F
licenseNot graded
qualityD
maintenanceEnables semantic search and question-answering over FAQ documents using RAG (Retrieval-Augmented Generation) with OpenAI embeddings and in-memory vector similarity.
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rag-mcp-lab
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@rag-mcp-labwhat are the steps to update the knowledge base?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
rag-mcp-lab
A small, production shaped RAG service that speaks MCP. Ask a question, get the most relevant chunks of your documents back, or a written answer with sources. Good for learning how embeddings, vector search and MCP fit together.
Try it
git clone https://github.com/loganathansellappa/rag-mcp-lab.git
cd rag-mcp-lab
pip install -r requirements.txt
python -m src.mainA sample knowledge base is already built (data/knowledge_base.json, from three short docs in data/docs/), so the server starts right away. It talks MCP over stdio, so point a client at it. See examples/mcp-client.json.
Related MCP server: search-context
Tools
Tool | What it does | Needs |
| Semantic search, returns chunks with relevance scores | nothing |
| Search, then an LLM writes the answer with sources |
|
Without a key, rag-generate returns an error and rag-search works as normal.
Use your own docs
Drop markdown files into data/docs/ and run make kb. Details in UPDATING_THE_KB.
Layout
src/ config, retrieval, MCP server, entry point
scripts/ build_kb.py (chunk and embed)
data/ sample docs and the built knowledge base
config/ default.json, production.json
deployment/ Dockerfile, Kubernetes manifest
tests/ unit tests and an MCP stdio round trip
docs/ architecture, KB updates
examples/ MCP client configMore in ARCHITECTURE.
Develop
pip install -r requirements-dev.txt
make testDeploy
make docker-build IMAGE=your-registry/rag-mcp:1.0.0
make k8s-deployThe manifest expects a rag-secrets secret with litellm-key and a rag-kb-data ConfigMap holding the KB file.
License
MIT
This server cannot be deployed
Maintenance
ActivityMaintained
ResponsivenessNo issues
Related MCP Connectors
Ingest, manage, and retrieve documents for RAG-powered AI applications
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Cloud or self-hosted knowledge for AI agents: hybrid search, reranking, GraphRAG, scoped MCP tools.
Build a RAG pipeline station by station and search your own documents with cited answers.
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