Add is_truncated to graph query for NetworkX graph db

lightrag/base.py

@@ -343,7 +343,18 @@ class BaseGraphStorage(StorageNameSpace, ABC):
     async def get_knowledge_graph(
         self, node_label: str, max_depth: int = 3, max_nodes: int = 1000
     ) -> KnowledgeGraph:
-        """Retrieve a subgraph of the knowledge graph starting from a given node."""
+        """
+        Retrieve a connected subgraph of nodes where the label includes the specified `node_label`.
+
+        Args:
+            node_label: Label of the starting node，* means all nodes
+            max_depth: Maximum depth of the subgraph, Defaults to 3
+            max_nodes: Maxiumu nodes to return by BFS, Defaults to 1000
+
+        Returns:
+            KnowledgeGraph object containing nodes and edges, with an is_truncated flag
+            indicating whether the graph was truncated due to max_nodes limit
+        """
 
 
 class DocStatus(str, Enum):

lightrag/kg/neo4j_impl.py

@@ -651,17 +651,14 @@ class Neo4JStorage(BaseGraphStorage):
     ) -> KnowledgeGraph:
         """
         Retrieve a connected subgraph of nodes where the label includes the specified `node_label`.
-        When reducing the number of nodes, the prioritization criteria are as follows:
-            1. Hops(path) to the staring node take precedence
-            2. Followed by the degree of the nodes
 
         Args:
-            node_label: Label of the starting node
+            node_label: Label of the starting node，* means all nodes
             max_depth: Maximum depth of the subgraph, Defaults to 3
-            max_nodes: Maxiumu nodes to return
+            max_nodes: Maxiumu nodes to return by BFS, Defaults to 1000
 
         Returns:
-            KnowledgeGraph: Complete connected subgraph for specified node
+            KnowledgeGraph object containing nodes and edges
         """
         result = KnowledgeGraph()
         seen_nodes = set()

lightrag/kg/networkx_impl.py

@@ -270,16 +270,24 @@ class NetworkXStorage(BaseGraphStorage):
             max_nodes: Maxiumu nodes to return by BFS, Defaults to 1000
 
         Returns:
-            KnowledgeGraph object containing nodes and edges
+            KnowledgeGraph object containing nodes and edges, with an is_truncated flag
+            indicating whether the graph was truncated due to max_nodes limit
         """
         graph = await self._get_graph()
 
+        result = KnowledgeGraph()
+        
         # Handle special case for "*" label
         if node_label == "*":
             # Get degrees of all nodes
             degrees = dict(graph.degree())
             # Sort nodes by degree in descending order and take top max_nodes
             sorted_nodes = sorted(degrees.items(), key=lambda x: x[1], reverse=True)
+            
+            # Check if graph is truncated
+            if len(sorted_nodes) > max_nodes:
+                result.is_truncated = True
+                
             limited_nodes = [node for node, _ in sorted_nodes[:max_nodes]]
             # Create subgraph with the highest degree nodes
             subgraph = graph.subgraph(limited_nodes)
@@ -293,23 +301,27 @@ class NetworkXStorage(BaseGraphStorage):
             bfs_nodes = []
             visited = set()
             queue = [node_label]
-
+            
             # Breadth-first search
             while queue and len(bfs_nodes) < max_nodes:
                 current = queue.pop(0)
                 if current not in visited:
                     visited.add(current)
                     bfs_nodes.append(current)
-
+                    
                     # Add neighbor nodes to queue
                     neighbors = list(graph.neighbors(current))
                     queue.extend([n for n in neighbors if n not in visited])
-
+            
+            # Check if graph is truncated - if we still have nodes in the queue
+            # and we've reached max_nodes, then the graph is truncated
+            if queue and len(bfs_nodes) >= max_nodes:
+                result.is_truncated = True
+            
             # Create subgraph with BFS discovered nodes
             subgraph = graph.subgraph(bfs_nodes)
 
         # Add nodes to result
-        result = KnowledgeGraph()
         seen_nodes = set()
         seen_edges = set()
         for node in subgraph.nodes():

lightrag/types.py

@@ -26,3 +26,4 @@ class KnowledgeGraphEdge(BaseModel):
 class KnowledgeGraph(BaseModel):
     nodes: list[KnowledgeGraphNode] = []
     edges: list[KnowledgeGraphEdge] = []
+    is_truncated: bool = False