Fix linting

lightrag/base.py

@@ -363,7 +363,7 @@ class BaseGraphStorage(StorageNameSpace, ABC):
 
     async def get_nodes_batch(self, node_ids: list[str]) -> dict[str, dict]:
         """Get nodes as a batch using UNWIND
-        
+
         Default implementation fetches nodes one by one.
         Override this method for better performance in storage backends
         that support batch operations.
@@ -377,7 +377,7 @@ class BaseGraphStorage(StorageNameSpace, ABC):
 
     async def node_degrees_batch(self, node_ids: list[str]) -> dict[str, int]:
         """Node degrees as a batch using UNWIND
-        
+
         Default implementation fetches node degrees one by one.
         Override this method for better performance in storage backends
         that support batch operations.
@@ -388,9 +388,11 @@ class BaseGraphStorage(StorageNameSpace, ABC):
             result[node_id] = degree
         return result
 
-    async def edge_degrees_batch(self, edge_pairs: list[tuple[str, str]]) -> dict[tuple[str, str], int]:
+    async def edge_degrees_batch(
+        self, edge_pairs: list[tuple[str, str]]
+    ) -> dict[tuple[str, str], int]:
         """Edge degrees as a batch using UNWIND also uses node_degrees_batch
-        
+
         Default implementation calculates edge degrees one by one.
         Override this method for better performance in storage backends
         that support batch operations.
@@ -401,9 +403,11 @@ class BaseGraphStorage(StorageNameSpace, ABC):
             result[(src_id, tgt_id)] = degree
         return result
 
-    async def get_edges_batch(self, pairs: list[dict[str, str]]) -> dict[tuple[str, str], dict]:
+    async def get_edges_batch(
+        self, pairs: list[dict[str, str]]
+    ) -> dict[tuple[str, str], dict]:
         """Get edges as a batch using UNWIND
-        
+
         Default implementation fetches edges one by one.
         Override this method for better performance in storage backends
         that support batch operations.
@@ -417,9 +421,11 @@ class BaseGraphStorage(StorageNameSpace, ABC):
                 result[(src_id, tgt_id)] = edge
         return result
 
-    async def get_nodes_edges_batch(self, node_ids: list[str]) -> dict[str, list[tuple[str, str]]]:
+    async def get_nodes_edges_batch(
+        self, node_ids: list[str]
+    ) -> dict[str, list[tuple[str, str]]]:
         """Get nodes edges as a batch using UNWIND
-        
+
         Default implementation fetches node edges one by one.
         Override this method for better performance in storage backends
         that support batch operations.

lightrag/kg/neo4j_impl.py

@@ -311,10 +311,10 @@ class Neo4JStorage(BaseGraphStorage):
     async def get_nodes_batch(self, node_ids: list[str]) -> dict[str, dict]:
         """
         Retrieve multiple nodes in one query using UNWIND.
-        
+
         Args:
             node_ids: List of node entity IDs to fetch.
-            
+
         Returns:
             A dictionary mapping each node_id to its node data (or None if not found).
         """
@@ -334,7 +334,9 @@ class Neo4JStorage(BaseGraphStorage):
                 node_dict = dict(node)
                 # Remove the 'base' label if present in a 'labels' property
                 if "labels" in node_dict:
-                    node_dict["labels"] = [label for label in node_dict["labels"] if label != "base"]
+                    node_dict["labels"] = [
+                        label for label in node_dict["labels"] if label != "base"
+                    ]
                 nodes[entity_id] = node_dict
             await result.consume()  # Make sure to consume the result fully
             return nodes
@@ -385,12 +387,12 @@ class Neo4JStorage(BaseGraphStorage):
     async def node_degrees_batch(self, node_ids: list[str]) -> dict[str, int]:
         """
         Retrieve the degree for multiple nodes in a single query using UNWIND.
-        
+
         Args:
             node_ids: List of node labels (entity_id values) to look up.
-        
+
         Returns:
-            A dictionary mapping each node_id to its degree (number of relationships). 
+            A dictionary mapping each node_id to its degree (number of relationships).
             If a node is not found, its degree will be set to 0.
         """
         async with self._driver.session(
@@ -407,13 +409,13 @@ class Neo4JStorage(BaseGraphStorage):
                 entity_id = record["entity_id"]
                 degrees[entity_id] = record["degree"]
             await result.consume()  # Ensure result is fully consumed
-            
+
             # For any node_id that did not return a record, set degree to 0.
             for nid in node_ids:
                 if nid not in degrees:
                     logger.warning(f"No node found with label '{nid}'")
                     degrees[nid] = 0
-            
+
             logger.debug(f"Neo4j batch node degree query returned: {degrees}")
             return degrees
 
