feat：新的记忆系统，deepthink插件，修复平行动作
This commit is contained in:
SengokuCola
@@ -3,9 +3,13 @@ from typing import Tuple
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from src.common.logger import get_logger
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from src.config.config import global_config
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from src.chat.utils.prompt_builder import Prompt
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from src.llm_models.payload_content.tool_option import ToolParamType
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from src.plugin_system import BaseAction, ActionActivationType
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from src.chat.memory_system.Hippocampus import hippocampus_manager
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from src.chat.utils.utils import cut_key_words
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from src.chat.memory_system.Memory_chest import global_memory_chest
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from src.plugin_system.base.base_tool import BaseTool
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from typing import Any
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logger = get_logger("memory")
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@@ -66,73 +70,153 @@ def init_prompt():
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)
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class BuildMemoryAction(BaseAction):
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"""关系动作 - 构建关系"""
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# class BuildMemoryAction(BaseAction):
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# """关系动作 - 构建关系"""
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# activation_type = ActionActivationType.LLM_JUDGE
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# parallel_action = True
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# # 动作基本信息
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# action_name = "build_memory"
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# action_description = (
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# "了解对于某个概念或者某件事的记忆，并存储下来，在之后的聊天中，你可以根据这条记忆来获取相关信息"
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# )
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# # 动作参数定义
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# action_parameters = {
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# "concept_name": "需要了解或记忆的概念或事件的名称",
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# "concept_description": "需要了解或记忆的概念或事件的描述，需要具体且明确",
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# }
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# # 动作使用场景
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# action_require = [
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# "了解对于某个概念或者某件事的记忆，并存储下来，在之后的聊天中，你可以根据这条记忆来获取相关信息",
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# "有你不了解的概念",
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# "有人要求你记住某个概念或者事件",
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# "你对某件事或概念有新的理解，或产生了兴趣",
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# ]
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# # 关联类型
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# associated_types = ["text"]
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# async def execute(self) -> Tuple[bool, str]:
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# """执行关系动作"""
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# try:
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# # 1. 获取构建关系的原因
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# concept_description = self.action_data.get("concept_description", "")
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# logger.info(f"{self.log_prefix} 添加记忆原因: {self.reasoning}")
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# concept_name = self.action_data.get("concept_name", "")
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# # 2. 获取目标用户信息
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# # 对 concept_name 进行jieba分词
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# concept_name_tokens = cut_key_words(concept_name)
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# # logger.info(f"{self.log_prefix} 对 concept_name 进行分词结果: {concept_name_tokens}")
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# filtered_concept_name_tokens = [
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# token
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# for token in concept_name_tokens
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# if all(keyword not in token for keyword in global_config.memory.memory_ban_words)
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# ]
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# if not filtered_concept_name_tokens:
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# logger.warning(f"{self.log_prefix} 过滤后的概念名称列表为空，跳过添加记忆")
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# return False, "过滤后的概念名称列表为空，跳过添加记忆"
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# similar_topics_dict = (
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# hippocampus_manager.get_hippocampus().parahippocampal_gyrus.get_similar_topics_from_keywords(
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# filtered_concept_name_tokens
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# )
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# )
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# await hippocampus_manager.get_hippocampus().parahippocampal_gyrus.add_memory_with_similar(
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# concept_description, similar_topics_dict
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# )
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# return True, f"成功添加记忆: {concept_name}"
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# except Exception as e:
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# logger.error(f"{self.log_prefix} 构建记忆时出错: {e}")
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# return False, f"构建记忆时出错: {e}"
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class GetMemoryTool(BaseTool):
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"""获取用户信息"""
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name = "get_memory"
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description = "在记忆中搜索，获取某个问题的答案"
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parameters = [
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("question", ToolParamType.STRING, "需要获取答案的问题", True, None)
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]
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available_for_llm = True
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async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
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"""执行比较两个数的大小
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Args:
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function_args: 工具参数
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Returns:
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dict: 工具执行结果
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"""
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question: str = function_args.get("question") # type: ignore
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answer = await global_memory_chest.get_answer_by_question(question=question)
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if not answer:
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return {"content": f"没有找到相关记忆"}
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return {"content": f"问题：{question}，答案：{answer}"}
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class GetMemoryAction(BaseAction):
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"""关系动作 - 获取记忆"""
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activation_type = ActionActivationType.LLM_JUDGE
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parallel_action = True
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# 动作基本信息
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action_name = "build_memory"
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# 动作基本信息
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action_name = "get_memory"
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action_description = (
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"了解对于某个概念或者某件事的记忆，并存储下来，在之后的聊天中，你可以根据这条记忆来获取相关信息"
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"在记忆中搜寻某个问题的答案"
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)
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# 动作参数定义
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action_parameters = {
