diff --git a/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py b/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
index 1ccdbf971c..a501113dc3 100644
--- a/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
+++ b/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
@@ -33,10 +33,10 @@ default_retrieval_model = {
     'score_threshold_enabled': False
 }
 
-class KnowledgeRetrievalNode(BaseNode):
 
+class KnowledgeRetrievalNode(BaseNode):
     _node_data_cls = KnowledgeRetrievalNodeData
-    _node_type = NodeType.TOOL
+    _node_type = NodeType.KNOWLEDGE_RETRIEVAL
 
     def _run(self, variable_pool: VariablePool) -> NodeRunResult:
         node_data: KnowledgeRetrievalNodeData = cast(self._node_data_cls, self.node_data)
@@ -67,7 +67,9 @@ class KnowledgeRetrievalNode(BaseNode):
                 inputs=variables,
                 error=str(e)
             )
-    def _fetch_dataset_retriever(self, node_data: KnowledgeRetrievalNodeData, variables: dict[str, Any]) -> list[dict[str, Any]]:
+
+    def _fetch_dataset_retriever(self, node_data: KnowledgeRetrievalNodeData, variables: dict[str, Any]) -> list[
+        dict[str, Any]]:
         """
         A dataset tool is a tool that can be used to retrieve information from a dataset
         :param node_data: node data
@@ -224,14 +226,14 @@ class KnowledgeRetrievalNode(BaseNode):
                 if score_threshold_enabled:
                     score_threshold = retrieval_model_config.get("score_threshold")
 
-                results = RetrievalService.retrieve(retrival_method=retrival_method, dataset_id=dataset.id, query=variables['#query#'],
-                                                top_k=top_k, score_threshold=score_threshold,
-                                                reranking_model=reranking_model)
+                results = RetrievalService.retrieve(retrival_method=retrival_method, dataset_id=dataset.id,
+                                                    query=variables['#query#'],
+                                                    top_k=top_k, score_threshold=score_threshold,
+                                                    reranking_model=reranking_model)
                 return results
 
-
-
-    def _fetch_model_config(self, node_data: KnowledgeRetrievalNodeData) -> tuple[ModelInstance, ModelConfigWithCredentialsEntity]:
+    def _fetch_model_config(self, node_data: KnowledgeRetrievalNodeData) -> tuple[
+        ModelInstance, ModelConfigWithCredentialsEntity]:
         """
         Fetch model config
         :param node_data: node data
@@ -333,7 +335,7 @@ class KnowledgeRetrievalNode(BaseNode):
 
         return all_documents
 
-    def _retriever(self, flask_app: Flask, dataset_id: str, query: str,  top_k: int, all_documents: list):
+    def _retriever(self, flask_app: Flask, dataset_id: str, query: str, top_k: int, all_documents: list):
         with flask_app.app_context():
             dataset = db.session.query(Dataset).filter(
                 Dataset.tenant_id == self.tenant_id,
@@ -368,4 +370,4 @@ class KnowledgeRetrievalNode(BaseNode):
                                                           if retrieval_model['reranking_enable'] else None
                                                           )
 
-                    all_documents.extend(documents)
\ No newline at end of file
+                    all_documents.extend(documents)