RAG-MCP Pipeline Research
by dzikrisyairozi
This project explores Retrieval-Augmented Generation (RAG) and Multi-Cloud Processing (MCP) server integration using free and open-source models. It provides a structured learning path for integrating Large Language Models (LLMs) with external services through MCP servers, focusing on practical business applications.
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What is RAG-MCP Pipeline Research?
This repository is a research project focused on integrating Large Language Models (LLMs) with external services using Retrieval-Augmented Generation (RAG) and Multi-Cloud Processing (MCP) techniques. It aims to provide a practical understanding of these concepts using free and open-source models.
How to use RAG-MCP Pipeline Research?
To use this project, clone the repository, set up the environment using the provided script, activate the virtual environment, and then follow the modules sequentially, completing the practical exercises in each section. The modules cover prerequisites, AI modeling, hosting strategies, MCP servers, API integration, and RAG.
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
Integrating LLMs with accounting software (e.g., QuickBooks)
Building AI-powered data entry and processing systems
Creating secure API gateways for external service integration
Developing scalable infrastructure for AI applications
FAQ from RAG-MCP Pipeline Research
Why use free models?
Why use free models?
To ensure accessibility, promote educational value, maintain privacy, offer flexibility for customization, and future-proof skills.
What are the prerequisites for this project?
What are the prerequisites for this project?
Basic knowledge of Python, Git/GitHub, Docker, machine learning, RESTful APIs, cloud services, transformers, RAG, and prompt engineering.
What is RAG?
What is RAG?
Retrieval-Augmented Generation is a technique that combines information retrieval with text generation to improve the quality and relevance of LLM outputs.
What is MCP?
What is MCP?
Multi-Cloud Processing refers to the use of multiple cloud platforms to distribute and process workloads, often for scalability, resilience, or cost optimization.
Can I use commercial APIs with this project?
Can I use commercial APIs with this project?
Yes, while this project focuses on free models, the concepts learned can be applied to commercial APIs for better performance in production applications.