RAG-MCP Pipeline Research
@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.
基本情報
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.
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.
「データと分析」の他のコンテンツ
Bright Data MCP
luminati-ioA powerful Model Context Protocol (MCP) server that provides an all-in-one solution for public web access.
Federal Reserve Economic Data MCP Server
stefanoamorelliOpen-source FRED MCP Server (Federal Reserve Economic Data)
MCP.science: Open Source MCP Servers for Scientific Research 🔍📚
pathintegral-instituteOpen Source MCP Servers for Scientific Research
Google Ads MCP
cohnenAn MCP tool that connects Google Ads with Claude AI/Cursor and others, allowing you to analyze your advertising data through natural language conversations. This integration gives you access to campaign information, performance metrics, keyword analytics, and ad management—all th
Google Analytics MCP Server
surendranbGoogle Analytics 4 data to AI agents, agentic workflows, and MCP clients. Give agents analysis-ready access to website traffic, user behavior, and performance data with schema discovery, server-side aggregation, and safe defaults that reduce data wrangling.
コメント