ref:将make_question从Plugin改为回复前工作；为tool添加chat_id字段
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SengokuCola
@@ -1,4 +1,6 @@
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from typing import Tuple
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import asyncio
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from datetime import datetime
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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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@@ -8,22 +10,73 @@ from src.plugin_system import BaseAction, ActionActivationType
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from src.chat.utils.utils import cut_key_words
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from src.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 src.plugin_system.apis.message_api import get_messages_by_time_in_chat, build_readable_messages
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import model_config
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from typing import Any
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logger = get_logger("memory")
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def parse_datetime_to_timestamp(value: str) -> float:
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"""
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接受多种常见格式并转换为时间戳（秒）
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支持示例：
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- 2025-09-29
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- 2025-09-29 00:00:00
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- 2025/09/29 00:00
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- 2025-09-29T00:00:00
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"""
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value = value.strip()
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fmts = [
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"%Y-%m-%d %H:%M:%S",
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"%Y-%m-%d %H:%M",
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"%Y/%m/%d %H:%M:%S",
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"%Y/%m/%d %H:%M",
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"%Y-%m-%d",
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"%Y/%m/%d",
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"%Y-%m-%dT%H:%M:%S",
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"%Y-%m-%dT%H:%M",
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]
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last_err = None
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for fmt in fmts:
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try:
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dt = datetime.strptime(value, fmt)
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return dt.timestamp()
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except Exception as e:
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last_err = e
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raise ValueError(f"无法解析时间: {value} ({last_err})")
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def parse_time_range(time_range: str) -> tuple[float, float]:
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"""
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解析时间范围字符串，返回开始和结束时间戳
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格式: "YYYY-MM-DD HH:MM:SS - YYYY-MM-DD HH:MM:SS"
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"""
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if " - " not in time_range:
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raise ValueError("时间范围格式错误，应使用 ' - ' 分隔开始和结束时间")
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start_str, end_str = time_range.split(" - ", 1)
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start_timestamp = parse_datetime_to_timestamp(start_str.strip())
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end_timestamp = parse_datetime_to_timestamp(end_str.strip())
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if start_timestamp > end_timestamp:
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raise ValueError("开始时间不能晚于结束时间")
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return start_timestamp, end_timestamp
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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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description = "在记忆中搜索，获取某个问题的答案，可以指定搜索的时间范围或时间点"
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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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("question", ToolParamType.STRING, "需要获取答案的问题", True, None),
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("time_point", ToolParamType.STRING, "需要获取记忆的时间点，格式为YYYY-MM-DD HH:MM:SS", False, None),
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("time_range", ToolParamType.STRING, "需要获取记忆的时间范围，格式为YYYY-MM-DD HH:MM:SS - YYYY-MM-DD HH:MM:SS", False, 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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"""执行记忆搜索
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Args:
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function_args: 工具参数
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@@ -32,12 +85,141 @@ class GetMemoryTool(BaseTool):
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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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time_point: str = function_args.get("time_point") # type: ignore
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time_range: str = function_args.get("time_range") # 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"问题：{question}，没有找到相关记忆"}
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# 检查是否指定了时间参数
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has_time_params = bool(time_point or time_range)
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return {"content": f"问题：{question}，答案：{answer}"}
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if has_time_params and not self.chat_id:
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return {"content": f"问题：{question}，无法获取聊天记录：缺少chat_id"}
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# 创建并行任务
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tasks = []
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# 原任务：从记忆仓库获取答案
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memory_task = asyncio.create_task(
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global_memory_chest.get_answer_by_question(question=question)
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)
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tasks.append(("memory", memory_task))
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# 新任务：从聊天记录获取答案（如果指定了时间参数）
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chat_task = None
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if has_time_params:
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chat_task = asyncio.create_task(
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self._get_answer_from_chat_history(question, time_point, time_range)
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)
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tasks.append(("chat", chat_task))
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# 等待所有任务完成
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results = {}
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for task_name, task in tasks:
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try:
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results[task_name] = await task
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except Exception as e:
