Added a Dependabot configuration to automate dependency updates for the Python package ecosystem. Dependabot will now check for updates on a daily basis, ensuring that our project dependencies remain up-to-date with the latest security patches and features without manual oversight. This proactive approach towards dependency management will aid in minimizing potential security vulnerabilities and compatibility issues, fostering a more secure and stable development environment.
Updated the README to strengthen the recommendation of using a virtual environment (venv) during installation. This adjustment aims to guide users towards best practices in Python environment management, potentially reducing common issues related to package dependencies and conflicts.
This commit simplifies the pyproject.toml structure for better readability and maintenance. Key changes include formatting author and license information, consolidating dependency lists into a more concise format, and adding the `future` package to dependencies to ensure forward-compatibility. Optional dependencies are now listed in a more compact style, and the development dependencies section has been cleaned up. These adjustments make the project configuration cleaner and more accessible, facilitating future updates and dependency management.
Corrected the default port for Pantalaimon from 8010 to 8009 in the README documentation. This change aligns the documentation with the latest Pantalaimon configuration standards, ensuring that users setting up their homeserver URL in the bot's config.ini file use the correct port. This update is crucial for new users during initial setup to avoid connectivity issues.
Updated the default listening port in pantalaimon.example.conf from 8010 to 8009. This alteration ensures compatibility with new network policies and avoids collision with commonly used ports in the default configuration. It's an important change for users setting up new instances, enabling smoother initial configurations without manual port adjustments.
Introduced the `ForceVision` configuration option to allow usage of third-party models for image recognition within the OpenAI setup. This change broadens the flexibility and applicability of the bot's image processing capabilities by not restricting to predefined vision models only. Also, added missing properties to the `OpenAI` class to provide comprehensive control over the bot's behavior, including options for forcing vision and tools usage, along with emulating tool capabilities in models not officially supporting them. These enhancements make the bot more adaptable to various models and user needs, especially for self-hosted setups.
Additionally, updated documentation and increment version to 0.3.12 to reflect these changes and improvements.
Refactored the handling of AI providers to support multiple AI services efficiently, introducing a `BaseAI` class from which all AI providers now inherit. This change modernizes our approach to AI integration, providing a more flexible and maintainable architecture for future expansions and enhancements.
- Adopted `gpt-4o` and `dall-e-3` as the default models for chat and image generation, respectively, aligning with the latest advancements in AI capabilities.
- Integrated `ruff` as a development dependency to enforce coding standards and improve code quality through consistent linting.
- Removed unused API keys and sections from `config.dist.ini` to streamline configuration management and clarify setup processes for new users.
- Updated the command line tool for improved usability and fixed previous issues preventing its effective operation.
- Enhanced OpenAI integration with advanced settings for temperature, top_p, frequency_penalty, and presence_penalty, enabling finer control over AI-generated outputs.
This comprehensive update not only enhances the bot's performance and usability but also lays the groundwork for incorporating additional AI providers, ensuring the project remains at the forefront of AI-driven chatbot technologies.
Resolves#13
Refactored the main execution pathway to introduce a `main_sync` function that wraps the existing asynchronous `main` function, facilitating compatibility with environments that necessitate or prefer synchronous execution. This change enhances the bot's flexibility in various deployment scenarios without altering the core asynchronous functionality.
In addition, expanded the exception handling in `get_version` to catch all exceptions instead of limiting to `DistributionNotFound`. This broadens the robustness of version retrieval, ensuring the application can gracefully handle unexpected issues during version lookup.
Whitespace adjustments improve code readability by clearly separating function definitions.
These adjustments contribute to the maintainability and operability of the application, allowing for broader usage contexts and easier integration into diverse environments.
This commit removes unnecessary imports across several modules, enhancing code readability and potentially improving performance. Notably, `KeysUploadError` and `requests` were removed where no longer used, reflecting a cleaner dependency structure. Furthermore, logging calls have been standardized, removing dynamic string generation in favor of static messages. This change not only makes the logs more consistent but also slightly reduces the computational overhead associated with log generation. The removal of unused type hints also contributes to a more focused and maintainable code base.
Additionally, the commit includes minor text adjustments for user messages, replacing dynamic content with fixed strings where the dynamism was not needed. This enhances both the clarity and security of user-directed messages by avoiding unnecessary string formatting operations.
