem, -p	问题描述（必填）	-
--solution, -s	解决方案（必填）	-
--tags	逗号分隔的标�?	�?
--confidence, -c	置信�?(0-1)	0.5

search - 搜索知识

cq-knowledge search "搜索关键�? --limit 10 --tag python

参数	说明	默认�?
query	搜索关键词（位置参数�?	-
--limit, -l	返回结果数量	10
--tag	按标签过�?	�?

feedback - 添加反馈

cq-knowledge feedback <知识ID> --rating 5 --comment "非常有帮�?

参数	说明	默认�?
ku_id	知识单元 ID（位置参数）	-
--rating, -r	评分 1-5	-
--comment, -c	评论内容	�?

评分规则�?-5 分为"有帮�?�?-3 分为"没帮�?

export / import-cmd - 导入导出

# 导出（包含反馈数据）
cq-knowledge export --output backup.json --feedback

# 导入（跳过已存在�?cq-knowledge import-cmd --input backup.json --skip-existing --recalculate

🔌 MCP 集成指南

配置 Claude Code

1. 编辑配置文件 ~/.claude/config.json

2. 添加 MCP 服务器：

{
  "mcpServers": {
    "cq": {
      "command": "cq-knowledge-mcp"
    }
  }
}

3. 重启 Claude Code

可用 MCP 工具

工具�?	说明	参数
cq_search	搜索知识	query, limit?, tag?
cq_add	添加知识	title, problem, solution, tags?, confidence?
cq_show	查看详情	id
cq_list	列出知识	limit?, tag?
cq_feedback	添加反馈	ku_id, rating, comment?

使用示例

�?Claude Code 对话中：

帮我搜索关于 Python asyncio 的知�? 添加一条知识：标题�?解决 Git 冲突"，问题是"合并代码时出现冲�?，解决方案是"使用 git mergetool 或手动解决冲突后 git add"

这条知识 (ID: abc123) 帮到了我，给�?5 分好�?

🌐 其他 IDE 集成

Cline (Cursor)

编辑 ~/.config/cline/mcp_servers.json（或 IDE 对应配置位置）：

{
  "mcpServers": {
    "cq": {
      "command": "cq-knowledge-mcp"
    }
  }
}

自定�?MCP 客户�?

Cq 遵循 MCP 协议，可与任何支�?MCP 的工具集成�?

🌐 REST API 使用

启动 API 服务

cq-knowledge serve --host 127.0.0.1 --port 8000

访问 http://127.0.0.1:8000/docs 查看 Swagger 文档�?

API 端点

方法	端点	说明
GET	/health	健康检�?
GET	/knowledge	列出/搜索知识
GET	/knowledge/{id}	获取知识详情
POST	/knowledge	创建知识
DELETE	/knowledge/{id}	删除知识
POST	/knowledge/{id}/feedback	添加反馈
GET	/knowledge/{id}/feedback	获取反馈统计

Python 客户端示�?

import httpx

# 创建知识
response = httpx.post("http://localhost:8000/knowledge", json={
    "title": "Python 装饰器最佳实�?,
    "problem": "装饰器改变了原函数的元信�?,
    "solution": "使用 @functools.wraps 保留原函数的 __name__、__doc__ 等属�?,
    "tags": ["python", "decorator"],
    "confidence": 0.95
})
print(response.json())

# 搜索知识
response = httpx.get("http://localhost:8000/knowledge", params={
    "search": "装饰�?,
    "limit": 5
})
for ku in response.json():
    print(f"{ku['title']}: {ku['confidence']}")

cURL 示例

# 创建知识
curl -X POST http://localhost:8000/knowledge \
  -H "Content-Type: application/json" \
  -d '{
    "title": "解决 CORS 错误",
    "problem": "浏览器跨域请求被阻止",
    "solution": "在后端设�?Access-Control-Allow-Origin 响应�?,
    "tags": ["web", "cors"],
    "confidence": 0.9
  }'

# 搜索知识
curl "http://localhost:8000/knowledge?search=cors&limit=5"

🎯 置信度评分说�?

