- Included the `ffmpeg` package in the Docker environment to support multimedia content processing.
- Added `trackingmore-api-tool` as a dependency to expand the bot's functionality with tracking capabilities.
- Adjusted the `all` dependencies list in `pyproject.toml` to include the `trackingmore` module, indicating a broader feature set for the application.
- Updated the bot class to prepare for integrating `TrackingMore` alongside existing services like `OpenAI` and `WolframAlpha`, highlighting an intention to make such integrations configurable in the future.
This enhancement enables the bot to interact with multimedia content more effectively and introduces package tracking features, laying groundwork for configurable service integrations.
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.
Moved the installation of build-essential and libpython3-dev from the Docker workflow to the Dockerfile itself. This change optimizes the Docker setup process, ensuring that all necessary dependencies are encapsulated within the Docker build context. It simplifies the CI workflow by removing redundant steps and centralizes dependency management, making the build process more straightforward and maintainable.
This adjustment aids in achieving a cleaner division between CI setup and application-specific build requirements, potentially improving build times and reducing complexity for future updates or dependency changes.
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.
