# PLAN for `uubed` (Project Home)

This plan tracks cross-repository activities that affect every other `uubed-*` repository. It contains only *umbrella* work items – language-specific or documentation-specific tasks live in their own repos.

## Current Status Overview

The uubed project has successfully completed its initial implementation phases and multi-repository restructuring. We now have a mature, high-performance position-safe encoding library with established community infrastructure and automated CI/CD pipelines.

**Completed Milestones:**
- ✅ Phase 1-4: Core implementation, Rust acceleration, packaging, and distribution
- ✅ Phase 5: Multi-repository organization and community infrastructure
- ✅ Repository metadata, CI/CD, research organization, and community setup
- ✅ Phase 6: Advanced Project Infrastructure & Matryoshka Integration

## 1. Governance & Road-mapping

### PROJECT.md as Single Source of Truth
**Objective**: Maintain PROJECT.md as the authoritative document for project vision, architecture, and milestones.

**Implementation Strategy**:
- Review PROJECT.md monthly for alignment with actual development progress
- Update architectural decisions and technical specifications as they evolve
- Ensure all major feature decisions are documented before implementation begins
- Coordinate with sub-repository maintainers to reflect their progress

**Success Metrics**: All team members and contributors reference PROJECT.md for authoritative project information.

### Milestone-Based Changelog Aggregation
**Objective**: Systematically aggregate accomplishments from sub-repositories into the main CHANGELOG.md after each milestone.

**Process**:
1. **Milestone Completion**: Each sub-repository completes their milestone-specific work
2. **Accomplishment Extraction**: Review sub-repository CHANGELOGs for significant achievements
3. **Cross-Cutting Impact Assessment**: Identify changes that affect multiple repositories
4. **Main Changelog Update**: Document milestone completion with links to detailed sub-repository changes
5. **Version Coordination**: Ensure version numbers are synchronized across the ecosystem

**Frequency**: After each major milestone (monthly or bi-monthly cadence).

### Marketing-Grade README Maintenance
**Objective**: Keep README.md current with latest performance benchmarks, feature highlights, and installation instructions.

**Content Strategy**:
- **Performance Section**: Update benchmarks quarterly or after significant performance improvements
- **Feature Highlights**: Refresh based on most compelling use cases and community feedback  
- **Installation Instructions**: Keep installation commands current with latest stable versions
- **Integration Examples**: Showcase real-world usage patterns and success stories

## 2. Continuous Integration / Delivery

### Cross-Repository Build Orchestration
**Implementation Details**:

**Workflow: `orchestrate-builds.yml`**
- **Trigger Events**: Push to main, pull requests, manual dispatch
- **Execution Order**: 
  1. `uubed-rs` (core Rust implementation)
  2. `uubed-py` (Python bindings, depends on Rust artifacts)
  3. `uubed-docs` (documentation, may reference Python examples)
- **Dependencies**: Each subsequent build waits for previous success
- **Failure Handling**: Halt pipeline on any repository failure
- **Artifact Passing**: Rust builds may produce artifacts consumed by Python builds

**Authentication**: Uses PAT_TOKEN for cross-repository workflow triggers.

### Release Coordination Strategy
**Implementation Details**:

**Version Bump Order**: `rs → py → docs`
- **Rationale**: Python bindings depend on Rust core; documentation references final API
- **Automation**: `release-coordination.yml` workflow manages the sequence
- **Version Synchronization**: All repositories maintain compatible version numbers
- **Rollback Strategy**: Ability to revert releases in reverse order if issues discovered

**Release Process**:
1. **Pre-Release**: Version validation, changelog review, test execution
2. **Rust Release**: Core library release to crates.io
3. **Python Release**: Bindings release to PyPI (consuming latest Rust version)
4. **Documentation Release**: Updated docs referencing current versions
5. **Coordination Release**: Main repository tags coordinated release

### Nightly Performance Regression Detection
**Implementation Details**:

**Benchmark Suite Coverage**:
- **Encoding Methods**: All four encoding schemes (Eq64, Shq64, T8q64, Zoq64)
- **Input Variations**: Various embedding sizes (128, 512, 1024, 4096 dimensions)
- **Hardware Profiles**: Different CPU architectures and memory configurations
- **Performance Metrics**: Throughput (MB/s), latency (μs), memory usage (MB)

**Regression Detection**:
- **Baseline Comparison**: Compare against previous 7-day average
- **Threshold Alerts**: >10% performance degradation triggers investigation
- **Bisection Support**: Identify specific commits causing regressions
- **Reporting**: Daily performance reports with trend analysis

## 3. Research & Ideation

### Matryoshka Embeddings Integration
**Strategic Direction**: Position uubed as the leading solution for position-safe hierarchical embeddings.

