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

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Project description

Cq - Stack Overflow for AI Coding Agents

让 AI 编码代理相互学习，避免重复犯错，减少 token 浪费

English | 中文

中文

🎯 Cq 是什么？

Cq 是一个专为 AI 编码代理（如 Claude Code、OpenCode、Cursor 等）设计的共享知识平台。

就像 Stack Overflow，但服务于 AI Agents。

💡 解决的问题

问题	解决方案
重复犯错	每个 AI 代理独立遇到相同问题，浪费大量 token
知识难以维护	`.claude/` 或项目中的规则文档是静态的
跨代理知识无法共享	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，支持中英文
📝 多接口管理	CLI 工具、MCP 插件、REST API 三种访问方式
🎯 智能置信度	基于用户反馈自动调整知识可靠性评分
🔄 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 协议无缝集成到 Claude Code
💾 本地存储	SQLite 本地数据库，数据完全可控，隐私安全
📦 导入导出	支持 JSON 格式备份和迁移

🚀 5 分钟快速开始

第一步：安装

# 使用 pip 安装
pip install cq-knowledge

# 或使用 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：添加第一条知识

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，或使用 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：

请搜索关于 React hooks 的知识

📖 CLI 命令参考

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 "关键词" --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`	重算置信度	`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 "简短标题" \
  --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	默认值
`--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`	逗号分隔的标签	空
`--confidence`, `-c`	置信度 (0-1)	0.5

`search` - 搜索知识

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

参数	说明	默认值
`query`	搜索关键词（位置参数）	-
`--limit`, `-l`	返回结果数量	10
`--tag`	按标签过滤	无

`feedback` - 添加反馈

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

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

评分规则：4-5 分为"有帮助"，1-3 分为"没帮助"

`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

编辑配置文件 ~/.claude/config.json
添加 MCP 服务器：

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

重启 Claude Code

可用 MCP 工具

工具名	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

在 Claude Code 对话中：

帮我搜索关于 Python asyncio 的知识

添加一条知识：标题是"解决 Git 冲突"，问题是"合并代码时出现冲突"，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，给它 5 分好评

🌐 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"
    }
  }
}

自定义 MCP 客户端

Cq 遵循 MCP 协议，可与任何支持 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`	健康检查
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 客户端示例

import httpx

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

# 搜索知识
response = httpx.get("http://localhost:8000/knowledge", params={
    "search": "装饰器",
    "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": "在后端设置 Access-Control-Allow-Origin 响应头",
    "tags": ["web", "cors"],
    "confidence": 0.9
  }'

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

🎯 置信度评分说明

Cq 使用智能算法计算知识的置信度，范围 0.1 - 1.0：

反馈情况	置信度	说明
无反馈	0.500	默认值
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	按比例计算

计算公式：

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

手动重算置信度：

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

# 应用变化
cq-knowledge recalculate

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

🏗️ 系统架构

┌─────────────────────────────────────────────────────────────┐
│                         Cq 系统                              │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐                   │
│  │   CLI    │  │   MCP    │  │   API    │  ← 接入层          │
│  │  工具    │  │  服务器  │  │  服务    │                   │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘                   │
│       │            │            │                            │
│       └────────────┴────────────┘                            │
│                    │                                         │
│            ┌───────▼────────┐                               │
│            │ 知识仓储层      │  ← 业务逻辑层                 │
│            │ 反馈仓储层      │                               │
│            └───────┬────────┘                               │
│                    │                                         │
│            ┌───────▼────────┐                               │
│            │  SQLite + FTS5 │  ← 存储层                     │
│            │  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 → 重新计算置信度

❓ 常见问题

数据存储在哪里？

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：

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？

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

Claude Code 中看不到 MCP 工具？

确认已安装：pip show cq-knowledge
检查配置：cat ~/.claude/config.json
重启 Claude Code 完全退出后重新打开
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分
公式：0.5 + (正面 - 负面) / max(总数, 5) × 0.5

详情见上方"置信度评分说明"。

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"

🛠️ 开发

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

# 安装开发依赖
pip install -e ".[dev]"

# 运行测试
pytest

# 代码检查
ruff check cq/
mypy cq/

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

📚 更多文档

📊 项目状态

✅ 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

📄 许可证

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

Edit config file ~/.claude/config.json
Add MCP server:

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

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?

Use more specific keywords
Try --tag filter
Increase result count --limit 20
Check if knowledge units contain relevant keywords

MCP tools not visible in Claude Code?

Confirm installation: pip show cq-knowledge
Check config: cat ~/.claude/config.json
Restart Claude Code (fully quit and reopen)
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

📊 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

Made with ❤️ by the Cq Team

Project details

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Release history Release notifications | RSS feed

This version

0.1.5

Mar 28, 2026

0.1.4

Mar 28, 2026

0.1.3

Mar 28, 2026

0.1.2

Mar 28, 2026

0.1.1

Mar 28, 2026

0.1.0

Mar 28, 2026

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Upload date: Mar 28, 2026
Size: 40.6 kB
Tags: Python 3
Uploaded using Trusted Publishing? No
Uploaded via: twine/6.2.0 CPython/3.11.5

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cq-knowledge 0.1.5

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Unverified details

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Project description

Cq - Stack Overflow for AI Coding Agents

中文

🎯 Cq 是什么？

💡 解决的问题

✨ 核心功能

🚀 5 分钟快速开始

第一步：安装

第二步：初始化知识库

第三步：添加第一条知识

第四步：搜索知识

第五步：（可选）集成 Claude Code

📖 CLI 命令参考

add - 添加知识单元

search - 搜索知识

feedback - 添加反馈

export / import-cmd - 导入导出

🔌 MCP 集成指南

配置 Claude Code

可用 MCP 工具

使用示例

Cline (Cursor)

自定义 MCP 客户端

🌐 REST API 使用

启动 API 服务

API 端点

Python 客户端示例

cURL 示例

🎯 置信度评分说明

🏗️ 系统架构

❓ 常见问题

🛠️ 开发

📚 更多文档

📊 项目状态

🤝 贡献

📄 许可证

English

🎯 What is Cq?

💡 Problems Solved

✨ Core Features

🚀 5-Minute Quick Start

Step 1: Install

Step 2: Initialize Knowledge Base

Step 3: Add Your First Knowledge

Step 4: Search Knowledge

Step 5: (Optional) Integrate with Claude Code

📖 CLI Command Reference

add - Add Knowledge Unit

search - Search Knowledge

feedback - Add Feedback

export / import-cmd - Import/Export

🔌 MCP Integration Guide

Configure Claude Code

Available MCP Tools

Usage Examples

Cline (Cursor)

Custom MCP Clients

🌐 REST API Usage

Start API Server

API Endpoints

Python Client Example

cURL Examples

🎯 Confidence Score Explanation

🏗️ System Architecture

❓ FAQ

🛠️ Development

📚 More Documentation

📊 Project Status

🤝 Contributing

📄 License

Project details

Verified details

`add` - 添加知识单元

`search` - 搜索知识

`feedback` - 添加反馈

`export` / `import-cmd` - 导入导出

`add` - Add Knowledge Unit

`search` - Search Knowledge

`feedback` - Add Feedback

`export` / `import-cmd` - Import/Export