Rag MCP Pipeline Research

🚀 Research on Workflow Based on Retrieval-Augmented Generation and Multi-Cloud Processing

This is a comprehensive research project that explores the integration of Retrieval-Augmented Generation (RAG) with Multi-Cloud Processing (MCP) servers, using only free and open-source models.

📋 Project Overview

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

✨ Features

• No paid API keys required - Use free Hugging Face models.
• Local operation - No external dependencies.
• Comprehensive step-by-step documentation - Beginner-friendly.
• Practical examples - Accompanied by working code.

📚 Research Modules

Module 0: Prerequisites

Build a solid foundation before delving into specific areas:

• Programming and tools: Python, Git/GitHub, Docker.
• Basic concepts: Machine learning, RESTful API, cloud services.
• AI and LLM fundamentals: Understand transformers, RAG, and prompt engineering techniques.
• Set up the development environment and configure free models.

Module 1: AI Modeling and LLM Integration

• Master different LLM architectures and their capabilities.
• Methods for integrating different LLM providers (Hugging Face, open-source models).
• Fine-tuning strategies for domain-specific tasks.
• Evaluation metrics and performance optimization.

Module 2: Host Deployment and AI Application Strategies

• Scalable infrastructure for AI applications.
• Cost optimization techniques.
• Model service options (serverless, containerized, dedicated instances).
• The importance of monitoring and observability for LLM applications.

Module 3: In-depth Exploration of MCP Servers

• Architecture and components of MCP servers.
• Build a secure API gateway for external service integration.
• Authentication and authorization models.
• Command execution protocols and standardization.

Module 4: API Integration and Command Execution

• Integration of business software APIs (e.g., QuickBooks).
• Data transformation and normalization.
• Error handling and resilience strategies.
• Testing and validation methods.

Module 5: RAG (Retrieval-Augmented Generation) and Alternative Strategies

• Selection and optimization of vector databases.
• Document processing pipelines.
• Hybrid retrieval methods.
• Alternative enhancement strategies for LLMs.

🎯 Project Objectives

1. Gain a comprehensive understanding of the concepts of RAG and MCP servers.
2. Build a prototype integration of popular business software.
3. Develop an AI-driven data entry and processing framework.
4. Create documentation and best practices for future implementation.

🚀 Quick Start

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 installation script to prepare the environment:

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

3. Activate the virtual environment:

# For Windows
venv\Scripts\activate

# For macOS/Linux
source venv/bin/activate

4. Start learning from Module 0: Prerequisites.
5. Complete each module in sequence.
6. Finish the practical exercises in each part.

💡 Why Use Free Models?

This project deliberately chooses free and open-source models like those on Hugging Face instead of commercial APIs (e.g., OpenAI) for the following reasons:

1. Accessibility - Anyone can participate without barriers.
2. Educational value - Gain a deeper understanding of the internal mechanisms of models.
3. Cost-effectiveness - Save resource investment in the early stage of R & D.
4. Transparency - The open source code facilitates debugging and optimization.
5. Diversity - The open-source community supports a rich ecosystem.

🎯 Project Goals

1. Explore the application potential of RAG technology in real-world scenarios.
2. Validate the feasibility and stability of the MCP server architecture.
3. Build a scalable and maintainable AI service framework.
4. Form a complete set of development documentation and best practices.

💻 Usage Examples

# Clone the repository and enter the directory
git clone https://github.com/your-username/rag-mcp-pipeline-research.git
cd rag-mcp-pipeline-research

# Install dependencies
pip install -r requirements.txt

# Start the environment
source venv/bin/activate

# Run the example code
python src/example.py

📄 License

This project is licensed under the MIT License.