Server data from the Official MCP Registry
Lightweight self-hosted RAG knowledge base with hybrid retrieval and cited answers.
About
Lightweight self-hosted RAG knowledge base with hybrid retrieval and cited answers.
Remote endpoints: sse: https://minirag.kimelody.de5.net/sse
Security Report
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
Endpoint verified · Requires authentication · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Found in Source Code
Found by scanning the linked source code. This listing connects to a hosted endpoint, so none of this runs on your machine: it describes what the server software does where it is hosted.
What You'll Need
Set these up before or after installing:
Environment variable: DEEPSEEK_BASE_URL
Environment variable: DEEPSEEK_API_KEY
Environment variable: EMBEDDING_BASE_URL
Environment variable: EMBEDDING_API_KEY
Environment variable: EMBEDDING_MODEL
Environment variable: DEMO_KEY
Environment variable: ADMIN_KEY
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-kimelody-minirag": {
"url": "https://minirag.kimelody.de5.net/sse"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
MiniRAG
A self-hosted RAG knowledge base in a single Spring Boot process: SQLite storage, hybrid retrieval, cited answers, and a built-in MCP server. No vector database, no message queue.
Live demo (ask the owner for the access code) · Evaluation report
Features
- Hybrid retrieval — in-memory cosine + SQLite FTS5 (BM25), fused with RRF; optional LLM rerank
- Cited answers — clickable
[n]citations; only sources actually cited are displayed - Streaming web UI — SSE, typewriter rendering, markdown, retrieval-mode switcher (vector / hybrid / rerank)
- Small footprint — single process, ~250MB RSS, runs on a 1GB VPS
- Multiple interfaces — MCP server (SSE transport), REST API, web UI, WeChat Work callback integration
Quick start
docker run -d --name minirag -p 18090:18090 --memory 512m \
-v $(pwd)/data:/app/data \
-e DEEPSEEK_API_KEY=sk-... \
-e EMBEDDING_API_KEY=sk-... \
ghcr.io/kimelody/minirag:latest
Open http://localhost:18090 and start asking. Data lives in ./data (SQLite, created automatically).
Configuration
| Env | Required | Default | Description |
|---|---|---|---|
DEEPSEEK_BASE_URL | no | https://api.deepseek.com | Any OpenAI-compatible chat endpoint |
DEEPSEEK_API_KEY | yes | — | Chat model API key |
EMBEDDING_BASE_URL | no | https://api.siliconflow.cn/v1 | OpenAI-compatible embeddings endpoint |
EMBEDDING_API_KEY | yes | — | Embeddings API key |
EMBEDDING_MODEL | no | BAAI/bge-m3 | Embedding model |
DEMO_KEY | no | — | Visitor access code for read APIs; unset = open (local dev) |
ADMIN_KEY | no | — | Admin key required for ingest APIs |
MCP
Two tools: query(question, mode?) — ask and get a cited answer; ingest_text(docId, text, adminKey) — add a document.
{
"mcpServers": {
"minirag": {
"url": "http://localhost:18090/sse"
}
}
}
The hosted demo endpoint requires basic auth: https://minirag.kimelody.de5.net/sse (ask the owner for credentials).
REST API
| Method | Path | Description |
|---|---|---|
| POST | /api/chat | Ask; returns answer + cited sources (JSON) |
| POST | /api/chat/stream | Same, streamed as SSE (meta / token / sources / done) |
| GET | /api/search?q=&mode= | Retrieval debug: vector | hybrid | rerank |
| POST | /api/documents/text | Ingest {docId, text} (requires X-Admin-Key) |
| POST | /api/documents | Ingest an uploaded file, multipart (requires X-Admin-Key) |
Evaluation
An eval harness ships in eval/: labeled question sets, Top-1 / Recall / MRR scoring over three retrieval modes — hybrid+rerank reaches Top-1 100% / MRR 1.0 on the demo set (report); a 20-question labor-law evaluation with corpus-cleaning scripts lives in eval/law/. Bring your own corpus; raw source texts are not redistributed.
Build from source
mvn package
java -jar target/minirag-0.1.0-SNAPSHOT.jar
Requires Java 21. API keys can be put in a .env file in the working directory (git-ignored).
中文说明
单进程私有化 RAG 知识库：SQLite 存储 + 混合检索（向量 + BM25，RRF 融合，可选 LLM 重排），回答带可点击引用角标，自带网页演示、REST API 与 MCP server（SSE），内存占用约 250MB，1GB 小服务器可跑。在线演示（访问码联系作者）。
License
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