RAG-MCP Pipeline Research

@dzikrisyairozi

代码仓库

基本信息

分类

数据与分析

传输方式

stdio

作者

dzikrisyairozi

关于 RAG-MCP Pipeline Research

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

RAG-MCP Pipeline Research 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

RAG-MCP Pipeline Research 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

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 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.

常见问题

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.

Can I use commercial APIs instead of free models?

Yes, for production applications you may choose commercial APIs for better performance, but the concepts learned in this project apply universally.

Is this project suitable for beginners?

Yes, it features comprehensive step-by-step documentation designed for beginners, with no paid API key requirements and full local execution.

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