@@ -436,25 +438,27 @@ class Neo4JStorage(BaseGraphStorage):
 
         degrees = int(src_degree) + int(trg_degree)
         return degrees
-    
-    async def edge_degrees_batch(self, edge_pairs: list[tuple[str, str]]) -> dict[tuple[str, str], int]:
+
+    async def edge_degrees_batch(
+        self, edge_pairs: list[tuple[str, str]]
+    ) -> dict[tuple[str, str], int]:
         """
         Calculate the combined degree for each edge (sum of the source and target node degrees)
         in batch using the already implemented node_degrees_batch.
-        
+
         Args:
             edge_pairs: List of (src, tgt) tuples.
-        
+
         Returns:
             A dictionary mapping each (src, tgt) tuple to the sum of their degrees.
         """
         # Collect unique node IDs from all edge pairs.
         unique_node_ids = {src for src, _ in edge_pairs}
         unique_node_ids.update({tgt for _, tgt in edge_pairs})
-        
+
         # Get degrees for all nodes in one go.
         degrees = await self.node_degrees_batch(list(unique_node_ids))
-        
+
         # Sum up degrees for each edge pair.
         edge_degrees = {}
         for src, tgt in edge_pairs:
@@ -547,13 +551,15 @@ class Neo4JStorage(BaseGraphStorage):
             )
             raise
 
-    async def get_edges_batch(self, pairs: list[dict[str, str]]) -> dict[tuple[str, str], dict]:
+    async def get_edges_batch(
+        self, pairs: list[dict[str, str]]
+    ) -> dict[tuple[str, str], dict]:
         """
         Retrieve edge properties for multiple (src, tgt) pairs in one query.
-        
+
         Args:
             pairs: List of dictionaries, e.g. [{"src": "node1", "tgt": "node2"}, ...]
-        
+
         Returns:
             A dictionary mapping (src, tgt) tuples to their edge properties.
         """
@@ -574,13 +580,23 @@ class Neo4JStorage(BaseGraphStorage):
                 if edges and len(edges) > 0:
                     edge_props = edges[0]  # choose the first if multiple exist
                     # Ensure required keys exist with defaults
-                    for key, default in {"weight": 0.0, "source_id": None, "description": None, "keywords": None}.items():
+                    for key, default in {
+                        "weight": 0.0,
+                        "source_id": None,
+                        "description": None,
+                        "keywords": None,
+                    }.items():
                         if key not in edge_props:
                             edge_props[key] = default
                     edges_dict[(src, tgt)] = edge_props
                 else:
                     # No edge found – set default edge properties
-                    edges_dict[(src, tgt)] = {"weight": 0.0, "source_id": None, "description": None, "keywords": None}
+                    edges_dict[(src, tgt)] = {
+                        "weight": 0.0,
+                        "source_id": None,
+                        "description": None,
+                        "keywords": None,
+                    }
             await result.consume()
             return edges_dict
 
@@ -644,17 +660,21 @@ class Neo4JStorage(BaseGraphStorage):
             logger.error(f"Error in get_node_edges for {source_node_id}: {str(e)}")
             raise
 
-    async def get_nodes_edges_batch(self, node_ids: list[str]) -> dict[str, list[tuple[str, str]]]:
+    async def get_nodes_edges_batch(
+        self, node_ids: list[str]
+    ) -> dict[str, list[tuple[str, str]]]:
         """
         Batch retrieve edges for multiple nodes in one query using UNWIND.
-        
+
         Args:
             node_ids: List of node IDs (entity_id) for which to retrieve edges.
-            
+
         Returns:
             A dictionary mapping each node ID to its list of edge tuples (source, target).
         """
-        async with self._driver.session(database=self._DATABASE, default_access_mode="READ") as session:
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
             query = """
                 UNWIND $node_ids AS id
                 MATCH (n:base {entity_id: id})

