docs: add ldg-times parameter for diskann graph locality optimization
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@@ -18,7 +18,7 @@ LEANN achieves this through *graph-based selective recomputation* with *high-deg
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**Ready to RAG Everything?** Transform your laptop into a personal AI assistant that can search your **[file system](#-personal-data-manager-process-any-documents-pdf-txt-md)**, **[emails](#-your-personal-email-secretary-rag-on-apple-mail)**, **[browser history](#-time-machine-for-the-web-rag-your-entire-browser-history)**, **[chat history](#-wechat-detective-unlock-your-golden-memories)**, or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
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> **🚀 NEW: Claude Code Integration!** LEANN now provides native MCP integration for Claude Code users. Index your codebase and get intelligent code assistance directly in Claude Code. [Setup Guide →](packages/leann-mcp/README.md)
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> **🚀 Claude Code Integration!** LEANN now provides native MCP integration for Claude Code users. Index your codebase and get intelligent code assistance directly in Claude Code. [Setup Guide →](packages/leann-mcp/README.md)
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@@ -97,6 +97,7 @@ For immediate testing without local model downloads:
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- ✅ **Faster search latency** (typically 2-8x speedup)
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- ✅ **Better scaling** for large datasets
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- ✅ **Smart storage management** with automatic partitioning
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- ✅ **Better graph locality** with `--ldg-times` parameter for SSD optimization
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- ⚠️ **Slightly larger index size** due to PQ tables and graph metadata
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```bash
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@@ -279,3 +280,4 @@ LEANN's recomputation feature provides exact distance calculations but can be di
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- [Lessons Learned Developing LEANN](https://yichuan-w.github.io/blog/lessons_learned_in_dev_leann/)
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- [LEANN Technical Paper](https://arxiv.org/abs/2506.08276)
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- [DiskANN Original Paper](https://papers.nips.cc/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf)
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- [SSD-based Graph Partitioning](https://github.com/SonglinLife/SSD_BASED_PLAN)
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