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import os
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from dataclasses import dataclass
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from typing import final
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from lightrag.types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge
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from lightrag.utils import logger
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from lightrag.base import BaseGraphStorage
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import pipmaster as pm
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if not pm.is_installed("networkx"):
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pm.install("networkx")
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if not pm.is_installed("graspologic"):
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pm.install("graspologic")
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import networkx as nx
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from .shared_storage import (
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get_storage_lock,
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get_update_flag,
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set_all_update_flags,
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)
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from dotenv import load_dotenv
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# use the .env that is inside the current folder
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# allows to use different .env file for each lightrag instance
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# the OS environment variables take precedence over the .env file
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load_dotenv(dotenv_path=".env", override=False)
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MAX_GRAPH_NODES = int(os.getenv("MAX_GRAPH_NODES", 1000))
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@final
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@dataclass
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class NetworkXStorage(BaseGraphStorage):
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@staticmethod
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def load_nx_graph(file_name) -> nx.Graph:
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if os.path.exists(file_name):
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return nx.read_graphml(file_name)
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return None
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@staticmethod
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def write_nx_graph(graph: nx.Graph, file_name):
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logger.info(
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f"Writing graph with {graph.number_of_nodes()} nodes, {graph.number_of_edges()} edges"
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)
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nx.write_graphml(graph, file_name)
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def __post_init__(self):
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self._graphml_xml_file = os.path.join(
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self.global_config["working_dir"], f"graph_{self.namespace}.graphml"
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)
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self._storage_lock = None
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self.storage_updated = None
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self._graph = None
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# Load initial graph
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preloaded_graph = NetworkXStorage.load_nx_graph(self._graphml_xml_file)
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if preloaded_graph is not None:
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logger.info(
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f"Loaded graph from {self._graphml_xml_file} with {preloaded_graph.number_of_nodes()} nodes, {preloaded_graph.number_of_edges()} edges"
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)
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else:
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logger.info("Created new empty graph")
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self._graph = preloaded_graph or nx.Graph()
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async def initialize(self):
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"""Initialize storage data"""
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# Get the update flag for cross-process update notification
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self.storage_updated = await get_update_flag(self.namespace)
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# Get the storage lock for use in other methods
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self._storage_lock = get_storage_lock()
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async def _get_graph(self):
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"""Check if the storage should be reloaded"""
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# Acquire lock to prevent concurrent read and write
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async with self._storage_lock:
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# Check if data needs to be reloaded
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if self.storage_updated.value:
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logger.info(
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f"Process {os.getpid()} reloading graph {self.namespace} due to update by another process"
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)
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# Reload data
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self._graph = (
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NetworkXStorage.load_nx_graph(self._graphml_xml_file) or nx.Graph()
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)
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# Reset update flag
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self.storage_updated.value = False
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return self._graph
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async def has_node(self, node_id: str) -> bool:
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graph = await self._get_graph()
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return graph.has_node(node_id)
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async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
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graph = await self._get_graph()
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return graph.has_edge(source_node_id, target_node_id)
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async def get_node(self, node_id: str) -> dict[str, str] | None:
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graph = await self._get_graph()
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return graph.nodes.get(node_id)
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async def node_degree(self, node_id: str) -> int:
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graph = await self._get_graph()
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return graph.degree(node_id)
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async def edge_degree(self, src_id: str, tgt_id: str) -> int:
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graph = await self._get_graph()
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return graph.degree(src_id) + graph.degree(tgt_id)
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async def get_edge(
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self, source_node_id: str, target_node_id: str
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) -> dict[str, str] | None:
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graph = await self._get_graph()
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return graph.edges.get((source_node_id, target_node_id))
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async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None:
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graph = await self._get_graph()
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if graph.has_node(source_node_id):
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return list(graph.edges(source_node_id))
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return None
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async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None:
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"""
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Importance notes:
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1. Changes will be persisted to disk during the next index_done_callback
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2. Only one process should updating the storage at a time before index_done_callback,
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KG-storage-log should be used to avoid data corruption
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"""
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graph = await self._get_graph()
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graph.add_node(node_id, **node_data)
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async def upsert_edge(
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self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
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) -> None:
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"""
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Importance notes:
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1. Changes will be persisted to disk during the next index_done_callback
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2. Only one process should updating the storage at a time before index_done_callback,
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KG-storage-log should be used to avoid data corruption
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"""
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graph = await self._get_graph()
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graph.add_edge(source_node_id, target_node_id, **edge_data)
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async def delete_node(self, node_id: str) -> None:
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"""
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Importance notes:
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1. Changes will be persisted to disk during the next index_done_callback
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2. Only one process should updating the storage at a time before index_done_callback,
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KG-storage-log should be used to avoid data corruption
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"""
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graph = await self._get_graph()
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if graph.has_node(node_id):
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graph.remove_node(node_id)
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logger.debug(f"Node {node_id} deleted from the graph.")