lightrag/kg/postgres_impl.py

@@ -1461,30 +1461,29 @@ class PGGraphStorage(BaseGraphStorage):
     async def get_nodes_batch(self, node_ids: list[str]) -> dict[str, dict]:
         """
         Retrieve multiple nodes in one query using UNWIND.
-        
+
         Args:
             node_ids: List of node entity IDs to fetch.
-            
+
         Returns:
             A dictionary mapping each node_id to its node data (or None if not found).
         """
         if not node_ids:
             return {}
-            
+
         # Format node IDs for the query
-        formatted_ids = ", ".join(['"' + node_id.replace('"', '') + '"' for node_id in node_ids])
-        
+        formatted_ids = ", ".join(
+            ['"' + node_id.replace('"', "") + '"' for node_id in node_ids]
+        )
+
         query = """SELECT * FROM cypher('%s', $$
                      UNWIND [%s] AS node_id
                      MATCH (n:base {entity_id: node_id})
                      RETURN node_id, n
-                   $$) AS (node_id text, n agtype)""" % (
-            self.graph_name, 
-            formatted_ids
-        )
-        
+                   $$) AS (node_id text, n agtype)""" % (self.graph_name, formatted_ids)
+
         results = await self._query(query)
-        
+
         # Build result dictionary
         nodes_dict = {}
         for result in results:
@@ -1492,28 +1491,32 @@ class PGGraphStorage(BaseGraphStorage):
                 node_dict = result["n"]["properties"]
                 # Remove the 'base' label if present in a 'labels' property
                 if "labels" in node_dict:
-                    node_dict["labels"] = [label for label in node_dict["labels"] if label != "base"]
+                    node_dict["labels"] = [
+                        label for label in node_dict["labels"] if label != "base"
+                    ]
                 nodes_dict[result["node_id"]] = node_dict
-                
+
         return nodes_dict
 
     async def node_degrees_batch(self, node_ids: list[str]) -> dict[str, int]:
         """
         Retrieve the degree for multiple nodes in a single query using UNWIND.
-        
+
         Args:
             node_ids: List of node labels (entity_id values) to look up.
-        
+
         Returns:
-            A dictionary mapping each node_id to its degree (number of relationships). 
+            A dictionary mapping each node_id to its degree (number of relationships).
             If a node is not found, its degree will be set to 0.
         """
         if not node_ids:
             return {}
-            
+
         # Format node IDs for the query
-        formatted_ids = ", ".join(['"' + node_id.replace('"', '') + '"' for node_id in node_ids])
-        
+        formatted_ids = ", ".join(
+            ['"' + node_id.replace('"', "") + '"' for node_id in node_ids]
+        )
+
         query = """SELECT * FROM cypher('%s', $$
                      UNWIND [%s] AS node_id
                      MATCH (n:base {entity_id: node_id})
@@ -1521,112 +1524,122 @@ class PGGraphStorage(BaseGraphStorage):
                      RETURN node_id, count(r) AS degree
                    $$) AS (node_id text, degree bigint)""" % (
             self.graph_name,
-            formatted_ids
+            formatted_ids,
         )
-        
+
         results = await self._query(query)
-        
+
         # Build result dictionary
         degrees_dict = {}
         for result in results:
             if result["node_id"] is not None:
                 degrees_dict[result["node_id"]] = int(result["degree"])
-                
+
         # Ensure all requested node_ids are in the result dictionary
         for node_id in node_ids:
             if node_id not in degrees_dict:
                 degrees_dict[node_id] = 0
-                
+
         return degrees_dict
-        
-    async def edge_degrees_batch(self, edges: list[tuple[str, str]]) -> dict[tuple[str, str], int]:
+
+    async def edge_degrees_batch(
+        self, edges: list[tuple[str, str]]
+    ) -> dict[tuple[str, str], int]:
         """
         Calculate the combined degree for each edge (sum of the source and target node degrees)
         in batch using the already implemented node_degrees_batch.
-        
+
         Args:
             edges: List of (source_node_id, target_node_id) tuples
-            
+
         Returns:
             Dictionary mapping edge tuples to their combined degrees
         """
         if not edges:
             return {}
-            
+
         # Use node_degrees_batch to get all node degrees efficiently
         all_nodes = set()
         for src, tgt in edges:
             all_nodes.add(src)
             all_nodes.add(tgt)
-            
+
         node_degrees = await self.node_degrees_batch(list(all_nodes))
-        
+
         # Calculate edge degrees
         edge_degrees_dict = {}
         for src, tgt in edges:
             src_degree = node_degrees.get(src, 0)
             tgt_degree = node_degrees.get(tgt, 0)
             edge_degrees_dict[(src, tgt)] = src_degree + tgt_degree
-            
+
         return edge_degrees_dict
-        
-    async def get_edges_batch(self, pairs: list[dict[str, str]]) -> dict[tuple[str, str], dict]:
+
+    async def get_edges_batch(
+        self, pairs: list[dict[str, str]]
+    ) -> dict[tuple[str, str], dict]:
         """
         Retrieve edge properties for multiple (src, tgt) pairs in one query.
-        
+
         Args:
             pairs: List of dictionaries, e.g. [{"src": "node1", "tgt": "node2"}, ...]
-        
+
         Returns:
             A dictionary mapping (src, tgt) tuples to their edge properties.
         """
         if not pairs:
             return {}
-            
+
         # 从字典列表中提取源节点和目标节点ID
         src_nodes = []
         tgt_nodes = []
         for pair in pairs:
-            src_nodes.append(pair["src"].replace('"', ''))
-            tgt_nodes.append(pair["tgt"].replace('"', ''))
-        
+            src_nodes.append(pair["src"].replace('"', ""))
+            tgt_nodes.append(pair["tgt"].replace('"', ""))
+
         # 构建查询，使用数组索引来匹配源节点和目标节点
         src_array = ", ".join([f'"{src}"' for src in src_nodes])
         tgt_array = ", ".join([f'"{tgt}"' for tgt in tgt_nodes])
-        
+
         query = f"""SELECT * FROM cypher('{self.graph_name}', $$
                      WITH [{src_array}] AS sources, [{tgt_array}] AS targets
                      UNWIND range(0, size(sources)-1) AS i
                      MATCH (a:base {{entity_id: sources[i]}})-[r:DIRECTED]-(b:base {{entity_id: targets[i]}})
                      RETURN sources[i] AS source, targets[i] AS target, properties(r) AS edge_properties
                    $$) AS (source text, target text, edge_properties agtype)"""
-        
+
         results = await self._query(query)
-        
+
         # 构建结果字典
         edges_dict = {}
         for result in results:
             if result["source"] and result["target"] and result["edge_properties"]:
-                edges_dict[(result["source"], result["target"])] = result["edge_properties"]
-                
+                edges_dict[(result["source"], result["target"])] = result[
+                    "edge_properties"
+                ]
+
         return edges_dict
-        
-    async def get_nodes_edges_batch(self, node_ids: list[str]) -> dict[str, list[tuple[str, str]]]:
+
+    async def get_nodes_edges_batch(
+        self, node_ids: list[str]
+    ) -> dict[str, list[tuple[str, str]]]:
         """
         Get all edges for multiple nodes in a single batch operation.
-        
+
         Args:
             node_ids: List of node IDs to get edges for
-            
+
         Returns:
             Dictionary mapping node IDs to lists of (source, target) edge tuples
         """
         if not node_ids:
             return {}
-            
+
         # Format node IDs for the query
-        formatted_ids = ", ".join(['"' + node_id.replace('"', '') + '"' for node_id in node_ids])
-        
+        formatted_ids = ", ".join(
+            ['"' + node_id.replace('"', "") + '"' for node_id in node_ids]
+        )
+
         query = """SELECT * FROM cypher('%s', $$
                      UNWIND [%s] AS node_id
                      MATCH (n:base {entity_id: node_id})
@@ -1634,11 +1647,11 @@ class PGGraphStorage(BaseGraphStorage):
                      RETURN node_id, connected.entity_id AS connected_id
                    $$) AS (node_id text, connected_id text)""" % (
             self.graph_name,
-            formatted_ids
+            formatted_ids,
         )
-        
+
         results = await self._query(query)
-        
+
         # Build result dictionary
         nodes_edges_dict = {node_id: [] for node_id in node_ids}
         for result in results:
@@ -1646,9 +1659,9 @@ class PGGraphStorage(BaseGraphStorage):
                 nodes_edges_dict[result["node_id"]].append(
                     (result["node_id"], result["connected_id"])
                 )
-                
+
         return nodes_edges_dict
-        
+
     async def get_all_labels(self) -> list[str]:
         """
         Get all labels (node IDs) in the graph.

