1035 lines
43 KiB
Python
1035 lines
43 KiB
Python
import time
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import json
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import asyncio
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from typing import List, Dict, Any, Optional, Tuple, Set
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from src.common.logger import get_logger
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from src.config.config import global_config, model_config
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.plugin_system.apis import llm_api
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from src.common.database.database_model import ThinkingBack
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from src.memory_system.retrieval_tools import get_tool_registry, init_all_tools
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from src.memory_system.memory_utils import parse_questions_json
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from src.llm_models.payload_content.message import MessageBuilder, RoleType, Message
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from src.jargon.jargon_explainer import match_jargon_from_text, retrieve_concepts_with_jargon
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logger = get_logger("memory_retrieval")
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THINKING_BACK_NOT_FOUND_RETENTION_SECONDS = 36000 # 未找到答案记录保留时长
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THINKING_BACK_CLEANUP_INTERVAL_SECONDS = 3000 # 清理频率
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_last_not_found_cleanup_ts: float = 0.0
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def _cleanup_stale_not_found_thinking_back() -> None:
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"""定期清理过期的未找到答案记录"""
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global _last_not_found_cleanup_ts
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now = time.time()
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if now - _last_not_found_cleanup_ts < THINKING_BACK_CLEANUP_INTERVAL_SECONDS:
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return
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threshold_time = now - THINKING_BACK_NOT_FOUND_RETENTION_SECONDS
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try:
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deleted_rows = (
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ThinkingBack.delete()
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.where((ThinkingBack.found_answer == 0) & (ThinkingBack.update_time < threshold_time))
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.execute()
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)
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if deleted_rows:
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logger.info(f"清理过期的未找到答案thinking_back记录 {deleted_rows} 条")
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_last_not_found_cleanup_ts = now
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except Exception as e:
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logger.error(f"清理未找到答案的thinking_back记录失败: {e}")
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def init_memory_retrieval_prompt():
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"""初始化记忆检索相关的 prompt 模板和工具"""
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# 首先注册所有工具
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init_all_tools()
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# 第一步：问题生成prompt
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Prompt(
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"""
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你的名字是{bot_name}。现在是{time_now}。
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群里正在进行的聊天内容：
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{chat_history}
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{recent_query_history}
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现在，{sender}发送了内容:{target_message},你想要回复ta。
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请仔细分析聊天内容，考虑以下几点：
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1. 对话中是否提到了过去发生的事情、人物、事件或信息
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2. 是否有需要回忆的内容（比如"之前说过"、"上次"、"以前"等）
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3. 是否有需要查找历史信息的问题
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4. 是否有问题可以搜集信息帮助你聊天
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重要提示：
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- **每次只能提出一个问题**，选择最需要查询的关键问题
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- 如果"最近已查询的问题和结果"中已经包含了类似的问题并得到了答案，请避免重复生成相同或相似的问题，不需要重复查询
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- 如果之前已经查询过某个问题但未找到答案，可以尝试用不同的方式提问或更具体的问题
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如果你认为需要从记忆中检索信息来回答，请根据上下文提出**一个**最关键的问题来帮助你回复目标消息，放入"questions"字段
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问题格式示例：
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- "xxx在前几天干了什么"
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- "xxx是什么，在什么时候提到过?"
