diff --git a/README-zh.md b/README-zh.md
index 685c9468..25273399 100644
--- a/README-zh.md
+++ b/README-zh.md
@@ -1108,40 +1108,98 @@ LightRAG 现已与 [RAG-Anything](https://github.com/HKUDS/RAG-Anything) 实现
pip install raganything
```
2. 处理多模态文档：
- ```python
- import asyncio
- from raganything import RAGAnything
- from lightrag.llm.openai import openai_complete_if_cache, openai_embed
+
+ RAGAnything 使用实例
+ ```python
+ import asyncio
+ from raganything import RAGAnything
+ from lightrag import LightRAG
+ from lightrag.llm.openai import openai_complete_if_cache, openai_embed
+ from lightrag.utils import EmbeddingFunc
+ import os
- async def main():
- # 使用LightRAG集成初始化RAGAnything
- rag = RAGAnything(
- working_dir="./rag_storage",
- llm_model_func=lambda prompt, **kwargs: openai_complete_if_cache(
- "gpt-4o-mini", prompt, api_key="your-api-key", **kwargs
- ),
- embedding_func=lambda texts: openai_embed(
- texts, model="text-embedding-3-large", api_key="your-api-key"
- ),
- embedding_dim=3072,
- )
+ async def load_existing_lightrag():
+ # 首先，创建或加载现有的 LightRAG 实例
+ lightrag_working_dir = "./existing_lightrag_storage"
- # 处理多模态文档
- await rag.process_document_complete(
- file_path="path/to/your/document.pdf",
- output_dir="./output"
- )
+ # 检查是否存在之前的 LightRAG 实例
+ if os.path.exists(lightrag_working_dir) and os.listdir(lightrag_working_dir):
+ print("✅ Found existing LightRAG instance, loading...")
+ else:
+ print("❌ No existing LightRAG instance found, will create new one")
- # 查询多模态内容
- result = await rag.query_with_multimodal(
- "图表中显示的主要发现是什么？",
- mode="hybrid"
- )
- print(result)
+ # 使用您的配置创建/加载 LightRAG 实例
+ lightrag_instance = LightRAG(
+ working_dir=lightrag_working_dir,
+ llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache(
+ "gpt-4o-mini",
+ prompt,
+ system_prompt=system_prompt,
+ history_messages=history_messages,
+ api_key="your-api-key",
+ **kwargs,
+ ),
+ embedding_func=EmbeddingFunc(
+ embedding_dim=3072,
+ max_token_size=8192,
+ func=lambda texts: openai_embed(
+ texts,
+ model="text-embedding-3-large",
+ api_key=api_key,
+ base_url=base_url,
+ ),
+ )
+ )
- if __name__ == "__main__":
- asyncio.run(main())
- ```
+ # 初始化存储（如果有现有数据，这将加载现有数据）
+ await lightrag_instance.initialize_storages()
+
+ # 现在使用现有的 LightRAG 实例初始化 RAGAnything
+ rag = RAGAnything(
+ lightrag=lightrag_instance, # 传递现有的 LightRAG 实例
+ # 仅需要视觉模型用于多模态处理
+ vision_model_func=lambda prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs: openai_complete_if_cache(
+ "gpt-4o",
+ "",
+ system_prompt=None,
+ history_messages=[],
+ messages=[
+ {"role": "system", "content": system_prompt} if system_prompt else None,
+ {"role": "user", "content": [
+ {"type": "text", "text": prompt},
+ {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}}
+ ]} if image_data else {"role": "user", "content": prompt}
+ ],
+ api_key="your-api-key",
+ **kwargs,
+ ) if image_data else openai_complete_if_cache(
+ "gpt-4o-mini",
+ prompt,
+ system_prompt=system_prompt,
+ history_messages=history_messages,
+ api_key="your-api-key",
+ **kwargs,
+ )
+ # 注意：working_dir、llm_model_func、embedding_func 等都从 lightrag_instance 继承
+ )
+
+ # 查询现有的知识库
+ result = await rag.query_with_multimodal(
+ "What data has been processed in this LightRAG instance?",
+ mode="hybrid"
+ )
+ print("Query result:", result)
+
+ # 向现有的 LightRAG 实例添加新的多模态文档
+ await rag.process_document_complete(
+ file_path="path/to/new/multimodal_document.pdf",
+ output_dir="./output"
+ )
+
+ if __name__ == "__main__":
+ asyncio.run(load_existing_lightrag())
+ ```
+
如需详细文档和高级用法，请参阅 [RAG-Anything 仓库](https://github.com/HKUDS/RAG-Anything)。
diff --git a/README.md b/README.md
index 5765e469..2c9e08b8 100644
--- a/README.md
+++ b/README.md
@@ -1159,99 +1159,98 @@ LightRAG now seamlessly integrates with [RAG-Anything](https://github.com/HKUDS/
pip install raganything
```
2. Process multimodal documents:
+
+ RAGAnything Usage Example
+ ```python
+ import asyncio
+ from raganything import RAGAnything
+ from lightrag import LightRAG
+ from lightrag.llm.openai import openai_complete_if_cache, openai_embed
+ from lightrag.utils import EmbeddingFunc
+ import os
-
- RAGAnything Usage Example
- ```python
- import asyncio
- from raganything import RAGAnything
- from lightrag import LightRAG
- from lightrag.llm.openai import openai_complete_if_cache, openai_embed
- from lightrag.utils import EmbeddingFunc
- import os
+ async def load_existing_lightrag():
+ # First, create or load an existing LightRAG instance
+ lightrag_working_dir = "./existing_lightrag_storage"
- async def load_existing_lightrag():
- # First, create or load an existing LightRAG instance
- lightrag_working_dir = "./existing_lightrag_storage"
+ # Check if previous LightRAG instance exists
+ if os.path.exists(lightrag_working_dir) and os.listdir(lightrag_working_dir):
+ print("✅ Found existing LightRAG instance, loading...")
