BREAKING CHANGE: Complete migration from TensorFlow.js to Transformers.js for embedding generation
This is a major architectural change that replaces TensorFlow.js (USE model) with Transformers.js (all-MiniLM-L6-v2) for significantly improved performance and reduced complexity.
Key Changes:
- Replace TensorFlow.js Universal Sentence Encoder with Transformers.js all-MiniLM-L6-v2
- Reduce model size from 525MB to 87MB (83% reduction)
- Reduce embedding dimensions from 512 to 384 (faster distance calculations)
- Remove TensorFlow.js Float32Array patching (caused ONNX conflicts)
- Implement smart bundled model detection for offline operation
- Add explicit model download script for Docker deployments
- Remove complex environment variables in favor of simple configuration
- Update all distance functions to use optimized pure JavaScript
- Remove TensorFlow-specific utilities and type definitions
Performance Improvements:
- Model loading: 5x faster (87MB vs 525MB)
- Memory usage: 75% reduction (~200-400MB vs ~1.5GB)
- Distance calculations: Faster pure JS vs GPU overhead for small vectors
- Cold start performance: Significantly improved
Files Changed:
- Updated package.json: New dependencies, simplified scripts
- Rewrote src/utils/embedding.ts: Complete Transformers.js implementation
- Updated src/utils/distance.ts: Optimized JavaScript distance functions
- Simplified src/setup.ts: Removed TensorFlow-specific patching
- Simplified src/utils/textEncoding.ts: Only Node.js TextEncoder/Decoder patches
- Deleted src/utils/robustModelLoader.ts: TensorFlow-specific loader
- Deleted src/types/tensorflowTypes.ts: TensorFlow type definitions
- Added scripts/download-models.cjs: Docker-compatible model downloader
- Added comprehensive documentation: README.md, OFFLINE_MODELS.md, analysis docs
Testing:
- All 19 tests passing
- Removed test mocking in favor of real implementation testing
- Updated test environment for Transformers.js compatibility
- Performance tests validate improved efficiency
This migration resolves production issues with Docker egress limitations and provides a more robust, performant foundation for vector operations.
- **Core**:
- Added `check-database.js` to verify database status and validate search functionality.
- Created `fix-dimension-mismatch.js` to handle re-embedding of existing data to resolve dimension mismatch from 3 to 512.
- Improved test cases by updating vector operations to support 512 dimensions, replacing previously hardcoded dimensions.
- **Migration**:
- Developed `DIMENSION_MISMATCH_SUMMARY.md`, detailing the root cause, solution, and preventive strategies for dimension mismatch issues.
- Added `production-migration-guide.md` for structured production migration with detailed steps on re-embedding strategies, batching, and error handling.
- **Tests**:
- Enhanced test coverage with 512-dimensional vector validation.
- Introduced helper functions for consistent vector testing behavior and streamlined search test cases.
- **Documentation**:
- Updated project documentation to highlight the resolution process for dimension mismatches, emphasizing preventive mechanisms such as auto-migration and version tracking.
**Purpose**: Address critical dimension mismatch issues caused by embedding changes, restore functionality, and provide a roadmap for robust prevention strategies and migration processes.
- **Test Enhancements**:
- Refactored test setup in `core.test.ts` for better isolation and clarity:
- Added explicit `.clear()` calls to ensure clean state between tests.
- Replaced old vector addition logic with simplified data insertion methods.
- Updated search operations to reflect current functionality and debug-friendly output.
- Introduced the `@vitest-environment jsdom` annotation in `environment.browser.test.ts` for accurate browser environment emulation.
- **Configuration Updates**:
- Enhanced `vitest.config.ts`:
- Introduced custom `reporters` for cleaner and focused test result presentation.
- Expanded console log filtering with additional patterns for reducing noise from TensorFlow.js and setup processes.
- **Purpose**:
- These updates improve test clarity, consistency, and robustness while streamlining the configuration to minimize distractions in test outputs.
- **Test Improvements**:
- Introduced data-clearing steps (`.clear()`) across critical test cases for ensuring better test isolation and preventing state leakage.
- Extended support for overriding global utilities (`testUtils`) and added fallback behaviors for test vector creation.
- **Configuration Updates**:
- Added support for `distanceFunction` as an alternative to `metric` in vector operations for consistency.
- Adjusted and unified asynchronous `timeout` handling across test suites for predictability.
- **Purpose**:
- These updates improve reliability, maintainability, and clarity in test cases while ensuring compatibility across diverse test environments.
- **New Tests Added**:
- Introduced multiple test suites covering core functionalities (`core.test.ts`), vector operations (`vector-operations.test.ts`), Node.js environment (`environment.node.test.ts`), browser setup (`environment.browser.test.ts`), and TensorFlow.js-specific behaviors (`tensorflow-patch.test.ts`).
- Added performance, scalability, and error-handling tests to ensure robust validation of vector addition, search, and text embedding functionalities.
- Introduced setup utilities (`tests/setup.ts`) and standardized test utilities for creating predictable test cases.
- **Configuration**:
- Created `vitest.config.ts` for custom test configurations, including support for modern test environments (`jsdom`, `happy-dom`) and extended timeouts for asynchronous operations.
- **Validation**:
- Includes compatibility checks for TensorFlow.js imports and ensures proper handling of `TextEncoder`/`TextDecoder` in Node.js environments.
This commit significantly enhances the testing coverage and structure, ensuring Brainy functionality is robust, cross-platform, and aligned with evolving reliability standards.
