# Anchoring AI [**Why Anchoring AI?**](#why-anchoring-ai) | [**Live Demo and Videos**](#live-demo-and-videos) | [**Docker Deployment**](#docker-deployment) | [**Installation Guide**](#installation-guide) ## Why Anchoring AI? 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: - **No-Code Interface**: Quickly build apps with language models. - **Modular Design**: Easily add your own models, datasets and extensions. - **Drag-and-Drop**: Chain components to create powerful apps. - **Batch Processing**: Efficiently handle evaluations and repetitive tasks. - **Prompt Management**: Effortlessly manage your prompt and chains. - **Easy Sharing**: Streamline collaboration and sharing. - **Secure Access**: Customizable authentication for team management. - **Langchain Integration**: Seamless compatibility with Langchain (Python). - **Optimized Caching**: Reduce costs and boost performance. ## Live Demo and Videos ### Live Website You can check out our Alpha Release [here](https://platform.anchoring.ai/). ### Videos ## Upcoming Features - **Expanded Language Model Support**: Integration with more language models. - **Extended Capabilities**: Additional extensions and a new chat mode. - **Advanced Evaluation Metrics**: Custom modules for calculating evaluation metrics. - **Robust Security**: Strengthened security measures. - **Enhanced Modularity**: Improved standard components for increased flexibility. ## Docker Deployment If you prefer to deploy Anchoring AI using Docker, this section provides a step-by-step guide to do so. ### Prerequisites - [Docker](https://www.docker.com/products/docker-desktop) must be installed on your system. ### Instructions 1. **Clone the GitHub Repository** If you haven't already, clone the repository to your local machine. ```bash git clone https://github.com/AnchoringAI/anchoring-ai.git ``` 2. **Navigate to the Project Root Directory** ```bash cd anchoring-ai ``` 3. **Build the Docker Image** ```bash docker-compose build ``` 4. **Run Docker Containers** ```bash docker-compose up ``` Your application should now be accessible at `localhost:3000`. ### Teardown - **Stop Docker Containers** ```bash docker-compose down ``` - **Remove All Docker Resources (Optional)** ```bash docker system prune -a ``` ## Installation Guide This guide is primarily designed for Linux and macOS. Windows users can still follow along with some adjustments specified below. ### Prerequisites Before starting the installation, ensure you have administrator-level access to your system. > ### Note for Windows Users > > 1. Install and start Redis which is not supported on Windows through Windows Subsystem for Linux (WSL). > 2. Comment out `uwsgi==2.0.21` in `back-end/requirements.txt` as this package is not supported for Windows. > 3. Add `--pool=solo` for the Celery worker args in `back-end/src/celery_worker.py` to support batch jobs. ### Step 1: Install MySQL 8.0 1. **Download MySQL 8.0**: Go to the [official MySQL downloads page](https://dev.mysql.com/downloads/mysql/) and download the MySQL 8.0 installer for your operating system. 2. **Install MySQL**: Run the installer and follow the on-screen instructions to install MySQL. - Choose a setup type (Developer Default, Server only, etc.) - Configure the server (if prompted) - Set the root password and optionally create other users 3. **Start MySQL**: - For Linux and macOS, you can usually start MySQL with the following command: ```bash sudo systemctl start mysql ``` - For Windows, it often starts automatically or you can start it through the Services application. 4. **Verify Installation**: Open a terminal and execute the following: ```bash mysql --version ``` This should display the installed MySQL version. ### Step 2: Install Redis 5.0.7 1. **Download Redis 5.0.7**: Visit the [official Redis downloads page](https://redis.io/download) and download the Redis 5.0.7 tarball or installer for your operating system. 2. **Install Redis**: - **For Linux and macOS**: Extract the tarball and run the following commands in the terminal: ```bash cd redis-5.0.7 make make install ``` - **For Windows**: You may need to use Windows Subsystem for Linux (WSL) or a Redis Windows port. 3. **Start Redis**: - **For Linux and macOS**: You can usually start Redis by running: ```bash redis-server ``` - **For Windows**: If you're using WSL, you can start it the same way as on Linux. 4. **Verify Installation**: Open a new terminal and run: ```bash redis-cli ping ``` If Redis is running, this will return "PONG". ### Step 3: Install Node.js v18.16.0 1. Download and install Node.js version 18.16.0 from the [official website](https://nodejs.org/en/download/). 2. Verify the installation by running `node -v` in the terminal. ## Step 4: Install Python 3.8.10 1. Download and install Python version 3.8.10 from the [official website](https://www.python.org/downloads/). 2. Verify the installation by running `python --version` or `python3 --version` in the terminal. ## Step 5: Clone the GitHub Repository Run the following command in the terminal: ```bash git clone https://github.com/AnchoringAI/anchoring-ai.git ``` ### Step 6: Initialize and Configure Database #### Initialize Database 1. Open your terminal and navigate to the `scripts` directory within your project: ```bash cd path/to/your/project/scripts ``` 2. Open the MySQL shell by entering the following command: ```bash mysql -u [your_username] -p ``` You will be prompted to enter the password for `[your_username]`. 3. 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: ```bash use [your_database]; ``` 4. Execute the `init_db.sql` script to initialize your MySQL database: ```bash source init_db.sql ``` #### Configure Database Connection in Code 1. Navigate to the `config.py` file located in the `back-end/src` directory: ```bash cd path/to/your/project/back-end/src ``` 2. Open `config.py` in your favorite text editor and locate the `DevelopmentConfig` class. 3. Update the database configuration class to match your MySQL settings: ```python 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. ### Step 7: Set Up Front-end 1. Change your current directory to the `front-end` folder: ```bash cd front-end ``` 2. Install all necessary packages: ```bash npm install ``` 3. Start the front-end server: ```bash npm start ``` ### Step 8: Set Up Back-end 1. Change your current directory to the root directory and then navigate to `back-end`: ```bash cd .. cd back-end ``` 2. Install all required Python packages: ```bash pip install -r requirements.txt ``` ### Step 9: Run the Application 1. **Navigate to the `src` directory**: ```bash cd src ``` 2. **Start the Python application**: - **For Linux and macOS**: ```bash python3 app.py ``` - **For Windows**: ```bash python app.py ``` 3. **Start the Celery worker in the background**: - **For Linux and macOS**: ```bash python3 celery_worker.py >> logs/celery_worker_log.txt 2>&1 ``` - **For Windows**: ```bash 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.