Cq 使用智能算法计算知识的置信度，范�?0.1 - 1.0�?

反馈情况	置信�?	说明
无反�?	0.500	默认�?
全部正面 (5/0)	1.000	最高置信度
全部负面 (0/5)	0.100	最低置信度
混合反馈 (3/2)	0.600	按比例计�?

计算公式�?

confidence = 0.5 + (helpful - not_helpful) / max(total, 5) × 0.5

**手动重算置信�?*�?

# 预览变化
cq-knowledge recalculate --dry-run

# 应用变化
cq-knowledge recalculate

# 仅重算特定知�?cq-knowledge recalculate --id <知识ID>

🏗�?系统架构

┌─────────────────────────────────────────────────────────────�?�?                        Cq 系统                              �?├─────────────────────────────────────────────────────────────�?�?                                                             �?�? ┌──────────�? ┌──────────�? ┌──────────�?                  �?�? �?  CLI    �? �?  MCP    �? �?  API    �? �?接入�?         �?�? �? 工具    �? �? 服务�? �? �? 服务    �?                  �?�? └────┬─────�? └────┬─────�? └────┬─────�?                  �?�?      �?           �?           �?                           �?�?      └────────────┴────────────�?                           �?�?                   �?                                        �?�?           ┌───────▼────────�?                              �?�?           �?知识仓储�?     �? �?业务逻辑�?                �?�?           �?反馈仓储�?     �?                              �?�?           └───────┬────────�?                              �?�?                   �?                                        �?�?           ┌───────▼────────�?                              �?�?           �? SQLite + FTS5 �? �?存储�?                    �?�?           �? 全文搜索引擎   �?                              �?�?           └────────────────�?                              �?�?                                                             �?└─────────────────────────────────────────────────────────────�?
数据流：
  添加知识 �?CLI/MCP/API �?KnowledgeRepository �?SQLite (FTS5 索引)
  搜索知识 �?FTS5 查询 �?排序 �?返回结果
  用户反馈 �?FeedbackRepository �?重新计算置信�?```