**Technical Approach**:
**Mq64 Encoding Scheme Development**:
- **Hierarchical Alphabet Design**: Position-safe alphabets that preserve hierarchy levels
- **Progressive Decoding**: Ability to decode partial embeddings (64, 128, 256 dimensions)
- **Compression Optimization**: Leverage redundancy between hierarchy levels
- **Compatibility**: Work with existing Matryoshka models (OpenAI, Nomic, Alibaba GTE)

**Implementation Phases**:
1. **Research Phase** (1-2 months): Analyze Matryoshka embedding structures, design alphabet systems
2. **Prototype Phase** (2-3 months): Implement Mq64 in Rust core, basic Python bindings
3. **Integration Phase** (2-3 months): Full API integration, performance optimization
4. **Ecosystem Phase** (1-2 months): Documentation, examples, vector database integrations

**Market Opportunity**: 
- Matryoshka embeddings adoption increasing (OpenAI, commercial providers)
- No existing position-safe solution for hierarchical embeddings
- Potential for 10x+ storage efficiency gains in production systems

### Advanced Encoding Research
**Quantization-Aware Position-Safe Encoding**:
- **Objective**: Combine position safety with aggressive quantization (4-bit, 2-bit)
- **Technical Challenge**: Maintain substring pollution protection with reduced precision
- **Applications**: Mobile deployment, edge computing, massive-scale systems

**Multi-Modal Encoding**:
- **Vision**: Extend position-safe encoding to image-text embeddings (CLIP-style)
- **Research Areas**: Cross-modal search applications, multimodal retrieval systems

## 4. Community & Ecosystem

### Blog-Style Release Communication
**Content Strategy**:

**Release Post Structure** (using `.github/RELEASE_TEMPLATE.md`):
- **Performance Highlights**: Concrete benchmark improvements
- **Feature Spotlights**: Real-world impact of new capabilities  
- **Developer Experience**: API improvements, integration examples
- **Community Contributions**: Acknowledge contributor efforts
- **Future Roadmap**: Tease upcoming developments

**Distribution Channels**:
- **Primary**: GitHub Releases with detailed changelog
- **Secondary**: Community forums, Reddit, Hacker News (for major releases)
- **Technical**: Blog posts on Medium/dev.to with deep technical details

**Release Cadence**: 
- **Major Releases** (x.0.0): Quarterly, with full blog posts
- **Minor Releases** (x.y.0): Monthly, with release notes
- **Patch Releases** (x.y.z): As needed, with brief summaries

### Community Metrics & Engagement
**Future Initiatives**:

**Metrics Dashboard**:
- **Usage Analytics**: PyPI downloads, GitHub stars, repository forks
- **Community Health**: Issue response time, PR merge time, contributor diversity
- **Performance Tracking**: Benchmark trends, regression frequency
- **Ecosystem Adoption**: Integration examples, third-party packages

**Community Programs**:
- **Contributor Recognition**: Monthly contributor highlights, contribution leaderboards
- **Integration Bounties**: Rewards for creating integrations with popular vector databases
- **Research Collaboration**: Academic partnerships for advanced encoding research

## Success Criteria

**Long-term (12 months)**:
- [ ] uubed recognized as industry standard for position-safe embedding encoding
- [ ] Matryoshka embeddings support driving significant adoption
- [ ] Self-sustaining contributor community with diverse expertise

**Immediate Action Items (based on current analysis)**:
- [ ] Enhance main repository testing infrastructure
- [ ] Create comprehensive API documentation using Sphinx
- [ ] Implement streaming API for large embedding batches
- [ ] Add integration tests for cross-repository workflows
- [ ] Create troubleshooting guide for common errors

This plan balances ambitious technical innovation (Matryoshka integration) with solid engineering practices (CI/CD, testing, documentation) and community building (engagement, contributions, ecosystem growth).