| # 🚀 ResumeAnalyse RAG Architecture |
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| **Advanced AI-powered resume matching with RAG architecture** |
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| [](https://github.com/deepanmpc/ResumeAnalyse_RAG-Architecture) |
| [](LICENSE) |
| [](https://python.org) |
| [](https://reactjs.org) |
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| </div> |
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| ## 🌟 Overview |
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| ResumeAnalyse RAG Architecture is a cutting-edge solution that revolutionizes resume screening and candidate matching using Retrieval-Augmented Generation (RAG) technology. Our system combines the power of vector databases, large language models, and modern web technologies to deliver intelligent, accurate, and efficient resume analysis. |
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| ### 🎯 Key Features |
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| - **🔍 Intelligent Resume Matching**: Advanced vector similarity search with customizable thresholds |
| - **💬 AI-Powered Chat Assistant**: Interactive analysis with LLM-driven insights |
| - **📊 Real-time Analytics**: Dynamic scoring and ranking of candidate matches |
| - **🎨 Modern UI/UX**: Futuristic design with glassmorphism effects and smooth animations |
| - **⚡ High Performance**: Optimized for speed with efficient document processing |
| - **🔧 Flexible Configuration**: Customizable matching parameters and preview modes |
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| ## 🛠️ Tech Stack |
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| ### Backend |
| - **Python 3.8+** - Core application logic |
| - **LangChain** - LLM orchestration and document processing |
| - **ChromaDB** - Vector database for semantic search |
| - **Ollama/Mistral** - Local LLM integration |
| - **FastAPI** - High-performance API framework |
| - **PyPDF2** - PDF document processing |
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| ### Frontend |
| - **React 18** - Modern component-based UI |
| - **TypeScript** - Type-safe development |
| - **Vite** - Lightning-fast build tool |
| - **TailwindCSS** - Utility-first styling |
| - **Framer Motion** - Smooth animations |
| - **Three.js** - 3D visualizations |
| - **shadcn/ui** - Premium UI components |
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| ### Infrastructure |
| - **Docker** - Containerization (optional) |
| - **Git** - Version control |
| - **GitHub Actions** - CI/CD pipeline |
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| ## 🚀 Quick Start |
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| ### Prerequisites |
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| - Python 3.8 or higher |
| - Node.js 16 or higher |
| - npm or yarn package manager |
| - Git |
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| ### 1. Clone Repository |
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| ```bash |
| git clone https://github.com/deepanmpc/ResumeAnalyse_RAG-Architecture.git |
| cd ResumeAnalyse_RAG-Architecture |
| ``` |
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| ### 2. Backend Setup |
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| ```bash |
| # Create virtual environment |
| python -m venv venv |
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| # Activate virtual environment |
| # On Windows: |
| venv\Scripts\activate |
| # On macOS/Linux: |
| source venv/bin/activate |
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| # Install dependencies |
| pip install -r requirements.txt |
| ``` |
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| ### 3. Environment Configuration |
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| ```bash |
| # Copy environment template |
| cp .env.example .env |
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| # Edit .env with your configurations |
| # Add your LLM API keys if needed |
| ``` |
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| ### 4. Frontend Setup |
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| ```bash |
| # Install frontend dependencies |
| npm install |
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| # Start development server |
| npm run dev |
| ``` |
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| ### 5. Run the Application |
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| ```bash |
| # Start the backend server |
| python app.py |
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| # Backend will be available at http://localhost:8000 |
| # Frontend will be available at http://localhost:5173 |
| ``` |
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| ## 📋 Usage |
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| ### 1. Upload Documents |
| - Navigate to the Resume Matching Dashboard |
| - Upload your job description PDF |
| - Upload candidate resume PDFs (supports multiple files) |
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| ### 2. Configure Matching Parameters |
| - **Top N Matches**: Select how many top candidates to display (3, 5, or 10) |
| - **Similarity Threshold**: Set minimum match score (0-100%) |
| - **Display Mode**: Choose between Preview or Full Text mode |
