diff --git a/api/core/rag/retrieval/dataset_retrieval.py b/api/core/rag/retrieval/dataset_retrieval.py
index 2fe46197f7..d48da0f941 100644
--- a/api/core/rag/retrieval/dataset_retrieval.py
+++ b/api/core/rag/retrieval/dataset_retrieval.py
@@ -900,7 +900,10 @@ class DatasetRetrieval:
             return str(inputs.get(key, f"{{{{{key}}}}}"))
 
         pattern = re.compile(r"\{\{(\w+)\}\}")
-        return pattern.sub(replacer, text)
+        output = pattern.sub(replacer, text)
+        if isinstance(output, str):
+            output = re.sub(r"[\r\n\t]+", " ", output).strip()
+        return output
 
     def _automatic_metadata_filter_func(
         self, dataset_ids: list, query: str, tenant_id: str, user_id: str, metadata_model_config: ModelConfig
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 5643e37ce0..bb825e7d42 100644
--- a/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
+++ b/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
@@ -1,5 +1,6 @@
 import json
 import logging
+import re
 import time
 from collections import defaultdict
 from collections.abc import Mapping, Sequence
@@ -360,8 +361,13 @@ class KnowledgeRetrievalNode(LLMNode):
                             if isinstance(expected_value, str):
                                 expected_value = self.graph_runtime_state.variable_pool.convert_template(
                                     expected_value
-                                ).text
-
+                                ).value[0]
+                                if expected_value.value_type == "number":
+                                    expected_value = expected_value.value
+                                elif expected_value.value_type == "string":
+                                    expected_value = re.sub(r"[\r\n\t]+", " ", expected_value.text).strip()
+                                else:
+                                    raise ValueError("Invalid expected metadata value type")
                             filters = self._process_metadata_filter_func(
                                 condition.comparison_operator, metadata_name, expected_value, filters
                             )