🧠 CodeScrybe - Legacy Repository AI

A comprehensive AI-powered platform that helps developers and organizations analyze, understand, and interact with legacy codebases through intelligent parsing, semantic chunking, and natural language queries.

🌟 Key Features

• 🔗 GitHub Integration: Connect and analyze any GitHub repository (Future Scope)
• 🧩 Smart Code Parsing: Semantically chunk code into meaningful units (functions, classes, blocks)
• 🤖 AI-Powered Analysis: Extract features and metadata using Google Gemini AI
• 📊 Knowledge Graph: Build interactive visualizations in Neo4j to map code relationships
• 💬 Natural Language Queries: Ask questions about your codebase using GraphRAG
• 🌐 Web Interface: User-friendly React.js frontend for seamless interaction

🏗️ Project Structure

CodeScrybe-Legacy-Repository-AI/
├── Server/                     # Backend API (FastAPI)
│   ├── backend/
│   │   ├── agents/           # Core parsing, graph builder and analysis modules
│   │   ├── database/         # models & services related to db
│   │   ├── app.py            # FastAPI application
│   │   └── pipeline.py       # End-to-end processing pipeline
│   ├── venv410/              # Python virtual environment
│   ├── .env                  # Environent Variables
│   └── README.md             # Server-specific documentation
├── Client/                   # Frontend (React.js)
│   ├── src/                  # All UI & Business Logic Files
│   ├── public/
│   ├── package.json
│   └── README.md             # Client-specific documentation
├── .gitignore
└── README.md                # This file

✅ Supported Languages

Language	Extensions	Parser Type	Chunk Type
C, C++	.c, .cpp, .h, .hpp	regex_chunker	Function + Context
Java	.java	regex_chunker	Function + Context
Shell	.sh, .bash	regex_chunker	Function-like blocks
Perl	.pl, .pm	regex_chunker	Subroutines
COBOL	.cob, .cbl, .cpy	sas_cobol_chunkers	Paragraphs
SAS	.sas	sas_cobol_chunkers	PROC & DATA blocks

🚀 Quick Start

Prerequisites

• Python 3.8+ for backend
• Node.js 18+ for frontend
• Neo4j Aura account (free tier available)
• Google AI Studio API key

1. Clone the Repository

git clone <repository-url>
cd CodeScrybe-Legacy-Repository-AI

2. Environment Setup

Create a .env file in the Server directory:

# Neo4j Configuration
NEO4J_URI=bolt://<your-neo4j-uri>
NEO4J_USERNAME=<your-username>
NEO4J_PASSWORD=<your-password>

# Google Gemini AI
GEMINI_API_KEY=<your-gemini-api-key>

Getting Your API Keys:

Neo4j Aura:

1. Visit Neo4j Aura
2. Create a free account and new instance
3. Copy the connection details to your .env

Google Gemini:

1. Go to Google AI Studio
2. Create a new API key
3. Add it to your .env file

3. Backend Setup

# Create virtual environment (if venv410 doesn't work)
python -m venv venv
source Server/venv/bin/activate      # Linux/Mac
# or
Server\venv410\Scripts\activate.bat         # Windows

# Install dependencies
pip install -r Server/requirements.txt

# Start the backend server
uvicorn Server.backend.app:app --reload

The backend will be available at: http://127.0.0.1:8000

4. Frontend Setup

# Navigate to client directory (from root)
cd Client

# Install dependencies
npm install

# Start the development server
npm start

The frontend will be available at: http://localhost:3000

📚 API Endpoints

Health Check

GET /health

Other Endpoints access from below

Interactive Documentation

Visit http://127.0.0.1:8000/docs for complete API documentation.

🔧 Development Workflow

Full Pipeline Testing

1. Test Individual Components:

   cd Server
   source venv410/bin/activate  # or your venv
   
   # Test repo cloning
   python backend/agents/repo_loader.py
   
   # Test code parsing
   python -m backend.agents.code_parsers.py
   
   # Test NER extraction
   python backend/agents/ner_extractor.py
   
   # Test knowledge graph building
   python backend/agents/graph_builder.py
   
   # Test GraphRAG queries
   python backend/agents/graph_rag.py

2. Run Complete Pipeline:

   python backend/pipeline.py

Frontend Development

cd client
npm start        # Start development server

🐛 Troubleshooting

Common Issues

Virtual Environment Issues:

• If venv410 doesn't work, create a new virtual environment
• Ensure Python 3.8+ is installed
• Check path issues when moving project directories

API Connection Issues:

• Verify .env file is in the Server directory
• Check Neo4j instance is running
• Validate Gemini API key is active

Port Conflicts:

• Backend default: 8000
• Frontend default: 3000
• Modify ports in respective configuration files if needed

📝 Usage Examples

Analyzing a Repository

1. Start both backend and frontend servers
2. Navigate to http://localhost:3000/dashboard
3. Click on Add Repo
4. Enter a GitHub repository URL
5. Wait for analysis to complete
6. Explore the knowledge graph and ask questions in their chat section

Sample Questions

• "What are the main functions in this codebase?"
• "Which files depend on the database module?"
• "Show me all the API endpoints defined"
• "What security-related functions are implemented?"

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit changes: git commit -m 'Add amazing feature'
4. Push to branch: git push origin feature/amazing-feature
5. Open a Pull Request

📄 License

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

🙏 Acknowledgments

• Google Gemini AI for intelligent code analysis
• Neo4j for graph database capabilities
• FastAPI for robust backend development
• React.js for modern frontend development

---

CodeScrybe - Legacy Repository AI | Transforming Legacy Code Understanding Through AI

Need Help? Check the individual README files in the Server/ and client/ directories for more detailed setup instructions.