RAG-MCP Pipeline Research

A comprehensive research project exploring Retrieval-Augmented Generation (RAG) and Multi-Cloud Processing (MCP) server integration using free and open-source models.

Project Overview

This repository serves as a structured learning and research path for understanding how to integrate Large Language Models (LLMs) with external services through MCP servers, with a focus on practical business applications such as accounting software integration (e.g., QuickBooks).

🌟 Key Features

• 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

Research Modules

Module 0: Prerequisites

Establish a solid foundation before diving into specific areas:

• Programming & Tools: Python, Git/GitHub, Docker
• Basic Concepts: Machine learning, RESTful APIs, cloud services
• AI & LLM Foundations: Understanding transformers, RAG, and prompt engineering
• Development environment setup with free models

Module 1: AI Modeling & LLM Integration

• Understanding different LLM architectures and capabilities
• Integration methods with various LLM providers (Hugging Face, open-source models)
• Fine-tuning strategies for domain-specific tasks
• Evaluation metrics and performance optimization

Module 2: Hosting & Deployment Strategies for AI

• Scalable infrastructure for AI applications
• Cost optimization techniques
• Model serving options (serverless, container-based, dedicated instances)
• Monitoring and observability for LLM applications

Module 3: Deep Dive into MCP Servers

• Architecture and components of MCP servers
• Building secure API gateways for external service integration
• Authentication and authorization patterns
• Command execution protocols and standardization

Module 4: API Integration & Command Execution

• Integration with business software APIs (QuickBooks, etc.)
• Data transformation and normalization
• Error handling and resilience strategies
• Testing and validation methodologies

Module 5: RAG (Retrieval Augmented Generation) & Alternative Strategies

• Vector database selection and optimization
• Document processing pipelines
• Hybrid retrieval approaches
• Alternative augmentation strategies for LLMs

Project Goals

1. Gain comprehensive understanding of RAG and MCP server concepts
2. Build prototype integrations with popular business software
3. Develop a framework for AI-powered data entry and processing
4. Create documentation and best practices for future implementations

Getting Started

1. Clone this repository to your local machine

   git clone https://github.com/your-username/rag-mcp-pipeline-research.git
   cd rag-mcp-pipeline-research
   

2. Run the setup script to prepare your environment

   # Navigate to the project directory
   python src/setup_environment.py
   

3. Activate the virtual environment

   # On Windows
   venv\Scripts\activate
   
   # On macOS/Linux
   source venv/bin/activate
   

4. Start with Module 0: Prerequisites

5. Progress through each module sequentially

6. Complete the practical exercises in each section

Why Free Models?

This project intentionally uses free, open-source models from Hugging Face instead of commercial APIs like OpenAI for several reasons:

1. Accessibility - Anyone can follow along without financial barriers
2. Educational Value - Better understanding of how models work internally
3. Privacy - All processing happens locally on your machine
4. Flexibility - Easier to customize and fine-tune models for specific needs
5. Future-Proofing - Skills transfer to any model, not tied to specific providers

For production applications, you may choose to use commercial APIs for better performance, but the concepts learned here apply universally.

License

MIT