Update README
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118
README-zh.md
118
README-zh.md
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@ -1108,40 +1108,98 @@ LightRAG 现已与 [RAG-Anything](https://github.com/HKUDS/RAG-Anything) 实现
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pip install raganything
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```
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2. 处理多模态文档：
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```python
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import asyncio
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from raganything import RAGAnything
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from lightrag.llm.openai import openai_complete_if_cache, openai_embed
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<details>
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<summary> <b> RAGAnything 使用实例 </b></summary>
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```python
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import asyncio
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from raganything import RAGAnything
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from lightrag import LightRAG
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from lightrag.llm.openai import openai_complete_if_cache, openai_embed
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from lightrag.utils import EmbeddingFunc
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import os
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async def main():
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# 使用LightRAG集成初始化RAGAnything
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rag = RAGAnything(
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working_dir="./rag_storage",
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llm_model_func=lambda prompt, **kwargs: openai_complete_if_cache(
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"gpt-4o-mini", prompt, api_key="your-api-key", **kwargs
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),
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embedding_func=lambda texts: openai_embed(
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texts, model="text-embedding-3-large", api_key="your-api-key"
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),
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embedding_dim=3072,
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)
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async def load_existing_lightrag():
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# 首先，创建或加载现有的 LightRAG 实例
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lightrag_working_dir = "./existing_lightrag_storage"
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# 处理多模态文档
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await rag.process_document_complete(
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file_path="path/to/your/document.pdf",
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output_dir="./output"
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)
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# 检查是否存在之前的 LightRAG 实例
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if os.path.exists(lightrag_working_dir) and os.listdir(lightrag_working_dir):
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print("✅ Found existing LightRAG instance, loading...")
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else:
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print("❌ No existing LightRAG instance found, will create new one")
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# 查询多模态内容
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result = await rag.query_with_multimodal(
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"图表中显示的主要发现是什么？",
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mode="hybrid"
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)
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print(result)
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# 使用您的配置创建/加载 LightRAG 实例
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lightrag_instance = LightRAG(
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working_dir=lightrag_working_dir,
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llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache(
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"gpt-4o-mini",
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key="your-api-key",
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**kwargs,
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),
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embedding_func=EmbeddingFunc(
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embedding_dim=3072,
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max_token_size=8192,
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func=lambda texts: openai_embed(
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texts,
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model="text-embedding-3-large",
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api_key=api_key,
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base_url=base_url,
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),
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)
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)
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if __name__ == "__main__":
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asyncio.run(main())
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```
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# 初始化存储（如果有现有数据，这将加载现有数据）
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await lightrag_instance.initialize_storages()
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# 现在使用现有的 LightRAG 实例初始化 RAGAnything
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rag = RAGAnything(
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lightrag=lightrag_instance, # 传递现有的 LightRAG 实例
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# 仅需要视觉模型用于多模态处理
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vision_model_func=lambda prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs: openai_complete_if_cache(
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"gpt-4o",
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"",
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system_prompt=None,
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history_messages=[],
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messages=[
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{"role": "system", "content": system_prompt} if system_prompt else None,
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{"role": "user", "content": [
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{"type": "text", "text": prompt},
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{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}}
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]} if image_data else {"role": "user", "content": prompt}
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],
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api_key="your-api-key",
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**kwargs,
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) if image_data else openai_complete_if_cache(
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"gpt-4o-mini",
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key="your-api-key",
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**kwargs,
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)
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# 注意：working_dir、llm_model_func、embedding_func 等都从 lightrag_instance 继承
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)
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# 查询现有的知识库
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result = await rag.query_with_multimodal(
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"What data has been processed in this LightRAG instance?",
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mode="hybrid"
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)
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print("Query result:", result)
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# 向现有的 LightRAG 实例添加新的多模态文档
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await rag.process_document_complete(
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file_path="path/to/new/multimodal_document.pdf",
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output_dir="./output"
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)
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if __name__ == "__main__":
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asyncio.run(load_existing_lightrag())
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```
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</details>
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如需详细文档和高级用法，请参阅 [RAG-Anything 仓库](https://github.com/HKUDS/RAG-Anything)。
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163
README.md
163
README.md
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@ -1159,99 +1159,98 @@ LightRAG now seamlessly integrates with [RAG-Anything](https://github.com/HKUDS/
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pip install raganything
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```
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2. Process multimodal documents:
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<details>
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<summary> <b> RAGAnything Usage Example </b></summary>
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```python
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import asyncio
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from raganything import RAGAnything
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from lightrag import LightRAG
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from lightrag.llm.openai import openai_complete_if_cache, openai_embed
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from lightrag.utils import EmbeddingFunc
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import os
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<details>
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<summary> <b> RAGAnything Usage Example </b></summary>
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```python
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import asyncio
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from raganything import RAGAnything
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from lightrag import LightRAG
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from lightrag.llm.openai import openai_complete_if_cache, openai_embed
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from lightrag.utils import EmbeddingFunc
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import os
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async def load_existing_lightrag():
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# First, create or load an existing LightRAG instance
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lightrag_working_dir = "./existing_lightrag_storage"
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async def load_existing_lightrag():
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# First, create or load an existing LightRAG instance
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lightrag_working_dir = "./existing_lightrag_storage"
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# Check if previous LightRAG instance exists
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if os.path.exists(lightrag_working_dir) and os.listdir(lightrag_working_dir):
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print("✅ Found existing LightRAG instance, loading...")
