* feat: Enhance annotation module with template management and validation
- Added DatasetMappingCreateRequest and DatasetMappingUpdateRequest schemas to handle dataset mapping requests with camelCase and snake_case support.
- Introduced Annotation Template schemas including CreateAnnotationTemplateRequest, UpdateAnnotationTemplateRequest, and AnnotationTemplateResponse for managing annotation templates.
- Implemented AnnotationTemplateService for creating, updating, retrieving, and deleting annotation templates, including validation of configurations and XML generation.
- Added utility class LabelStudioConfigValidator for validating Label Studio configurations and XML formats.
- Updated database schema for annotation templates and labeling projects to include new fields and constraints.
- Seeded initial annotation templates for various use cases including image classification, object detection, and text classification.
* feat: Enhance TemplateForm with improved validation and dynamic field rendering; update LabelStudio config validation for camelCase support
* feat: Update docker-compose.yml to mark datamate dataset volume and network as external
* feat: Add tag configuration management and related components
- Introduced new components for tag selection and browsing in the frontend.
- Added API endpoint to fetch tag configuration from the backend.
- Implemented tag configuration management in the backend, including loading from YAML.
- Enhanced template service to support dynamic tag rendering based on configuration.
- Updated validation utilities to incorporate tag configuration checks.
- Refactored existing code to utilize the new tag configuration structure.
* feat: Refactor LabelStudioTagConfig for improved configuration loading and validation
* feat: Update Makefile to include backend-python-docker-build in the build process
* feat: Migrate to poetry for better deps management
* Add pyyaml dependency and update Dockerfile to use Poetry for dependency management
- Added pyyaml (>=6.0.3,<7.0.0) to pyproject.toml dependencies.
- Updated Dockerfile to install Poetry and manage dependencies using it.
- Improved layer caching by copying only dependency files before the application code.
- Removed unnecessary installation of build dependencies to keep the final image size small.
* feat: Remove duplicated backend-python-docker-build target from Makefile
* fix: airflow is not ready for adding yet
* feat: update Python version to 3.12 and remove project installation step in Dockerfile
* feat: Refactor configuration and sync logic for improved dataset handling and logging
* feat: Enhance annotation synchronization and dataset file management
- Added new fields `tags_updated_at` to `DatasetFiles` model for tracking the last update time of tags.
- Implemented new asynchronous methods in the Label Studio client for fetching, creating, updating, and deleting task annotations.
- Introduced bidirectional synchronization for annotations between DataMate and Label Studio, allowing for flexible data management.
- Updated sync service to handle annotation conflicts based on timestamps, ensuring data integrity during synchronization.
- Enhanced dataset file response model to include tags and their update timestamps.
- Modified database initialization script to create a new column for `tags_updated_at` in the dataset files table.
- Updated requirements to ensure compatibility with the latest dependencies.
