From 5780765cb738552ac1d5381a83f1074fb7966ee6 Mon Sep 17 00:00:00 2001
From: 2829798842 <2829798842@qq.com>
Date: Wed, 28 May 2025 19:15:09 +0800
Subject: [PATCH] Update get_knowledge.py

---
 src/tools/tool_can_use/get_knowledge.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/tools/tool_can_use/get_knowledge.py b/src/tools/tool_can_use/get_knowledge.py
index fd37f11e..df73b286 100644
--- a/src/tools/tool_can_use/get_knowledge.py
+++ b/src/tools/tool_can_use/get_knowledge.py
@@ -13,12 +13,12 @@ class SearchKnowledgeTool(BaseTool):
     """从知识库中搜索相关信息的工具"""
 
     name = "search_knowledge"
-    description = "使用工具从知识库中搜索相关信息"
+    description = "Use tool to search relevant information from the knowledge base"
     parameters = {
         "type": "object",
         "properties": {
-            "query": {"type": "string", "description": "搜索查询关键词"},
-            "threshold": {"type": "number", "description": "相似度阈值，0.0到1.0之间"},
+            "query": {"type": "string", "description": "Search query keywords"},
+            "threshold": {"type": "number", "description": "Similarity threshold, between 0.0 and 1.0"},
         },
         "required": ["query"],
     }
@@ -42,14 +42,14 @@ class SearchKnowledgeTool(BaseTool):
             if embedding:
                 knowledge_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
                 if knowledge_info:
-                    content = f"你知道这些知识: {knowledge_info}"
+                    content = f"You know this knowledge: {knowledge_info}"
                 else:
-                    content = f"你不太了解有关{query}的知识"
+                    content = f"You don't know much about {query}"
                 return {"type": "knowledge", "id": query, "content": content}
-            return {"type": "info", "id": query, "content": f"无法获取关于'{query}'的嵌入向量，你知识库炸了"}
+            return {"type": "info", "id": query, "content": f"Unable to get embedding vector for '{query}', knowledge base failed"}
         except Exception as e:
-            logger.error(f"知识库搜索工具执行失败: {str(e)}")
-            return {"type": "info", "id": query, "content": f"知识库搜索失败，炸了: {str(e)}"}
+            logger.error(f"Knowledge base search tool execution failed: {str(e)}")
+            return {"type": "info", "id": query, "content": f"Knowledge base search failed: {str(e)}"}
 
     @staticmethod
     def _cosine_similarity(vec1: List[float], vec2: List[float]) -> float: