MCP-RAG: Modular RAG Pipeline using MCP & GroundX

@sujithadr

代码仓库

基本信息

分类

记忆与知识

许可证

MIT license

传输方式

stdio

作者

sujithadr

关于 MCP-RAG: Modular RAG Pipeline using MCP & GroundX

MCP server Implantation for RAG (GroundX API)

#mathgpt #math-solver #math-assistant

MCP-RAG: Modular RAG Pipeline using MCP & GroundX 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

MCP-RAG: Modular RAG Pipeline using MCP & GroundX 工具

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工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

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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