refactor: 优化知识库搜索逻辑，移除冗余代码

src/tools/tool_can_use/lpmm_get_knowledge.py

@@ -38,125 +38,22 @@ class SearchKnowledgeFromLPMMTool(BaseTool):
             # threshold = function_args.get("threshold", 0.4)
 
             # 调用知识库搜索
-            embedding = await get_embedding(query, request_type="info_retrieval")
-            if embedding:
+
             knowledge_info = qa_manager.get_knowledge(query)
+            
             logger.debug(f"知识库查询结果: {knowledge_info}")
+            
             if knowledge_info:
                 content = f"你知道这些知识: {knowledge_info}"
             else:
                 content = f"你不太了解有关{query}的知识"
             return {"type": "lpmm_knowledge", "id": query, "content": content}
-            # 如果获取嵌入失败
-            return {"type": "info", "id": query, "content": f"无法获取关于'{query}'的嵌入向量，你lpmm知识库炸了"}
         except Exception as e:
+            # 捕获异常并记录错误
             logger.error(f"知识库搜索工具执行失败: {str(e)}")
             # 在其他异常情况下，确保 id 仍然是 query (如果它被定义了)
             query_id = query if "query" in locals() else "unknown_query"
             return {"type": "info", "id": query_id, "content": f"lpmm知识库搜索失败，炸了: {str(e)}"}
 
-    # def get_info_from_db(
-    #     self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
-    # ) -> Union[str, list]:
-    #     """从数据库中获取相关信息
-
-    #     Args:
-    #         query_embedding: 查询的嵌入向量
-    #         limit: 最大返回结果数
-    #         threshold: 相似度阈值
-    #         return_raw: 是否返回原始结果
-
-    #     Returns:
-    #         Union[str, list]: 格式化的信息字符串或原始结果列表
-    #     """
-    #     if not query_embedding:
-    #         return "" if not return_raw else []
-
-    #     # 使用余弦相似度计算
-    #     pipeline = [
-    #         {
-    #             "$addFields": {
-    #                 "dotProduct": {
-    #                     "$reduce": {
-    #                         "input": {"$range": [0, {"$size": "$embedding"}]},
-    #                         "initialValue": 0,
-    #                         "in": {
-    #                             "$add": [
-    #                                 "$$value",
-    #                                 {
-    #                                     "$multiply": [
-    #                                         {"$arrayElemAt": ["$embedding", "$$this"]},
-    #                                         {"$arrayElemAt": [query_embedding, "$$this"]},
-    #                                     ]
-    #                                 },
-    #                             ]
-    #                         },
-    #                     }
-    #                 },
-    #                 "magnitude1": {
-    #                     "$sqrt": {
-    #                         "$reduce": {
-    #                             "input": "$embedding",
-    #                             "initialValue": 0,
-    #                             "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
-    #                         }
-    #                     }
-    #                 },
-    #                 "magnitude2": {
-    #                     "$sqrt": {
-    #                         "$reduce": {
-    #                             "input": query_embedding,
-    #                             "initialValue": 0,
-    #                             "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
-    #                         }
-    #                     }
-    #                 },
-    #             }
-    #         },
-    #         {"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
-    #         {
-    #             "$match": {
-    #                 "similarity": {"$gte": threshold}  # 只保留相似度大于等于阈值的结果
-    #             }
-    #         },
-    #         {"$sort": {"similarity": -1}},
-    #         {"$limit": limit},
-    #         {"$project": {"content": 1, "similarity": 1}},
-    #     ]
-
-    #     results = list(db.knowledges.aggregate(pipeline))
-    #     logger.debug(f"知识库查询结果数量: {len(results)}")
-
-    #     if not results:
-    #         return "" if not return_raw else []
-
-    #     if return_raw:
-    #         return results
-    #     else:
-    #         # 返回所有找到的内容，用换行分隔
-    #         return "\n".join(str(result["content"]) for result in results)
-
-    def _format_results(self, results: list) -> str:
-        """格式化结果"""
-        if not results:
-            return "未找到相关知识。"
-
-        formatted_string = "我找到了一些相关知识：\n"
-        for i, result in enumerate(results):
-            # chunk_id = result.get("chunk_id")
-            text = result.get("text", "")
-            source = result.get("source", "未知来源")
-            source_type = result.get("source_type", "未知类型")
-            similarity = result.get("similarity", 0.0)
-
-            formatted_string += (
-                f"{i + 1}. (相似度: {similarity:.2f}) 类型: {source_type}, 来源: {source} \n内容片段: {text}\n\n"
-            )
-            # 暂时去掉chunk_id
-            # formatted_string += f"{i + 1}. (相似度: {similarity:.2f}) 类型: {source_type}, 来源: {source}, Chunk ID: {chunk_id} \n内容片段: {text}\n\n"
-
-        return formatted_string
 
 
-# 注册工具
-# register_tool(SearchKnowledgeTool)