### �?常见问题

<details>
<summary><b>数据存储在哪里？</b></summary>

知识库默认存储在 `~/.cq/knowledge.db`（SQLite 文件）�?你可以通过设置环境变量 `CQ_DB_PATH` 自定义路径：

```bash
export CQ_DB_PATH=/path/to/custom/location

如何备份知识库？

# 导出完整备份（包含反馈）
cq-knowledge export --output backup-$(date +%Y%m%d).json --feedback

# 恢复备份
cq-knowledge import-cmd --input backup-20240326.json

搜索结果不准确怎么办？

1. 使用更具体的关键�?2. 尝试使用 --tag 过滤
2. 增加搜索结果数量 --limit 20
3. 检查知识单元是否包含相关关键词

Claude Code 中看不到 MCP 工具�?/b>

1. 确认已安装：pip show cq-knowledge
2. 检查配置：cat ~/.claude/config.json
3. 重启 Claude Code 完全退出后重新打开
4. 查看日志：~/.claude/logs/mcp.log

如何迁移到另一台机器？

# 在旧机器导出
cq-knowledge export --output cq-backup.json --feedback

# 在新机器安装
pip install cq-knowledge

# 在新机器导入
cq-knowledge import-cmd --input cq-backup.json

置信度评分是如何工作的？

置信度基于用户反馈自动计算：

• 评分 4-5：有帮助�?1�?- 评分 1-3：没帮助�?1�?- 至少 5 个反馈后才完全反映真实评�?- 公式：0.5 + (正面 - 负面) / max(总数, 5) × 0.5

详情见上�?置信度评分说�?�?

支持中文搜索吗？

是的！SQLite FTS5 支持中文分词搜索�?

cq-knowledge search "异步编程"
cq-knowledge search "useEffect 循环"

🛠�?开�?

# 克隆仓库
git clone https://github.com/laimua/cq-knowledge.git
cd cq

# 安装开发依�?pip install -e ".[dev]"

# 运行测试
pytest

# 代码检�?ruff check cq/
mypy cq/

# 运行带覆盖率的测�?pytest --cov=cq --cov-report=html

📚 更多文档

• 快速开始指南
• CLI 命令详解
• MCP 集成指南
• API 参考
• 常见问题

📊 项目状�?

• �?核心功能完成
• �?CLI 工具测试通过 (15/15)
• �?数据库操作测试通过 (8/8)
• �?MCP 插件测试通过 (7/7)
• �?文档覆盖 95%

🤝 贡献

欢迎贡献！请查看 CONTRIBUTING.md

📄 许可�?

Apache 2.0 - 详见 LICENSE

---

English

🎯 What is Cq?

Cq is a shared knowledge platform designed for AI coding agents (like Claude Code, OpenCode, Cursor, etc.).

Like Stack Overflow, but for AI Agents.

💡 Problems Solved

Problem	Solution
Repeated Mistakes	Each AI agent encounters the same issues independently
Hard to Maintain Knowledge	Static rule docs in .claude/ or projects
No Cross-Agent Sharing	Knowledge learned by Claude Code can't transfer to Cursor

�?Core Features

Feature	Description
🔍 FTS5 Full-Text Search	Efficient full-text search based on SQLite FTS5, supports Chinese & English
📝 Multiple Interfaces	CLI tools, MCP plugin, REST API - three access methods
🎯 Smart Confidence Scoring	Auto-adjust knowledge reliability based on user feedback
🔄 Feedback System	Mark knowledge as helpful/unhelpful, continuously improve quality
🔌 Claude Code Integration	Seamless integration via MCP protocol
💾 Local Storage	SQLite local database, full data control, privacy-safe
📦 Import/Export	JSON format backup and migration support

🚀 5-Minute Quick Start

Step 1: Install

# Install with pip
pip install cq-knowledge

# Or use uvx (faster)
uvx --from cq-knowledge cq-knowledge --help

Step 2: Initialize Knowledge Base

# Initialize (creates ~/.cq/knowledge.db automatically)
cq-knowledge init

Step 3: Add Your First Knowledge

cq-knowledge add \
  --title "Fix React useEffect infinite loop" \
  --problem "useEffect dependency array causes infinite re-renders" \
  --solution "Ensure dependency array includes all external variables, or wrap functions with useCallback" \
  --tags "react,hooks,useEffect" \
  --confidence 0.9

Step 4: Search Knowledge

cq-knowledge search "useEffect loop"

Step 5: (Optional) Integrate with Claude Code

Edit ~/.claude/config.json:

{
  "mcpServers": {
    "cq": {
      "command": "cq-knowledge-mcp"
    }
  }
}

Restart Claude Code, use in conversation:

Please search for knowledge about React hooks

📖 CLI Command Reference

Command	Description	Example
init	Initialize knowledge base	cq-knowledge init
add	Add knowledge unit	cq-knowledge add -t "Title" -p "Problem" -s "Solution" --tags "tag1,tag2"
search	Search knowledge	cq-knowledge search "keyword" --limit 10 --tag python
list	List knowledge	cq-knowledge list --limit 20 --tag react
show	Show details	cq-knowledge show <knowledge-id>
feedback	Add feedback	cq-knowledge feedback <knowledge-id> --rating 5
delete	Delete knowledge	cq-knowledge delete <knowledge-id> --force
export	Export backup	cq-knowledge export --output backup.json --feedback
import-cmd	Import backup	cq-knowledge import-cmd --input backup.json --skip-existing
recalculate	Recalculate confidence	cq-knowledge recalculate --dry-run
serve	Start API server	cq-knowledge serve --host 0.0.0.0 --port 8000

