docs: Reorganize parameter documentation structure
- Move common parameters to a dedicated section before all examples - Rename sections to 'X-Specific Arguments' for clarity - Remove duplicate common parameters from individual examples - Better information architecture for users
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54
README.md
54
README.md
@@ -166,6 +166,27 @@ ollama pull llama3.2:1b
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</details>
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### Common Parameters (Available in All Examples)
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All RAG examples share these common parameters:
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```bash
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# Core Parameters
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--index-dir DIR # Directory to store the index (default: current directory)
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--query "YOUR QUESTION" # Single query to run (interactive mode if omitted)
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--max-items N # Max items to process (default: 1000, -1 for all)
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--force-rebuild # Force rebuild index even if it exists
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# Embedding Parameters
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--embedding-model MODEL # e.g., facebook/contriever, text-embedding-3-small
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--embedding-mode MODE # sentence-transformers, openai, or mlx
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# LLM Parameters
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--llm TYPE # openai, ollama, or hf
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--llm-model MODEL # e.g., gpt-4o, llama3.2:1b, Qwen/Qwen2.5-1.5B-Instruct
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--top-k N # Number of results to retrieve (default: 20)
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```
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### 📄 Personal Data Manager: Process Any Documents (`.pdf`, `.txt`, `.md`)!
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Ask questions directly about your personal PDFs, documents, and any directory containing your files!
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@@ -182,26 +203,9 @@ python ./examples/document_rag.py --query "What are the main techniques LEANN ex
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```
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<details>
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<summary><strong>📋 Click to expand: User Configurable Arguments</strong></summary>
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<summary><strong>📋 Click to expand: Document-Specific Arguments</strong></summary>
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#### Core Parameters (All Examples Share These)
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```bash
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--index-dir DIR # Directory to store the index
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--query "YOUR QUESTION" # Single query to run (interactive mode if omitted)
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--max-items N # Max items to process (default: 1000, -1 for all)
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--force-rebuild # Force rebuild index even if it exists
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# Embedding Parameters
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--embedding-model MODEL # e.g., facebook/contriever, text-embedding-3-small
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--embedding-mode MODE # sentence-transformers, openai, or mlx
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# LLM Parameters
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--llm TYPE # openai, ollama, or hf
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--llm-model MODEL # e.g., gpt-4o, llama3.2:1b
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--top-k N # Number of results to retrieve (default: 20)
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```
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#### Document-Specific Parameters
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#### Parameters
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```bash
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--data-dir DIR # Directory containing documents to process (default: examples/data)
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--file-types .ext .ext # File extensions to process (default: .pdf .txt .md)
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@@ -237,9 +241,9 @@ python examples/email_rag.py --query "What's the food I ordered by DoorDash or U
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**780K email chunks → 78MB storage.** Finally, search your email like you search Google.
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<details>
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<summary><strong>📋 Click to expand: User Configurable Arguments</strong></summary>
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<summary><strong>📋 Click to expand: Email-Specific Arguments</strong></summary>
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#### Email-Specific Parameters
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#### Parameters
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```bash
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--mail-path PATH # Path to specific mail directory (auto-detects if omitted)
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--include-html # Include HTML content in processing (useful for newsletters)
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@@ -277,9 +281,9 @@ python examples/browser_rag.py --query "Tell me my browser history about machine
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**38K browser entries → 6MB storage.** Your browser history becomes your personal search engine.
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<details>
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<summary><strong>📋 Click to expand: User Configurable Arguments</strong></summary>
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<summary><strong>📋 Click to expand: Browser-Specific Arguments</strong></summary>
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#### Browser-Specific Parameters
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#### Parameters
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```bash
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--chrome-profile PATH # Path to Chrome profile directory (auto-detects if omitted)
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```
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@@ -354,9 +358,9 @@ Failed to find or export WeChat data. Exiting.
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</details>
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<details>
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<summary><strong>📋 Click to expand: User Configurable Arguments</strong></summary>
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<summary><strong>📋 Click to expand: WeChat-Specific Arguments</strong></summary>
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#### WeChat-Specific Parameters
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#### Parameters
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```bash
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--export-dir DIR # Directory to store exported WeChat data (default: wechat_export_direct)
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--force-export # Force re-export even if data exists
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