docs: follow yichuan's suggestion
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Andy Lee
95
README.md
95
README.md
@@ -24,13 +24,13 @@ LEANN achieves this through *graph-based selective recomputation* with *high-deg
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<img src="assets/effects.png" alt="LEANN vs Traditional Vector DB Storage Comparison" width="100%">
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</p>
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**The numbers speak for themselves:** Index 60 million Wikipedia articles in just 6GB instead of 201GB. Finally, your MacBook can handle enterprise-scale datasets. [See detailed benchmarks below ↓](#storage-usage-comparison)
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**The numbers speak for themselves:** Index 60 million Wikipedia articles in just 6GB instead of 201GB. From emails to browser history, everything fits on your laptop. [See detailed benchmarks below ↓](#storage-usage-comparison)
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## Why This Matters
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🔒 **Privacy:** Your data never leaves your laptop. No OpenAI, no cloud, no "terms of service".
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🪶 **Lightweight:** Minimal resource requirements - runs smoothly on any laptop without specialized hardware.
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🪶 **Lightweight:** Smart graph pruning means less storage, less memory usage, better performance on your existing hardware.
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📈 **Scalability:** Organize our messy personal data that would crash traditional vector DBs, with performance that gets better as your data grows more personalized.
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@@ -93,16 +93,19 @@ Just 3 lines of code. Our declarative API makes RAG as easy as writing a config
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```python
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from leann.api import LeannBuilder, LeannSearcher
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# Index your entire email history (90K emails = 14MB vs 305MB)
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# 1. Build index (no embeddings stored!)
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builder = LeannBuilder(backend_name="hnsw")
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builder.add_from_mailbox("~/Library/Mail") # Your actual emails
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builder.build_index("my_life.leann")
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builder.add_text("C# is a powerful programming language")
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builder.add_text("Python is a powerful programming language")
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builder.add_text("Machine learning transforms industries")
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builder.add_text("Neural networks process complex data")
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builder.add_text("Leann is a great storage saving engine for RAG on your macbook")
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builder.build_index("knowledge.leann")
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# Ask questions about your own data
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searcher = LeannSearcher("my_life.leann")
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searcher.search("What did my boss say about the deadline?")
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searcher.search("Find emails about vacation requests")
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searcher.search("Show me all conversations with John about the project")
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# 2. Search with real-time embeddings
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searcher = LeannSearcher("knowledge.leann")
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results = searcher.search("C++ programming languages", top_k=2, recompute_beighbor_embeddings=True)
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print(results)
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```
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**That's it.** No cloud setup, no API keys, no "fine-tuning". Just your data, your questions, your laptop.
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@@ -160,6 +163,15 @@ python examples/mail_reader_leann.py --query "What did my boss say about deadlin
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</details>
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<details>
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<summary><strong>📋 Click to expand: Example queries you can try</strong></summary>
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Once the index is built, you can ask questions like:
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- "Find emails from my boss about deadlines"
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- "What did John say about the project timeline?"
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- "Show me emails about travel expenses"
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</details>
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### 🌐 Time Machine for the Web
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```bash
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python examples/google_history_reader_leann.py
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@@ -187,14 +199,54 @@ python examples/google_history_reader_leann.py --query "What websites did I visi
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</details>
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<details>
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<summary><strong>📋 Click to expand: How to find your Chrome profile</strong></summary>
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The default Chrome profile path is configured for a typical macOS setup. If you need to find your specific Chrome profile:
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1. Open Terminal
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2. Run: `ls ~/Library/Application\ Support/Google/Chrome/`
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3. Look for folders like "Default", "Profile 1", "Profile 2", etc.
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4. Use the full path as your `--chrome-profile` argument
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**Common Chrome profile locations:**
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- macOS: `~/Library/Application Support/Google/Chrome/Default`
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- Linux: `~/.config/google-chrome/Default`
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</details>
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<details>
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<summary><strong>💬 Click to expand: Example queries you can try</strong></summary>
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Once the index is built, you can ask questions like:
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- "What websites did I visit about machine learning?"
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- "Find my search history about programming"
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- "What YouTube videos did I watch recently?"
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- "Show me websites I visited about travel planning"
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</details>
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### 💬 WeChat Detective
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```bash
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python examples/wechat_history_reader_leann.py
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# "我想买魔术师约翰逊的球衣，给我一些对应聊天记录"
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python examples/wechat_history_reader_leann.py
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# "Show me all group chats about weekend plans"
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```
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**400K messages → 64MB.** Search years of chat history in any language.
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<details>
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<summary><strong>🔧 Click to expand: Installation Requirements</strong></summary>
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First, you need to install the WeChat exporter:
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```bash
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sudo packages/wechat-exporter/wechattweak-cli install
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```
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**Troubleshooting**: If you encounter installation issues, check the [WeChatTweak-CLI issues page](https://github.com/sunnyyoung/WeChatTweak-CLI/issues/41).
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</details>
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<details>
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<summary><strong>📋 Click to expand: Command Examples</strong></summary>
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@@ -202,7 +254,7 @@ python examples/wechat_history_reader_leann.py
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# Use default settings (recommended for first run)
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python examples/wechat_history_reader_leann.py
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# Run with custom export directory
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# Run with custom export directory and wehn we run the first time, LEANN will export all chat history automatically for you
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python examples/wechat_history_reader_leann.py --export-dir "./my_wechat_exports"
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# Run with custom index directory
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@@ -217,21 +269,14 @@ python examples/wechat_history_reader_leann.py --query "Show me conversations ab
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</details>
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### 📚 Personal Wikipedia
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```bash
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# Index 60M Wikipedia articles in 6GB (not 201GB)
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python examples/build_massive_index.py --source wikipedia
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# "Explain quantum computing like I'm 5"
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# "What are the connections between philosophy and AI?"
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```
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<details>
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<summary><strong>💬 Click to expand: Example queries you can try</strong></summary>
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**PDF RAG Demo (using LlamaIndex for document parsing and Leann for indexing/search)**
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Once the index is built, you can ask questions like:
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This demo showcases how to build a RAG system for PDF/md documents using Leann.
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1. Place your PDF files (and other supported formats like .docx, .pptx, .xlsx) into the `examples/data/` directory.
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2. Ensure you have an `OPENAI_API_KEY` set in your environment variables or in a `.env` file for the LLM to function.
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- "我想买魔术师约翰逊的球衣，给我一些对应聊天记录?" (Chinese: Show me chat records about buying Magic Johnson's jersey)
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</details>
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## 🏗️ Architecture & How It Works
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