Finally, the simplification of the migration script and the adjustment in the tools module underscore an ongoing effort to maintain clean and efficient code infrastructure.
Added `LogLevel` and `UseKeyring` configuration options to the example configuration file to provide users with more control over logging verbosity and the decision to utilize a system keyring for credentials storage. The LogLevel option allows for easier debugging by adjusting the verbosity of logs, whereas the UseKeyring option offers flexibility in credential management, catering to environments where a system keyring may not be preferred or available.
These changes enhance the tool's usability and adaptability to various user environments and debugging needs.
Enhanced bot flexibility by enabling the specification of room IDs in the allowed users' list, broadening access control capabilities. This change allows for more granular control over who can interact with the bot, particularly useful in scenarios where the bot's usage needs to be restricted to specific rooms. Additionally, updated documentation and configurations reflect the inclusion of new AI models and self-hosted API support, catering to a wider range of use cases and setups. The README.md and config.dist.ini files have been updated to offer clearer guidance on setup, configuration, and troubleshooting, aiming to improve user experience and ease of deployment.
- Introduced the ability for room-specific bot access, enhancing user and room management flexibility.
- Expanded AI model support, including `gpt-4o` and `ollama`, increases the bot's versatility and application scenarios.
- Updated Python version compatibility to 3.12 to ensure users are leveraging the latest language features and improvements.
- Improved troubleshooting documentation to assist users in resolving common issues more efficiently.
Introduces logging for cases where OpenAI's API returns an empty response, ensuring that such occurrences are captured for debugging purposes. This change enhances visibility into the interaction with OpenAI's endpoint, facilitating easier identification and resolution of issues where empty responses are received, potentially indicating API limitations, network issues, or unexpected behavior from the AI model.
This update allows users to provide a location name for their weather reports, which can be useful when requesting weather information for specific locations.
When processing large volumes of data, it's essential to handle errors gracefully and provide clear feedback to users. This change introduces additional checks to ensure robust error handling during user authentication, reducing the likelihood of errors propagating further down the pipeline.
This improvement not only enhances the overall stability of the system but also provides a better user experience by providing more informative error messages in the event of an issue.
Renamed `pantalaimon_first_login.py` to `fetch_access_token.py` to better reflect its purpose. Additionally, updated README to remove obsolete instructions for using pantalaimon with the bot.
Release version 0.3.9 introduces Docker support, enhancing deployment options by allowing the bot to run in a containerized environment. This update greatly simplifies deployment and scaling processes. Additionally, the inclusion of the TrackingMore dependency expands the bot's functionality, enabling advanced tracking features. These changes collectively aim to improve the bot's adaptability and efficiency in handling tasks.
- Updated the Docker CI/CD workflow to trigger on pushes to the main branch, aligning with standard Git flow practices for production deployment.
- Advanced project version to 0.3.9, marking a new release with consolidated features and bug fixes.
This adjustment ensures that the Docker images are built and deployed in a more streamlined manner, reflecting our shift towards a unified branching strategy for releases. The version bump signifies the stabilization of new functionalities and enhancements for broader usage.
This update introduces the ability for the bot to use a Matrix UserID and password for authentication, in addition to the existing Access Token method. Upon the first run with UserID and password, the bot automatically converts these credentials into an Access Token, updates the configuration with this token, and removes the password for security purposes. This enhancement simplifies the initial setup process for new users by directly utilizing Matrix login credentials, aligning with common user authentication workflows and enhancing security by not storing passwords long-term.
Refactored the bot initialization process in `GPTBot.from_config` to support dynamic login method selection based on provided credentials, and implemented automatic configuration updating to reflect the newly obtained Access Token and cleaned credentials.
Minor adjustments include formatting and comment clarification for better readability and maintenance.
This change addresses the need for a more straightforward and secure authentication process for bot deployment and user experience improvement.
Removed detailed Docker setup instructions, opting to simplify the Docker usage section by retaining only the Docker Compose method. This change aims to declutter the README and encourage a more standardized setup process for users, reducing potential confusion and maintaining focus on the primary installation method via Docker Compose. The update includes a minor adjustment to the database initialization step, ensuring users are guided to prepare their environment fully before running the bot. This revision makes the setup process more approachable and efficient, especially for newcomers.
