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

docs/configuration-guide.md

@@ -52,7 +52,7 @@ Based on our experience developing LEANN, embedding models fall into three categ
 ### Quick Start: Cloud and Local Embedding Options
 
 **OpenAI Embeddings (Fastest Setup)**
-For immediate testing without local model downloads:
+For immediate testing without local model downloads(also if you [do not have GPU](https://github.com/yichuan-w/LEANN/issues/43) and do not care that much about your document leak, you should use this, we compute the embedding and recompute using openai API):
 ```bash
 # Set OpenAI embeddings (requires OPENAI_API_KEY)
 --embedding-mode openai --embedding-model text-embedding-3-small
@@ -97,16 +97,30 @@ ollama pull nomic-embed-text
 ```
 
 ### DiskANN
-**Best for**: Large datasets (> 10M vectors, 10GB+ index size) - **⚠️ Beta version, still in active development**
-- Uses Product Quantization (PQ) for coarse filtering during graph traversal
-- Novel approach: stores only PQ codes, performs rerank with exact computation in final step
-- Implements a corner case of double-queue: prunes all neighbors and recomputes at the end
+**Best for**: Performance-critical applications and large datasets - **Production-ready with automatic graph partitioning**
+
+**How it works:**
+- **Product Quantization (PQ) + Real-time Reranking**: Uses compressed PQ codes for fast graph traversal, then recomputes exact embeddings for final candidates
+- **Automatic Graph Partitioning**: When `is_recompute=True`, automatically partitions large indices and safely removes redundant files to save storage
+- **Superior Speed-Accuracy Trade-off**: Faster search than HNSW while maintaining high accuracy
+
+**Trade-offs compared to HNSW:**
+- ✅ **Faster search latency** (typically 2-8x speedup)
+- ✅ **Better scaling** for large datasets
+- ✅ **Smart storage management** with automatic partitioning
+- ✅ **Better graph locality** with `--ldg-times` parameter for SSD optimization
+- ⚠️ **Slightly larger index size** due to PQ tables and graph metadata
 
 ```bash
-# For billion-scale deployments
+# Recommended for most use cases
+--backend-name diskann --graph-degree 32 --build-complexity 64
+
+# For large-scale deployments
 --backend-name diskann --graph-degree 64 --build-complexity 128
 ```
 
+**Performance Benchmark**: Run `python benchmarks/diskann_vs_hnsw_speed_comparison.py` to compare DiskANN and HNSW on your system.
+
 ## LLM Selection: Engine and Model Comparison
 
 ### LLM Engines
@@ -352,3 +366,4 @@ Trade-offs:
 - [Lessons Learned Developing LEANN](https://yichuan-w.github.io/blog/lessons_learned_in_dev_leann/)
 - [LEANN Technical Paper](https://arxiv.org/abs/2506.08276)
 - [DiskANN Original Paper](https://papers.nips.cc/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf)
+- [SSD-based Graph Partitioning](https://github.com/SonglinLife/SSD_BASED_PLAN)