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AnchoringAI / repository
An open-source no-code tool for teams to collaborate on building, evaluating, and hosting applications leveraging GPT and other large language models. You could easily build and share LLM-powered apps, manage your budget and run batch jobs.
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Why Anchoring AI? | Live Demo and Videos | Docker Deployment | Installation Guide
Anchoring AI is an open-source no-code tool for teams to collaborate on building, evaluating, and hosting applications leveraging GPT and other large language models. You could easily build and share LLM-powered apps, manage your budget and run batch jobs. With Anchoring AI, managing access, controlling budgets, and running batch jobs is a breeze. We aim to be the destination of choice for transforming your team into an AI-centric powerhouse.
We provide:
You can check out our Alpha Release here.
https://github.com/AnchoringAI/anchoring-ai/assets/20156958/eece7096-7e54-476e-a0f9-93926918ada1
If you prefer to deploy Anchoring AI using Docker, this section provides a step-by-step guide to do so.
Clone the GitHub Repository
If you haven't already, clone the repository to your local machine.
git clone https://github.com/AnchoringAI/anchoring-ai.git
Navigate to the Project Root Directory
cd anchoring-ai
Build the Docker Image
docker-compose build
Run Docker Containers
docker-compose up
Your application should now be accessible at localhost:3000.
Stop Docker Containers
docker-compose down
Remove All Docker Resources (Optional)
docker system prune -a
This guide is primarily designed for Linux and macOS. Windows users can still follow along with some adjustments specified below.
Before starting the installation, ensure you have administrator-level access to your system.
Note for Windows Users
- Install and start Redis which is not supported on Windows through Windows Subsystem for Linux (WSL).
- Comment out
uwsgi==2.0.21inback-end/requirements.txtas this package is not supported for Windows.- Add
--pool=solofor the Celery worker args inback-end/src/celery_worker.pyto support batch jobs.
Download MySQL 8.0: Go to the official MySQL downloads page and download the MySQL 8.0 installer for your operating system.
Install MySQL: Run the installer and follow the on-screen instructions to install MySQL.
Start MySQL:
sudo systemctl start mysql
Verify Installation: Open a terminal and execute the following:
mysql --version
This should display the installed MySQL version.
Download Redis 5.0.7: Visit the official Redis downloads page and download the Redis 5.0.7 tarball or installer for your operating system.
Install Redis:
cd redis-5.0.7
make
make install
Start Redis:
redis-server
Verify Installation: Open a new terminal and run:
redis-cli ping
If Redis is running, this will return "PONG".
node -v in the terminal.python --version or python3 --version in the terminal.Run the following command in the terminal:
git clone https://github.com/AnchoringAI/anchoring-ai.git
Open your terminal and navigate to the scripts directory within your project:
cd path/to/your/project/scripts
Open the MySQL shell by entering the following command:
mysql -u [your_username] -p
You will be prompted to enter the password for [your_username].
Once inside the MySQL shell, switch to the database you intend to use (if it already exists). Replace [your_database] with the name of your database:
use [your_database];
Execute the init_db.sql script to initialize your MySQL database:
source init_db.sql
Navigate to the config.py file located in the back-end/src directory:
cd path/to/your/project/back-end/src
Open config.py in your favorite text editor and locate the DevelopmentConfig class.
Update the database configuration class to match your MySQL settings:
class DevelopmentConfig(BaseConfig):
USERNAME = '[your_username]'
PASSWORD = '[your_password]'
HOST = 'localhost'
PORT = '3306'
DATABASE = '[your_database]'
DB_URI = f'mysql+pymysql://{USERNAME}:{PASSWORD}@{HOST}:{PORT}/{DATABASE}?charset=utf8'
SQLALCHEMY_DATABASE_URI = DB_URI
Replace [your_username], [your_password], and [your_database] with the MySQL username, password, and database name you've chosen.
After completing these steps, your database should be initialized and your application configured to connect to it.
front-end folder:cd front-end
npm install
npm start
back-end:cd ..
cd back-end
pip install -r requirements.txt
Navigate to the src directory:
cd src
Start the Python application:
python3 app.py
python app.py
Start the Celery worker in the background:
python3 celery_worker.py >> logs/celery_worker_log.txt 2>&1
python celery_worker.py >> logs/celery_worker_log.txt 2>&1
After completing these steps, you should be able to see the app running at localhost:3000.