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feat: Update documentation based on review feedback

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- Add MLX embedding example to README
- Clarify examples/data content description (two papers, Pride and Prejudice, Chinese README)
- Move chunk parameters to common parameters section
- Remove duplicate chunk parameters from document-specific section
This commit is contained in:
Andy Lee
2025-07-30 18:05:39 -07:00
parent 274bbb19ea
commit c1124eb349
1 changed files with 6 additions and 4 deletions
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README.md
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@@ -184,7 +184,7 @@ All RAG examples share these common parameters. **Interactive mode** is availabl
# Embedding Parameters # Embedding Parameters
--embedding-model MODEL # e.g., facebook/contriever, text-embedding-3-small --embedding-model MODEL # e.g., facebook/contriever, text-embedding-3-small
--embedding-mode MODE # sentence-transformers, openai, or mlx --embedding-mode MODE # sentence-transformers, openai, or mlx (e.g., use with mlx-community/multilingual-e5-base-mlx)
# LLM Parameters (Text generation models) # LLM Parameters (Text generation models)
--llm TYPE # LLM backend: openai, ollama, or hf (default: openai) --llm TYPE # LLM backend: openai, ollama, or hf (default: openai)
@@ -194,6 +194,10 @@ All RAG examples share these common parameters. **Interactive mode** is availabl
--top-k N # Number of results to retrieve (default: 20) --top-k N # Number of results to retrieve (default: 20)
--search-complexity N # Search complexity for graph traversal (default: 64) --search-complexity N # Search complexity for graph traversal (default: 64)
# Chunking Parameters
--chunk-size N # Size of text chunks (default varies by source: 256 for most, 192 for WeChat)
--chunk-overlap N # Overlap between chunks (default varies: 25-128 depending on source)
# Index Building Parameters # Index Building Parameters
--backend-name NAME # Backend to use: hnsw or diskann (default: hnsw) --backend-name NAME # Backend to use: hnsw or diskann (default: hnsw)
--graph-degree N # Graph degree for index construction (default: 32) --graph-degree N # Graph degree for index construction (default: 32)
@@ -212,7 +216,7 @@ Ask questions directly about your personal PDFs, documents, and any directory co
<img src="videos/paper_clear.gif" alt="LEANN Document Search Demo" width="600"> <img src="videos/paper_clear.gif" alt="LEANN Document Search Demo" width="600">
</p> </p>
The example below asks a question about summarizing two papers (uses default data in `examples/data`) and this is the easiest example to run here: The example below asks a question about summarizing our paper (uses default data in `examples/data`, which contains two papers, Pride and Prejudice, and a README in Chinese) and this is the easiest example to run here:
```bash ```bash
source .venv/bin/activate # Don't forget to activate the virtual environment source .venv/bin/activate # Don't forget to activate the virtual environment
@@ -226,8 +230,6 @@ python ./examples/document_rag.py --query "What are the main techniques LEANN ex
```bash ```bash
--data-dir DIR # Directory containing documents to process (default: examples/data) --data-dir DIR # Directory containing documents to process (default: examples/data)
--file-types .ext .ext # Filter by specific file types (optional - all LlamaIndex supported types if omitted) --file-types .ext .ext # Filter by specific file types (optional - all LlamaIndex supported types if omitted)
--chunk-size N # Size of text chunks (default: 256) - larger for papers, smaller for code
--chunk-overlap N # Overlap between chunks (default: 128)
``` ```
#### Example Commands #### Example Commands