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Device Management System

A comprehensive solution AI transalter base on RAG

Features

  • Simple Translator

Base Config

set frontend environment variables

  • edit vite-project/.env under vite-porject eg: 
    VITE_FLASK_URL=http://localhost:5000

set backend environment variables

  • edit backend/.env under backend eg:

    USE_STREAM=false
OPENAI_API_KEY=xxxxxx
DEEPSEEK_API_KEY=xxxxxxxxx

  • edit backend/config.yml under backend to config the model eg:

    models:
- name: gpt-3.5-turbo
  providers: [OpenAI, DeepSeek, Custom]
  max_tokens: 1000
  temperature: 0.7

Run in one command

You can setup the env by one command

docker-compose up -d

Vue Frontend Environment Setup

Prerequisites

  • Node.js (v14+)
  • npm or yarn

Install pre-requirement

cd vite-project
npm install

Installation

npm install

Running the Application

npm start

# vite project
npm run dev

Usage

  • Access the web interface at http://localhost:3000
  • Use the API endpoints as documented in the /docs folder

Python Backend Environment Setup

Prerequisites

  • Python 3.11 or higher
  • pip (Python package manager)

Installation Steps

  1. Install Python dependencies:

    pip install -r requirements.txt

  2. Set environment variables:

    • Copy .env to your working directory and update credentials as needed. eg:
    USE_STREAM=false

  3. Run the backend server:

    cd backend
python app.py

    The backend API will be available at http://localhost:5000.

Notes

  • The backend uses SQLite for translator data storage (backend/config.db).
  • For development, the server runs with debug=True for hot-reloading.
  • Make sure to keep your .env file secure and do not commit sensitive credentials.

Contributing

Contributions are welcome! Please open issues or submit pull requests.

License

This project is licensed under the MIT License.