rUv
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13600cc572
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feat: Add REFRAG pipeline example demonstrating 30x RAG latency reduction
Implements a complete Compress-Sense-Expand architecture as standalone example:
- **Compress Layer**: Binary tensor storage with 4 compression strategies
- None (1x), Float16 (2x), Int8 (4x), Binary (32x)
- **Sense Layer**: Policy network for COMPRESS/EXPAND routing decisions
- ThresholdPolicy (~2μs), LinearPolicy (~5μs), MLPPolicy (~15μs)
- **Expand Layer**: Dimension projection with LLM registry
- Supports LLaMA, GPT-4, Claude, Mistral, Phi-3
- **RefragStore**: Hybrid search returning mixed tensor/text results
This example demonstrates REFRAG concepts (arXiv:2509.01092) without
modifying ruvector-core, serving as proof-of-concept for Issue #10.
Includes:
- 25 passing unit tests
- Interactive demo (cargo run --bin refrag-demo)
- Performance benchmarks (cargo run --bin refrag-benchmark)
- Criterion benchmarks for CI integration
Refs: #10, #22
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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2025-11-27 20:59:23 +00:00 |
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