better：心流升级，大大减少了复读情况，并且灵活调用工具来实现知识和记忆检索
This commit is contained in:
SengokuCola
@@ -68,20 +68,24 @@ class ChattingObservation(Observation):
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self.translate_message_list_to_str()
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# 更新观察次数
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self.observe_times += 1
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# self.observe_times += 1
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self.last_observe_time = new_messages[-1]["time"]
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# 检查是否需要更新summary
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current_time = int(datetime.now().timestamp())
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if current_time - self.last_summary_time >= 30: # 如果超过30秒，重置计数
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self.summary_count = 0
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self.last_summary_time = current_time
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# current_time = int(datetime.now().timestamp())
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# if current_time - self.last_summary_time >= 30: # 如果超过30秒，重置计数
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# self.summary_count = 0
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# self.last_summary_time = current_time
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if self.summary_count < self.max_update_in_30s: # 如果30秒内更新次数小于2次
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await self.update_talking_summary(new_messages_str)
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self.summary_count += 1
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# if self.summary_count < self.max_update_in_30s: # 如果30秒内更新次数小于2次
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# await self.update_talking_summary(new_messages_str)
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# print(f"更新聊天总结：{self.observe_info}11111111111111")
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# self.summary_count += 1
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updated_observe_info = await self.update_talking_summary(new_messages_str)
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print(f"更新聊天总结：{updated_observe_info}11111111111111")
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self.observe_info = updated_observe_info
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return self.observe_info
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return updated_observe_info
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async def carefully_observe(self):
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# 查找新消息，限制最多40条
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@@ -110,43 +114,46 @@ class ChattingObservation(Observation):
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self.observe_times += 1
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self.last_observe_time = new_messages[-1]["time"]
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await self.update_talking_summary(new_messages_str)
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return self.observe_info
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updated_observe_info = await self.update_talking_summary(new_messages_str)
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self.observe_info = updated_observe_info
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return updated_observe_info
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async def update_talking_summary(self, new_messages_str):
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# 基于已经有的talking_summary，和新的talking_message，生成一个summary
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# print(f"更新聊天总结：{self.talking_summary}")
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# 开始构建prompt
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prompt_personality = "你"
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# person
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individuality = Individuality.get_instance()
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# prompt_personality = "你"
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# # person
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# individuality = Individuality.get_instance()
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personality_core = individuality.personality.personality_core
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prompt_personality += personality_core
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# personality_core = individuality.personality.personality_core
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# prompt_personality += personality_core
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personality_sides = individuality.personality.personality_sides
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random.shuffle(personality_sides)
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prompt_personality += f",{personality_sides[0]}"
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# personality_sides = individuality.personality.personality_sides
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# random.shuffle(personality_sides)
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# prompt_personality += f",{personality_sides[0]}"
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identity_detail = individuality.identity.identity_detail
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random.shuffle(identity_detail)
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prompt_personality += f",{identity_detail[0]}"
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# identity_detail = individuality.identity.identity_detail
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# random.shuffle(identity_detail)
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# prompt_personality += f",{identity_detail[0]}"
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personality_info = prompt_personality
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# personality_info = prompt_personality
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prompt = ""
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prompt += f"{personality_info}，请注意识别你自己的聊天发言"
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prompt += f"你的名字叫：{self.name}，你的昵称是：{self.nick_name}\n"
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# prompt += f"{personality_info}"
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prompt += f"你的名字叫：{self.name}\n，标识'{self.name}'的都是你自己说的话"
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prompt += f"你正在参与一个qq群聊的讨论，你记得这个群之前在聊的内容是：{self.observe_info}\n"
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prompt += f"现在群里的群友们产生了新的讨论，有了新的发言，具体内容如下：{new_messages_str}\n"
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prompt += """以上是群里在进行的聊天，请你对这个聊天内容进行总结，总结内容要包含聊天的大致内容，
