TBNilles/LEANN
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

docs: Make example commands more representative

Browse Source 
- Add default values to parameter descriptions
- Replace generic examples with real-world use cases
- Focus on data-source-specific features in examples
- Remove redundant demonstrations of common parameters
This commit is contained in:
Andy Lee
2025-07-29 13:59:29 -07:00
parent 34e313f64a
commit bd4451bf50
1 changed files with 20 additions and 62 deletions
Download Patch File Download Diff File Expand all files Collapse all files

82
README.md
View File

@@ -203,25 +203,19 @@ python ./examples/document_rag.py --query "What are the main techniques LEANN ex
#### Document-Specific Parameters #### Document-Specific Parameters
```bash ```bash
--data-dir DIR # Directory containing documents to process --data-dir DIR # Directory containing documents to process (default: examples/data)
--file-types .ext .ext # File extensions to process (e.g., .pdf .txt .md) --file-types .ext .ext # File extensions to process (default: .pdf .txt .md)
--chunk-size N # Size of text chunks (default: 2048) --chunk-size N # Size of text chunks (default: 2048)
--chunk-overlap N # Overlap between chunks (default: 25) --chunk-overlap N # Overlap between chunks (default: 25)
``` ```
#### Example Commands #### Example Commands
```bash ```bash
# Process custom documents # Process your research papers folder
python examples/document_rag.py --data-dir "./my_documents" --file-types .pdf .txt .md python examples/document_rag.py --data-dir "~/Documents/Papers" --file-types .pdf
# Process with custom chunking # Process code documentation with smaller chunks
python examples/document_rag.py --chunk-size 512 --chunk-overlap 256 python examples/document_rag.py --data-dir "./docs" --chunk-size 512 --file-types .md .rst
# Use local LLM for privacy
python examples/document_rag.py --llm ollama --llm-model llama3.2:1b
# Use OpenAI embeddings
python examples/document_rag.py --embedding-model text-embedding-3-small --embedding-mode openai
``` ```
</details> </details>
@@ -248,28 +242,16 @@ python examples/email_rag.py --query "What's the food I ordered by DoorDash or U
#### Email-Specific Parameters #### Email-Specific Parameters
```bash ```bash
--mail-path PATH # Path to specific mail directory (auto-detects if omitted) --mail-path PATH # Path to specific mail directory (auto-detects if omitted)
--include-html # Include HTML content in processing --include-html # Include HTML content in processing (useful for newsletters)
``` ```
#### Example Commands #### Example Commands
```bash ```bash
# Auto-detect and process all Apple Mail accounts # Search work emails from a specific account
python examples/email_rag.py python examples/email_rag.py --mail-path "~/Library/Mail/V10/WORK_ACCOUNT"
# Process specific mail directory # Find all receipts and order confirmations (includes HTML)
python examples/email_rag.py --mail-path "~/Library/Mail/V10/..." python examples/email_rag.py --query "receipt order confirmation invoice" --include-html
# Process all emails (may take time)
python examples/email_rag.py --max-items -1
# Include HTML content
python examples/email_rag.py --include-html
# Use OpenAI embeddings for better results
python examples/email_rag.py --embedding-model text-embedding-3-small --embedding-mode openai
# Use local LLM for privacy
python examples/email_rag.py --llm ollama --llm-model llama3.2:1b
``` ```
</details> </details>
@@ -304,23 +286,11 @@ python examples/browser_rag.py --query "Tell me my browser history about machine
#### Example Commands #### Example Commands
```bash ```bash
# Auto-detect and process all Chrome profiles # Search work-related browsing in your work profile
python examples/browser_rag.py python examples/browser_rag.py --chrome-profile "~/Library/Application Support/Google/Chrome/Profile 1"
# Process specific Chrome profile # Interactive mode to explore your research history
python examples/browser_rag.py --chrome-profile "~/Library/Application Support/Google/Chrome/Default" python examples/browser_rag.py --query "machine learning papers arxiv"
# Limit history entries for testing
python examples/browser_rag.py --max-items 500
# Interactive search mode
python examples/browser_rag.py # Without --query for interactive mode
# Use local LLM for privacy
python examples/browser_rag.py --llm ollama --llm-model llama3.2:1b
# Use better embeddings
python examples/browser_rag.py --embedding-model text-embedding-3-small --embedding-mode openai
``` ```
</details> </details>
@@ -388,29 +358,17 @@ Failed to find or export WeChat data. Exiting.
#### WeChat-Specific Parameters #### WeChat-Specific Parameters
```bash ```bash
--export-dir DIR # Directory to store exported WeChat data --export-dir DIR # Directory to store exported WeChat data (default: wechat_export_direct)
--force-export # Force re-export even if data exists --force-export # Force re-export even if data exists
``` ```
#### Example Commands #### Example Commands
```bash ```bash
# Auto-export and index WeChat data # Search for travel plans discussed in group chats
python examples/wechat_rag.py python examples/wechat_rag.py --query "旅游 travel 机票 酒店" --max-items 10000
# Use custom export directory # Re-export and search recent chats (useful after new messages)
python examples/wechat_rag.py --export-dir "./my_wechat_exports" python examples/wechat_rag.py --force-export --query "最近的工作安排"
# Force re-export even if data exists
python examples/wechat_rag.py --force-export
# Limit chat entries for testing
python examples/wechat_rag.py --max-items 1000
# Use HuggingFace model for Chinese support
python examples/wechat_rag.py --llm hf --llm-model Qwen/Qwen2.5-1.5B-Instruct
# Use Qwen embedding model (better for Chinese)
python examples/wechat_rag.py --embedding-model Qwen/Qwen3-Embedding-0.6B
``` ```
</details> </details>