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MCP-RAG: Modular RAG Pipeline using MCP & GroundX

@sujithadr

リポジトリ

基本情報

カテゴリ

メモリとナレッジ

ライセンス

MIT license

トランスポート

stdio

作者

sujithadr

MCP-RAG: Modular RAG Pipeline using MCP & GroundX について

MCP server Implantation for RAG (GroundX API)

#mathgpt#math-solver#math-assistant

MCP-RAG: Modular RAG Pipeline using MCP & GroundX の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-rag-sujithadr": {
      "command": "uv",
      "args": [
        "pip",
        "install",
        "-r",
        "pyproject.toml"
      ]
    }
  }
}

MCP-RAG: Modular RAG Pipeline using MCP & GroundX のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

1

What dependencies are required?

Python >=3.12, an OpenAI API key, a GroundX API key, and the `uv` package manager.

2

How do I ingest a PDF?

Run `mcp call ingest_documents --args '{"file_path": "data/sample.pdf"}'`.

3

How do I perform a semantic search?

Run `mcp call process_search_query --args '{"query": "What is explained in section 3?"}'`.

4

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.

5

Where is the ingested data stored?

Data is stored in the GroundX vector store (via the ingestion tool).

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