diff --git a/data/chroma/kb_2/714cba61-f48b-47c5-bcb8-eed8ffd400b0/data_level0.bin b/data/chroma/kb_2/714cba61-f48b-47c5-bcb8-eed8ffd400b0/data_level0.bin
index 69e7080..d352f28 100644
Binary files a/data/chroma/kb_2/714cba61-f48b-47c5-bcb8-eed8ffd400b0/data_level0.bin and b/data/chroma/kb_2/714cba61-f48b-47c5-bcb8-eed8ffd400b0/data_level0.bin differ
diff --git a/data/chroma/kb_2/chroma.sqlite3 b/data/chroma/kb_2/chroma.sqlite3
index c4673cd..504c313 100644
Binary files a/data/chroma/kb_2/chroma.sqlite3 and b/data/chroma/kb_2/chroma.sqlite3 differ
diff --git a/th_agenter/models/workflow.py b/th_agenter/models/workflow.py
index 1df374d..bec2add 100644
--- a/th_agenter/models/workflow.py
+++ b/th_agenter/models/workflow.py
@@ -23,6 +23,7 @@ class NodeType(enum.Enum):
     CODE = "code"            # 代码执行节点
     HTTP = "http"            # HTTP请求节点
     TOOL = "tool"            # 工具节点
+    KNOWLEDGE_BASE = "knowledge-base"  # 知识库节点
 
 class ExecutionStatus(enum.Enum):
     """执行状态枚举"""
diff --git a/th_agenter/services/workflow_engine.py b/th_agenter/services/workflow_engine.py
index d958a4f..5fddb48 100644
--- a/th_agenter/services/workflow_engine.py
+++ b/th_agenter/services/workflow_engine.py
@@ -458,6 +458,8 @@ class WorkflowEngine:
                 output_data = await self._execute_code_node(node, input_data)
             elif node_type == 'http':
                 output_data = await self._execute_http_node(node, input_data)
+            elif node_type == 'knowledge-base':
+                output_data = await self._execute_knowledge_base_node(node, input_data)
             else:
                 raise ValueError(f"不支持的节点类型: {node_type}")
             
@@ -663,14 +665,22 @@ class WorkflowEngine:
                     model_id = model_value
                 else:
                     # 如果是字符串，按名称查询
-                    llm_config = self.session.query(LLMConfig).filter(LLMConfig.model_name == model_value).first()
+                    from sqlalchemy import select
+                    result = await self.session.execute(
+                        select(LLMConfig).where(LLMConfig.model_name == model_value)
+                    )
+                    llm_config = result.scalar_one_or_none()
                     if llm_config:
                         model_id = llm_config.id
         
         if not model_id:
             raise ValueError("未指定有效的大模型配置")
         
-        llm_config = self.session.query(LLMConfig).filter(LLMConfig.id == model_id).first()
+        from sqlalchemy import select
+        result = await self.session.execute(
+            select(LLMConfig).where(LLMConfig.id == model_id)
+        )
+        llm_config = result.scalar_one_or_none()
         if not llm_config:
             raise ValueError(f"大模型配置 {model_id} 不存在")
         
@@ -935,6 +945,84 @@ class WorkflowEngine:
         except Exception as e:
             logger.error(f"HTTP请求失败: {str(e)}")
             raise ValueError(f"HTTP请求失败: {str(e)}")
+    
+    async def _execute_knowledge_base_node(self, node: Dict[str, Any], input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """执行知识库节点"""
+        from ..services.document import DocumentService
+        
+        config = input_data['node_config']
+        # 支持多种字段名：knowledge_base_id, knowledgeBase, kb_id
+        knowledge_base_id = config.get('knowledge_base_id') or config.get('knowledgeBase') or config.get('kb_id')
+        query = config.get('query', '')
+        top_k = config.get('top_k', config.get('topK', 5))
+        similarity_threshold = config.get('similarity_threshold', config.get('similarityThreshold', 0.7))
+        
+        if not knowledge_base_id:
+            raise ValueError("知识库节点配置缺少 knowledge_base_id (或 knowledgeBase/kb_id)")
+        
+        if not query:
+            # 尝试从 resolved_inputs 中获取查询文本
+            resolved_inputs = input_data.get('resolved_inputs', {})
+            query = resolved_inputs.get('query', '')
+            # 如果还是没有，尝试从 workflow_input 中获取
+            if not query:
+                workflow_input = input_data.get('workflow_input', {})
+                query = workflow_input.get('query', '')
+            # 如果还是没有，尝试从 previous_outputs 中获取（可能是上一个节点的输出）
+            if not query:
+                previous_outputs = input_data.get('previous_outputs', {})
+                # 尝试从上一个节点的输出中获取查询文本
+                for node_id, output in previous_outputs.items():
+                    if isinstance(output, dict):
+                        query = output.get('query') or output.get('data', {}).get('query', '')
+                        if query:
+                            break
+            # 如果还是没有，使用默认查询或抛出错误
+            if not query:
+                # 如果没有查询文本，可以返回空结果而不是抛出错误
+                logger.warning(f"知识库节点缺少查询文本，将返回空结果")
+                return {
+                    'success': True,
+                    'query': '',
+                    'knowledge_base_id': knowledge_base_id,
+                    'results': [],
+                    'total_results': 0,
+                    'top_k': top_k,
+                    'similarity_threshold': similarity_threshold,
+                    'warning': '缺少查询文本，返回空结果'
+                }
+        
+        try:
+            # 直接使用 document_processor 进行搜索
+            # 注意：get_document_processor 期望同步 Session，但这里传入 None 以避免类型不匹配
+            from ..services.document_processor import get_document_processor
+            document_processor = await get_document_processor(None)
+            results = document_processor.search_similar_documents(
+                knowledge_base_id=knowledge_base_id,
+                query=query,
+                k=top_k
+            )
+            
+            # 过滤相似度阈值
+            filtered_results = []
+            for result in results:
+                score = result.get('normalized_score', result.get('similarity_score', 0))
+                if score >= similarity_threshold:
+                    filtered_results.append(result)
+            
+            return {
+                'success': True,
+                'query': query,
+                'knowledge_base_id': knowledge_base_id,
+                'results': filtered_results,
+                'total_results': len(filtered_results),
+                'top_k': top_k,
+                'similarity_threshold': similarity_threshold
+            }
+            
+        except Exception as e:
+            logger.error(f"知识库搜索失败: {str(e)}")
+            raise ValueError(f"知识库搜索失败: {str(e)}")
 
 
 # 工作流引擎实例