By directing users to the `Running` section for config file setup instructions, we maintain consistency and avoid duplicative content, keeping the README streamlined and more manageable.
Transitioned from the deprecated `pkg_resources` to `importlib.metadata` for package version retrieval, improving performance and future compatibility.
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.
- 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.
Removed redundant internal Docker CI/CD workflow and unified naming for external Docker workflows to improve clarity and maintainability. Introduced a new workflow for tagging pushes, aligning deployments closer with best practices for version management and distribution. This change simplifies the CI/CD pipeline, reducing duplication and potential confusion, while ensuring that Docker images are built and pushed efficiently for both internal developments and tagged releases.
- Docker CI/CD internals were removed, focusing efforts on standardized workflows.
- Docker CI/CD workflow names were harmonized to reinforce their universal role across projects.
- A new tagging workflow supports better version control and facilitates automatic releases to Docker Hub, promoting consistency and reliability in image distribution.
This adjustment lays the groundwork for more streamlined and robust CI/CD operations, providing a solid framework for future enhancements and scalability.
Introduced a new CI/CD workflow specifically for building and pushing Docker images to the Forgejo Docker Registry, triggered by pushes to the 'docker' branch. This addition aims to streamline Docker image management and deployment within Forgejo's infrastructure, ensuring a more isolated and secure handling of images. Concurrently, the existing workflow for Docker Hub has been refined to clarify its purpose: it is now explicitly focused on pushing to Docker Hub, removing the overlap with Forgejo Docker Registry operations. This delineation enhances the clarity of our CI/CD processes and ensures a cleaner separation of concerns between public and internal image repositories.
Removed a duplicate and unnecessary line specifying the `latest` tag for the docker image in the workflow. This change simplifies the tag specification process, avoiding redundancy, and ensuring clear declaration of both the `latest` and SHA-specific tags for our docker images in CI/CD pipelines.
Migrated from direct GitHub context references to environment variables for GitHub SHA, actor, and token within the Docker build and push actions. This enhances portability and consistency across different execution environments, ensuring better compatibility and security when interfacing with GitHub and Forgejo Docker registries.
Enhanced Docker build workflows in `.forgejo/workflows/docker-latest.yml` to include dynamic tagging based on the GITHUB_SHA, alongside the existing 'latest' tag for both the kumitterer/matrix-gptbot and git.private.coffee/privatecoffee/matrix-gptbot images. This change allows for more precise versioning and traceability of images, facilitating rollback and specific version deployment. Also standardized authentication token variables for Docker login to the Forgejo Docker Registry, improving readability and consistency in CI/CD configurations.
Enhanced the CI pipeline for Docker images by supporting an additional push to the Forgejo Docker Registry alongside the existing push to Docker Hub. This change allows for better integration with private infrastructures and provides an alternative for users and systems that prefer or require images to be stored in a more controlled or private registry. It involves logging into both Docker Hub and Forgejo with respective credentials and pushing the built images to both, ensuring broader availability and redundancy of our Docker images.
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.
Updated the docker-latest workflow to install additional critical build dependencies including build-essential and libpython3-dev, alongside docker.io. This enhancement is geared towards supporting a wider range of development scenarios and facilitating more complex build requirements directly within the CI pipeline.
Updated the Docker CI/CD pipeline in the `.forgejo/workflows/docker-latest.yml` to support better integration and efficiency. Key enhancements include setting a container environment with Node 20 on Debian Bookworm for consistency across builds, and installing Docker directly within the runner to streamline the process. This refinement simplifies the setup steps, reduces potential for errors, and possibly decreases pipeline execution time. These changes ensure that our Docker images are built and pushed in a more reliable and faster environment.
Removed the specific runner designation from the Docker workflow to allow for dynamic runner selection. This change aims to increase flexibility and compatibility across different CI environments. It reduces the dependency on a single OS version, potentially leading to better resource availability and efficiency in workflow execution.
Introduced a new Docker CI/CD workflow to automatically build and push images to Docker Hub on pushes to the 'docker' branch. This automation ensures that the latest version of the bot is readily available for deployment, facilitating easier distribution and deployment for users.
