mirror of
https://github.com/HKUDS/LightRAG.git
synced 2025-08-06 07:41:54 +00:00
294 lines
10 KiB
Python
294 lines
10 KiB
Python
import os
|
|
from dataclasses import dataclass
|
|
from typing import Any, final
|
|
import numpy as np
|
|
|
|
from lightrag.types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge
|
|
from lightrag.utils import logger
|
|
from lightrag.base import BaseGraphStorage
|
|
|
|
import pipmaster as pm
|
|
|
|
if not pm.is_installed("networkx"):
|
|
pm.install("networkx")
|
|
|
|
if not pm.is_installed("graspologic"):
|
|
pm.install("graspologic")
|
|
|
|
import networkx as nx
|
|
from graspologic import embed
|
|
from threading import Lock as ThreadLock
|
|
|
|
|
|
@final
|
|
@dataclass
|
|
class NetworkXStorage(BaseGraphStorage):
|
|
@staticmethod
|
|
def load_nx_graph(file_name) -> nx.Graph:
|
|
if os.path.exists(file_name):
|
|
return nx.read_graphml(file_name)
|
|
return None
|
|
|
|
@staticmethod
|
|
def write_nx_graph(graph: nx.Graph, file_name):
|
|
logger.info(
|
|
f"Writing graph with {graph.number_of_nodes()} nodes, {graph.number_of_edges()} edges"
|
|
)
|
|
nx.write_graphml(graph, file_name)
|
|
|
|
@staticmethod
|
|
def _stabilize_graph(graph: nx.Graph) -> nx.Graph:
|
|
"""Refer to https://github.com/microsoft/graphrag/index/graph/utils/stable_lcc.py
|
|
Ensure an undirected graph with the same relationships will always be read the same way.
|
|
"""
|
|
fixed_graph = nx.DiGraph() if graph.is_directed() else nx.Graph()
|
|
|
|
sorted_nodes = graph.nodes(data=True)
|
|
sorted_nodes = sorted(sorted_nodes, key=lambda x: x[0])
|
|
|
|
fixed_graph.add_nodes_from(sorted_nodes)
|
|
edges = list(graph.edges(data=True))
|
|
|
|
if not graph.is_directed():
|
|
|
|
def _sort_source_target(edge):
|
|
source, target, edge_data = edge
|
|
if source > target:
|
|
temp = source
|
|
source = target
|
|
target = temp
|
|
return source, target, edge_data
|
|
|
|
edges = [_sort_source_target(edge) for edge in edges]
|
|
|
|
def _get_edge_key(source: Any, target: Any) -> str:
|
|
return f"{source} -> {target}"
|
|
|
|
edges = sorted(edges, key=lambda x: _get_edge_key(x[0], x[1]))
|
|
|
|
fixed_graph.add_edges_from(edges)
|
|
return fixed_graph
|
|
|
|
def __post_init__(self):
|
|
self._graphml_xml_file = os.path.join(
|
|
self.global_config["working_dir"], f"graph_{self.namespace}.graphml"
|
|
)
|
|
self._storage_lock = ThreadLock()
|
|
|
|
with self._storage_lock:
|
|
preloaded_graph = NetworkXStorage.load_nx_graph(self._graphml_xml_file)
|
|
if preloaded_graph is not None:
|
|
logger.info(
|
|
f"Loaded graph from {self._graphml_xml_file} with {preloaded_graph.number_of_nodes()} nodes, {preloaded_graph.number_of_edges()} edges"
|
|
)
|
|
else:
|
|
logger.info("Created new empty graph")
|
|
self._graph = preloaded_graph or nx.Graph()
|
|
self._node_embed_algorithms = {
|
|
"node2vec": self._node2vec_embed,
|
|
}
|
|
|
|
def _get_graph(self):
|
|
"""Check if the shtorage should be reloaded"""
|
|
return self._graph
|
|
|
|
async def index_done_callback(self) -> None:
|
|
with self._storage_lock:
|
|
NetworkXStorage.write_nx_graph(self._get_graph(), self._graphml_xml_file)
|
|
|
|
async def has_node(self, node_id: str) -> bool:
|
|
return self._get_graph().has_node(node_id)
|
|
|
|
async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
|
|
return self._get_graph().has_edge(source_node_id, target_node_id)
|
|
|
|
async def get_node(self, node_id: str) -> dict[str, str] | None:
|
|
return self._get_graph().nodes.get(node_id)
|
|
|
|
async def node_degree(self, node_id: str) -> int:
|
|
return self._get_graph().degree(node_id)
|
|
|
|
async def edge_degree(self, src_id: str, tgt_id: str) -> int:
|
|
return self._get_graph().degree(src_id) + self._get_graph().degree(tgt_id)
|
|
|
|
async def get_edge(
|
|
self, source_node_id: str, target_node_id: str
|
|
) -> dict[str, str] | None:
|
|
return self._get_graph().edges.get((source_node_id, target_node_id))
|
|
|
|
async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None:
|
|
if self._get_graph().has_node(source_node_id):
|
|
return list(self._get_graph().edges(source_node_id))
|
|
return None
|
|
|
|
async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None:
|
|
self._get_graph().add_node(node_id, **node_data)
|
|
|
|
async def upsert_edge(
|
|
self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
|
|
) -> None:
|
|
self._get_graph().add_edge(source_node_id, target_node_id, **edge_data)
|
|
|
|
async def delete_node(self, node_id: str) -> None:
|
|
if self._get_graph().has_node(node_id):
|
|
self._get_graph().remove_node(node_id)
|
|
logger.debug(f"Node {node_id} deleted from the graph.")
|
|
else:
|
|
logger.warning(f"Node {node_id} not found in the graph for deletion.")
