# Realizar v0.2.0 - Phase 2: Production Optimization Features

We're excited to announce Realizar v0.2.0, delivering Phase 2 production optimization features focused on performance, scalability, and developer experience.

## 🚀 What's New

### Batch Inference API
Process multiple prompts in a single HTTP request, reducing network overhead and improving throughput:
- `POST /batch/tokenize` - Tokenize multiple texts at once
- `POST /batch/generate` - Generate text for multiple prompts
- Linear scaling performance characteristics
- Full test coverage and benchmarks

```bash
curl -X POST http://localhost:8080/batch/generate \
  -H "Content-Type: application/json" \
  -d '{"prompts": ["hello", "world"], "max_tokens": 10}'
```

### Server-Sent Events (SSE) Streaming
Real-time token-by-token generation for better perceived latency:
- `POST /stream/generate` - Stream tokens as they're generated
- Token events and completion events
- Reduced time-to-first-token
- JavaScript and Python client examples included

```javascript
const eventSource = new EventSource('/stream/generate');
eventSource.addEventListener('token', (e) => {
  const token = JSON.parse(e.data);
  console.log(token.text);
});
```

### Model Caching Infrastructure
LRU cache for reduced cold start latency:
- Thread-safe concurrent access with `Arc<RwLock>`
- Configurable capacity with automatic LRU eviction
- Cache metrics (hits, misses, evictions, hit rate)
- **Performance**: 40ns cache hits, 14µs cache misses
- Zero-cost abstraction over raw model access

### SafeTensors Interoperability
Seamless integration with the aprender ML ecosystem:
- Load models trained with aprender
- Complete SafeTensors format support
- Property-based tests for robustness
- Example code included

### Comprehensive Benchmarks
Three complete benchmark suites:
- **Cache benchmarks**: Hit/miss latency, eviction, concurrency
- **Batch benchmarks**: Scaling characteristics
- **Inference benchmarks**: End-to-end generation performance

## 📊 Quality Improvements

- **Test Coverage**: 95.76% (up from 95.46%)
- **Tests**: 286 total (228 unit + 6 integration + 52 property)
- **TDG Score**: 96.4/100 (A+ grade, up from 93.9)
- **Dead Code**: 0%
- **Clippy**: 0 warnings
- **Benchmarks**: 3 comprehensive suites

## 🐛 Bug Fixes

- Fixed race condition in concurrent cache access test
- Fixed float comparison clippy warnings in cache metrics
- Reduced type complexity with strategic type aliases

## 📈 Performance Numbers

| Operation | Latency | Improvement |
|-----------|---------|-------------|
| Cache hit | 40 ns | ⚡ New feature |
| Cache miss + load | 14 µs | ⚡ New feature |
| Concurrent access (4 threads) | 94 µs | ⚡ New feature |
| Metrics access | 4.6 ns | ⚡ New feature |
| Forward pass (1 token) | 17.5 µs | Maintained |
| 10-token generation | 1.54 ms | Maintained |

## 📚 Documentation

- Complete API documentation for all new endpoints
- SSE streaming guides with client examples
- Cache architecture and usage documentation
- Expanded mdBook (168 documentation files)

## 🔧 Installation

```bash
cargo install realizar --version 0.2.0
```

Or add to your `Cargo.toml`:
```toml
[dependencies]
realizar = "0.2.0"
```

## 🎯 What's Next

Phase 2 is complete! Future releases will focus on:
- Additional model format support
- Advanced sampling strategies
- Performance optimizations
- Extended GPU acceleration

## 🙏 Acknowledgments

Built with EXTREME TDD methodology:
- 286 comprehensive tests
- Property-based testing with proptest
- Mutation testing
- Zero tolerance for defects

## 📝 Full Changelog

See [CHANGELOG.md](https://github.com/paiml/realizar/blob/main/CHANGELOG.md) for complete details.

---

**Realizar** - Pure Rust ML inference engine built from scratch
- 🦀 100% Rust, zero unsafe in public API
- ⚡ SIMD-accelerated via Trueno
- 🎯 EXTREME TDD, 96.4/100 quality score
- 📦 GGUF and SafeTensors support
- 🌐 Production-ready HTTP API

**Links**: [GitHub](https://github.com/paiml/realizar) | [Documentation](https://github.com/paiml/realizar/tree/main/book) | [crates.io](https://crates.io/crates/realizar)
