This commit introduces a new application layer to the codebase, which decouples the GraphQL resolvers from the data layer. The resolvers now call application services, which in turn call the repositories. This change improves the separation of concerns and makes the code more testable and maintainable.
Additionally, this commit introduces dataloaders to solve the N+1 problem in the GraphQL resolvers. The dataloaders are used to batch and cache database queries, which significantly improves the performance of the API.
The following changes were made:
- Created application services for most of the domains.
- Refactored the GraphQL resolvers to use the new application services.
- Implemented dataloaders for the `Author` aggregate.
- Updated the `app.Application` struct to hold the application services instead of the repositories.
- Fixed a large number of compilation errors in the test files that arose from these changes.
There are still some compilation errors in the `internal/adapters/graphql/integration_test.go` file. These errors are due to the test files still trying to access the repositories directly from the `app.Application` struct. The remaining work is to update these tests to use the new application services.
This commit includes the following changes:
- Refactored all data repositories in `internal/data/sql/` to use a consistent `sql` package and to align with the new `domain` models.
- Fixed the GraphQL structure by moving the server creation logic from `internal/app` to `cmd/api`, which resolved an import cycle.
- Corrected numerous incorrect import paths for packages like `graph`, `linguistics`, `syncjob`, and the legacy `models` package.
- Resolved several package and function redeclaration errors.
- Removed legacy migration code.
- Core Go application with GraphQL API using gqlgen
- Comprehensive data models for literary works, authors, translations
- Repository pattern with caching layer
- Authentication and authorization system
- Linguistics analysis capabilities with multiple adapters
- Vector search integration with Weaviate
- Docker containerization support
- Python data migration and analysis scripts
- Clean architecture with proper separation of concerns
- Production-ready configuration and middleware
- Proper .gitignore excluding vendor/, database files, and build artifacts