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else:
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logger.warning(f"Node {node_id} not found in the graph for deletion.")
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async def remove_nodes(self, nodes: list[str]):
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"""Delete multiple nodes
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Importance notes:
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1. Changes will be persisted to disk during the next index_done_callback
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2. Only one process should updating the storage at a time before index_done_callback,
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KG-storage-log should be used to avoid data corruption
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Args:
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nodes: List of node IDs to be deleted
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"""
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graph = await self._get_graph()
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for node in nodes:
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if graph.has_node(node):
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graph.remove_node(node)
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async def remove_edges(self, edges: list[tuple[str, str]]):
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"""Delete multiple edges
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Importance notes:
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1. Changes will be persisted to disk during the next index_done_callback
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2. Only one process should updating the storage at a time before index_done_callback,
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KG-storage-log should be used to avoid data corruption
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Args:
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edges: List of edges to be deleted, each edge is a (source, target) tuple
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"""
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graph = await self._get_graph()
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for source, target in edges:
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if graph.has_edge(source, target):
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graph.remove_edge(source, target)
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async def get_all_labels(self) -> list[str]:
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"""
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Get all node labels in the graph
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Returns:
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[label1, label2, ...] # Alphabetically sorted label list
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"""
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graph = await self._get_graph()
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labels = set()
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for node in graph.nodes():
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labels.add(str(node)) # Add node id as a label
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# Return sorted list
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return sorted(list(labels))
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async def get_knowledge_graph(
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self,
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node_label: str,
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max_depth: int = 3,
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max_nodes: int = MAX_GRAPH_NODES,
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) -> KnowledgeGraph:
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"""
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Retrieve a connected subgraph of nodes where the label includes the specified `node_label`.
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Args:
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node_label: Label of the starting node，* means all nodes
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max_depth: Maximum depth of the subgraph, Defaults to 3
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max_nodes: Maxiumu nodes to return by BFS, Defaults to 1000
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Returns:
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KnowledgeGraph object containing nodes and edges, with an is_truncated flag
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indicating whether the graph was truncated due to max_nodes limit
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"""
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graph = await self._get_graph()
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result = KnowledgeGraph()
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# Handle special case for "*" label
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if node_label == "*":
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# Get degrees of all nodes
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degrees = dict(graph.degree())
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# Sort nodes by degree in descending order and take top max_nodes
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sorted_nodes = sorted(degrees.items(), key=lambda x: x[1], reverse=True)
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# Check if graph is truncated
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if len(sorted_nodes) > max_nodes:
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result.is_truncated = True
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logger.info(
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f"Graph truncated: {len(sorted_nodes)} nodes found, limited to {max_nodes}"
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)
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limited_nodes = [node for node, _ in sorted_nodes[:max_nodes]]
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# Create subgraph with the highest degree nodes
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subgraph = graph.subgraph(limited_nodes)
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else:
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# Check if node exists
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if node_label not in graph:
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logger.warning(f"Node {node_label} not found in the graph")
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return KnowledgeGraph() # Return empty graph
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# Use modified BFS to get nodes, prioritizing high-degree nodes at the same depth
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bfs_nodes = []
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visited = set()
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# Store (node, depth, degree) in the queue
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queue = [(node_label, 0, graph.degree(node_label))]
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# Modified breadth-first search with degree-based prioritization
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while queue and len(bfs_nodes) < max_nodes:
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# Get the current depth from the first node in queue
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current_depth = queue[0][1]
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# Collect all nodes at the current depth
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current_level_nodes = []
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while queue and queue[0][1] == current_depth:
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current_level_nodes.append(queue.pop(0))
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# Sort nodes at current depth by degree (highest first)
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current_level_nodes.sort(key=lambda x: x[2], reverse=True)
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# Process all nodes at current depth in order of degree
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for current_node, depth, degree in current_level_nodes:
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if current_node not in visited:
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visited.add(current_node)
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bfs_nodes.append(current_node)
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# Only explore neighbors if we haven't reached max_depth
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if depth < max_depth:
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# Add neighbor nodes to queue with incremented depth
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neighbors = list(graph.neighbors(current_node))
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# Filter out already visited neighbors
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unvisited_neighbors = [
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n for n in neighbors if n not in visited
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]
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# Add neighbors to the queue with their degrees
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for neighbor in unvisited_neighbors:
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neighbor_degree = graph.degree(neighbor)
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queue.append((neighbor, depth + 1, neighbor_degree))
