keqing-kb
MCP server for querying the Keqing customer service knowledge base, enabling AI agents to search structured knowledge entries and retrieve authoritative, citable answers.
README
科情客服智能知识库
基于钉钉 AI 表格「科情OA知识库数据梳理」+ 钉钉知识库双数据源构建的客服智能知识库。 面向客服机器人 / AI Agent / 客服人员:准确定位问题 → 引用权威答案 → 精准回答。
产品形态
| 组件 | 路径 | 说明 |
|---|---|---|
| 数据层 | knowledge/ |
2485 条结构化知识条目(单一事实源) |
| 采集层 | scripts/ |
钉钉同步 / 清洗 / 索引脚本 |
| Web 门户 | portal/ |
VitePress 站点(浏览器访问、搜索、分类树) |
| API 服务 | server/ |
REST API + MCP Server(供系统与 AI Agent 调用) |
| CI/CD | .github/workflows/ |
自动构建门户并发布 GitHub Pages |
快速开始
1. 浏览门户
cd portal
pnpm install
pnpm run dev # 本地预览 http://localhost:5173
pnpm run build # 构建静态站点 → .vitepress/dist
2. 调用 API
cd server
npm install
npm run api # REST API → http://localhost:8787
# 检索示例
curl "http://localhost:8787/api/search?q=U8%20登录失败&top_k=5"
curl "http://localhost:8787/api/stats"
3. 调用 MCP(供 AI Agent)
cd server
npm run mcp # stdio MCP Server
客户端配置:
{ "mcpServers": { "kb": { "command": "node", "args": ["<仓库路径>/server/src/mcp.mjs"] } } }
4. 命令行查询
cd scripts
python3 query.py "客户提问的问题描述" # 全文检索 Top-5
python3 query.py --keyword "WebView2" # 关键词检索
python3 query.py --id FX-20221130-059 # 精确获取
维护流程
# 1. 从钉钉同步最新数据
bash scripts/sync_from_dingtalk.sh
# 2. 重建索引
python3 scripts/build_index.py
# 3. 重新生成门户页面
cd portal && node scripts/generate.mjs
# 4. 构建并提交
cd portal && pnpm run build
cd .. && git add -A && git commit -m "同步最新知识" && git push
目录结构
kb/
├── raw/ # 原始采集数据(只读)
├── knowledge/ # 清洗后知识库(entries + index + markdown)
├── scripts/ # 采集/清洗/索引/查询脚本
├── portal/ # VitePress Web 门户
│ ├── scripts/generate.mjs # 知识条目 → 门户页面生成器
│ └── docs/ # 生成的站点页面
├── server/ # API 服务(REST + MCP)
│ └── src/
│ ├── core.mjs # 检索核心(共享)
│ ├── api.mjs # REST API
│ └── mcp.mjs # MCP Server
├── docs/ # 使用文档(机器人集成等)
└── .github/workflows/ # CI 部署
设计原则
- 单一事实源:钉钉端数据源是权威,本地知识库是投影
- 双向同步:增量拉取 → 清洗/分类/索引;本地修正可回写
- 自进化:每次同步自动发现新增/修改/删除,增量更新
- 机器可读 + 人可读:JSON 供程序消费,Markdown/门户供人阅读
- 引用可溯源:每条知识携带来源(钉钉文档 URL / 表格记录),回答必须引用
- 多人协作:Git 版本管理 + PR 审查 + 自动发布
详细文档
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