@dzikrisyairozi
Basic information
A learning repository exploring Retrieval-Augmented Generation (RAG) and Multi-Cloud Processing (MCP) server integration using free and open-source models.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"rag-mcp-pipeline-research": {
"command": "python",
"args": [
"src/setup_environment.py"
]
}
}
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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).
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.
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.
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.
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.
Yes, for production applications you may choose commercial APIs for better performance, but the concepts learned in this project apply universally.
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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