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# Check if we've reached max_nodes
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if len(bfs_nodes) >= max_nodes:
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break
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# Check if graph is truncated - if we still have nodes in the queue
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# and we've reached max_nodes, then the graph is truncated
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if queue and len(bfs_nodes) >= max_nodes:
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result.is_truncated = True
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logger.info(
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f"Graph truncated: breadth-first search limited to {max_nodes} nodes"
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)
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# Create subgraph with BFS discovered nodes
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subgraph = graph.subgraph(bfs_nodes)
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# Add nodes to result
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seen_nodes = set()
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seen_edges = set()
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for node in subgraph.nodes():
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if str(node) in seen_nodes:
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continue
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node_data = dict(subgraph.nodes[node])
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# Get entity_type as labels
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labels = []
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if "entity_type" in node_data:
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if isinstance(node_data["entity_type"], list):
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labels.extend(node_data["entity_type"])
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else:
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labels.append(node_data["entity_type"])
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# Create node with properties
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node_properties = {k: v for k, v in node_data.items()}
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result.nodes.append(
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KnowledgeGraphNode(
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id=str(node), labels=[str(node)], properties=node_properties
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)
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)
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seen_nodes.add(str(node))
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# Add edges to result
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for edge in subgraph.edges():
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source, target = edge
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# Esure unique edge_id for undirect graph
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if str(source) > str(target):
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source, target = target, source
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edge_id = f"{source}-{target}"
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if edge_id in seen_edges:
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continue
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edge_data = dict(subgraph.edges[edge])
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# Create edge with complete information
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result.edges.append(
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KnowledgeGraphEdge(
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id=edge_id,
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type="DIRECTED",
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source=str(source),
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target=str(target),
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properties=edge_data,
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)
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)
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seen_edges.add(edge_id)
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logger.info(
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f"Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}"
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)
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return result
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async def index_done_callback(self) -> bool:
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"""Save data to disk"""
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async with self._storage_lock:
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# Check if storage was updated by another process
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if self.storage_updated.value:
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# Storage was updated by another process, reload data instead of saving
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logger.info(
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f"Graph for {self.namespace} was updated by another process, reloading..."
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)
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self._graph = (
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NetworkXStorage.load_nx_graph(self._graphml_xml_file) or nx.Graph()
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)
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# Reset update flag
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self.storage_updated.value = False
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return False # Return error
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# Acquire lock and perform persistence
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async with self._storage_lock:
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try:
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# Save data to disk
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NetworkXStorage.write_nx_graph(self._graph, self._graphml_xml_file)
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# Notify other processes that data has been updated
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await set_all_update_flags(self.namespace)
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# Reset own update flag to avoid self-reloading
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self.storage_updated.value = False
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return True # Return success
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except Exception as e:
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logger.error(f"Error saving graph for {self.namespace}: {e}")
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return False # Return error
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return True
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async def drop(self) -> dict[str, str]:
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"""Drop all graph data from storage and clean up resources
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This method will:
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1. Remove the graph storage file if it exists
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2. Reset the graph to an empty state
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3. Update flags to notify other processes
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4. Changes is persisted to disk immediately
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Returns:
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dict[str, str]: Operation status and message
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- On success: {"status": "success", "message": "data dropped"}
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- On failure: {"status": "error", "message": "<error details>"}
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"""
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try:
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async with self._storage_lock:
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# delete _client_file_name
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if os.path.exists(self._graphml_xml_file):
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os.remove(self._graphml_xml_file)
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self._graph = nx.Graph()
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# Notify other processes that data has been updated
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await set_all_update_flags(self.namespace)
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# Reset own update flag to avoid self-reloading
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self.storage_updated.value = False
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logger.info(
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f"Process {os.getpid()} drop graph {self.namespace} (file:{self._graphml_xml_file})"
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)
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return {"status": "success", "message": "data dropped"}
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except Exception as e:
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logger.error(f"Error dropping graph {self.namespace}: {e}")
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return {"status": "error", "message": str(e)}
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