RAG-Domains-Adopters

This repository showcases practical implementations of Retrieval-Augmented Generation (RAG) across different business domains. Each subdirectory contains a complete, domain-specific RAG application that demonstrates how to effectively leverage RAG for real-world use cases.

Project Structure

The repository is organized into domain-specific directories:

🏭 Energy Domain (/energy)

A RAG-powered application focused on predictive maintenance in the energy sector:

• Equipment maintenance prediction and monitoring
• Technical documentation analysis
• Performance data analysis and pattern recognition
• Real-time monitoring and alerts
• Customized query interface for maintenance staff

💰 Finance Domain (/finance)

A RAG implementation for financial analysis and compliance:

• Financial document processing
• Market data analysis
• Investment insights generation

🏥 Healthcare Domain (/healthcare)

A regulatory compliance assistant powered by Google's Gemini Pro:

• Healthcare regulatory document analysis
• Interactive compliance query system
• Source context display for transparency
• Automatic question generation from documents
• Support for PDF and text documents

🏘️ Real Estate Domain (/realestate)

A comprehensive real estate analysis system:

• Property listing and market data analysis
• Legal document processing for agreements
• Market trend identification
• Automated property valuation insights
• Contract analysis and legal clause detection

🏏 Sports Domain (/sports)

A cricket match analysis system powered by Google's Gemini Pro:

• Comprehensive match analysis and statistics
• Partnership and player performance tracking
• Phase-wise analysis (powerplay, middle overs, death overs)
• Risk analysis and scoring patterns
• Interactive match visualization

Features

• Domain-Specific RAG Implementations: Each domain showcases tailored RAG solutions
• Multiple LLM Support: Integration with both OpenAI and Google's Gemini Pro
• Interactive Web Interfaces: Built with Streamlit for user-friendly interaction
• Document Processing: Efficient ingestion and processing of domain-specific documents
• Intelligent Querying: Natural language querying with context-aware responses
• Visualization: Interactive data visualization and insights

Prerequisites

• Python 3.8+
• OpenAI API key (for OpenAI-based implementations)
• Google API key (for Gemini Pro implementations)
• Required Python packages (specified in each domain's requirements.txt or environment.yml)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/RAG-Domains-Adopters.git
   cd RAG-Domains-Adopters

2. Choose your preferred setup method for specific domain:

   Using Conda (Recommended)

   cd <domain-directory>
   conda env create -f environment.yml
   conda activate <domain>-rag

   Using Python venv

   cd <domain-directory>
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt

3. Set up environment variables:

   • Copy .env.template to .env in the domain directory
   • Add required API keys to .env

4. Run the application:

   streamlit run app.py

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.