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以及聊天中的一些重要信息，注意识别你自己的发言，记得不要分点，不要太长，精简的概括成一段文本\n"""
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prompt += """以上是群里在进行的聊天，请你对这个聊天内容进行总结，总结内容要包含聊天的大致内容，目前最新讨论的话题
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以及聊天中的一些重要信息，记得不要分点，精简的概括成一段文本\n"""
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prompt += "总结概括："
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try:
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self.observe_info, reasoning_content = await self.llm_summary.generate_response_async(prompt)
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updated_observe_info, reasoning_content = await self.llm_summary.generate_response_async(prompt)
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except Exception as e:
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print(f"获取总结失败: {e}")
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self.observe_info = ""
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updated_observe_info = ""
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return updated_observe_info
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# print(f"prompt：{prompt}")
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# print(f"self.observe_info：{self.observe_info}")
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@@ -17,7 +17,7 @@ from src.plugins.chat.chat_stream import ChatStream
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from src.plugins.person_info.relationship_manager import relationship_manager
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from src.plugins.chat.utils import get_recent_group_speaker
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import json
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from src.heart_flow.tool_use import ToolUser
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from src.do_tool.tool_use import ToolUser
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subheartflow_config = LogConfig(
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# 使用海马体专用样式
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@@ -133,14 +133,13 @@ class SubHeartflow:
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tool_result = await self.tool_user.use_tool(message_txt, sender_name, chat_stream)
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# 如果工具被使用且获得了结果，将收集到的信息合并到思考中
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collected_info = ""
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if tool_result.get("used_tools", False):
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logger.info("使用工具收集了信息")
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# 如果有收集到的信息，将其添加到当前思考中
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if "collected_info" in tool_result:
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collected_info = tool_result["collected_info"]
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# 开始构建prompt
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prompt_personality = f"你的名字是{self.bot_name},你"
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@@ -178,6 +177,7 @@ class SubHeartflow:
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)
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prompt = ""
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# prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
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if tool_result.get("used_tools", False):
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prompt += f"{collected_info}\n"
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prompt += f"{relation_prompt_all}\n"
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@@ -187,10 +187,10 @@ class SubHeartflow:
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prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
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prompt += f"你现在{mood_info}\n"
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prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n"
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prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，"
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prompt += "思考时可以想想如何对群聊内容进行回复。回复的要求是：平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话 ，注意只输出回复内容。"
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prompt += "请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，"
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prompt += f"记得结合上述的消息，生成符合内心想法的内心独白，文字不要浮夸，注意你就是{self.bot_name}，{self.bot_name}指的就是你。"
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prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白"
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prompt += "思考时可以想想如何对群聊内容进行回复。回复的要求是：平淡一些，简短一些，说中文，尽量不要说你说过的话\n"
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prompt += "请注意不要输出多余内容(包括前后缀，冒号和引号，括号， 表情，其他描述等)，"
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prompt += f"记得结合上述的消息，生成内心想法，文字不要浮夸，注意你就是{self.bot_name}，{self.bot_name}指的就是你。"
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try:
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response, reasoning_content = await self.llm_model.generate_response_async(prompt)
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@@ -1,561 +0,0 @@
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from src.plugins.models.utils_model import LLM_request
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from src.plugins.config.config import global_config
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from src.plugins.chat.chat_stream import ChatStream
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from src.plugins.memory_system.Hippocampus import HippocampusManager
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from src.common.database import db
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import time
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import json
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from src.common.logger import get_module_logger
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from src.plugins.chat.utils import get_embedding
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from typing import Union
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logger = get_module_logger("tool_use")
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class ToolUser:
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def __init__(self):
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self.llm_model_tool = LLM_request(
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model=global_config.llm_heartflow, temperature=0.2, max_tokens=1000, request_type="tool_use"
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)
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async def _build_tool_prompt(self, message_txt:str, sender_name:str, chat_stream:ChatStream):
