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.
Why Anchoring AI? | Live Demo and Videos | Docker Deployment | Installation Guide
Anchoring AI is your gateway to effortless AI integration. Our open-source, no-code platform empowers teams to create, assess, share, and host applications powered by Large Language Models (LLMs), complete with rich component support. 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==2.0.21
inback-end/requirements.txt
as this package is not supported for Windows.- Add
--pool=solo
for the Celery worker args inback-end/src/celery_worker.py
to 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.