MCP-RAG: Modular RAG Pipeline using MCP & GroundX
@sujithadr
About MCP-RAG: Modular RAG Pipeline using MCP & GroundX
MCP server Implantation for RAG (GroundX API)
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-rag-sujithadr": {
"command": "uv",
"args": [
"pip",
"install",
"-r",
"pyproject.toml"
]
}
}
}Tools
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Overview
What is MCP-RAG: Modular RAG Pipeline using MCP & GroundX?
MCP-RAG is a modular, production-grade Retrieval-Augmented Generation (RAG) system that integrates GroundX for semantic search and ingestion, OpenAI GPT-4 for response generation, and the Model Context Protocol (MCP) for standardized tool orchestration. It is designed for teams building AI-driven applications with reusable components.
How to use MCP-RAG: Modular RAG Pipeline using MCP & GroundX?
Set up a .env file with your OPENAI_API_KEY and GROUNDEX_API_KEY, then install dependencies with uv pip install -r pyproject.toml. Start the MCP server with mcp dev server.py. Ingest a PDF using mcp call ingest_documents --args '{"file_path": "data/sample.pdf"}' and perform a search with mcp call process_search_query --args '{"query": "What is explained in section 3?"}'.
Key features of MCP-RAG: Modular RAG Pipeline using MCP & GroundX
- Modular tool design using MCP server interface
- YAML-based prompt templates with Jinja2 rendering
- PDF file ingestion into GroundX vector store
- Real-time semantic search via GroundX Search Tool
- Plug-and-play API integration for new tools
Use cases of MCP-RAG: Modular RAG Pipeline using MCP & GroundX
- Ingest PDF documents and perform semantic search on their content
- Build a modular RAG pipeline with clean separation of concerns
- Use standardized MCP tool orchestration for AI applications
FAQ from MCP-RAG: Modular RAG Pipeline using MCP & GroundX
What dependencies are required?
Python >=3.12, an OpenAI API key, a GroundX API key, and the uv package manager.
How do I ingest a PDF?
Run mcp call ingest_documents --args '{"file_path": "data/sample.pdf"}'.
How do I perform a semantic search?
Run mcp call process_search_query --args '{"query": "What is explained in section 3?"}'.
What is the architectural flow?
User query → MCP server routes to Search Tool → Search Tool queries GroundX API → snippets are rendered via YAML prompt → OpenAI API generates final LLM response.
Where is the ingested data stored?
Data is stored in the GroundX vector store (via the ingestion tool).
Frequently asked questions
What dependencies are required?
Python >=3.12, an OpenAI API key, a GroundX API key, and the `uv` package manager.
How do I ingest a PDF?
Run `mcp call ingest_documents --args '{"file_path": "data/sample.pdf"}'`.
How do I perform a semantic search?
Run `mcp call process_search_query --args '{"query": "What is explained in section 3?"}'`.
What is the architectural flow?
User query → MCP server routes to Search Tool → Search Tool queries GroundX API → snippets are rendered via YAML prompt → OpenAI API generates final LLM response.
Where is the ingested data stored?
Data is stored in the GroundX vector store (via the ingestion tool).
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