The README.md has been updated to improve clarity around installation methods, emphasizing PyPI as the recommended installation method while expanding on Docker usage. It now includes detailed instructions for Docker Hub images, local image building, and Docker Compose deployment, catering to a broader range of users and deployment scenarios. This update aims to make the bot more accessible and manageable by providing clear, step-by-step guidance for different deployment strategies.
Related to these changes, the documentation has been restructured to better organize information related to configuration and running the bot, ensuring users have a smoother experience setting up and deploying it in their environment.
These changes reflect our commitment to enhancing user experience and streamlining deployment processes, making it easier for users to adopt and maintain the matrix-gptbot in various environments.
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.
Extended the README to include a new section on bot configuration setup, emphasizing the necessity of a config.ini file for operation. This update clarifies the setup process for new users, ensuring they understand the requirement of configuring the bot before use. Additionally, outlined the repository policy regarding the use of the `main` branch for development and the process for contributing through feature branches and pull requests, aiming to streamline contribution workflows and maintain code quality.
The formatting improvements across the README enhance readability and ensure consistency in documentation presentation.
Removed an extraneous log statement that recorded the first message content in the OpenAI class. This change streamlines the logging process by eliminating unnecessary log clutter, improving the readability of logs and potentially enhancing performance by reducing I/O operations on the logging system. This adjustment is pivotal for maintaining a clean and efficient codebase, especially in production environments where excessive logging can lead to inflated log sizes and make troubleshooting more challenging.
- Initialized preparations for the unreleased 0.3.9 version in the changelog.
- Updated copyright information to include 2024 and added Private.coffee Team alongside Kumi Mitterer to reflect the collaborative nature of the project going forward.
- Incremented the project version to 0.3.9.dev0 in pyproject.toml to align with upcoming development efforts.
- Modified all references from Kumi's personal repo to the Private.coffee Team's repo in README.md, LICENSE, and pyproject.toml, ensuring future contributions and issues are directed to the correct repository. This change facilitates a broader collaboration platform and acknowledges the team's growing involvement in the project's development.
These updates are critical for the upcoming development phase and for accurately representing the collaborative efforts behind the project.
Updated the CHANGELOG.md to document enhancements in versions 0.3.7 and 0.3.8, including updates to URLs in the pyproject.toml and migration of the build pipeline to Forgejo Actions. These changes aim to improve project dependency management and streamline the CI/CD process, ensuring a more efficient development workflow.
Bumped project version to 0.3.8 for the next release cycle. Updated Homepage and Bug Tracker URLs to reflect the new hosting location, aiming for improved accessibility and collaboration. Additionally, introduced a Source Code URL for direct access to the repository, facilitating developers' engagement and contributions.
Simplified the publishing to PyPI steps in the release workflow by consolidating the Python environment setup, tool installation, package building, and publishing into a single job. This change makes the workflow more efficient and reduces the number of steps required to publish a package. It ensures that the environment setup, dependencies installation, package building, and publishing are done in one go, which can help in minimizing potential issues arising from multiple, separate steps.
This approach leverages the existing Python and PyPI tools more effectively and could potentially shorten the release cycle time. It also makes the workflow file cleaner and easier to maintain.
Switched the virtual environment activation command to be compliant with POSIX shell syntax. The previous `source` command is replaced with `.`, making the script more broadly compatible with different shell environments without requiring bash specifically. This change ensures greater portability and compatibility of the release workflow across diverse CI/CD execution environments.
Updated the GitHub Actions workflow in `.forgejo/workflows/release.yml` to explicitly use `python3` instead of `python` for both version checking and virtual environment setup. This change ensures compatibility and clarity across environments where `python` might still point to Python 2.x, preventing potential conflicts and erasing ambiguity. The adjustment aligns with modern best practices, acknowledging the widespread transition to Python 3 and its tooling.
Updated the CI/CD pipeline in `.forgejo/workflows/release.yml` to utilize the `node:20-bookworm` image instead of `python:3.10`. This necessitated adding steps for installing Python dependencies, given the switch to a Node.js-based image. This change accommodates projects that require both Node.js and Python environments for their setup and publishing processes, ensuring better compatibility and flexibility in handling mixed-technology stacks.
This update enhances our pipeline's capability to manage dependencies more efficiently across different technologies, catering to the evolving needs of our projects.
