yangdx
commited on
Commit
·
e5f9f74
1
Parent(s):
7fb023a
Refactor storage initialization to separate object creation from data loading
Browse files• Split __post_init__ and initialize()
• Move data loading to initialize()
• Add FastAPI lifespan integration
lightrag/api/lightrag_server.py
CHANGED
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@@ -135,14 +135,16 @@ def create_app(args):
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# Initialize database connections
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await rag.initialize_storages()
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-
#
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-
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-
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-
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-
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get_storage_lock,
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)
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# Check if a task is already running (with lock protection)
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pipeline_status = await get_namespace_data("pipeline_status")
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should_start_task = False
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# Initialize database connections
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await rag.initialize_storages()
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# Import necessary functions from shared_storage
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from lightrag.kg.shared_storage import (
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get_namespace_data,
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get_storage_lock,
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initialize_pipeline_namespace,
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)
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await initialize_pipeline_namespace()
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# Auto scan documents if enabled
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if args.auto_scan_at_startup:
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# Check if a task is already running (with lock protection)
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pipeline_status = await get_namespace_data("pipeline_status")
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should_start_task = False
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lightrag/kg/json_doc_status_impl.py
CHANGED
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@@ -24,11 +24,14 @@ from .shared_storage import (
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class JsonDocStatusStorage(DocStatusStorage):
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"""JSON implementation of document status storage"""
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-
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working_dir = self.global_config["working_dir"]
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self._file_name = os.path.join(working_dir, f"kv_store_{self.namespace}.json")
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self._storage_lock = get_storage_lock()
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# check need_init must before get_namespace_data
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need_init = try_initialize_namespace(self.namespace)
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self._data = await get_namespace_data(self.namespace)
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class JsonDocStatusStorage(DocStatusStorage):
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"""JSON implementation of document status storage"""
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def __post_init__(self):
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working_dir = self.global_config["working_dir"]
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self._file_name = os.path.join(working_dir, f"kv_store_{self.namespace}.json")
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self._storage_lock = get_storage_lock()
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self._data = None
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async def initialize(self):
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"""Initialize storage data"""
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# check need_init must before get_namespace_data
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need_init = try_initialize_namespace(self.namespace)
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self._data = await get_namespace_data(self.namespace)
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lightrag/kg/json_kv_impl.py
CHANGED
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@@ -20,11 +20,14 @@ from .shared_storage import (
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@final
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@dataclass
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class JsonKVStorage(BaseKVStorage):
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-
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working_dir = self.global_config["working_dir"]
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self._file_name = os.path.join(working_dir, f"kv_store_{self.namespace}.json")
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self._storage_lock = get_storage_lock()
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# check need_init must before get_namespace_data
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need_init = try_initialize_namespace(self.namespace)
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self._data = await get_namespace_data(self.namespace)
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@final
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@dataclass
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class JsonKVStorage(BaseKVStorage):
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def __post_init__(self):
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working_dir = self.global_config["working_dir"]
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self._file_name = os.path.join(working_dir, f"kv_store_{self.namespace}.json")
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self._storage_lock = get_storage_lock()
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self._data = None
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async def initialize(self):
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"""Initialize storage data"""
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# check need_init must before get_namespace_data
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need_init = try_initialize_namespace(self.namespace)
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self._data = await get_namespace_data(self.namespace)
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lightrag/kg/nano_vector_db_impl.py
CHANGED
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@@ -16,15 +16,16 @@ if not pm.is_installed("nano-vectordb"):
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pm.install("nano-vectordb")
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from nano_vectordb import NanoVectorDB
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-
from
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@final
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@dataclass
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class NanoVectorDBStorage(BaseVectorStorage):
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def __post_init__(self):
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# Initialize
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self._storage_lock =
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# Use global config value if specified, otherwise use default
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kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {})
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@@ -40,7 +41,9 @@ class NanoVectorDBStorage(BaseVectorStorage):
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)
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self._max_batch_size = self.global_config["embedding_batch_num"]
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-
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self._client = NanoVectorDB(
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self.embedding_func.embedding_dim,
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storage_file=self._client_file_name,
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@@ -163,5 +166,5 @@ class NanoVectorDBStorage(BaseVectorStorage):
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logger.error(f"Error deleting relations for {entity_name}: {e}")
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async def index_done_callback(self) -> None:
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with self._storage_lock:
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self._get_client().save()
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pm.install("nano-vectordb")
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from nano_vectordb import NanoVectorDB
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from .shared_storage import get_storage_lock
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@final
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@dataclass
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class NanoVectorDBStorage(BaseVectorStorage):
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def __post_init__(self):
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# Initialize basic attributes
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self._storage_lock = get_storage_lock()
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self._client = None
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# Use global config value if specified, otherwise use default
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kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {})
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)
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self._max_batch_size = self.global_config["embedding_batch_num"]
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async def initialize(self):
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"""Initialize storage data"""
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async with self._storage_lock:
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self._client = NanoVectorDB(
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self.embedding_func.embedding_dim,
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storage_file=self._client_file_name,
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logger.error(f"Error deleting relations for {entity_name}: {e}")
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async def index_done_callback(self) -> None:
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async with self._storage_lock:
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self._get_client().save()
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lightrag/kg/shared_storage.py
CHANGED
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@@ -125,13 +125,21 @@ def initialize_share_data(workers: int = 1):
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# Mark as initialized
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_initialized = True
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# Initialize pipeline status for document indexing control
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pipeline_namespace = get_namespace_data("pipeline_status")
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-
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-
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-
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-
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"busy": False, # Control concurrent processes
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"job_name": "Default Job", # Current job name (indexing files/indexing texts)
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"job_start": None, # Job start time
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@@ -141,8 +149,8 @@ def initialize_share_data(workers: int = 1):
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"request_pending": False, # Flag for pending request for processing
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"latest_message": "", # Latest message from pipeline processing
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"history_messages": history_messages, # 使用共享列表对象
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}
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-
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async def get_update_flags(namespace: str):
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# Mark as initialized
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_initialized = True
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async def initialize_pipeline_namespace():
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"""
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Initialize pipeline namespace with default values.
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"""
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pipeline_namespace = await get_namespace_data("pipeline_status")
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async with get_storage_lock():
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# Check if already initialized by checking for required fields
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if "busy" in pipeline_namespace:
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return
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# Create a shared list object for history_messages
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history_messages = _manager.list() if is_multiprocess else []
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pipeline_namespace.update({
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"busy": False, # Control concurrent processes
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"job_name": "Default Job", # Current job name (indexing files/indexing texts)
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"job_start": None, # Job start time
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"request_pending": False, # Flag for pending request for processing
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"latest_message": "", # Latest message from pipeline processing
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"history_messages": history_messages, # 使用共享列表对象
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})
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direct_log(f"Process {os.getpid()} Pipeline namespace initialized")
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async def get_update_flags(namespace: str):
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