- **Core**: Introduced a `getCurrentAugmentation` method for detecting active augmentation names. Updated metadata handling to include `createdBy`, `createdAt`, and `updatedAt` attributes for improved tracking.
- **Storage**: Added support for service-based statistics tracking with new methods such as `incrementStatistic`, `decrementStatistic`, and `updateHnswIndexSize`. Implemented persistence for statistics in storage adapters.
- **Statistics**: Enhanced `getStatistics` functionality to provide service-specific breakdowns and support filtering by services. Improved noun, verb, and metadata tracking mechanisms.
- **Search**: Added `service` option to filter results during searches for nouns, verbs, and metadata, ensuring accurate service-based query results.
- **Refactor**: Simplified search logic by integrating HNSW index filtering for better performance when retrieving service-specific results.
- **Tests**: Added comprehensive test coverage for service-level statistics and filtering by service.
**Purpose**: Improve service-level data tracking and analytics while enhancing functionality for filtering and maintaining metadata accuracy to support detailed insights for diverse use cases.
- **Core**: Introduced a new `getStatistics` utility function in `statistics.ts` for fetching database statistics at the root level of the library. Enhanced `BrainyData` methods to ensure metadata includes `id` field and refined statistics calculations, excluding verbs from the noun count.
- **Tests**: Added comprehensive test coverage in `statistics.test.ts` for the new utility function, validating proper error handling, statistics accuracy, and consistent results between instance methods and standalone function.
- **Storage Config**: Enabled dynamic support for AWS S3, Cloudflare R2, and Google Cloud Storage in web service configuration, utilizing environment variables for adapter setup. Addressed a race condition in `FileSystemStorage` initialization by deferring path module imports.
**Purpose**: Enhance database analytics by introducing a reusable `getStatistics` function, improve flexibility in storage configuration, and ensure robust testing for reliability and accuracy.
- **New Method**:
- Introduced a new `getStatistics` method in `brainyData.ts` to retrieve key database metrics:
- Counts for nouns, verbs, metadata entries, and HNSW index size.
- **Error Handling**:
- Added try-catch blocks to ensure robust error management when fetching metadata or logging failures.
- **Purpose**:
- Enhances observability of the database state, providing valuable insights for diagnostics and monitoring.
- Added `autoCreateMissingNouns` and `missingNounMetadata` options to the `addVerb` method in `brainyData.ts`, enabling automatic creation of missing source or target nouns.
- Improved error handling and logging for auto-creation failures, ensuring better feedback during runtime.
- Reformatted existing code for storage options validation to improve readability and maintain consistency.
**Purpose**: Simplify the addition of relationships by automating the process for non-existing nouns and enhance developer experience with better error handling and logging.
- Added `getFile` method to `FileSystemHandle` interface for enhanced TypeScript compatibility with File System Access API.
- Improved maintainability by reformatting and aligning imports in `brainyData.ts`.
- Standardized spacing and indentation across structured comments and interface fields.
Purpose: Improve TypeScript support for file system operations and enhance code readability with consistent formatting and import management.
- Refactor embedding functions to support customizable verbosity through new helper methods (`getDefaultEmbeddingFunction`, `getDefaultBatchEmbeddingFunction`).
- Ensure metadata initialization with default values when null during search results.
- **Logging Cleanup**:
- Removed redundant `console.log` statements in `brainyData.ts` and `embedding.ts` related to Universal Sentence Encoder initialization and load function detection.
- Replaced detailed logs with concise comments to streamline debugging and reduce noisy outputs.
- **Purpose**:
- Improves code readability, reduces runtime logging noise, and aligns logging verbosity with the project's streamlined debugging practices.
- **Vector Handling Updates**:
- Added a `dimensions` property to `BrainyDataConfig` for specifying vector dimensions.
- Introduced validation for vector dimensions during database creation and insertion to ensure consistency.
- Enhanced error handling and logging for dimension mismatches.
- **Model Loading Improvements**:
- Implemented retry logic for Universal Sentence Encoder model loading to handle network instability and JSON parsing errors gracefully.
- Improved logging and debugging support for failures during model initialization and embedding operations.
- **Compatibility Enhancements**:
- Updated polyfills to support TensorFlow.js compatibility across diverse server environments (Node.js, serverless, etc.).
- Introduced and refactored global `TextEncoder`/`TextDecoder` definitions for seamless operation in non-browser environments.
- Simplified TensorFlow.js backend setup with streamlined imports and logging for GPU/WebGL fallback.
- **Purpose**:
- These updates improve BrainyData's robustness, enforce correct vector usage, and extend compatibility with varied runtime environments. The changes enhance the usability, reliability, and cross-platform readiness of core functionalities.
- Applied consistent formatting improvements, including line breaks, parentheses usage, and object destructuring, to enhance code readability and maintainability.
- Enhanced the fallback mechanism during Universal Sentence Encoder initialization by implementing a retry approach with error handling.
- Refactored `addBatch` processing for both vector and text items to improve clarity and adhere to project coding standards.
- Optimized initialization safeguards with structured retry implementations, ensuring robust error resiliency.
These changes align the codebase with established formatting guidelines and improve the reliability of embedding initialization processes.
- Added batch embedding support with `defaultBatchEmbeddingFunction`, leveraging shared model instances for optimized performance.
- Integrated `isInitializing` flag to prevent recursive initialization and ensure smooth concurrent operation handling during `BrainyData` initialization.
- Pre-loaded Universal Sentence Encoder in `BrainyData` to prevent delays during embedding.
- Introduced fallback mechanisms in embedding initialization for better error resiliency and model reusability.
- Updated `addBatch` with support for batchSize and refactored text/vector processing logic for clearer separation and memory management.
- Improved GPU and CPU backend selection in Universal Sentence Encoder for compatibility across environments.
- Enhanced memory management by cleaning tensors after embedding operations.
- Updated README with instructions for batch embedding, threading updates, and GPU/CPU optimizations.
Applied consistent formatting adjustments across `src/brainyData.ts`, including line breaks, parentheses, and object destructuring. These changes enhance code readability, maintainability, and alignment with the project's style guidelines without altering functionality.
