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docs: update -h and config advice

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yichuan520030910320
2025-08-13 14:26:35 -07:00
parent 46565b9249
commit 21f7d8e031
3 changed files with 6 additions and 6 deletions
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2
README.md
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@@ -190,7 +190,7 @@ All RAG examples share these common parameters. **Interactive mode** is availabl
--force-rebuild # Force rebuild index even if it exists --force-rebuild # Force rebuild index even if it exists
# Embedding Parameters # Embedding Parameters
--embedding-model MODEL # e.g., facebook/contriever, text-embedding-3-small, nomic-embed-text,mlx-community/Qwen3-Embedding-0.6B-8bit or nomic-embed-text --embedding-model MODEL # e.g., facebook/contriever, text-embedding-3-small, mlx-community/Qwen3-Embedding-0.6B-8bit or nomic-embed-text
--embedding-mode MODE # sentence-transformers, openai, mlx, or ollama --embedding-mode MODE # sentence-transformers, openai, mlx, or ollama
# LLM Parameters (Text generation models) # LLM Parameters (Text generation models)

8
apps/base_rag_example.py
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@@ -69,14 +69,14 @@ class BaseRAGExample(ABC):
"--embedding-model", "--embedding-model",
type=str, type=str,
default=embedding_model_default, default=embedding_model_default,
help=f"Embedding model to use (default: {embedding_model_default})", help=f"Embedding model to use (default: {embedding_model_default}), we provide facebook/contriever, text-embedding-3-small,mlx-community/Qwen3-Embedding-0.6B-8bit or nomic-embed-text",
) )
embedding_group.add_argument( embedding_group.add_argument(
"--embedding-mode", "--embedding-mode",
type=str, type=str,
default="sentence-transformers", default="sentence-transformers",
choices=["sentence-transformers", "openai", "mlx", "ollama"], choices=["sentence-transformers", "openai", "mlx", "ollama"],
help="Embedding backend mode (default: sentence-transformers)", help="Embedding backend mode (default: sentence-transformers), we provide sentence-transformers, openai, mlx, or ollama",
) )
# LLM parameters # LLM parameters
@@ -86,13 +86,13 @@ class BaseRAGExample(ABC):
type=str, type=str,
default="openai", default="openai",
choices=["openai", "ollama", "hf", "simulated"], choices=["openai", "ollama", "hf", "simulated"],
help="LLM backend to use (default: openai)", help="LLM backend: openai, ollama, or hf (default: openai)",
) )
llm_group.add_argument( llm_group.add_argument(
"--llm-model", "--llm-model",
type=str, type=str,
default=None, default=None,
help="LLM model name (default: gpt-4o for openai, llama3.2:1b for ollama)", help="Model name (default: gpt-4o) e.g., gpt-4o-mini, llama3.2:1b, Qwen/Qwen2.5-1.5B-Instruct",
) )
llm_group.add_argument( llm_group.add_argument(
"--llm-host", "--llm-host",

2
docs/configuration-guide.md
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@@ -52,7 +52,7 @@ Based on our experience developing LEANN, embedding models fall into three categ
### Quick Start: Cloud and Local Embedding Options ### Quick Start: Cloud and Local Embedding Options
**OpenAI Embeddings (Fastest Setup)** **OpenAI Embeddings (Fastest Setup)**
For immediate testing without local model downloads: For immediate testing without local model downloads(also if you [do not have GPU](https://github.com/yichuan-w/LEANN/issues/43) and do not care that much about your document leak, you should use this, we compute the embedding and recompute using openai API):
```bash ```bash
# Set OpenAI embeddings (requires OPENAI_API_KEY) # Set OpenAI embeddings (requires OPENAI_API_KEY)
--embedding-mode openai --embedding-model text-embedding-3-small --embedding-mode openai --embedding-model text-embedding-3-small