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"concept_name": "需要了解或记忆的概念或事件的名称",
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"concept_description": "需要了解或记忆的概念或事件的描述，需要具体且明确",
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"question": "需要搜寻或回答的问题",
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}
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# 动作使用场景
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action_require = [
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"了解对于某个概念或者某件事的记忆，并存储下来，在之后的聊天中，你可以根据这条记忆来获取相关信息",
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"在记忆中搜寻某个问题的答案",
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"有你不了解的概念",
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"有人要求你记住某个概念或者事件",
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"你对某件事或概念有新的理解，或产生了兴趣",
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"有人提问关于过去的事情"
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"你需要根据记忆回答某个问题",
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]
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# 关联类型
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associated_types = ["text"]
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async def execute(self) -> Tuple[bool, str]:
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"""执行关系动作"""
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try:
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# 1. 获取构建关系的原因
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concept_description = self.action_data.get("concept_description", "")
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logger.info(f"{self.log_prefix} 添加记忆原因: {self.reasoning}")
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concept_name = self.action_data.get("concept_name", "")
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# 2. 获取目标用户信息
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# 对 concept_name 进行jieba分词
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concept_name_tokens = cut_key_words(concept_name)
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# logger.info(f"{self.log_prefix} 对 concept_name 进行分词结果: {concept_name_tokens}")
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filtered_concept_name_tokens = [
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token
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for token in concept_name_tokens
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if all(keyword not in token for keyword in global_config.memory.memory_ban_words)
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]
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if not filtered_concept_name_tokens:
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logger.warning(f"{self.log_prefix} 过滤后的概念名称列表为空，跳过添加记忆")
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return False, "过滤后的概念名称列表为空，跳过添加记忆"
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similar_topics_dict = (
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hippocampus_manager.get_hippocampus().parahippocampal_gyrus.get_similar_topics_from_keywords(
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filtered_concept_name_tokens
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)
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question = self.action_data.get("question", "")
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answer = await global_memory_chest.get_answer_by_question(self.chat_id, question)
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if not answer:
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await self.store_action_info(
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action_build_into_prompt=True,
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action_prompt_display=f"你回忆了有关问题：{question}的记忆，但是没有找到相关记忆",
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action_done=True,
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)
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await hippocampus_manager.get_hippocampus().parahippocampal_gyrus.add_memory_with_similar(
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concept_description, similar_topics_dict
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)
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return True, f"成功添加记忆: {concept_name}"
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except Exception as e:
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logger.error(f"{self.log_prefix} 构建记忆时出错: {e}")
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return False, f"构建记忆时出错: {e}"
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return False, f"没有找到相关记忆"
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await self.store_action_info(
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action_build_into_prompt=True,
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action_prompt_display=f"你回忆了有关问题：{question}的记忆，答案是：{answer}",
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action_done=True,
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)
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return True, f"成功获取记忆: {answer}"
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# 还缺一个关系的太多遗忘和对应的提取
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@@ -1,25 +1,23 @@
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from typing import List, Tuple, Type
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# 导入新插件系统
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from src.plugin_system import BasePlugin, ComponentInfo
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from src.plugin_system import BasePlugin, ComponentInfo, register_plugin
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from src.plugin_system.base.config_types import ConfigField
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# 导入依赖的系统组件
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from src.common.logger import get_logger
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from src.plugins.built_in.memory.build_memory import BuildMemoryAction
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from src.plugins.built_in.memory.build_memory import GetMemoryAction, GetMemoryTool
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logger = get_logger("relation_actions")
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logger = get_logger("memory_build")
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# @register_plugin
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@register_plugin
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class MemoryBuildPlugin(BasePlugin):
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"""关系动作插件
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"""记忆构建插件
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系统内置插件，提供基础的聊天交互功能：
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- Reply: 回复动作
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- NoReply: 不回复动作
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- Emoji: 表情动作
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- GetMemory: 获取记忆
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注意：插件基本信息优先从_manifest.json文件中读取
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"""
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@@ -43,9 +41,6 @@ class MemoryBuildPlugin(BasePlugin):
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"enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
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"config_version": ConfigField(type=str, default="1.1.0", description="配置文件版本"),
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},
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"components": {
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"memory_max_memory_num": ConfigField(type=int, default=10, description="记忆最大数量"),
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},
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}
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def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
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@@ -53,6 +48,7 @@ class MemoryBuildPlugin(BasePlugin):
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# --- 根据配置注册组件 ---
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components = []
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components.append((BuildMemoryAction.get_action_info(), BuildMemoryAction))
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components.append((GetMemoryAction.get_action_info(), GetMemoryAction))
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components.append((GetMemoryTool.get_tool_info(), GetMemoryTool))
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return components
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