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docs: highlight diskann readiness and add performance comparison

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Andy Lee
2025-08-06 22:10:43 -07:00
parent 1d657fd9f6
commit f28f15000c
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README.md
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@@ -524,12 +524,16 @@ Options:
- **Dynamic batching:** Efficiently batch embedding computations for GPU utilization
- **Two-level search:** Smart graph traversal that prioritizes promising nodes
**Backends:** HNSW (default) for most use cases, with optional DiskANN support for billion-scale datasets.
**Backends:**
- **HNSW** (default): Ideal for most datasets with maximum storage savings through full recomputation
- **DiskANN**: Advanced option with superior search performance, using PQ-based graph traversal with real-time reranking for the best speed-accuracy trade-off
## Benchmarks
**[DiskANN vs HNSW Performance Comparison →](benchmarks/diskann_vs_hnsw_speed_comparison.py)** - Compare search performance between both backends
**[Simple Example: Compare LEANN vs FAISS →](benchmarks/compare_faiss_vs_leann.py)** - See storage savings in action
**[Simple Example: Compare LEANN vs FAISS →](benchmarks/compare_faiss_vs_leann.py)**
### 📊 Storage Comparison
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