AI Language Notes

A language learning assistant application that uses AI to analyze text inputs and provide language learning feedback.

Features

• Language Learning Analysis: Submit text in your target language and receive instant AI-powered feedback
• Personalized Learning: Set your native language and language you're learning
• Smart Note Organization: Auto-tagging system for categorizing language notes
• User Authentication: Secure user accounts with JWT authentication

Screenshots

Home Page

Dashboard

Tech Stack

• Backend: Go with Gin web framework
• Database: PostgreSQL
• Caching: Redis
• AI Integration: DeepSeek and OpenAI APIs
• Authentication: JWT
• Deployment: Docker and Docker Compose

Getting Started

Prerequisites

• Go 1.24+
• PostgreSQL 16+
• Redis
• DeepSeek or OpenAI API key

Running with Docker

1. Clone the repository:

git clone https://github.com/yourusername/ai-language-notes.git
cd ai-language-notes

2. Create a .env file in the docker directory using this template:

APP_PORT=8080
HTTP_PORT=8080
DB_HOST=db
DB_PORT=5432
DB_NAME=lang_learn_db
DB_USER=postgres
DB_PASSWORD=your_secure_password
DB_SSLMODE=disable
JWT_SECRET=your_jwt_secret
JWT_EXPIRATION_HOURS=72h
REDIS_PASSWORD=your_redis_password
REDIS_DB=0
DEEPSEEK_API_KEY=your_deepseek_api_key
LLM_PROVIDER=deepseek

3. Start the application:

cd deployment/docker
docker-compose up -d

4. Access the application at http://localhost:8080

Manual Setup

1. Clone the repository
2. Create a .env file in the root directory
3. Set up a PostgreSQL database and Redis
4. Run the migration:

go run migrations/migrate.go up

5. Start the server:

go run cmd/api/main.go

API Endpoints

Authentication

• POST /api/v1/auth/register - Register a new user
• POST /api/v1/auth/login - Login user

Notes

• GET /api/v1/notes - Get all notes for authenticated user
• POST /api/v1/notes - Create a new note
• GET /api/v1/notes/:id - Get a specific note
• DELETE /api/v1/notes/:id - Delete a note

User

• GET /api/v1/user/profile - Get user profile
• PUT /api/v1/user/profile - Update user profile

Architecture

The project follows a clean architecture approach:

• api: Application entry point
• api: HTTP API handlers and router
• repository: Data access layer
• models: Domain models
• services: Business logic services
• ai: AI service integrations
• config: Configuration management
• auth: Authentication utilities
• storage: Database connections