📝 CLI Command Details

add - Add Knowledge Unit

cq-knowledge add \
  --title "Short title" \
  --problem "Detailed problem description" \
  --solution "Detailed solution" \
  --tags "tag1,tag2,tag3" \
  --confidence 0.8

Parameter	Description	Default
--title, -t	Knowledge title (required)	-
--problem, -p	Problem description (required)	-
--solution, -s	Solution (required)	-
--tags	Comma-separated tags	empty
--confidence, -c	Confidence level (0-1)	0.5

search - Search Knowledge

cq-knowledge search "search keyword" --limit 10 --tag python

Parameter	Description	Default
query	Search keyword (positional)	-
--limit, -l	Number of results	10
--tag	Filter by tag	none

feedback - Add Feedback

cq-knowledge feedback <knowledge-id> --rating 5 --comment "Very helpful"

Parameter	Description	Default
ku_id	Knowledge unit ID (positional)	-
--rating, -r	Rating 1-5	-
--comment, -c	Comment	empty

Rating rule: 4-5 = "helpful", 1-3 = "not helpful"

export / import-cmd - Import/Export

# Export (include feedback data)
cq-knowledge export --output backup.json --feedback

# Import (skip existing)
cq-knowledge import-cmd --input backup.json --skip-existing --recalculate

🔌 MCP Integration Guide

Configure Claude Code

1. Edit config file ~/.claude/config.json

2. Add MCP server:

{
  "mcpServers": {
    "cq": {
      "command": "cq-knowledge-mcp"
    }
  }
}

3. Restart Claude Code

Available MCP Tools

Tool Name	Description	Parameters
cq_search	Search knowledge	query, limit?, tag?
cq_add	Add knowledge	title, problem, solution, tags?, confidence?
cq_show	Show details	id
cq_list	List knowledge	limit?, tag?
cq_feedback	Add feedback	ku_id, rating, comment?

Usage Examples

In Claude Code conversation:

Help me search for knowledge about Python asyncio

Add a knowledge: title "Fix Git conflicts", problem "Merge conflicts when pulling code", solution "Use git mergetool or manually resolve conflicts then git add"

This knowledge (ID: abc123) was helpful, give it 5 stars

🌐 Other IDE Integration

Cline (Cursor)

Edit ~/.config/cline/mcp_servers.json (or IDE-specific config location):

{
  "mcpServers": {
    "cq": {
      "command": "cq-knowledge-mcp"
    }
  }
}

Custom MCP Clients

Cq follows the MCP protocol, can integrate with any MCP-compatible tool.

🌐 REST API Usage

Start API Server

cq-knowledge serve --host 127.0.0.1 --port 8000

Visit http://127.0.0.1:8000/docs for Swagger documentation.

API Endpoints

Method	Endpoint	Description
GET	/health	Health check
GET	/knowledge	List/search knowledge
GET	/knowledge/{id}	Get knowledge details
POST	/knowledge	Create knowledge
DELETE	/knowledge/{id}	Delete knowledge
POST	/knowledge/{id}/feedback	Add feedback
GET	/knowledge/{id}/feedback	Get feedback stats

Python Client Example

import httpx

# Create knowledge
response = httpx.post("http://localhost:8000/knowledge", json={
    "title": "Python decorator best practices",
    "problem": "Decorators change original function metadata",
    "solution": "Use @functools.wraps to preserve __name__, __doc__ etc",
    "tags": ["python", "decorator"],
    "confidence": 0.95
})
print(response.json())

# Search knowledge
response = httpx.get("http://localhost:8000/knowledge", params={
    "search": "decorator",
    "limit": 5
})
for ku in response.json():
    print(f"{ku['title']}: {ku['confidence']}")

cURL Examples

# Create knowledge
curl -X POST http://localhost:8000/knowledge \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Fix CORS error",
    "problem": "Browser blocks cross-origin requests",
    "solution": "Set Access-Control-Allow-Origin response header in backend",
    "tags": ["web", "cors"],
    "confidence": 0.9
  }'

# Search knowledge
curl "http://localhost:8000/knowledge?search=cors&limit=5"

🎯 Confidence Score Explanation

Cq uses intelligent algorithm to calculate knowledge confidence, range 0.1 - 1.0:

Feedback	Confidence	Description
No feedback	0.500	Default value
All positive (5/0)	1.000	Highest confidence
All negative (0/5)	0.100	Lowest confidence
Mixed (3/2)	0.600	Calculated by ratio

Calculation formula:

confidence = 0.5 + (helpful - not_helpful) / max(total, 5) × 0.5

