- Deleted the following obsolete files:
- `CHANGES.md`, `changes-summary.md`, `CHANGES_SUMMARY.md`: Contained redundant or outdated change logs and implementation summaries.
- `COMPATIBILITY.md`: Detailed compatibility behavior no longer relevant after environment detection updates.
- `fix-documentation.md`: Addressed a resolved issue regarding `process.memoryUsage` errors in testing.
- `DIMENSION_MISMATCH_SUMMARY.md`: Provided a legacy summary of resolved embedding dimension mismatch issues.
- `demo.md`: Documented an outdated demo process for testing Brainy features.
- `CONCURRENCY_IMPLEMENTATION_SUMMARY.md`: Summarized already-documented concurrency features.
- `IMPLEMENTATION_SUMMARY.md`: Detailed an obsolete implementation of optional model bundling.
- Purpose:
- Streamline and declutter archive by removing redundant or outdated documentation.
- Align repository with current feature set and documentation standards.
- Introduced new documentation files under `docs/`:
- `model-bundling-analysis.md`: Provides detailed analysis of current, bundled, hybrid, and dynamic model loading approaches, including pros, cons, and recommendations.
- `model-management.md`: Explains how Brainy manages Universal Sentence Encoder models, including setup, usage, and troubleshooting.
- `optional-model-bundling.md`: Details the `@soulcraft/brainy-models` package for offline reliability with pre-bundled models.
- Added `src/utils/robustModelLoader.ts`:
- Implements enhanced model loading with retry mechanisms, timeout handling, fallback URLs, and optional local model bundling.
- Supports Node.js and browser environments with exponential backoff logic.
- Key Updates:
- **Hybrid Loading Strategy**: Recommended for balancing reliability and flexibility via hybrid online/offline mechanisms.
- **Enhanced Fallback Scenarios**: Robust loader improves network-dependent reliability for embedding workflows.
- **Offline Reliability Support**: Optional model bundling eliminates dependency on external services, supporting air-gapped and edge environments.
**Purpose**: Introduce a hybrid model loading approach with robust options for
