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 d352f28..eded1b7 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 504c313..78f49c3 100644
Binary files a/data/chroma/kb_2/chroma.sqlite3 and b/data/chroma/kb_2/chroma.sqlite3 differ
diff --git a/th_agenter/services/workflow_engine.py b/th_agenter/services/workflow_engine.py
index 5ad3b66..337d346 100644
--- a/th_agenter/services/workflow_engine.py
+++ b/th_agenter/services/workflow_engine.py
@@ -1030,14 +1030,35 @@ class WorkflowEngine:
             # 如果还是没有，尝试从 workflow_input 中获取
             if not query:
                 workflow_input = input_data.get('workflow_input', {})
+                # 首先尝试获取 'query' 字段
                 query = workflow_input.get('query', '')
+                # 如果没有 'query' 字段，尝试获取第一个非空的字符串值作为查询文本
+                if not query and isinstance(workflow_input, dict):
+                    for key, value in workflow_input.items():
+                        if isinstance(value, str) and value.strip():
+                            query = value.strip()
+                            logger.info(f"从工作流输入的 '{key}' 字段获取查询文本: {query}")
+                            break
             # 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', '')
+                        # 首先尝试从根级别获取
+                        query = output.get('query', '')
+                        if not query:
+                            # 尝试从 data 字段中获取
+                            data = output.get('data', {})
+                            if isinstance(data, dict):
+                                query = data.get('query', '')
+                                # 如果 data 中没有 query，尝试获取第一个非空字符串值
+                                if not query:
+                                    for key, value in data.items():
+                                        if isinstance(value, str) and value.strip():
+                                            query = value.strip()
+                                            logger.info(f"从节点 {node_id} 输出的 data.{key} 字段获取查询文本: {query}")
+                                            break
                         if query:
                             break
             # 如果还是没有，使用默认查询或抛出错误
@@ -1057,30 +1078,47 @@ class WorkflowEngine:
         
         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
+            # 注意：get_document_processor 需要 session 来初始化嵌入模型
             from ..services.document_processor import get_document_processor
-            document_processor = await get_document_processor(None)
+            # 传入 self.session 以便初始化嵌入模型（虽然类型不匹配，但 get_document_processor 会处理）
+            document_processor = await get_document_processor(self.session)
             results = document_processor.search_similar_documents(
                 knowledge_base_id=knowledge_base_id,
                 query=query,
                 k=top_k
             )
             
+            logger.info(f"知识库 {knowledge_base_id} 搜索查询 '{query}' 返回 {len(results)} 个原始结果")
+            
             # 过滤相似度阈值
             filtered_results = []
+            all_results = []
             for result in results:
                 score = result.get('normalized_score', result.get('similarity_score', 0))
+                all_results.append({
+                    **result,
+                    'score': score
+                })
                 if score >= similarity_threshold:
                     filtered_results.append(result)
             
+            logger.info(f"应用相似度阈值 {similarity_threshold} 后，剩余 {len(filtered_results)} 个结果")
+            
+            # 如果过滤后结果为空，但原始结果不为空，返回所有结果并添加警告
+            if not filtered_results and results:
+                logger.warning(f"所有搜索结果都被相似度阈值 {similarity_threshold} 过滤，返回前 {min(len(results), top_k)} 个结果")
+                filtered_results = results[:top_k]
+            
             return {
                 'success': True,
                 'query': query,
                 'knowledge_base_id': knowledge_base_id,
                 'results': filtered_results,
                 'total_results': len(filtered_results),
+                'raw_results_count': len(results),
                 'top_k': top_k,
-                'similarity_threshold': similarity_threshold
+                'similarity_threshold': similarity_threshold,
+                'all_results_scores': [r.get('score', 0) for r in all_results[:5]] if all_results else []
             }
             
         except Exception as e: