Spending pattern analysis #124
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…ection. Removed redundant embedding store implementations, transitioning to a unified factory-based approach. Introduced seasonal spending pattern detection with corresponding database schema changes and providers. Updated logging and configuration to support the new features.
Introduced rule engine integration in transaction processing, enabling automated rule application with detailed logging and efficient pagination. Updated spending insights and patterns database schema for improved precision using `decimal` type and `BIGSERIAL` for IDs. Adjusted scheduled task for spending analysis to execute on the 15th of each month.
Introduce the AnalyzeJob framework for scheduling, processing, and persisting spending analysis tasks. This includes JPA entities, repositories, message commands, a scheduler, and logging enhancements to streamline the monthly analysis workflow.
Refactored code for spending pattern detection to improve readability, maintainability, and functionality. Added comprehensive unit tests for `SeasonalPattern`, `AmountPattern`, and anomaly detection to ensure robust logic and error handling. Minor optimizations and cleanup in related modules.
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Unresolved for now:
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Reformatted and condensed multiline lambda expressions into a more concise single-line or cleaner format across various classes. This improves readability and consistency throughout the codebase without altering functionality.
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Introduced custom `MockitoConfiguration` for mock behaviors with better handling of `Optional`, `Sequence`, and `ResultPage` types. Enhanced `PatternDetector` and `AnomalyDetector` with new test cases and refined logic for detecting and updating spending patterns, improving reliability and coverage.
Revised `CreateSpendingInsight` and `CreateSpendingPattern` commands to use flat field structures instead of nested objects. Updated related JPA providers and tests to reflect these changes, enhancing clarity and reducing coupling.
Introduced a test to verify that EmbeddingStoreFiller correctly executes the callback for transactions. It ensures event publishing and proper integration with executor services are working as expected.
Introduced new endpoints to retrieve spending insights and patterns for a specific year and month. Implemented respective response models, endpoint logic, and comprehensive test coverage to ensure correct functionality and data integrity.
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Breaking change
Proposed change
Add the initial version for spending pattern detection and spending insight detection.
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