lightrag/operate.py

@@ -1323,14 +1323,14 @@ async def _get_node_data(
 
     if not len(results):
         return "", "", ""
-    
+
     # Extract all entity IDs from your results list
     node_ids = [r["entity_name"] for r in results]
 
     # Call the batch node retrieval and degree functions concurrently.
     nodes_dict, degrees_dict = await asyncio.gather(
-        knowledge_graph_inst.get_nodes_batch(node_ids), 
-        knowledge_graph_inst.node_degrees_batch(node_ids)
+        knowledge_graph_inst.get_nodes_batch(node_ids),
+        knowledge_graph_inst.node_degrees_batch(node_ids),
     )
 
     # Now, if you need the node data and degree in order:
@@ -1459,7 +1459,7 @@ async def _find_most_related_text_unit_from_entities(
         for dp in node_datas
         if dp["source_id"] is not None
     ]
-    
+
     node_names = [dp["entity_name"] for dp in node_datas]
     batch_edges_dict = await knowledge_graph_inst.get_nodes_edges_batch(node_names)
     # Build the edges list in the same order as node_datas.
@@ -1472,10 +1472,14 @@ async def _find_most_related_text_unit_from_entities(
         all_one_hop_nodes.update([e[1] for e in this_edges])
 
     all_one_hop_nodes = list(all_one_hop_nodes)
-    
+
     # Batch retrieve one-hop node data using get_nodes_batch
-    all_one_hop_nodes_data_dict = await knowledge_graph_inst.get_nodes_batch(all_one_hop_nodes)
-    all_one_hop_nodes_data = [all_one_hop_nodes_data_dict.get(e) for e in all_one_hop_nodes]
+    all_one_hop_nodes_data_dict = await knowledge_graph_inst.get_nodes_batch(
+        all_one_hop_nodes
+    )
+    all_one_hop_nodes_data = [
+        all_one_hop_nodes_data_dict.get(e) for e in all_one_hop_nodes
+    ]
 
     # Add null check for node data
     all_one_hop_text_units_lookup = {
@@ -1571,13 +1575,13 @@ async def _find_most_related_edges_from_entities(
     edge_pairs_dicts = [{"src": e[0], "tgt": e[1]} for e in all_edges]
     # For edge degrees, use tuples.
     edge_pairs_tuples = list(all_edges)  # all_edges is already a list of tuples
-    
+
     # Call the batched functions concurrently.
     edge_data_dict, edge_degrees_dict = await asyncio.gather(
         knowledge_graph_inst.get_edges_batch(edge_pairs_dicts),
-        knowledge_graph_inst.edge_degrees_batch(edge_pairs_tuples)
+        knowledge_graph_inst.edge_degrees_batch(edge_pairs_tuples),
     )
-    
+
     # Reconstruct edge_datas list in the same order as the deduplicated results.
     all_edges_data = []
     for pair in all_edges:
@@ -1590,7 +1594,6 @@ async def _find_most_related_edges_from_entities(
             }
             all_edges_data.append(combined)
 
-
     all_edges_data = sorted(
         all_edges_data, key=lambda x: (x["rank"], x["weight"]), reverse=True
     )
@@ -1634,7 +1637,7 @@ async def _get_edge_data(
     # Call the batched functions concurrently.
     edge_data_dict, edge_degrees_dict = await asyncio.gather(
         knowledge_graph_inst.get_edges_batch(edge_pairs_dicts),
-        knowledge_graph_inst.edge_degrees_batch(edge_pairs_tuples)
+        knowledge_graph_inst.edge_degrees_batch(edge_pairs_tuples),
     )
 
     # Reconstruct edge_datas list in the same order as results.
@@ -1652,7 +1655,7 @@ async def _get_edge_data(
                 **edge_props,
             }
             edge_datas.append(combined)
-    
+
     edge_datas = sorted(
         edge_datas, key=lambda x: (x["rank"], x["weight"]), reverse=True
     )
@@ -1761,7 +1764,7 @@ async def _find_most_related_entities_from_relationships(
     # Batch approach: Retrieve nodes and their degrees concurrently with one query each.
     nodes_dict, degrees_dict = await asyncio.gather(
         knowledge_graph_inst.get_nodes_batch(entity_names),
-        knowledge_graph_inst.node_degrees_batch(entity_names)
+        knowledge_graph_inst.node_degrees_batch(entity_names),
     )
 