|
|
|
|
async def embed_nodes(
|
|
self, algorithm: str
|
|
) -> tuple[np.ndarray[Any, Any], list[str]]:
|
|
if algorithm not in self._node_embed_algorithms:
|
|
raise ValueError(f"Node embedding algorithm {algorithm} not supported")
|
|
return await self._node_embed_algorithms[algorithm]()
|
|
|
|
# TODO: NOT USED
|
|
async def _node2vec_embed(self):
|
|
graph = self._get_graph()
|
|
embeddings, nodes = embed.node2vec_embed(
|
|
graph,
|
|
**self.global_config["node2vec_params"],
|
|
)
|
|
nodes_ids = [graph.nodes[node_id]["id"] for node_id in nodes]
|
|
return embeddings, nodes_ids
|
|
|
|
def remove_nodes(self, nodes: list[str]):
|
|
"""Delete multiple nodes
|
|
|
|
Args:
|
|
nodes: List of node IDs to be deleted
|
|
"""
|
|
graph = self._get_graph()
|
|
for node in nodes:
|
|
if graph.has_node(node):
|
|
graph.remove_node(node)
|
|
|
|
def remove_edges(self, edges: list[tuple[str, str]]):
|
|
"""Delete multiple edges
|
|
|
|
Args:
|
|
edges: List of edges to be deleted, each edge is a (source, target) tuple
|
|
"""
|
|
graph = self._get_graph()
|
|
for source, target in edges:
|
|
if graph.has_edge(source, target):
|
|
graph.remove_edge(source, target)
|
|
|
|
async def get_all_labels(self) -> list[str]:
|
|
"""
|
|
Get all node labels in the graph
|
|
Returns:
|
|
[label1, label2, ...] # Alphabetically sorted label list
|
|
"""
|
|
labels = set()
|
|
for node in self._get_graph().nodes():
|
|
labels.add(str(node)) # Add node id as a label
|
|
|
|
# Return sorted list
|
|
return sorted(list(labels))
|
|
|
|
async def get_knowledge_graph(
|
|
self, node_label: str, max_depth: int = 5
|
|
) -> KnowledgeGraph:
|
|
"""
|
|
Get complete connected subgraph for specified node (including the starting node itself)
|
|
|
|
Args:
|
|
node_label: Label of the starting node
|
|
max_depth: Maximum depth of the subgraph
|
|
|
|
Returns:
|
|
KnowledgeGraph object containing nodes and edges
|
|
"""
|
|
result = KnowledgeGraph()
|
|
seen_nodes = set()
|
|
seen_edges = set()
|
|
|
|
graph = self._get_graph()
|
|
|
|
# Handle special case for "*" label
|
|
if node_label == "*":
|
|
# For "*", return the entire graph including all nodes and edges
|
|
subgraph = (
|
|
graph.copy()
|
|
) # Create a copy to avoid modifying the original graph
|
|
else:
|
|
# Find nodes with matching node id (partial match)
|
|
nodes_to_explore = []
|
|
for n, attr in graph.nodes(data=True):
|
|
if node_label in str(n): # Use partial matching
|
|
nodes_to_explore.append(n)
|
|
|
|
if not nodes_to_explore:
|
|
logger.warning(f"No nodes found with label {node_label}")
|
|
return result
|
|
|
|
# Get subgraph using ego_graph
|
|
subgraph = nx.ego_graph(graph, nodes_to_explore[0], radius=max_depth)
|
|
|
|
# Check if number of nodes exceeds max_graph_nodes
|
|
max_graph_nodes = 500
|
|
if len(subgraph.nodes()) > max_graph_nodes:
|
|
origin_nodes = len(subgraph.nodes())
|
|
node_degrees = dict(subgraph.degree())
|
|
top_nodes = sorted(node_degrees.items(), key=lambda x: x[1], reverse=True)[
|
|
:max_graph_nodes
|
|
]
|
|
top_node_ids = [node[0] for node in top_nodes]
|
|
# Create new subgraph with only top nodes
|
|
subgraph = subgraph.subgraph(top_node_ids)
|
|
logger.info(
|
|
f"Reduced graph from {origin_nodes} nodes to {max_graph_nodes} nodes (depth={max_depth})"
|
|
)
|
|
|
|
# Add nodes to result
|
|
for node in subgraph.nodes():
|
|
if str(node) in seen_nodes:
|
|
continue
|
|
|
|
node_data = dict(subgraph.nodes[node])
|
|
# Get entity_type as labels
|
|
labels = []
|
|
if "entity_type" in node_data:
|
|
if isinstance(node_data["entity_type"], list):
|
|
labels.extend(node_data["entity_type"])
|
|
else:
|
|
labels.append(node_data["entity_type"])
|
|
|
|
# Create node with properties
|
|
node_properties = {k: v for k, v in node_data.items()}
|
|
|
|
result.nodes.append(
|
|
KnowledgeGraphNode(
|
|
id=str(node), labels=[str(node)], properties=node_properties
|
|
)
|
|
)
|
|
seen_nodes.add(str(node))
|
|
|
|
# Add edges to result
|
|
for edge in subgraph.edges():
|
|
source, target = edge
|
|
edge_id = f"{source}-{target}"
|
|
if edge_id in seen_edges:
|
|
continue
|
|
|
|
edge_data = dict(subgraph.edges[edge])
|
|
|
|
# Create edge with complete information
|
|
result.edges.append(
|
|
KnowledgeGraphEdge(
|
|
id=edge_id,
|
|
type="DIRECTED",
|
|
source=str(source),
|
|
target=str(target),
|
|
properties=edge_data,
|
|
)
|
|
)
|
|
seen_edges.add(edge_id)
|
|
|
|
logger.info(
|
|
f"Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}"
|
|
)
|
|
return result
|