- Add complete Universal Sentence Encoder Lite model (27MB)
- Include vocab.json for tokenization support
- Update package to work with @tensorflow-models/universal-sentence-encoder
- Ensure offline model loading capability for Docker deployments
- Published to npm as @soulcraft/brainy-models@0.8.0
Remove development artifacts, test files, and redundant directories:
- Delete debug/reproduction scripts and temporary test files
- Remove brainy-models-package/ (redundant with main models/ directory)
- Remove test-consumer/ development testing directory
- Remove build artifacts (coverage/, test-results.json)
- Remove large brainy-data/ test artifact directory
This cleanup reduces repository size significantly and prepares the project for a clean release.
- **Scripts**:
- Refactored logging within `release-workflow.js` for improved readability and maintainability.
- Added a fallback mechanism in `download-full-models.js` to inject the "format" field into `model.json` for TensorFlow.js compatibility.
- **Documentation**:
- Updated `README.md` with a redesigned structure:
- Enhanced readability using emojis and a cleaner presentation.
- Expanded `Overview`, `Features`, and `Quick Start` sections.
- Refined "Use Cases" and added better explanations for bundled model benefits.
- **Models**:
- Adjusted `model.json` to include the "format" field for compatibility with TensorFlow.js.
- Updated `metadata.json` with a recent download timestamp.
**Purpose
- Introduced `@soulcraft/brainy-models` package with pre-bundled TensorFlow models for enhanced offline reliability.
- Added `index.d.ts` and `index.js` allowing offline embedding workflows with the Universal Sentence Encoder model.
- Included utility scripts for model compression, size retrieval, and availability checks.
- Added `metadata.json` and `model.json` defining the Universal Sentence Encoder configuration with offline bundling.
- Ensured comprehensive model documentation, error handling, and robust logging for seamless integration.
- Supported optional model quantization placeholders for future TensorFlow.js enhancements.
**Purpose**: Enable fully offline-ready embedding workflows via pre-bundled Universal Sentence Encoder models, ensuring maximum reliability and air-gapped environment compatibility.