     # Rebuild the list in the same order as entity_names

lightrag_webui/src/stores/graph.ts

@@ -136,7 +136,7 @@ interface GraphState {
   // Version counter to trigger data refresh
   graphDataVersion: number
   incrementGraphDataVersion: () => void
-  
+
   // Methods for updating graph elements and UI state together
   updateNodeAndSelect: (nodeId: string, entityId: string, propertyName: string, newValue: string) => Promise<void>
   updateEdgeAndSelect: (edgeId: string, dynamicId: string, sourceId: string, targetId: string, propertyName: string, newValue: string) => Promise<void>
@@ -252,40 +252,40 @@ const useGraphStoreBase = create<GraphState>()((set, get) => ({
     // Get current state
     const state = get()
     const { sigmaGraph, rawGraph } = state
-    
+
     // Validate graph state
     if (!sigmaGraph || !rawGraph || !sigmaGraph.hasNode(nodeId)) {
       return
     }
-    
+
     try {
       const nodeAttributes = sigmaGraph.getNodeAttributes(nodeId)
-      
+
       console.log('updateNodeAndSelect', nodeId, entityId, propertyName, newValue)
-      
+
       // For entity_id changes (node renaming) with NetworkX graph storage
       if ((nodeId === entityId) && (propertyName === 'entity_id')) {
         // Create new node with updated ID but same attributes
         sigmaGraph.addNode(newValue, { ...nodeAttributes, label: newValue })
-        
+
         const edgesToUpdate: EdgeToUpdate[] = []
-        
+
         // Process all edges connected to this node
         sigmaGraph.forEachEdge(nodeId, (edge, attributes, source, target) => {
           const otherNode = source === nodeId ? target : source
           const isOutgoing = source === nodeId
-          
+
           // Get original edge dynamic ID for later reference
           const originalEdgeDynamicId = edge
           const edgeIndexInRawGraph = rawGraph.edgeDynamicIdMap[originalEdgeDynamicId]
-          
+
           // Create new edge with updated node reference
           const newEdgeId = sigmaGraph.addEdge(
             isOutgoing ? newValue : otherNode,
             isOutgoing ? otherNode : newValue,
             attributes
           )
-          
+
           // Track edges that need updating in the raw graph
           if (edgeIndexInRawGraph !== undefined) {
             edgesToUpdate.push({
@@ -294,14 +294,14 @@ const useGraphStoreBase = create<GraphState>()((set, get) => ({
               edgeIndex: edgeIndexInRawGraph
             })
           }
-          
+
           // Remove the old edge
           sigmaGraph.dropEdge(edge)
         })
-        
+
         // Remove the old node after all edges are processed
         sigmaGraph.dropNode(nodeId)
-        
+
         // Update node reference in raw graph data
         const nodeIndex = rawGraph.nodeIdMap[nodeId]
         if (nodeIndex !== undefined) {
@@ -311,7 +311,7 @@ const useGraphStoreBase = create<GraphState>()((set, get) => ({
           delete rawGraph.nodeIdMap[nodeId]
           rawGraph.nodeIdMap[newValue] = nodeIndex
         }
-        
+
         // Update all edge references in raw graph data
         edgesToUpdate.forEach(({ originalDynamicId, newEdgeId, edgeIndex }) => {
           if (rawGraph.edges[edgeIndex]) {
@@ -322,14 +322,14 @@ const useGraphStoreBase = create<GraphState>()((set, get) => ({
             if (rawGraph.edges[edgeIndex].target === nodeId) {
               rawGraph.edges[edgeIndex].target = newValue
             }
-            
+
             // Update dynamic ID mappings
             rawGraph.edges[edgeIndex].dynamicId = newEdgeId
             delete rawGraph.edgeDynamicIdMap[originalDynamicId]
             rawGraph.edgeDynamicIdMap[newEdgeId] = edgeIndex
           }
         })
-        
+
         // Update selected node in store
         set({ selectedNode: newValue, moveToSelectedNode: true })
       } else {
@@ -342,7 +342,7 @@ const useGraphStoreBase = create<GraphState>()((set, get) => ({
             sigmaGraph.setNodeAttribute(String(nodeId), 'label', newValue)
           }
         }
-        
+
         // Trigger a re-render by incrementing the version counter
         set((state) => ({ graphDataVersion: state.graphDataVersion + 1 }))
       }
@@ -351,17 +351,17 @@ const useGraphStoreBase = create<GraphState>()((set, get) => ({
       throw new Error('Failed to update node in graph')
     }
   },
-  
+
   updateEdgeAndSelect: async (edgeId: string, dynamicId: string, sourceId: string, targetId: string, propertyName: string, newValue: string) => {
     // Get current state
     const state = get()
     const { sigmaGraph, rawGraph } = state
-    
+
     // Validate graph state
     if (!sigmaGraph || !rawGraph) {
       return
     }
-    
+
     try {
       const edgeIndex = rawGraph.edgeIdMap[String(edgeId)]
       if (edgeIndex !== undefined && rawGraph.edges[edgeIndex]) {
@@ -370,10 +370,10 @@ const useGraphStoreBase = create<GraphState>()((set, get) => ({
           sigmaGraph.setEdgeAttribute(dynamicId, 'label', newValue)
         }
       }
-      
+
       // Trigger a re-render by incrementing the version counter
       set((state) => ({ graphDataVersion: state.graphDataVersion + 1 }))
-      
+
       // Update selected edge in store to ensure UI reflects changes
       set({ selectedEdge: dynamicId })
     } catch (error) {

lightrag_webui/src/utils/graphOperations.ts

@@ -3,7 +3,7 @@ import { useGraphStore } from '@/stores/graph'
 /**
  * Update node in the graph visualization
  * This function is now a wrapper around the store's updateNodeAndSelect method
- * 
+ *
  * @param nodeId - ID of the node to update
  * @param entityId - ID of the entity
  * @param propertyName - Name of the property being updated

tests/test_graph_storage.py

@@ -510,35 +510,66 @@ async def test_graph_batch_operations(storage):
         assert node1_id in nodes_dict, f"{node1_id} 应在返回结果中"
         assert node2_id in nodes_dict, f"{node2_id} 应在返回结果中"
         assert node3_id in nodes_dict, f"{node3_id} 应在返回结果中"
-        assert nodes_dict[node1_id]["description"] == node1_data["description"], f"{node1_id} 描述不匹配"
-        assert nodes_dict[node2_id]["description"] == node2_data["description"], f"{node2_id} 描述不匹配"
-        assert nodes_dict[node3_id]["description"] == node3_data["description"], f"{node3_id} 描述不匹配"
+        assert (
+            nodes_dict[node1_id]["description"] == node1_data["description"]
+        ), f"{node1_id} 描述不匹配"
+        assert (
+            nodes_dict[node2_id]["description"] == node2_data["description"]
+        ), f"{node2_id} 描述不匹配"
+        assert (
+            nodes_dict[node3_id]["description"] == node3_data["description"]
+        ), f"{node3_id} 描述不匹配"
 
