12b03ecb8d
- 将“get_module_logger”替换为新模块“logger_manager”中的“get_logger”，以实现一致的日志设置。 - 移除了单独的日志配置设置，转而采用集中式日志管理。 - 更新了多个文件中的日志初始化方法，包括“config.py”、“change_mood.py”、“change_relationship.py”等，以简化日志记录实践。 - 引入“logger_manager.py”，用于根据模块特定的样式处理日志配置。
137 lines
5.1 KiB
Python
137 lines
5.1 KiB
Python
from src.do_tool.tool_can_use.base_tool import BaseTool
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from src.plugins.chat.utils import get_embedding
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from src.common.database import db
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from src.common.logger_manager import get_logger
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from typing import Dict, Any, Union
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logger = get_logger("get_knowledge_tool")
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class SearchKnowledgeTool(BaseTool):
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"""从知识库中搜索相关信息的工具"""
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name = "search_knowledge"
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description = "从知识库中搜索相关信息"
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parameters = {
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"type": "object",
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"properties": {
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"query": {"type": "string", "description": "搜索查询关键词"},
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"threshold": {"type": "number", "description": "相似度阈值，0.0到1.0之间"},
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},
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"required": ["query"],
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}
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async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]:
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"""执行知识库搜索
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Args:
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function_args: 工具参数
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message_txt: 原始消息文本
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Returns:
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Dict: 工具执行结果
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"""
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try:
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query = function_args.get("query")
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threshold = function_args.get("threshold", 0.4)
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# 调用知识库搜索
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embedding = await get_embedding(query, request_type="info_retrieval")
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if embedding:
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knowledge_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
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if knowledge_info:
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content = f"你知道这些知识: {knowledge_info}"
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else:
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content = f"你不太了解有关{query}的知识"
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return {"name": "search_knowledge", "content": content}
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return {"name": "search_knowledge", "content": f"无法获取关于'{query}'的嵌入向量"}
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except Exception as e:
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logger.error(f"知识库搜索工具执行失败: {str(e)}")
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return {"name": "search_knowledge", "content": f"知识库搜索失败: {str(e)}"}
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@staticmethod
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def get_info_from_db(
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query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
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) -> Union[str, list]:
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"""从数据库中获取相关信息
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Args:
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query_embedding: 查询的嵌入向量
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limit: 最大返回结果数
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threshold: 相似度阈值
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return_raw: 是否返回原始结果
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Returns:
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Union[str, list]: 格式化的信息字符串或原始结果列表
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"""
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if not query_embedding:
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return "" if not return_raw else []
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# 使用余弦相似度计算
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pipeline = [
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{
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"$addFields": {
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"dotProduct": {
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"$reduce": {
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"input": {"$range": [0, {"$size": "$embedding"}]},
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"initialValue": 0,
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"in": {
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"$add": [
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"$$value",
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{
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"$multiply": [
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{"$arrayElemAt": ["$embedding", "$$this"]},
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{"$arrayElemAt": [query_embedding, "$$this"]},
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]
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},
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]
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},
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}
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},
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"magnitude1": {
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"$sqrt": {
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"$reduce": {
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"input": "$embedding",
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"initialValue": 0,
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"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
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}
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}
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},
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"magnitude2": {
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"$sqrt": {
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"$reduce": {
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"input": query_embedding,
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"initialValue": 0,
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"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
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}
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}
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},
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}
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},
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{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
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{
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"$match": {
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"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
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}
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},
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{"$sort": {"similarity": -1}},
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{"$limit": limit},
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{"$project": {"content": 1, "similarity": 1}},
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]
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results = list(db.knowledges.aggregate(pipeline))
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logger.debug(f"知识库查询结果数量: {len(results)}")
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if not results:
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return "" if not return_raw else []
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if return_raw:
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return results
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else:
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# 返回所有找到的内容，用换行分隔
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return "\n".join(str(result["content"]) for result in results)
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# 注册工具
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# register_tool(SearchKnowledgeTool)
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