Added search mode and min degree filtering for NetworkX

- Implemented exact and inclusive search modes
- Added min degree filtering for nodes
- Updated API to parse label for search options

lightrag/api/routers/graph_routes.py

@@ -34,6 +34,11 @@ def create_graph_routes(rag, api_key: Optional[str] = None):
             2. Followed by nodes directly connected to the matching nodes
             3. Finally, the degree of the nodes
         Maximum number of nodes is limited to env MAX_GRAPH_NODES(default: 1000)
+        Control search mode by label content:
+            1. only label-name :  exact search with the label name (selecting from the label list return previously)
+            2. label-name follow by '>n' : exact search of nodes with degree more than n
+            3. label-name follow by* :  inclusive search of nodes with degree more than n
+            4. label-name follow by '>n*' : inclusive search 
 
         Args:
             label (str): Label to get knowledge graph for
@@ -42,6 +47,37 @@ def create_graph_routes(rag, api_key: Optional[str] = None):
         Returns:
             Dict[str, List[str]]: Knowledge graph for label
         """
-        return await rag.get_knowledge_graph(node_label=label, max_depth=max_depth)
+        # Parse label to extract search mode and min degree if specified
+        search_mode = "exact"  # Default search mode
+        min_degree = 0  # Default minimum degree
+        original_label = label
+        
+        # First check if label ends with *
+        if label.endswith("*"):
+            search_mode = "inclusive"  # Always set to inclusive if ends with *
+            label = label[:-1].strip()  # Remove trailing *
+            
+            # Try to parse >n if it exists
+            if ">" in label:
+                try:
+                    degree_pos = label.rfind(">")
+                    degree_str = label[degree_pos + 1:].strip()
+                    min_degree = int(degree_str) + 1
+                    label = label[:degree_pos].strip()
+                except ValueError:
+                    # If degree parsing fails, just remove * and keep the rest as label
+                    label = original_label[:-1].strip()
+        # If no *, check for >n pattern
+        elif ">" in label:
+            try:
+                degree_pos = label.rfind(">")
+                degree_str = label[degree_pos + 1:].strip()
+                min_degree = int(degree_str) + 1
+                label = label[:degree_pos].strip()
+            except ValueError:
+                # If degree parsing fails, treat the whole string as label
+                label = original_label
+                    
+        return await rag.get_knowledge_graph(node_label=label, max_depth=max_depth, search_mode=search_mode, min_degree=min_degree)
 
     return router

lightrag/kg/networkx_impl.py

@@ -232,7 +232,7 @@ class NetworkXStorage(BaseGraphStorage):
         return sorted(list(labels))
 
     async def get_knowledge_graph(
-        self, node_label: str, max_depth: int = 5
+        self, node_label: str, max_depth: int = 5, search_mode: str = "exact", min_degree: int = 0
     ) -> KnowledgeGraph:
         """
         Retrieve a connected subgraph of nodes where the label includes the specified `node_label`.
@@ -245,6 +245,8 @@ class NetworkXStorage(BaseGraphStorage):
         Args:
             node_label: Label of the starting node
             max_depth: Maximum depth of the subgraph
+            search_mode (str, optional): Search mode, either "exact" or "inclusive". Defaults to "exact".
+            min_degree (int, optional): Minimum degree of nodes to include. Defaults to 0.
 
         Returns:
             KnowledgeGraph object containing nodes and edges
@@ -262,11 +264,16 @@ class NetworkXStorage(BaseGraphStorage):
                 graph.copy()
             )  # Create a copy to avoid modifying the original graph
         else:
-            # Find nodes with matching node id (partial match)
+            # Find nodes with matching node id based on search_mode
             nodes_to_explore = []
             for n, attr in graph.nodes(data=True):
-                if node_label in str(n):  # Use partial matching
+                node_str = str(n)
-                    nodes_to_explore.append(n)
+                if search_mode == "exact":
+                    if node_label == node_str:  # Use exact matching
+                        nodes_to_explore.append(n)
+                else:  # inclusive mode
+                    if node_label in node_str:  # Use partial matching
+                        nodes_to_explore.append(n)
 
             if not nodes_to_explore:
                 logger.warning(f"No nodes found with label {node_label}")
@@ -277,6 +284,12 @@ class NetworkXStorage(BaseGraphStorage):
             for start_node in nodes_to_explore:
                 node_subgraph = nx.ego_graph(graph, start_node, radius=max_depth)
                 combined_subgraph = nx.compose(combined_subgraph, node_subgraph)
+            
+            # Filter nodes based on min_degree
+            if min_degree > 0:
+                nodes_to_keep = [node for node, degree in combined_subgraph.degree() if degree >= min_degree]
+                combined_subgraph = combined_subgraph.subgraph(nodes_to_keep)
+            
             subgraph = combined_subgraph
 
         # Check if number of nodes exceeds max_graph_nodes

lightrag/lightrag.py

@@ -504,10 +504,24 @@ class LightRAG:
         return text
 
     async def get_knowledge_graph(
-        self, node_label: str, max_depth: int
+        self, node_label: str, max_depth: int, search_mode: str = "exact", min_degree: int = 0
     ) -> KnowledgeGraph:
+        """Get knowledge graph for a given label
+
+        Args:
+            node_label (str): Label to get knowledge graph for
+            max_depth (int): Maximum depth of graph
+            search_mode (str, optional): Search mode, either "exact" or "inclusive". Defaults to "exact".
+            min_degree (int, optional): Minimum degree of nodes to include. Defaults to 0.
+
+        Returns:
+            KnowledgeGraph: Knowledge graph containing nodes and edges
+        """
         return await self.chunk_entity_relation_graph.get_knowledge_graph(
-            node_label=node_label, max_depth=max_depth
+            node_label=node_label,
+            max_depth=max_depth,
+            search_mode=search_mode,
+            min_degree=min_degree
         )
 
     def _get_storage_class(self, storage_name: str) -> Callable[..., Any]: