scholar-mcp
little-librarian
一个本地 MCP 服务器,用于索引 .epub 文件并提供由 pplx-embed-context-v1(后期分块)和 Qdrant 支持的语义搜索工具。
MCP client (Claude Desktop, Claude Code, …)
│
▼ tool calls via MCP
server.py
pplx-embed-context-v1-0.6b + Qdrant (local)文件
文件 | 角色 |
| MCP 服务器 — epub 摄入、嵌入、搜索、Qdrant 存储 |
| 用于代码的独立 MCP 服务器(基于 AST 的分块) |
Related MCP server: ragi
为什么选择 pplx-embed-context-v1
使用后期分块 (late chunking):来自同一章节的所有分块都经过单次前向传递,因此每个分块的嵌入都能捕获完整的文档上下文,而无需在推理时添加文档前缀。在 ConTEB 上得分为 81.96 nDCG@10。
快速开始
# 1. install
pip install -e .
# 2. ingest your library (runs embedding, then exits)
HF_HUB_OFFLINE=0 python server.py --index ./library
# 3. start the MCP server
python server.py
# optional: preload the model at startup
python server.py --preloadMCP 工具
工具 | 描述 |
| 语义搜索,返回带有分数的 top-k 段落 |
| 按 |
| 检索书籍/章节的全文 |
| 列出所有已索引的书籍及其章节数 |
| Qdrant 集合信息(点数、向量大小) |
| 内容完整细分:书籍、章节、每本书的分块数、平均分块长度 |
| 显示用于嵌入的设备(CPU/GPU) |
Claude Desktop 配置
{
"mcpServers": {
"little-librarian": {
"command": "python",
"args": ["/path/to/server.py"]
}
}
}硬件指南
设置 | 最小显存 |
仅 CPU | 0 GB |
GPU (pplx-embed-0.6b) | ~2 GB |
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