         # 3. 测试 node_degrees_batch - 批量获取多个节点的度数
         print("== 测试 node_degrees_batch")
         node_degrees = await storage.node_degrees_batch(node_ids)
         print(f"批量获取节点度数结果: {node_degrees}")
-        assert len(node_degrees) == 3, f"应返回3个节点的度数，实际返回 {len(node_degrees)} 个"
+        assert (
+            len(node_degrees) == 3
+        ), f"应返回3个节点的度数，实际返回 {len(node_degrees)} 个"
         assert node1_id in node_degrees, f"{node1_id} 应在返回结果中"
         assert node2_id in node_degrees, f"{node2_id} 应在返回结果中"
         assert node3_id in node_degrees, f"{node3_id} 应在返回结果中"
-        assert node_degrees[node1_id] == 3, f"{node1_id} 度数应为3，实际为 {node_degrees[node1_id]}"
-        assert node_degrees[node2_id] == 2, f"{node2_id} 度数应为2，实际为 {node_degrees[node2_id]}"
-        assert node_degrees[node3_id] == 3, f"{node3_id} 度数应为3，实际为 {node_degrees[node3_id]}"
+        assert (
+            node_degrees[node1_id] == 3
+        ), f"{node1_id} 度数应为3，实际为 {node_degrees[node1_id]}"
+        assert (
+            node_degrees[node2_id] == 2
+        ), f"{node2_id} 度数应为2，实际为 {node_degrees[node2_id]}"
+        assert (
+            node_degrees[node3_id] == 3
+        ), f"{node3_id} 度数应为3，实际为 {node_degrees[node3_id]}"
 
         # 4. 测试 edge_degrees_batch - 批量获取多个边的度数
         print("== 测试 edge_degrees_batch")
         edges = [(node1_id, node2_id), (node2_id, node3_id), (node3_id, node4_id)]
         edge_degrees = await storage.edge_degrees_batch(edges)
         print(f"批量获取边度数结果: {edge_degrees}")
-        assert len(edge_degrees) == 3, f"应返回3条边的度数，实际返回 {len(edge_degrees)} 条"
-        assert (node1_id, node2_id) in edge_degrees, f"边 {node1_id} -> {node2_id} 应在返回结果中"
-        assert (node2_id, node3_id) in edge_degrees, f"边 {node2_id} -> {node3_id} 应在返回结果中"
-        assert (node3_id, node4_id) in edge_degrees, f"边 {node3_id} -> {node4_id} 应在返回结果中"
+        assert (
+            len(edge_degrees) == 3
+        ), f"应返回3条边的度数，实际返回 {len(edge_degrees)} 条"
+        assert (
+            node1_id,
+            node2_id,
+        ) in edge_degrees, f"边 {node1_id} -> {node2_id} 应在返回结果中"
+        assert (
+            node2_id,
+            node3_id,
+        ) in edge_degrees, f"边 {node2_id} -> {node3_id} 应在返回结果中"
+        assert (
+            node3_id,
+            node4_id,
+        ) in edge_degrees, f"边 {node3_id} -> {node4_id} 应在返回结果中"
         # 验证边的度数是否正确（源节点度数 + 目标节点度数）
-        assert edge_degrees[(node1_id, node2_id)] == 5, f"边 {node1_id} -> {node2_id} 度数应为5，实际为 {edge_degrees[(node1_id, node2_id)]}"
-        assert edge_degrees[(node2_id, node3_id)] == 5, f"边 {node2_id} -> {node3_id} 度数应为5，实际为 {edge_degrees[(node2_id, node3_id)]}"
-        assert edge_degrees[(node3_id, node4_id)] == 5, f"边 {node3_id} -> {node4_id} 度数应为5，实际为 {edge_degrees[(node3_id, node4_id)]}"
+        assert (
+            edge_degrees[(node1_id, node2_id)] == 5
+        ), f"边 {node1_id} -> {node2_id} 度数应为5，实际为 {edge_degrees[(node1_id, node2_id)]}"
+        assert (
+            edge_degrees[(node2_id, node3_id)] == 5
+        ), f"边 {node2_id} -> {node3_id} 度数应为5，实际为 {edge_degrees[(node2_id, node3_id)]}"
+        assert (
+            edge_degrees[(node3_id, node4_id)] == 5
+        ), f"边 {node3_id} -> {node4_id} 度数应为5，实际为 {edge_degrees[(node3_id, node4_id)]}"
 