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| ### 3. Analyze Results |
| - Click "Analyze Matches" to process documents |
| - View ranked candidates with match scores |
| - Explore detailed skill breakdowns and highlights |
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| ### 4. Interactive Chat |
| - Use the AI assistant for deeper insights |
| - Ask questions like: |
| - "Summarize Alex Chen's strengths" |
| - "What are the top skills across all candidates?" |
| - "Compare the top 3 candidates" |
| - "Identify skill gaps in the candidate pool" |
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| ## 🎮 Features Walkthrough |
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| ### Smart Resume Matching |
| Our RAG architecture processes job descriptions and resumes to create semantic embeddings, enabling intelligent matching that goes beyond keyword searches. |
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| ### AI Chat Assistant |
| Interactive conversational AI that provides deep insights into candidate profiles, skill analyses, and recruitment recommendations. |
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| ### Real-time Analytics |
| Dynamic visualization of match scores, skill distributions, and candidate rankings with beautiful animations. |
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| ## 🔧 Configuration |
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| ### Model Configuration |
| Edit `config.py` to customize: |
| - LLM model selection (Ollama, OpenAI, etc.) |
| - Vector database settings |
| - Similarity thresholds |
| - Processing parameters |
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| ### UI Customization |
| Modify the design system in: |
| - `src/index.css` - Color schemes and themes |
| - `tailwind.config.ts` - Custom animations and utilities |
| - Component files - Individual styling and behavior |
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| ## 🐳 Docker Deployment |
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| ```bash |
| # Build and run with Docker Compose |
| docker-compose up --build |
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| # Or run individual services |
| docker build -t resume-analyser . |
| docker run -p 8000:8000 resume-analyser |
| ``` |
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| ## 🧪 Testing |
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| ```bash |
| # Run backend tests |
| pytest tests/ |
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| # Run frontend tests |
| npm run test |
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| # Run E2E tests |
| npm run test:e2e |
| ``` |
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| ## 📊 Performance Metrics |
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| - **Document Processing**: < 2 seconds per resume |
| - **Vector Search**: Sub-millisecond query times |
| - **UI Responsiveness**: 60 FPS animations |
| - **Memory Usage**: Optimized for large document sets |
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| ## 🤝 Contributing |
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| We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details. |
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| 1. Fork the repository |
| 2. Create your feature branch (`git checkout -b feature/amazing-feature`) |
| 3. Commit your changes (`git commit -m 'Add amazing feature'`) |
| 4. Push to the branch (`git push origin feature/amazing-feature`) |
| 5. Open a Pull Request |
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| ## 📄 License |
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| This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. |
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| ## 🙏 Acknowledgments |
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| - **LangChain** team for the excellent RAG framework |
| - **ChromaDB** for the powerful vector database |
| - **Ollama** for local LLM deployment |
| - **shadcn/ui** for the beautiful UI components |
| - **Framer Motion** for smooth animations |
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| ## 📞 Support |
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| - 📧 Email: support@resumeanalyse.com |
| - 💬 Discord: [Join our community](https://discord.gg/resumeanalyse) |
| - 📖 Documentation: [docs.resumeanalyse.com](https://docs.resumeanalyse.com) |
| - 🐛 Issues: [GitHub Issues](https://github.com/deepanmpc/ResumeAnalyse_RAG-Architecture/issues) |
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| --- |
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| <div align="center"> |
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| **Made with ❤️ by the ResumeAnalyse Team** |
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| [⭐ Star this repo](https://github.com/deepanmpc/ResumeAnalyse_RAG-Architecture) • [🍴 Fork](https://github.com/deepanmpc/ResumeAnalyse_RAG-Architecture/fork) • [📢 Share](https://twitter.com/intent/tweet?text=Check%20out%20this%20amazing%20RAG-powered%20resume%20analysis%20tool!&url=https://github.com/deepanmpc/ResumeAnalyse_RAG-Architecture) |
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| </div> |
