merge: finalize compat resolution (delegate to PassageManager; keep relative hints in meta); resolve conflicts

This commit is contained in:
Andy Lee
2025-08-14 01:09:39 -07:00
25 changed files with 1911 additions and 477 deletions

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@@ -192,7 +192,7 @@ All RAG examples share these common parameters. **Interactive mode** is availabl
--force-rebuild # Force rebuild index even if it exists
# Embedding Parameters
--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
--embedding-model MODEL # e.g., facebook/contriever, text-embedding-3-small, mlx-community/Qwen3-Embedding-0.6B-8bit or nomic-embed-text
--embedding-mode MODE # sentence-transformers, openai, mlx, or ollama
# LLM Parameters (Text generation models)
@@ -457,7 +457,7 @@ leann --help
**To make it globally available:**
```bash
# Install the LEANN CLI globally using uv tool
uv tool install leann
uv tool install leann-core
# Now you can use leann from anywhere without activating venv
leann --help
@@ -545,12 +545,16 @@ 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)**
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