ickyMCP
ickyMCP
用于语义文档搜索的 RAG MCP 服务器。它使用 sqlite-vec 将本地文档集合索引到 SQLite 中,然后提供用于索引、搜索、刷新、列出和删除已索引文档的 MCP 工具。
功能特性
对 PDF、Word、PowerPoint、Excel、Markdown 和文本文件进行语义搜索
支持用户级数据库隔离,并提供可选的传统单数据库模式
支持用于聊天或特定事项范围检索的文档 ID 过滤器
基于文件大小和修改时间的增量索引
默认使用 Voyage AI 嵌入,并提供离线本地 sentence-transformers 后端
便携式 SQLite 存储,自动忽略本地数据库制品
Related MCP server: MCP-RAGNAR
设置
python -m venv .venv
.\.venv\Scripts\activate
pip install -r requirements.txt从示例创建本地环境设置:
copy .env.example .env使用默认的 Voyage 后端时,请设置 ICKY_VOYAGE_API_KEY。如需离线使用,请设置 ICKY_EMBEDDING_PROVIDER=local。
配置
变量 | 默认值 | 描述 |
|
|
|
| 未设置 | 使用 Voyage 嵌入时必需 |
|
| Voyage 嵌入模型 |
|
| Voyage 输出维度 |
|
| 为并行 Voyage 工作流预留 |
|
| 本地 sentence-transformers 模型 |
|
| 每个分块的大致 token 数 |
|
| 分块之间的大致重叠 token 数 |
|
| 用户数据库的基础目录 |
| 特定于提供程序 | 传统的单数据库路径 |
Claude MCP 配置
{
"mcpServers": {
"ickyMCP": {
"command": "python",
"args": ["C:\\Users\\devan\\OneDrive\\Desktop\\Projects\\ickyMCP\\run.py"],
"env": {
"ICKY_EMBEDDING_PROVIDER": "voyage",
"ICKY_VOYAGE_API_KEY": "YOUR_VOYAGE_API_KEY",
"ICKY_CHUNK_SIZE": "5000",
"ICKY_CHUNK_OVERLAP": "500"
}
}
}
}工具
index: 索引文件或目录;接受user_id、patterns、exclude和force参数search: 对已索引的分块进行语义查询;接受document_ids、path_filter和file_types参数similar: 查找与所提供文本相似的分块refresh: 重新索引已更改的文件并从索引中删除已删除的文件list: 列出已索引的文档及其 IDdelete: 按路径、文档 ID 或删除所有文档status: 返回数据库、嵌入和分块状态
验证
python -m compileall -q src run.py fast_index.py
python -c "from src.config import EMBEDDING_PROVIDER; print(EMBEDDING_PROVIDER)"根目录下的 test_*.py 文件是集成脚本,期望在 docs/ 下有本地文档,并且对于默认后端,需要配置 Voyage API 密钥。
数据清理
git 会忽略生成的数据库和本地文档文件夹:*.db、*.db.bak、*.sqlite、*.sqlite3、data/ 和 docs/。请将真实的 API 密钥保存在环境变量或 .env 文件中,不要保存在被追踪的文件中。
许可证
MIT
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