From 72328b890cc489120d3af0c2b2eec61401f10741 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=A2=A8=E6=A2=93=E6=9F=92?= <1787882683@qq.com> Date: Thu, 29 May 2025 09:41:31 +0800 Subject: [PATCH] =?UTF-8?q?refactor:=20=E4=BC=98=E5=8C=96=E7=9F=A5?= =?UTF-8?q?=E8=AF=86=E5=BA=93=E6=90=9C=E7=B4=A2=E9=80=BB=E8=BE=91=EF=BC=8C?= =?UTF-8?q?=E7=A7=BB=E9=99=A4=E5=86=97=E4=BD=99=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/tools/tool_can_use/lpmm_get_knowledge.py | 125 ++----------------- 1 file changed, 11 insertions(+), 114 deletions(-) diff --git a/src/tools/tool_can_use/lpmm_get_knowledge.py b/src/tools/tool_can_use/lpmm_get_knowledge.py index f7c0bd753..f8b9e95dc 100644 --- a/src/tools/tool_can_use/lpmm_get_knowledge.py +++ b/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"} + + 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} 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)