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README.md
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README.md
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The smallest vector index in the world. RAG Everything with LEANN!
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The smallest vector index in the world. RAG Everything with LEANN!
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</h2>
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</h2>
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LEANN is a revolutionary vector database that democratizes personal AI. Transform your laptop into a powerful RAG system that can index and search through millions of documents while using **[97% less storage]** than traditional solutions **without accuracy loss**.
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LEANN is a revolutionary vector database that democratizes personal AI. Transform your laptop into a powerful RAG system that can index and search through millions of documents while using **97% less storage** than traditional solutions **without accuracy loss**.
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LEANN achieves this through *graph-based selective recomputation* with *high-degree preserving pruning*, computing embeddings on-demand instead of storing them all. [Illustration →](#️-architecture--how-it-works) | [Paper →](https://arxiv.org/abs/2506.08276)
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LEANN achieves this through *graph-based selective recomputation* with *high-degree preserving pruning*, computing embeddings on-demand instead of storing them all. [Illustration →](#️-architecture--how-it-works) | [Paper →](https://arxiv.org/abs/2506.08276)
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@@ -125,15 +125,15 @@ response = chat.ask(
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)
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)
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```
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```
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## Wild Things You Can Do
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## RAG on Everything!
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LEANN supports RAGing a lot of data sources, like .pdf, .txt, .md, and also supports RAGing your WeChat, Google Search History, and more.
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LEANN supports RAG on various data sources including documents (.pdf, .txt, .md), Apple Mail, Google Search History, WeChat, and more.
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### Process Any Documents (.pdf, .txt, .md)
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### Process Any Documents (.pdf, .txt, .md)
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Above we showed the Python API, while this CLI script demonstrates the same concepts while directly processing PDFs and documents, and even any directory that stores your personal files!
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Ask questions directly about your personal PDFs, documents, and any directory containing your files!
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The following scripts use Ollama `qwen3:8b` by default, so you need `ollama pull qwen3:8b` first. For other models: `--llm openai --model gpt-4o` (requires `OPENAI_API_KEY` environment variable) or `--llm hf --model Qwen/Qwen3-4B`.
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The example below asks a question about summarizing two papers (uses default data in `examples/data`):
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```bash
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```bash
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# Drop your PDFs, .txt, .md files into examples/data/
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# Drop your PDFs, .txt, .md files into examples/data/
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**Works with any text format** - research papers, personal notes, presentations. Built with LlamaIndex for document parsing.
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### Search Your Entire Life
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### Search Your Entire Life
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**Note:** You need to grant full disk access to your terminal/VS Code in System Preferences → Privacy & Security → Full Disk Access.
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**Note:** You need to grant full disk access to your terminal/VS Code in System Preferences → Privacy & Security → Full Disk Access.
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@@ -259,9 +257,9 @@ First, you need to install the WeChat exporter:
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sudo packages/wechat-exporter/wechattweak-cli install
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sudo packages/wechat-exporter/wechattweak-cli install
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```
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```
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**Troubleshooting**:
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**Troubleshooting:**
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- If you encounter installation issues, check the [WeChatTweak-CLI issues page](https://github.com/sunnyyoung/WeChatTweak-CLI/issues/41).
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- **Installation issues**: Check the [WeChatTweak-CLI issues page](https://github.com/sunnyyoung/WeChatTweak-CLI/issues/41)
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- If you encounter the error below, try restart your wechat
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- **Export errors**: If you encounter the error below, try restarting WeChat
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```
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```
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Failed to export WeChat data. Please ensure WeChat is running and WeChatTweak is installed.
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Failed to export WeChat data. Please ensure WeChat is running and WeChatTweak is installed.
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Failed to find or export WeChat data. Exiting.
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Failed to find or export WeChat data. Exiting.
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