Project Sophia

Project Sophia is a comprehensive knowledge management system designed to help developers organize, manage, and share code snippets, solutions, and technical insights. It combines powerful AI capabilities with robust organization features to streamline your coding workflow.

About the Project

Project Sophia addresses the common problem developers face in managing an ever-growing collection of code snippets, solutions to problems, and general technical knowledge. It provides a structured environment to:

• Organize Code Snippets - Create, categorize, and tag code snippets by language and collection
• Problem Solving - Add LeetCode-style problems and generate AI-powered solutions
• AI-Powered Development - Generate code, explain existing code, format, and refactor using AI
• Personal Knowledge Base - Create notes and document your technical knowledge
• Gamification - Track progress with points, badges, and activity streaks
• Advanced Search - Find snippets using keyword and semantic search with embeddings

Tech Stack

• Backend: Flask (Python)
• Database: SQLite (SQLAlchemy ORM)
• Frontend: HTML, CSS (Bootstrap + custom styles), JavaScript
• AI Integration:
  • Google Gemini API (gemini-2.5-flash, gemini-1.5-flash, gemini-2.5-pro)
  • Minimax API (minimax-m2:free)
• Authentication: Flask-Login with password hashing (Werkzeug)
• Forms: WTForms with validation
• Database Migrations: Alembic/Flask-Migrate
• Syntax Highlighting: Pygments
• Markdown: python-markdown for note rendering

Documentation

• [User Guide](User Guide.md) - Complete user manual with tutorials and examples
• [Developer Guide](Developer Guide.md) - Technical documentation for contributors
• Features - Detailed feature descriptions and demos
• Troubleshooting - Common issues and solutions
• Updates - Version history and migration guide

Getting Started

Prerequisites

• Python 3.9+
• pip (Python package installer)
• Git

Installation

1. Clone the repository:

   git clone https://github.com/AlexBiobelemo/Project-Sophia.git
   cd Project-Sophia

2. Create and activate a virtual environment:

   python -m venv venv
   \venv\Scripts\activate  # On Windows
   source venv/bin/activate # On macOS/Linux

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables: Create a .env file in the root directory and add the following:

   SECRET_KEY='your_secret_key_here'
   DATABASE_URL='sqlite:///app.db'
   GEMINI_API_KEY='your_gemini_api_key_here'
   MINIMAX_API_KEY='your_minimax_api_key_here'  # Optional
   FLASK_APP=run.py
   FLASK_ENV=development
   

   • Replace GEMINI_API_KEY with your actual Google Gemini API key
   • MINIMAX_API_KEY is optional for using the Minimax AI provider

5. Initialize the database:

   flask db upgrade

6. Initialize badges:

   python -c "from app.badge_system import initialize_default_badges; from app import create_app, db; app = create_app(); with app.app_context(): initialize_default_badges()"

Usage

To run the application:

flask run 

The application will typically be available at http://127.0.0.1:5000/. Open this URL in your web browser.

Key Features

Code Snippet Management

• Create, edit, delete code snippets
• Syntax highlighting for multiple programming languages
• Tag-based organization
• Version history with rollback capability
• AI-powered code explanation and formatting

Collection Organization

• Hierarchical collections with nested folders
• Drag-and-drop reordering
• Move and copy snippets between collections
• Collection statistics

AI-Powered Features

• Code Generation: Generate code from natural language prompts
• Code Explanation: Get detailed explanations for any code snippet
• Code Formatting: AI-powered code formatting for consistency
• Tag Suggestion: Automatic tag suggestions for code
• Code Refinement: Fix bugs and improve code based on error messages
• Multi-Step Thinking: Architecture → Coder → Tester → Refiner pipeline
• Streaming Generation: Real-time code and explanation streaming

LeetCode Integration

• Add programming problems with descriptions and difficulty levels
• Generate AI solutions in multiple programming languages
• Solution approval workflow
• Solution explanation and classification

Notes System

• Create and manage personal notes
• AI-powered note explanation
• Rich text support with Markdown

Chat Assistant

• AI-powered coding assistant
• Conversation history with multiple sessions
• Context-aware responses based on your snippets

Gamification

• Points system for activities (snippet creation, solution approval, etc.)
• 25+ badges for achievements
• Activity streaks tracking
• User profile analytics and statistics

Search & Discovery

• Advanced search with filters (language, tags, date, collection)
• Semantic search using vector embeddings
• Search highlighting and scoring
• Solution search for approved solutions

Export & Backup

• Export snippets as Markdown files
• Export filtered snippets
• Bulk export to ZIP archive
• Automatic daily snapshots

User Management

• Secure registration and login
• Account lockout after failed login attempts
• Profile customization with avatars
• User preferences (dark mode, AI model selection, UI settings)

API Configuration

Project Sophia supports multiple AI providers:

Google Gemini (Default)

• Set GEMINI_API_KEY environment variable
• Default model: gemini-2.5-flash
• Supports: gemini-1.5-flash, gemini-2.5-flash, gemini-2.5-pro

Minimax (Optional)

• Set MINIMAX_API_KEY environment variable
• Model: minimax-m2:free
• Fallback to Gemini if Minimax is unavailable

Users can select their preferred AI model in the User Settings.

Project Structure

Project-Sophia/
├── app/
│   ├── __init__.py          # Flask app factory
│   ├── routes.py            # All route handlers
│   ├── models.py            # SQLAlchemy models
│   ├── forms.py             # WTForms forms
│   ├── ai_services.py       # AI service router
│   ├── ai_services_minimax.py  # Minimax AI implementation
│   ├── badge_system.py      # Badge and gamification
│   ├── utils/               # Utility functions
│   ├── static/              # Static assets (CSS, JS, uploads)
│   └── templates/           # Jinja2 templates
├── migrations/              # Alembic migrations
├── snapshots/               # Automatic daily snapshots
├── config.py                # Configuration
├── requirements.txt         # Python dependencies
└── run.py                   # Application entry point

Security Features

• Password hashing with Werkzeug
• Session management with Flask-Login
• Account lockout after 5 failed login attempts (30-minute lockout)
• CSRF protection via WTForms
• Security headers (X-Content-Type-Options, X-Frame-Options, etc.)
• Input validation and sanitization

Contact/Acknowledgments

• Developed by Alex Biobelemo
• Inspired by personal needs for better code organization
• Uses Google Gemini API for AI capabilities
• Uses Minimax API for additional AI capabilities