Added a volume to the `matrix-gptbot` service configuration in `docker-compose.yml`, mounting the local `database.db` file into the container. This change enables persistent storage for the bot's data, ensuring that data is not lost when the container is restarted or redeployed. It enhances data management and allows for more robust operation of the bot service by leveraging persistency.
This development is crucial for scenarios requiring data retention across bot updates and system maintenance activities.
Moved from building the GPT bot Docker container on the fly to using a pre-built image, enhancing the setup's efficiency and reducing build times for deployments. Adjusted the server's exposed port to resolve conflicts and standardize deployment configurations. Additionally, locked in the `future` package version to ensure compatibility with legacy Python code, mitigating potential future incompatibility issues.
Changed the volume mapping for GPTbot service in `docker-compose.yml` from `settings.ini` to `config.ini`. This modification aligns the container configuration with the new application configuration file naming convention, facilitating easier configuration management and clarity for development and deployment processes.
This change is essential for maintaining consistency across our documentation and deployment scripts, ensuring that all references to configuration files are accurate and up to date.
Redesigned the Docker setup to enhance project structure and configuration management. Changes include a more organized directory structure within the Docker container, separating source code, project metadata, and licenses explicitly to the `/app` directory for better clarity and management. Additionally, integrated `pantalaimon` as a dependency service in `docker-compose.yml`, enabling secure communication with Matrix services by automatically managing settings and configurations through mounted files. This setup simplifies the development environment setup and streamlines deployments.
Introduced Dockerfile and docker-compose.yml to encapsulate GPTBot into a Docker container. This simplifies deployment and ensures consistent environments across development and production setups. The Dockerfile outlines the necessary steps to build the image, incl. setting up the working directory, copying the current directory into the container, installing all dependencies, and defining the command to run the bot. The docker-compose.yml file further streamlines the deployment process by specifying service configuration, leveraging Docker Compose version 3.8 for its extended feature set and compatibility.
By containerizing GPTBot, we enhance portability, reduce set-up times for new contributors, and minimize "works on my machine" issues, fostering a more robust development lifecycle.