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logger.error(f"任务 {task_name} 执行失败: {e}")
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results[task_name] = None
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# 处理结果
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memory_answer = results.get("memory")
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chat_answer = results.get("chat")
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# 构建返回内容
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content_parts = [f"问题：{question}"]
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if memory_answer:
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content_parts.append(f"记忆仓库答案：{memory_answer}")
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else:
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content_parts.append("记忆仓库：没有找到相关记忆")
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if chat_answer:
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content_parts.append(f"聊天记录答案：{chat_answer}")
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elif has_time_params:
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content_parts.append("聊天记录：没有找到相关记录")
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return {"content": "\n".join(content_parts)}
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async def _get_answer_from_chat_history(self, question: str, time_point: str = None, time_range: str = None) -> str:
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"""从聊天记录中获取问题的答案"""
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try:
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# 确定时间范围
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if time_point:
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# 时间点：搜索前后25条记录
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target_timestamp = parse_datetime_to_timestamp(time_point)
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# 获取前后各25条记录，总共50条
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messages_before = get_messages_by_time_in_chat(
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chat_id=self.chat_id,
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start_time=0,
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end_time=target_timestamp,
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limit=25,
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limit_mode="latest"
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)
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messages_after = get_messages_by_time_in_chat(
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chat_id=self.chat_id,
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start_time=target_timestamp,
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end_time=float('inf'),
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limit=25,
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limit_mode="earliest"
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)
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messages = messages_before + messages_after
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elif time_range:
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# 时间范围：搜索范围内最多50条记录
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start_timestamp, end_timestamp = parse_time_range(time_range)
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messages = get_messages_by_time_in_chat(
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chat_id=self.chat_id,
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start_time=start_timestamp,
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end_time=end_timestamp,
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limit=50,
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limit_mode="latest"
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)
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else:
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return "未指定时间参数"
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if not messages:
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return "没有找到相关聊天记录"
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# 将消息转换为可读格式
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chat_content = build_readable_messages(messages, timestamp_mode="relative")
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if not chat_content.strip():
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return "聊天记录为空"
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# 使用LLM分析聊天内容并回答问题
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try:
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llm_request = LLMRequest(
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model_set=model_config.model_task_config.utils_small,
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request_type="chat_history_analysis"
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)
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analysis_prompt = f"""请根据以下聊天记录内容，回答用户的问题。
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聊天记录：
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{chat_content}
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用户问题：{question}
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请仔细分析聊天记录，提取与问题相关的信息，并给出准确的答案。如果聊天记录中没有相关信息，请说明"聊天记录中没有找到相关信息"。
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答案："""
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response, (reasoning, model_name, tool_calls) = await llm_request.generate_response_async(
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prompt=analysis_prompt,
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temperature=0.3,
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max_tokens=500
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)
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return f"基于聊天记录分析：{response}"
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except Exception as llm_error:
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logger.error(f"LLM分析聊天记录失败: {llm_error}")
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# 如果LLM分析失败，返回聊天内容的摘要
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if len(chat_content) > 300:
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chat_content = chat_content[:300] + "..."
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return f"聊天记录摘要：{chat_content}"
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except Exception as e:
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logger.error(f"从聊天记录获取答案失败: {e}")
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return f"聊天记录分析失败: {str(e)}"
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class GetMemoryAction(BaseAction):
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"""关系动作 - 获取记忆"""
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@@ -45,7 +227,7 @@ class GetMemoryAction(BaseAction):
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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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# 动作基本信息
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action_name = "get_memory"
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action_description = (
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"在记忆中搜寻某个问题的答案"
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@@ -60,7 +242,7 @@ class GetMemoryAction(BaseAction):
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action_require = [
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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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"有人提问关于过去的事情"
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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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