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"""构建工具使用的提示词
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Args:
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message_txt: 用户消息文本
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sender_name: 发送者名称
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chat_stream: 聊天流对象
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Returns:
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str: 构建好的提示词
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"""
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from src.plugins.config.config import global_config
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new_messages = list(
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db.messages.find({"chat_id": chat_stream.stream_id, "time": {"$gt": time.time()}})
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.sort("time", 1)
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.limit(15)
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)
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new_messages_str = ""
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for msg in new_messages:
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if "detailed_plain_text" in msg:
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new_messages_str += f"{msg['detailed_plain_text']}"
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# 这些信息应该从调用者传入，而不是从self获取
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bot_name = global_config.BOT_NICKNAME
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prompt = ""
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prompt += "你正在思考如何回复群里的消息。\n"
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prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n"
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prompt += f"注意你就是{bot_name}，{bot_name}指的就是你。"
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prompt += "你现在需要对群里的聊天内容进行回复，现在请你思考，你是否需要额外的信息，或者一些工具来帮你回复，比如回忆或者搜寻已有的知识，或者了解你现在正在做什么，请输出你需要的工具，或者你需要的额外信息。"
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return prompt
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def _define_tools(self):
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"""定义可用的工具列表
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Returns:
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list: 工具定义列表
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"""
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tools = [
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{
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"type": "function",
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"function": {
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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": {
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"type": "string",
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"description": "搜索查询关键词"
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},
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"threshold": {
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"type": "number",
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"description": "相似度阈值，0.0到1.0之间"
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}
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},
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"required": ["query"]
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "get_memory",
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"description": "从记忆系统中获取相关记忆",
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"parameters": {
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"type": "object",
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"properties": {
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"text": {
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"type": "string",
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"description": "要查询的相关文本"
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},
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"max_memory_num": {
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"type": "integer",
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"description": "最大返回记忆数量"
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}
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},
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"required": ["text"]
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "get_current_task",
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"description": "获取当前正在做的事情/最近的任务",
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"parameters": {
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"type": "object",
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"properties": {
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"num": {
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"type": "integer",
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"description": "要获取的任务数量"
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},
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"time_info": {
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"type": "boolean",
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"description": "是否包含时间信息"
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}
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},
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"required": []
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}
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}
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}
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]
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return tools
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async def _execute_tool_call(self, tool_call, message_txt:str):
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"""执行特定的工具调用
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Args:
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tool_call: 工具调用对象
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message_txt: 原始消息文本
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Returns:
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dict: 工具调用结果
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"""
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try:
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function_name = tool_call["function"]["name"]
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function_args = json.loads(tool_call["function"]["arguments"])
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if function_name == "search_knowledge":
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return await self._execute_search_knowledge(tool_call, function_args, message_txt)
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elif function_name == "get_memory":