         # 5. 测试 get_edges_batch - 批量获取多个边的属性
         print("== 测试 get_edges_batch")
@@ -547,28 +578,54 @@ async def test_graph_batch_operations(storage):
         edges_dict = await storage.get_edges_batch(edge_dicts)
         print(f"批量获取边属性结果: {edges_dict.keys()}")
         assert len(edges_dict) == 3, f"应返回3条边的属性，实际返回 {len(edges_dict)} 条"
-        assert (node1_id, node2_id) in edges_dict, f"边 {node1_id} -> {node2_id} 应在返回结果中"
-        assert (node2_id, node3_id) in edges_dict, f"边 {node2_id} -> {node3_id} 应在返回结果中"
-        assert (node3_id, node4_id) in edges_dict, f"边 {node3_id} -> {node4_id} 应在返回结果中"
-        assert edges_dict[(node1_id, node2_id)]["relationship"] == edge1_data["relationship"], f"边 {node1_id} -> {node2_id} 关系不匹配"
-        assert edges_dict[(node2_id, node3_id)]["relationship"] == edge2_data["relationship"], f"边 {node2_id} -> {node3_id} 关系不匹配"
-        assert edges_dict[(node3_id, node4_id)]["relationship"] == edge5_data["relationship"], f"边 {node3_id} -> {node4_id} 关系不匹配"
+        assert (
+            node1_id,
+            node2_id,
+        ) in edges_dict, f"边 {node1_id} -> {node2_id} 应在返回结果中"
+        assert (
+            node2_id,
+            node3_id,
+        ) in edges_dict, f"边 {node2_id} -> {node3_id} 应在返回结果中"
+        assert (
+            node3_id,
+            node4_id,
+        ) in edges_dict, f"边 {node3_id} -> {node4_id} 应在返回结果中"
+        assert (
+            edges_dict[(node1_id, node2_id)]["relationship"]
+            == edge1_data["relationship"]
+        ), f"边 {node1_id} -> {node2_id} 关系不匹配"
+        assert (
+            edges_dict[(node2_id, node3_id)]["relationship"]
+            == edge2_data["relationship"]
+        ), f"边 {node2_id} -> {node3_id} 关系不匹配"
+        assert (
+            edges_dict[(node3_id, node4_id)]["relationship"]
+            == edge5_data["relationship"]
+        ), f"边 {node3_id} -> {node4_id} 关系不匹配"
 
         # 6. 测试 get_nodes_edges_batch - 批量获取多个节点的所有边
         print("== 测试 get_nodes_edges_batch")
         nodes_edges = await storage.get_nodes_edges_batch([node1_id, node3_id])
         print(f"批量获取节点边结果: {nodes_edges.keys()}")
-        assert len(nodes_edges) == 2, f"应返回2个节点的边，实际返回 {len(nodes_edges)} 个"
+        assert (
+            len(nodes_edges) == 2
+        ), f"应返回2个节点的边，实际返回 {len(nodes_edges)} 个"
         assert node1_id in nodes_edges, f"{node1_id} 应在返回结果中"
         assert node3_id in nodes_edges, f"{node3_id} 应在返回结果中"
-        assert len(nodes_edges[node1_id]) == 3, f"{node1_id} 应有3条边，实际有 {len(nodes_edges[node1_id])} 条"
-        assert len(nodes_edges[node3_id]) == 3, f"{node3_id} 应有3条边，实际有 {len(nodes_edges[node3_id])} 条"
+        assert (
+            len(nodes_edges[node1_id]) == 3
+        ), f"{node1_id} 应有3条边，实际有 {len(nodes_edges[node1_id])} 条"
+        assert (
+            len(nodes_edges[node3_id]) == 3
+        ), f"{node3_id} 应有3条边，实际有 {len(nodes_edges[node3_id])} 条"
 
         # 7. 清理数据
         print("== 测试 drop")
         result = await storage.drop()
         print(f"清理结果: {result}")
-        assert result["status"] == "success", f"清理应成功，实际状态为 {result['status']}"
+        assert (
+            result["status"] == "success"
+        ), f"清理应成功，实际状态为 {result['status']}"
 
         print("\n批量操作测试完成")
         return True
@@ -630,7 +687,7 @@ async def main():
             if basic_result:
                 ASCIIColors.cyan("\n=== 开始高级测试 ===")
                 advanced_result = await test_graph_advanced(storage)
-                
+
                 if advanced_result:
                     ASCIIColors.cyan("\n=== 开始批量操作测试 ===")
                     await test_graph_batch_operations(storage)