Remove auto_manage_storages_states option

- Always manage storage states by LightRAG
- Remove rag.initialize_storages() from all examples

README-zh.md

@@ -204,7 +204,6 @@ async def initialize_rag():
         embedding_func=openai_embed,
         llm_model_func=gpt_4o_mini_complete,
     )
-    await rag.initialize_storages()
     await initialize_pipeline_status()
     return rag
 
@@ -400,7 +399,6 @@ async def initialize_rag():
         )
     )
 
-    await rag.initialize_storages()
     await initialize_pipeline_status()
 
     return rag
@@ -547,7 +545,6 @@ async def initialize_rag():
         ),
     )
 
-    await rag.initialize_storages()
     await initialize_pipeline_status()
 
     return rag
@@ -765,8 +762,6 @@ async def initialize_rag():
         graph_storage="Neo4JStorage", #<-----------覆盖KG默认值
     )
 
-    # 初始化数据库连接
-    await rag.initialize_storages()
     # 初始化文档处理的管道状态
     await initialize_pipeline_status()
 
@@ -1193,9 +1188,6 @@ LightRAG 现已与 [RAG-Anything](https://github.com/HKUDS/RAG-Anything) 实现
                 )
             )
 
-            # 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

README.md

@@ -181,8 +181,7 @@ For a streaming response implementation example, please see `examples/lightrag_o
 
 ### ⚠️ Important: Initialization Requirements
 
-**LightRAG requires explicit initialization before use.** You must call both `await rag.initialize_storages()` and `await initialize_pipeline_status()` after creating a LightRAG instance, otherwise you will encounter errors like:
-- `AttributeError: __aenter__` - if storages are not initialized
+**LightRAG requires explicit initialization before use.** You must call `await initialize_pipeline_status()` after creating a LightRAG instance, otherwise you will encounter errors like:
 - `KeyError: 'history_messages'` - if pipeline status is not initialized
 
 ### A Simple Program
@@ -209,9 +208,8 @@ async def initialize_rag():
         embedding_func=openai_embed,
         llm_model_func=gpt_4o_mini_complete,
     )
-    # IMPORTANT: Both initialization calls are required!
-    await rag.initialize_storages()  # Initialize storage backends
-    await initialize_pipeline_status()  # Initialize processing pipeline
+    # IMPORTANT: Initialize document processing pipeline status is required!
+    await initialize_pipeline_status()  #
     return rag
 
 async def main():
@@ -401,7 +399,6 @@ async def initialize_rag():
         )
     )
 
-    await rag.initialize_storages()
     await initialize_pipeline_status()
 
     return rag
@@ -550,7 +547,6 @@ async def initialize_rag():
         ),
     )
 
-    await rag.initialize_storages()
     await initialize_pipeline_status()
 
     return rag
@@ -776,8 +772,6 @@ async def initialize_rag():
         graph_storage="Neo4JStorage", #<-----------override KG default
     )
 
-    # Initialize database connections
-    await rag.initialize_storages()
     # Initialize pipeline status for document processing
     await initialize_pipeline_status()
 
@@ -862,8 +856,6 @@ async def initialize_rag():
         graph_storage="MemgraphStorage", #<-----------override KG default
     )
 
-    # Initialize database connections
-    await rag.initialize_storages()
     # Initialize pipeline status for document processing
     await initialize_pipeline_status()
 
@@ -1239,8 +1231,6 @@ LightRAG now seamlessly integrates with [RAG-Anything](https://github.com/HKUDS/
                     ),
                 )
             )
-            # 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
@@ -1433,24 +1423,16 @@ Valid modes are:
 
 ### Common Initialization Errors
 
-If you encounter these errors when using LightRAG:
+If you encounter the following error when using LightRAG:
 
-1. **`AttributeError: __aenter__`**
-   - **Cause**: Storage backends not initialized
-   - **Solution**: Call `await rag.initialize_storages()` after creating the LightRAG instance
+- **`KeyError: 'history_messages'`**
+- **Cause**: Pipeline status not initialized
+- **Solution**: Call `await initialize_pipeline_status()` after initializing storages
 
-2. **`KeyError: 'history_messages'`**
-   - **Cause**: Pipeline status not initialized
-   - **Solution**: Call `await initialize_pipeline_status()` after initializing storages
-
-3. **Both errors in sequence**
-   - **Cause**: Neither initialization method was called
-   - **Solution**: Always follow this pattern:
-   ```python
-   rag = LightRAG(...)
-   await rag.initialize_storages()
-   await initialize_pipeline_status()
-   ```
+```python
+rag = LightRAG(...)
+await initialize_pipeline_status()
+```
 
 ### Model Switching Issues
 

docs/rerank_integration.md

@@ -174,7 +174,6 @@ async def main():
         rerank_model_func=my_rerank_func,
     )
 
-    await rag.initialize_storages()
     await initialize_pipeline_status()
 
