A learning repository exploring Retrieval-Augmented Generation (RAG) and Multi-Cloud Processing (MCP) server integration using free and open-source models.
Overview
What is RAG-MCP Pipeline Research?
RAG-MCP Pipeline Research is a comprehensive learning repository that explores the integration of Retrieval-Augmented Generation (RAG) and Multi-Cloud Processing (MCP) servers using free and open-source models.
How to use RAG-MCP Pipeline Research?
To use this project, clone the repository from GitHub, set up your environment by running the provided setup script, and follow the structured modules sequentially to learn about RAG and MCP integration.
Key features of RAG-MCP Pipeline Research?
- No paid API keys required, utilizing free Hugging Face models.
- Ability to run everything locally without external dependencies.
- Comprehensive step-by-step documentation tailored for beginners.
- Practical examples with working code to facilitate learning.
Use cases of RAG-MCP Pipeline Research?
- Integrating AI models with business software like QuickBooks.
- Building prototypes for AI-powered data entry and processing.
- Developing frameworks for scalable AI applications.
FAQ from RAG-MCP Pipeline Research?
- Is prior programming knowledge required?
Yes, familiarity with Python and basic programming concepts is recommended.
- Can I use commercial APIs instead of free models?
Yes, while the project focuses on free models, you can apply the concepts learned to commercial APIs for better performance.
- What are the prerequisites for starting this project?
Basic knowledge of machine learning, RESTful APIs, and cloud services is beneficial.