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refactor: reorgnize all examples/ and test/

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This commit is contained in:
Andy Lee
2025-08-03 22:37:45 -07:00
parent 58556ef44c
commit b0239b6e4d
41 changed files with 127 additions and 1926 deletions
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docs/features.md
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@@ -13,7 +13,7 @@
- **🚀 High-throughput Embedding Pipeline** - Optimized batched processing for maximum efficiency
- **🎯 Two-level Search** - Novel coarse-to-fine search overlap for accelerated query processing (optional)
- **💾 Memory-mapped Indices** - Fast startup with raw text mapping to reduce memory overhead
- **🚀 MLX Support** - Ultra-fast recompute/build with quantized embedding models, accelerating building and search ([minimal example](test/build_mlx_index.py))
- **🚀 MLX Support** - Ultra-fast recompute/build with quantized embedding models, accelerating building and search ([minimal example](../examples/mlx_demo.py))
## 🎨 Developer Experience

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docs/normalized_embeddings.md
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@@ -72,4 +72,4 @@ Using the wrong distance metric with normalized embeddings can lead to:
- **Incorrect ranking** of search results
- **Suboptimal performance** compared to using the correct metric
For more details on why this happens, see our analysis of [OpenAI embeddings with MIPS](../examples/document_rag.py).
For more details on why this happens, see our analysis in the [embedding detection code](../packages/leann-core/src/leann/api.py) which automatically handles normalized embeddings and MIPS distance metric issues.