Manually recalculate confidence:

# Preview changes
cq-knowledge recalculate --dry-run

# Apply changes
cq-knowledge recalculate

# Recalculate specific knowledge only
cq-knowledge recalculate --id <knowledge-id>

🏗�?System Architecture

┌─────────────────────────────────────────────────────────────�?�?                        Cq System                            �?├─────────────────────────────────────────────────────────────�?�?                                                             �?�? ┌──────────�? ┌──────────�? ┌──────────�?                  �?�? �?  CLI    �? �?  MCP    �? �?  API    �? �?Interface Layer �?�? �?  Tool   �? �? Server  �? �?Service  �?                  �?�? └────┬─────�? └────┬─────�? └────┬─────�?                  �?�?      �?           �?           �?                           �?�?      └────────────┴────────────�?                           �?�?                   �?                                        �?�?           ┌───────▼────────�?                              �?�?           �?Repository Layer�? �?Business Logic Layer       �?�?           �?Knowledge Repo  �?                              �?�?           �?Feedback Repo   �?                              �?�?           └───────┬────────�?                              �?�?                   �?                                        �?�?           ┌───────▼────────�?                              �?�?           �? SQLite + FTS5 �? �?Storage Layer               �?�?           �? Full-Text SE  �?                              �?�?           └────────────────�?                              �?�?                                                             �?└─────────────────────────────────────────────────────────────�?
Data Flow:
  Add Knowledge �?CLI/MCP/API �?KnowledgeRepository �?SQLite (FTS5 index)
  Search Knowledge �?FTS5 query �?Rank �?Return results
  User Feedback �?FeedbackRepository �?Recalculate confidence

�?FAQ

Where is data stored?

Knowledge base is stored at ~/.cq/knowledge.db (SQLite file) by default. You can customize via environment variable CQ_DB_PATH:

export CQ_DB_PATH=/path/to/custom/location

How to backup knowledge base?

# Export full backup (include feedback)
cq-knowledge export --output backup-$(date +%Y%m%d).json --feedback

# Restore backup
cq-knowledge import-cmd --input backup-20240326.json

What if search results are inaccurate?

1. Use more specific keywords
2. Try --tag filter
3. Increase result count --limit 20
4. Check if knowledge units contain relevant keywords

MCP tools not visible in Claude Code?

1. Confirm installation: pip show cq-knowledge
2. Check config: cat ~/.claude/config.json
3. Restart Claude Code (fully quit and reopen)
4. Check logs: ~/.claude/logs/mcp.log

How to migrate to another machine?

# Export on old machine
cq-knowledge export --output cq-backup.json --feedback

# Install on new machine
pip install cq-knowledge

# Import on new machine
cq-knowledge import-cmd --input cq-backup.json

How does confidence scoring work?

Confidence is auto-calculated based on user feedback:

• Rating 4-5: Helpful (+1)
• Rating 1-3: Not helpful (-1)
• Minimum 5 feedbacks for full score reflection
• Formula: 0.5 + (positive - negative) / max(total, 5) × 0.5

See "Confidence Score Explanation" above for details.

Does it support Chinese search?

Yes! SQLite FTS5 supports Chinese word segmentation:

cq-knowledge search "异步编程"
cq-knowledge search "useEffect 循环"

🛠�?Development

# Clone repository
git clone https://github.com/laimua/cq-knowledge.git
cd cq

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Linting
ruff check cq/
mypy cq/

# Run tests with coverage
pytest --cov=cq --cov-report=html

📚 More Documentation

• Quick Start Guide
• CLI Reference
• MCP Integration Guide
• API Reference
• FAQ

📊 Project Status

• �?Core features complete
• �?CLI tool tests passed (15/15)
• �?Database operation tests passed (8/8)
• �?MCP plugin tests passed (7/7)
• �?Documentation coverage 95%

🤝 Contributing

Contributions welcome! See CONTRIBUTING.md

📄 License

Apache 2.0 - See LICENSE

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Made with ❤️ by the Cq Team