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else:
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print("❌ No existing LightRAG instance found, will create new one")
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# Check if previous LightRAG instance exists
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if os.path.exists(lightrag_working_dir) and os.listdir(lightrag_working_dir):
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print("✅ Found existing LightRAG instance, loading...")
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else:
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print("❌ No existing LightRAG instance found, will create new one")
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# Create/Load LightRAG instance with your configurations
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lightrag_instance = LightRAG(
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working_dir=lightrag_working_dir,
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llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache(
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"gpt-4o-mini",
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key="your-api-key",
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**kwargs,
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),
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embedding_func=EmbeddingFunc(
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embedding_dim=3072,
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max_token_size=8192,
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func=lambda texts: openai_embed(
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texts,
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model="text-embedding-3-large",
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api_key=api_key,
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base_url=base_url,
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# Create/Load LightRAG instance with your configurations
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lightrag_instance = LightRAG(
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working_dir=lightrag_working_dir,
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llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache(
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"gpt-4o-mini",
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key="your-api-key",
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**kwargs,
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),
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embedding_func=EmbeddingFunc(
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embedding_dim=3072,
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max_token_size=8192,
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func=lambda texts: openai_embed(
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texts,
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model="text-embedding-3-large",
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api_key=api_key,
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base_url=base_url,
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),
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)
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)
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)
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# Initialize storage (this will load existing data if available)
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await lightrag_instance.initialize_storages()
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# Initialize storage (this will load existing data if available)
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await lightrag_instance.initialize_storages()
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# Now initialize RAGAnything with the existing LightRAG instance
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rag = RAGAnything(
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lightrag=lightrag_instance, # Pass the existing LightRAG instance
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# Only need vision model for multimodal processing
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vision_model_func=lambda prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs: openai_complete_if_cache(
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"gpt-4o",
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"",
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system_prompt=None,
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history_messages=[],
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messages=[
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{"role": "system", "content": system_prompt} if system_prompt else None,
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{"role": "user", "content": [
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{"type": "text", "text": prompt},
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{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}}
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]} if image_data else {"role": "user", "content": prompt}
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],
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api_key="your-api-key",
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**kwargs,
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) if image_data else openai_complete_if_cache(
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"gpt-4o-mini",
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key="your-api-key",
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**kwargs,
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# Now initialize RAGAnything with the existing LightRAG instance
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rag = RAGAnything(
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lightrag=lightrag_instance, # Pass the existing LightRAG instance
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# Only need vision model for multimodal processing
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vision_model_func=lambda prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs: openai_complete_if_cache(
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"gpt-4o",
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"",
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system_prompt=None,
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history_messages=[],
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messages=[
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{"role": "system", "content": system_prompt} if system_prompt else None,
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{"role": "user", "content": [
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{"type": "text", "text": prompt},
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{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}}
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]} if image_data else {"role": "user", "content": prompt}
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],
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api_key="your-api-key",
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**kwargs,
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) if image_data else openai_complete_if_cache(
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"gpt-4o-mini",
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key="your-api-key",
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**kwargs,
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)
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# Note: working_dir, llm_model_func, embedding_func, etc. are inherited from lightrag_instance
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)
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# Note: working_dir, llm_model_func, embedding_func, etc. are inherited from lightrag_instance
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)
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# Query the existing knowledge base
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result = await rag.query_with_multimodal(
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"What data has been processed in this LightRAG instance?",
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mode="hybrid"
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)
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print("Query result:", result)
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# Query the existing knowledge base
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result = await rag.query_with_multimodal(
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"What data has been processed in this LightRAG instance?",
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mode="hybrid"
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)
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print("Query result:", result)
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# Add new multimodal documents to the existing LightRAG instance
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await rag.process_document_complete(
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file_path="path/to/new/multimodal_document.pdf",
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output_dir="./output"
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)
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# Add new multimodal documents to the existing LightRAG instance
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await rag.process_document_complete(
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file_path="path/to/new/multimodal_document.pdf",
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output_dir="./output"
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)
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if __name__ == "__main__":
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asyncio.run(load_existing_lightrag())
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```
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</details>
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if __name__ == "__main__":
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asyncio.run(load_existing_lightrag())
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```
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</details>
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For detailed documentation and advanced usage, please refer to the [RAG-Anything repository](https://github.com/HKUDS/RAG-Anything).
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