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return await self._execute_get_memory(tool_call, function_args, message_txt)
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elif function_name == "get_current_task":
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return await self._execute_get_current_task(tool_call, function_args)
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logger.warning(f"未知工具名称: {function_name}")
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return None
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except Exception as e:
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logger.error(f"执行工具调用时发生错误: {str(e)}")
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return None
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async def _execute_search_knowledge(self, tool_call, function_args, message_txt:str):
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"""执行知识库搜索工具
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Args:
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tool_call: 工具调用对象
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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: 工具调用结果
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"""
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try:
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query = function_args.get("query", message_txt)
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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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return {
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"tool_call_id": tool_call["id"],
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"role": "tool",
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"name": "search_knowledge",
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"content": f"知识库搜索结果: {knowledge_info}"
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}
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return None
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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 None
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async def _execute_get_memory(self, tool_call, function_args, message_txt:str):
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"""执行记忆获取工具
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Args:
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tool_call: 工具调用对象
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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: 工具调用结果
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"""
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try:
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text = function_args.get("text", message_txt)
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max_memory_num = function_args.get("max_memory_num", 2)
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# 调用记忆系统
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related_memory = await HippocampusManager.get_instance().get_memory_from_text(
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text=text,
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max_memory_num=max_memory_num,
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max_memory_length=2,
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max_depth=3,
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fast_retrieval=False
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)
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memory_info = ""
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if related_memory:
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for memory in related_memory:
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memory_info += memory[1] + "\n"
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return {
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"tool_call_id": tool_call["id"],
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"role": "tool",
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"name": "get_memory",
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"content": f"记忆系统结果: {memory_info if memory_info else '没有找到相关记忆'}"
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}
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except Exception as e:
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logger.error(f"记忆获取工具执行失败: {str(e)}")
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return None
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async def _execute_get_current_task(self, tool_call, function_args):
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"""执行获取当前任务工具
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Args:
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tool_call: 工具调用对象
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function_args: 工具参数
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Returns:
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dict: 工具调用结果
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"""
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try:
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from src.plugins.schedule.schedule_generator import bot_schedule
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# 获取参数，如果没有提供则使用默认值
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num = function_args.get("num", 1)
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time_info = function_args.get("time_info", False)
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# 调用日程系统获取当前任务
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current_task = bot_schedule.get_current_num_task(num=num, time_info=time_info)
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# 格式化返回结果
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if current_task:
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task_info = current_task
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else:
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task_info = "当前没有正在进行的任务"
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return {
|
||||
"tool_call_id": tool_call["id"],
|
||||
"role": "tool",
|
||||
"name": "get_current_task",
|
||||
"content": f"当前任务信息: {task_info}"
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"获取当前任务工具执行失败: {str(e)}")
|
||||
return None
|
||||
|
||||
async def use_tool(self, message_txt:str, sender_name:str, chat_stream:ChatStream):
|
||||
"""使用工具辅助思考，判断是否需要额外信息
|
||||
|
||||
Args:
|
||||
message_txt: 用户消息文本
|
||||
sender_name: 发送者名称
|
||||
chat_stream: 聊天流对象
|
||||
|
||||
Returns:
|
||||
dict: 工具使用结果
|
||||
"""
|
||||
try:
|
||||
# 构建提示词
|
||||
prompt = await self._build_tool_prompt(message_txt, sender_name, chat_stream)
|
||||
|
||||
# 定义可用工具
|
||||
tools = self._define_tools()
|
||||
|
||||
# 使用llm_model_tool发送带工具定义的请求
|
||||
payload = {
|
||||