     # Insert documents

examples/lightrag_openai_compatible_demo.py

@@ -4,7 +4,7 @@ import inspect
 import logging
 import logging.config
 from lightrag import LightRAG, QueryParam
-from lightrag.llm.openai import openai_complete_if_cache
+from lightrag.llm.openai import openai_complete_if_cache, openai_embed
 from lightrag.llm.ollama import ollama_embed
 from lightrag.utils import EmbeddingFunc, logger, set_verbose_debug
 from lightrag.kg.shared_storage import initialize_pipeline_status
@@ -99,6 +99,26 @@ async def llm_model_func(
     )
 
 
+ollama_embedding_func = EmbeddingFunc(
+    embedding_dim=int(os.getenv("EMBEDDING_DIM", "1024")),
+    func=lambda texts: ollama_embed(
+        texts,
+        embed_model=os.getenv("EMBEDDING_MODEL", "bge-m3:latest"),
+        host=os.getenv("EMBEDDING_BINDING_HOST", "http://localhost:11434"),
+    ),
+)
+
+openai_embedding_func = EmbeddingFunc(
+    embedding_dim=int(os.getenv("EMBEDDING_DIM", "1024")),
+    func=lambda texts: openai_embed(
+        texts,
+        model=os.getenv("EMBEDDING_MODEL", "BAAI/bge-m3"),
+        base_url=os.getenv("EMBEDDING_BINDING_HOST", "https://api.deepseek.com"),
+        api_key=os.getenv("EMBEDDING_BINDING_API_KEY") or os.getenv("OPENAI_API_KEY"),
+    ),
+)
+
+
 async def print_stream(stream):
     async for chunk in stream:
         if chunk:
@@ -109,18 +129,9 @@ async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
         llm_model_func=llm_model_func,
-        embedding_func=EmbeddingFunc(
-            embedding_dim=int(os.getenv("EMBEDDING_DIM", "1024")),
-            max_token_size=int(os.getenv("MAX_EMBED_TOKENS", "8192")),
-            func=lambda texts: ollama_embed(
-                texts,
-                embed_model=os.getenv("EMBEDDING_MODEL", "bge-m3:latest"),
-                host=os.getenv("EMBEDDING_BINDING_HOST", "http://localhost:11434"),
-            ),
-        ),
+        embedding_func=openai_embedding_func,
     )
 
-    await rag.initialize_storages()
     await initialize_pipeline_status()
 
     return rag

lightrag/api/lightrag_server.py

@@ -149,9 +149,6 @@ def create_app(args):
         app.state.background_tasks = set()
 
         try:
-            # Initialize database connections
-            await rag.initialize_storages()
-
             await initialize_pipeline_status()
             pipeline_status = await get_namespace_data("pipeline_status")
 
@@ -401,7 +398,6 @@ def create_app(args):
             enable_llm_cache_for_entity_extract=args.enable_llm_cache_for_extract,
             enable_llm_cache=args.enable_llm_cache,
             rerank_model_func=rerank_model_func,
-            auto_manage_storages_states=False,
             max_parallel_insert=args.max_parallel_insert,
             max_graph_nodes=args.max_graph_nodes,
             addon_params={"language": args.summary_language},
@@ -431,7 +427,6 @@ def create_app(args):
             enable_llm_cache_for_entity_extract=args.enable_llm_cache_for_extract,
             enable_llm_cache=args.enable_llm_cache,
             rerank_model_func=rerank_model_func,
-            auto_manage_storages_states=False,
             max_parallel_insert=args.max_parallel_insert,
             max_graph_nodes=args.max_graph_nodes,
             addon_params={"language": args.summary_language},

lightrag/lightrag.py

@@ -334,6 +334,7 @@ class LightRAG:
     # Storages Management
     # ---
 
+    # TODO: Deprecated (LightRAG will always manage storages states)
     auto_manage_storages_states: bool = field(default=True)
     """If True, lightrag will automatically calls initialize_storages and finalize_storages at the appropriate times."""
 
@@ -531,12 +532,14 @@ class LightRAG:
 
         self._storages_status = StoragesStatus.CREATED
 
-        if self.auto_manage_storages_states:
-            self._run_async_safely(self.initialize_storages, "Storage Initialization")
+        # Initialize storages
+        self._run_async_safely(self.initialize_storages, "Storage Initialization")
+
+        # Check and perform data migration if needed
+        self._run_async_safely(self._check_and_migrate_data, "Data Migration Check")
 
     def __del__(self):
-        if self.auto_manage_storages_states:
-            self._run_async_safely(self.finalize_storages, "Storage Finalization")
+        self._run_async_safely(self.finalize_storages, "Storage Finalization")
 
     def _run_async_safely(self, async_func, action_name=""):
         """Safely execute an async function, avoiding event loop conflicts."""

reproduce/Step_1.py

@@ -35,7 +35,6 @@ if not os.path.exists(WORKING_DIR):
 async def initialize_rag():
     rag = LightRAG(working_dir=WORKING_DIR)
 
-    await rag.initialize_storages()
     await initialize_pipeline_status()
 
     return rag

reproduce/Step_1_openai_compatible.py

@@ -70,7 +70,6 @@ async def initialize_rag():
         embedding_func=EmbeddingFunc(embedding_dim=4096, func=embedding_func),
     )
 
-    await rag.initialize_storages()
     await initialize_pipeline_status()
 
     return rag