RAG-MCP Pipeline Research

Name: RAG-MCP Pipeline Research
Author: dzikrisyairozi

@dzikrisyairozi

RAG-MCP Pipeline Research について

A learning repository exploring Retrieval-Augmented Generation (RAG) and Multi-Cloud Processing (MCP) server integration using free and open-source models.

#rag #llms #mcp-server

基本情報

カテゴリ

データと分析

ランタイム

python

トランスポート

stdio

公開者

dzikrisyairozi

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": {
    "rag-mcp-pipeline-research": {
      "command": "python",
      "args": [
        "src/setup_environment.py"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

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 RAG-MCP Pipeline Research?

It is a research project exploring Retrieval-Augmented Generation (RAG) and Multi-Cloud Processing (MCP) server integration using free, open-source models. It provides a structured learning path for integrating LLMs with external services via MCP servers, with practical examples for business applications like accounting software (e.g., QuickBooks).

How to use RAG-MCP Pipeline Research?

Clone the repository, run python src/setup_environment.py to prepare the environment, and activate the virtual environment. Start with Module 0 (Prerequisites) and progress sequentially through the modules, completing the practical exercises in each section.

Key features of RAG-MCP Pipeline Research

No paid API keys required; uses free Hugging Face models.
Run everything locally without external dependencies.
Comprehensive step-by-step documentation for beginners.
Practical examples with working code.

Use cases of RAG-MCP Pipeline Research

Learning how to integrate LLMs with MCP servers for business software.
Building prototype integrations with accounting APIs like QuickBooks.
Developing a framework for AI-powered data entry and processing.
Understanding vector databases, document pipelines, and hybrid retrieval.

FAQ from RAG-MCP Pipeline Research

Why does this project use free models?

Free, open-source models from Hugging Face make the project accessible without financial barriers, offer better educational insight into model internals, ensure privacy (all processing is local), allow easy customization, and transfer skills to any model provider.

How do I get started?

Clone the repository, run python src/setup_environment.py, activate the virtual environment, then start with Module 0 (Prerequisites). Progress through each module and complete the practical exercises.

What are the prerequisites?

A solid foundation in Python, Git/GitHub, Docker, basic machine learning concepts, RESTful APIs, cloud services, and familiarity with transformers, RAG, and prompt engineering. Module 0 covers all of these.