A comprehensive AI-powered programming assistant system featuring a VS Code extension, web interface, and FastAPI backend for intelligent code analysis, generation, and assistance.

This project provides a complete ecosystem for AI-assisted programming, specializing in Perl development but extensible to other languages. The system combines real-time code analysis, intelligent suggestions, error detection, and multi-modal content processing through three integrated components.

git_check/
├── backend/           # FastAPI backend server
├── frontend/          # React web interface  
├── VsCodeExtension/   # VS Code extension
└── README.md         # This file

• 🤖 AI-Powered Code Generation: Convert comments to functional code
• 🔍 Intelligent Error Detection: Real-time code analysis and error highlighting
• 📋 Alternative Code Suggestions: Multiple implementation approaches for any code block
• 💬 Interactive Chat Interface: Web-based AI programming assistant
• 📁 Multi-Modal Processing: Analyze repositories, PDFs, and web content
• 🔐 Secure Authentication: JWT-based user management
• 🔄 Real-time Streaming: Live AI responses in both web and IDE
• 🧠 Vector-Based Search: Semantic code matching using LanceDB embeddings and FAISS indexing
• 📚 Persistent Session Storage: FAISS-powered document storage for conversation context

FastAPI-based server providing AI integration and content processing.

Tech Stack: FastAPI, MongoDB, FAISS, Ollama LLM, JWT Authentication

Key Features:

• AI chat with session management
• Comment-to-code generation
• Repository and PDF analysis
• Error detection and code validation
• FAISS vector storage for semantic search and document persistence
• HuggingFace embeddings (all-MiniLM-L6-v2) for document indexing

Quick Start:

cd backend
pip install -r requirements.txt
uvicorn main:app --reload --port 8000

Modern React web interface for interacting with the AI assistant.

Tech Stack: React 19, Vite, Tailwind CSS, Radix UI

Key Features:

• Interactive chat interface
• Multi-session management with FAISS-backed document storage
• File upload and repository integration
• Real-time streaming responses
• Code syntax highlighting
• Persistent conversation context using vector embeddings

Quick Start:

cd frontend
npm install
npm run dev

Integrated development environment extension for seamless coding assistance.

Key Features:

• Real-time code suggestions
• Error detection and highlighting
• Sidebar alternative suggestions
• Context-aware code generation
• LanceDB vector search integration for semantic code matching

Quick Start:

cd VsCodeExtension
npm install
# Press F5 in VS Code to launch Extension Development Host

The system uses advanced vector storage technologies for intelligent content retrieval:

• FAISS (Facebook AI Similarity Search): High-performance similarity search for document storage and retrieval
• HuggingFace Embeddings: Uses all-MiniLM-L6-v2 model for generating semantic embeddings
• Persistent Session Storage: User-specific and session-specific document indexes for maintaining conversation context
• LanceDB Integration: Additional vector database support in VS Code extension for semantic code search
• Document Management: Automatic indexing, filtering, and removal of documents based on metadata

• Node.js: 14.0+
• Python: 3.9+
• MongoDB: 4.4+
• Ollama: With perlbot3:latest model
• VS Code: 1.60.0+ (for extension)

1. Clone the repository

   git clone <repository-url>
   cd git_check

2. Start Backend

   cd backend
   pip install -r requirements.txt
   cp .env.example .env  # Configure environment variables
   uvicorn main:app --reload --port 8000

3. Start Frontend

   cd frontend
   npm install
   npm run dev  # Runs on http://localhost:5173

4. Install VS Code Extension

   cd VsCodeExtension
   npm install
   npm install -g vsce
   vsce package
   # Install the generated .vsix file in VS Code

Backend Environment (.env):

MONGODB_URL=mongodb://localhost:27017
DATABASE_NAME=archelon_ai
JWT_SECRET_KEY=your-secret-key
OLLAMA_MODEL=perlbot3:latest
OLLAMA_BASE_URL=http://localhost:11434

Frontend: Automatically connects to http://localhost:8000

VS Code Extension: Configure through VS Code settings (perlCodeGeneration.*)

# Create a subroutine that validates email addresses
# AI generates:
sub validate_email {
    my ($email) = @_;
    return $email =~ /^[^\s@]+@[^\s@]+\.[^\s@]+$/;
}

• Start conversations with the AI assistant
• Upload files, analyze repositories
• Get real-time streaming responses
• Manage multiple chat sessions with persistent context
• FAISS-powered document retrieval for relevant conversation history

• Select code blocks for alternative suggestions
• Automatic error detection and highlighting
• Context-aware code completion
• Semantic search through codebase using vector embeddings

Once the backend is running:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

• POST /chats/chat - AI chat interaction with vector-based context retrieval
• POST /commentCode/suggest - Code generation
• POST /validate/error - Error detection
• POST /upload - File processing with FAISS indexing
• POST /analyze-repo - Repository analysis

# Backend
cd backend && python -m pytest tests/

# Frontend  
cd frontend && npm test

# VS Code Extension
cd VsCodeExtension && npm test

# Frontend
cd frontend && npm run build

# VS Code Extension
cd VsCodeExtension && vsce package

1. Backend Connection Failed

   • Ensure MongoDB is running
   • Verify Ollama model is installed: ollama pull perlbot3:latest
   • Check environment variables in .env

2. FAISS Index Issues

   • Verify write permissions to session storage directory
   • Check for sufficient disk space for vector indexes
   • Ensure HuggingFace model downloads correctly

3. VS Code Extension Not Working

   • Verify backend is running on port 8000
   • Check extension logs in VS Code Output panel
   • Ensure workspace contains Perl files

4. Frontend API Errors

   • Confirm backend server is accessible
   • Check browser console for CORS issues
   • Verify authentication tokens

1. Fork the repository
2. Create 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 Pull Request

This project is licensed under [LICENSE]. See individual component directories for specific licensing information.

• FastAPI Documentation
• VS Code Extension API
• React Documentation
• Ollama Models
• FAISS Documentation
• LanceDB Documentation

Built with ❤️ using modern AI and web technologies

For detailed component documentation, see the README files in each subdirectory.