+ else:
+ print("❌ No existing LightRAG instance found, will create new one")
- # Check if previous LightRAG instance exists
- if os.path.exists(lightrag_working_dir) and os.listdir(lightrag_working_dir):
- print("✅ Found existing LightRAG instance, loading...")
- else:
- print("❌ No existing LightRAG instance found, will create new one")
-
- # Create/Load LightRAG instance with your configurations
- lightrag_instance = LightRAG(
- working_dir=lightrag_working_dir,
- llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache(
- "gpt-4o-mini",
- prompt,
- system_prompt=system_prompt,
- history_messages=history_messages,
- api_key="your-api-key",
- **kwargs,
- ),
- embedding_func=EmbeddingFunc(
- embedding_dim=3072,
- max_token_size=8192,
- func=lambda texts: openai_embed(
- texts,
- model="text-embedding-3-large",
- api_key=api_key,
- base_url=base_url,
+ # Create/Load LightRAG instance with your configurations
+ lightrag_instance = LightRAG(
+ working_dir=lightrag_working_dir,
+ llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache(
+ "gpt-4o-mini",
+ prompt,
+ system_prompt=system_prompt,
+ history_messages=history_messages,
+ api_key="your-api-key",
+ **kwargs,
),
+ embedding_func=EmbeddingFunc(
+ embedding_dim=3072,
+ max_token_size=8192,
+ func=lambda texts: openai_embed(
+ texts,
+ model="text-embedding-3-large",
+ api_key=api_key,
+ base_url=base_url,
+ ),
+ )
)
- )
- # Initialize storage (this will load existing data if available)
- await lightrag_instance.initialize_storages()
+ # Initialize storage (this will load existing data if available)
+ await lightrag_instance.initialize_storages()
- # Now initialize RAGAnything with the existing LightRAG instance
- rag = RAGAnything(
- lightrag=lightrag_instance, # Pass the existing LightRAG instance
- # Only need vision model for multimodal processing
- vision_model_func=lambda prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs: openai_complete_if_cache(
- "gpt-4o",
- "",
- system_prompt=None,
- history_messages=[],
- messages=[
- {"role": "system", "content": system_prompt} if system_prompt else None,
- {"role": "user", "content": [
- {"type": "text", "text": prompt},
- {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}}
- ]} if image_data else {"role": "user", "content": prompt}
- ],
- api_key="your-api-key",
- **kwargs,
- ) if image_data else openai_complete_if_cache(
- "gpt-4o-mini",
- prompt,
- system_prompt=system_prompt,
- history_messages=history_messages,
- api_key="your-api-key",
- **kwargs,
+ # Now initialize RAGAnything with the existing LightRAG instance
+ rag = RAGAnything(
+ lightrag=lightrag_instance, # Pass the existing LightRAG instance
+ # Only need vision model for multimodal processing
+ vision_model_func=lambda prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs: openai_complete_if_cache(
+ "gpt-4o",
+ "",
+ system_prompt=None,
+ history_messages=[],
+ messages=[
+ {"role": "system", "content": system_prompt} if system_prompt else None,
+ {"role": "user", "content": [
+ {"type": "text", "text": prompt},
+ {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}}
+ ]} if image_data else {"role": "user", "content": prompt}
+ ],
+ api_key="your-api-key",
+ **kwargs,
+ ) if image_data else openai_complete_if_cache(
+ "gpt-4o-mini",
+ prompt,
+ system_prompt=system_prompt,
+ history_messages=history_messages,
+ api_key="your-api-key",
+ **kwargs,
+ )
+ # Note: working_dir, llm_model_func, embedding_func, etc. are inherited from lightrag_instance
)
- # Note: working_dir, llm_model_func, embedding_func, etc. are inherited from lightrag_instance
- )
- # Query the existing knowledge base
- result = await rag.query_with_multimodal(
- "What data has been processed in this LightRAG instance?",
- mode="hybrid"
- )
- print("Query result:", result)
+ # Query the existing knowledge base
+ result = await rag.query_with_multimodal(
+ "What data has been processed in this LightRAG instance?",
+ mode="hybrid"
+ )
+ print("Query result:", result)
- # Add new multimodal documents to the existing LightRAG instance
- await rag.process_document_complete(
- file_path="path/to/new/multimodal_document.pdf",
- output_dir="./output"
- )
+ # Add new multimodal documents to the existing LightRAG instance
+ await rag.process_document_complete(
+ file_path="path/to/new/multimodal_document.pdf",
+ output_dir="./output"
+ )
- if __name__ == "__main__":
- asyncio.run(load_existing_lightrag())
- ```
-
+ if __name__ == "__main__":
+ asyncio.run(load_existing_lightrag())
+ ```
+
For detailed documentation and advanced usage, please refer to the [RAG-Anything repository](https://github.com/HKUDS/RAG-Anything).