# TODO

## Completed Tasks ✅
- [x] Core implementation and analysis completed
- [x] Repository structure and coordination established
- [x] Advanced research on Matryoshka embeddings completed

## Outstanding Tasks

### Cross-Repository Coordination (Medium Priority)
- [x] Provide GitHub Actions that trigger downstream builds ✅ (orchestrate-builds.yml completed)
- [x] Establish release tagging policy ✅ (release-coordination.yml completed)
- [x] Create comprehensive API reference using Sphinx ✅
- [x] Add more code examples in docstrings ✅
- [x] Create troubleshooting guide for common errors ✅

### Performance and Testing (High Priority)
- [x] Profile the code to identify bottlenecks ✅ (benchmarking suite completed)
- [x] Consider implementing parallel processing for batch operations ✅ (Rust implementation)
- [ ] Add caching for frequently encoded embeddings
- [x] Add unit tests for all identified edge cases ✅ (test suites completed)
- [ ] Create integration tests for streaming operations
- [x] Add performance benchmarks for different encoding methods ✅ (benchmarking completed)

### Project Management & Quality (Medium Priority)
- [x] Create proper test suite for main repository (currently only research validation) ✅
- [x] Set up proper Python packaging structure for main repository ✅
- [x] Add automated testing for research code validation ✅
- [x] Create CI/CD pipeline for main repository testing ✅

## Next Priority Tasks

### Documentation & Integration
- [ ] Build and deploy Sphinx documentation to GitHub Pages
- [ ] Create integration examples for more vector databases (Pinecone, Weaviate, Qdrant)
- [ ] Add performance comparison charts to documentation
- [ ] Create migration guide from other encoding methods

### Advanced Features
- [ ] Implement caching layer for frequently encoded embeddings
- [ ] Create streaming API for large embedding batches
- [ ] Add compression benchmarks for different embedding sizes
- [ ] Implement parallel encoding for batch operations in Python