"model": self.llm_model_tool.model_name,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"max_tokens": global_config.max_response_length,
|
||||
"tools": tools,
|
||||
"temperature": 0.2
|
||||
}
|
||||
|
||||
logger.debug(f"发送工具调用请求，模型: {self.llm_model_tool.model_name}")
|
||||
# 发送请求获取模型是否需要调用工具
|
||||
response = await self.llm_model_tool._execute_request(
|
||||
endpoint="/chat/completions",
|
||||
payload=payload,
|
||||
prompt=prompt
|
||||
)
|
||||
|
||||
# 根据返回值数量判断是否有工具调用
|
||||
if len(response) == 3:
|
||||
content, reasoning_content, tool_calls = response
|
||||
logger.info(f"工具思考: {tool_calls}")
|
||||
|
||||
# 检查响应中工具调用是否有效
|
||||
if not tool_calls:
|
||||
logger.info("模型返回了空的tool_calls列表")
|
||||
return {"used_tools": False, "thinking": self.current_mind}
|
||||
|
||||
logger.info(f"模型请求调用{len(tool_calls)}个工具")
|
||||
tool_results = []
|
||||
collected_info = ""
|
||||
|
||||
# 执行所有工具调用
|
||||
for tool_call in tool_calls:
|
||||
result = await self._execute_tool_call(tool_call, message_txt)
|
||||
if result:
|
||||
tool_results.append(result)
|
||||
# 将工具结果添加到收集的信息中
|
||||
collected_info += f"\n{result['name']}返回结果: {result['content']}\n"
|
||||
|
||||
# 如果有工具结果，直接返回收集的信息
|
||||
if collected_info:
|
||||
logger.info(f"工具调用收集到信息: {collected_info}")
|
||||
return {
|
||||
"used_tools": True,
|
||||
"collected_info": collected_info,
|
||||
"thinking": self.current_mind # 保持原始思考不变
|
||||
}
|
||||
else:
|
||||
# 没有工具调用
|
||||
content, reasoning_content = response
|
||||
logger.info("模型没有请求调用任何工具")
|
||||
|
||||
# 如果没有工具调用或处理失败，直接返回原始思考
|
||||
return {
|
||||
"used_tools": False,
|
||||
"thinking": self.current_mind
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"工具调用过程中出错: {str(e)}")
|
||||
return {
|
||||
"used_tools": False,
|
||||
"error": str(e),
|
||||
"thinking": self.current_mind
|
||||
}
|
||||
|
||||
|
||||
|
||||
async def get_prompt_info(self, message: str, threshold: float):
|
||||
start_time = time.time()
|
||||
related_info = ""
|
||||
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
|
||||
|
||||
# 1. 先从LLM获取主题，类似于记忆系统的做法
|
||||
topics = []
|
||||
# try:
|
||||
# # 先尝试使用记忆系统的方法获取主题
|
||||
# hippocampus = HippocampusManager.get_instance()._hippocampus
|
||||
# topic_num = min(5, max(1, int(len(message) * 0.1)))
|
||||
# topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
|
||||
|
||||
# # 提取关键词
|
||||
# topics = re.findall(r"<([^>]+)>", topics_response[0])
|
||||
# if not topics:
|
||||
# topics = []
|
||||
# else:
|
||||
# topics = [
|
||||
# topic.strip()
|
||||
# for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||||
# if topic.strip()
|
||||
# ]
|
||||
|
||||
# logger.info(f"从LLM提取的主题: {', '.join(topics)}")
|
||||
# except Exception as e:
|
||||
# logger.error(f"从LLM提取主题失败: {str(e)}")
|
||||
# # 如果LLM提取失败，使用jieba分词提取关键词作为备选
|
||||
# words = jieba.cut(message)
|
||||
# topics = [word for word in words if len(word) > 1][:5]
|
||||
# logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
|
||||
|
||||
# 如果无法提取到主题，直接使用整个消息
|
||||
if not topics:
|
||||
logger.debug("未能提取到任何主题，使用整个消息进行查询")
|
||||
embedding = await get_embedding(message, request_type="info_retrieval")
|
||||
if not embedding:
|
||||
logger.error("获取消息嵌入向量失败")
|
||||
return ""
|
||||
|
||||
related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
|
||||
logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
|
||||
return related_info, {}
|
||||
|
||||
# 2. 对每个主题进行知识库查询
|
||||
logger.info(f"开始处理{len(topics)}个主题的知识库查询")
|
||||
|
||||
# 优化：批量获取嵌入向量，减少API调用
|
||||
embeddings = {}
|
||||
topics_batch = [topic for topic in topics if len(topic) > 0]
|
||||
if message: # 确保消息非空
|
||||
topics_batch.append(message)
|
||||
|
||||
# 批量获取嵌入向量
|
||||
embed_start_time = time.time()
|
||||
for text in topics_batch:
|
||||
if not text or len(text.strip()) == 0:
|
||||
continue
|
||||
|
||||
try:
|
||||
embedding = await get_embedding(text, request_type="info_retrieval")
|
||||
if embedding:
|
||||
embeddings[text] = embedding
|
||||
else:
|
||||
logger.warning(f"获取'{text}'的嵌入向量失败")
|
||||
except Exception as e:
|
||||
logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
|
||||
|
||||
logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
|
||||
|
||||
if not embeddings:
|
||||
logger.error("所有嵌入向量获取失败")
|
||||
return ""
|
||||
|
||||
# 3. 对每个主题进行知识库查询
|
||||
all_results = []
|
||||
query_start_time = time.time()
|
||||
|
||||
# 首先添加原始消息的查询结果
|
||||
if message in embeddings:
|
||||
original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
|
||||
if original_results:
|
||||
for result in original_results:
|
||||
result["topic"] = "原始消息"
|
||||
all_results.extend(original_results)
|
||||
logger.info(f"原始消息查询到{len(original_results)}条结果")
|
||||
|
||||
# 然后添加每个主题的查询结果
|
||||
for topic in topics:
|
||||
if not topic or topic not in embeddings:
|
||||
continue
|
||||
|
||||
try:
|
||||
topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
|
||||
if topic_results:
|
||||
# 添加主题标记
|
||||
for result in topic_results:
|
||||
result["topic"] = topic
|
||||
all_results.extend(topic_results)
|
||||
logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
|
||||
except Exception as e:
|
||||
logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
|
||||
|
||||
logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
|
||||
|
||||
# 4. 去重和过滤
|
||||
process_start_time = time.time()
|
||||
unique_contents = set()
|
||||
filtered_results = []
|
||||
for result in all_results:
|
||||
content = result["content"]
|
||||
if content not in unique_contents:
|
||||
unique_contents.add(content)
|
||||
filtered_results.append(result)
|
||||
|
||||
# 5. 按相似度排序
|
||||
filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
|
||||
|
||||
# 6. 限制总数量（最多10条）
|
||||
filtered_results = filtered_results[:10]
|
||||
logger.info(
|
||||
f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果"
|
||||
)
|
||||
|
||||
# 7. 格式化输出
|
||||
if filtered_results:
|
||||
format_start_time = time.time()
|
||||
grouped_results = {}
|
||||
for result in filtered_results:
|
||||
topic = result["topic"]
|
||||
if topic not in grouped_results:
|
||||
grouped_results[topic] = []
|
||||
grouped_results[topic].append(result)
|
||||
|
||||
# 按主题组织输出
|
||||
for topic, results in grouped_results.items():
|
||||
related_info += f"【主题: {topic}】\n"
|
||||
for _i, result in enumerate(results, 1):
|
||||
_similarity = result["similarity"]
|
||||
content = result["content"].strip()
|
||||
# 调试：为内容添加序号和相似度信息
|
||||
# related_info += f"{i}. [{similarity:.2f}] {content}\n"
|
||||
related_info += f"{content}\n"
|
||||
related_info += "\n"
|
||||
|
||||
logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
|
||||
|
||||
logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
|
||||
return related_info, grouped_results
|
||||
|
||||
def get_info_from_db(
|
||||
self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
|
||||
) -> Union[str, list]:
|
||||
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)
|
||||
Reference in New Issue
Block a user