MCP-RAG: Modular RAG Pipeline using MCP & GroundX

Created By
sujithadra year ago
MCP server Implantation for RAG (GroundX API)
Overview

what is MCP-RAG?

MCP-RAG is a modular, production-grade implementation of a Retrieval-Augmented Generation (RAG) system, designed to facilitate AI-driven applications with reusable components.

how to use MCP-RAG?

To use MCP-RAG, start the server and utilize the provided commands to ingest documents and perform searches. You can set up your environment by creating a .env file with your API keys and installing the necessary dependencies.

key features of MCP-RAG?

  • 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 and services

use cases of MCP-RAG?

  1. Ingesting and processing PDF documents for information retrieval.
  2. Performing semantic searches to generate contextual responses.
  3. Integrating various tools and services for enhanced AI capabilities.

FAQ from MCP-RAG?

  • What is the purpose of MCP-RAG?

MCP-RAG is designed to provide a scalable and flexible framework for building AI-driven applications.

  • How do I set up the environment?

You need to create a .env file with your API keys and install the dependencies using the provided commands.

  • Can I integrate other tools with MCP-RAG?

Yes! MCP-RAG supports plug-and-play API integration for new tools and services.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
sujithadr
Star
0
Language
Python
License
MIT license

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