merge: finalize compat resolution (delegate to PassageManager; keep relative hints in meta); resolve conflicts
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12
README.md
12
README.md
@@ -192,7 +192,7 @@ All RAG examples share these common parameters. **Interactive mode** is availabl
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--force-rebuild # Force rebuild index even if it exists
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# Embedding Parameters
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--embedding-model MODEL # e.g., facebook/contriever, text-embedding-3-small, nomic-embed-text,mlx-community/Qwen3-Embedding-0.6B-8bit or nomic-embed-text
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--embedding-model MODEL # e.g., facebook/contriever, text-embedding-3-small, mlx-community/Qwen3-Embedding-0.6B-8bit or nomic-embed-text
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--embedding-mode MODE # sentence-transformers, openai, mlx, or ollama
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# LLM Parameters (Text generation models)
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@@ -457,7 +457,7 @@ leann --help
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**To make it globally available:**
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```bash
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# Install the LEANN CLI globally using uv tool
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uv tool install leann
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uv tool install leann-core
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# Now you can use leann from anywhere without activating venv
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leann --help
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@@ -545,12 +545,16 @@ Options:
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- **Dynamic batching:** Efficiently batch embedding computations for GPU utilization
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- **Two-level search:** Smart graph traversal that prioritizes promising nodes
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**Backends:** HNSW (default) for most use cases, with optional DiskANN support for billion-scale datasets.
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**Backends:**
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- **HNSW** (default): Ideal for most datasets with maximum storage savings through full recomputation
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- **DiskANN**: Advanced option with superior search performance, using PQ-based graph traversal with real-time reranking for the best speed-accuracy trade-off
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## Benchmarks
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**[DiskANN vs HNSW Performance Comparison →](benchmarks/diskann_vs_hnsw_speed_comparison.py)** - Compare search performance between both backends
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**[Simple Example: Compare LEANN vs FAISS →](benchmarks/compare_faiss_vs_leann.py)** - See storage savings in action
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**[Simple Example: Compare LEANN vs FAISS →](benchmarks/compare_faiss_vs_leann.py)**
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### 📊 Storage Comparison
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| System | DPR (2.1M) | Wiki (60M) | Chat (400K) | Email (780K) | Browser (38K) |
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