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- "xxxx和xxx的关系是什么"
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- "xxx在某个时间点发生了什么"
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问题要说明前因后果和上下文，使其全面且精准
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输出格式示例（需要检索时）：
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```json
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{{
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"questions": ["张三在前几天干了什么"] #问题数组（字符串数组），如果不需要检索记忆则输出空数组[]，如果需要检索则只输出包含一个问题的数组
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}}
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```
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输出格式示例（不需要检索时）：
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```json
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{{
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"questions": []
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}}
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```
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请只输出JSON对象，不要输出其他内容：
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""",
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name="memory_retrieval_question_prompt",
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)
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# 第二步：ReAct Agent prompt（使用function calling，要求先思考再行动）
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Prompt(
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"""你的名字是{bot_name}。现在是{time_now}。
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你正在参与聊天，你需要搜集信息来回答问题，帮助你参与聊天。
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当前需要解答的问题：{question}
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已收集的信息：
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{collected_info}
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**执行步骤：**
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- 如果涉及过往事件，或者查询某个过去可能提到过的概念，或者某段时间发生的事件。可以使用聊天记录查询工具查询过往事件
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- 如果涉及人物，可以使用人物信息查询工具查询人物信息
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- 如果没有可靠信息，且查询时间充足，或者不确定查询类别，也可以使用lpmm知识库查询，作为辅助信息
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- **如果信息不足需要使用tool，说明需要查询什么，并输出为纯文本说明，然后调用相应工具查询（可并行调用多个工具）**
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- **如果当前已收集的信息足够回答问题，且能找到明确答案，调用found_answer工具标记已找到答案**
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**思考**
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- 你可以对查询思路给出简短的思考：思考要简短，直接切入要点
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- 如果信息不足，你必须给出使用什么工具进行查询
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- 如果信息足够，你必须调用found_answer工具
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""",
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name="memory_retrieval_react_prompt_head",
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)
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# 额外，如果最后一轮迭代：ReAct Agent prompt（使用function calling，要求先思考再行动）
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Prompt(
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"""你的名字是{bot_name}。现在是{time_now}。
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你正在参与聊天，你需要根据搜集到的信息判断问题是否可以回答问题。
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当前问题：{question}
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已收集的信息：
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{collected_info}
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**执行步骤：**
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分析：
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- 当前信息是否足够回答问题？
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- **如果信息足够且能找到明确答案**，在思考中直接给出答案，格式为：found_answer(answer="你的答案内容")
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- **如果信息不足或无法找到答案**，在思考中给出：not_enough_info(reason="信息不足或无法找到答案的原因")
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**重要规则：**
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- 你已经经过几轮查询，尝试了信息搜集，现在你需要总结信息，选择回答问题或判断问题无法回答
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- 必须严格使用检索到的信息回答问题，不要编造信息
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- 答案必须精简，不要过多解释
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- **只有在检索到明确、具体的答案时，才使用found_answer**
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- **如果信息不足、无法确定、找不到相关信息，必须使用not_enough_info，不要使用found_answer**
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- 答案必须给出，格式为 found_answer(answer="...") 或 not_enough_info(reason="...")。
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""",
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name="memory_retrieval_react_final_prompt",
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)
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def _log_conversation_messages(
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conversation_messages: List[Message],
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head_prompt: Optional[str] = None,
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final_status: Optional[str] = None,
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) -> None:
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"""输出对话消息列表的日志
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Args:
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conversation_messages: 对话消息列表
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head_prompt: 第一条系统消息（head_prompt）的内容，可选
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final_status: 最终结果状态描述（例如：找到答案/未找到答案），可选
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"""
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if not global_config.debug.show_memory_prompt:
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return
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log_lines: List[str] = []
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# 如果有head_prompt，先添加为第一条消息
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if head_prompt:
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msg_info = "========================================\n[消息 1] 角色: System 内容类型: 文本\n-----------------------------"
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msg_info += f"\n{head_prompt}"
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log_lines.append(msg_info)
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start_idx = 2
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else:
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start_idx = 1
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if not conversation_messages and not head_prompt:
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return
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for idx, msg in enumerate(conversation_messages, start_idx):
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role_name = msg.role.value if hasattr(msg.role, "value") else str(msg.role)
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# 处理内容 - 显示完整内容，不截断
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if isinstance(msg.content, str):
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full_content = msg.content
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content_type = "文本"
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elif isinstance(msg.content, list):
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text_parts = [item for item in msg.content if isinstance(item, str)]
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image_count = len([item for item in msg.content if isinstance(item, tuple)])
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full_content = "".join(text_parts) if text_parts else ""
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content_type = f"混合({len(text_parts)}段文本, {image_count}张图片)"
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else:
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full_content = str(msg.content)
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content_type = "未知"
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# 构建单条消息的日志信息
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msg_info = f"\n========================================\n[消息 {idx}] 角色: {role_name} 内容类型: {content_type}\n-----------------------------"
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if full_content:
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msg_info += f"\n{full_content}"
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if msg.tool_calls:
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msg_info += f"\n 工具调用: {len(msg.tool_calls)}个"
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for tool_call in msg.tool_calls:
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msg_info += f"\n - {tool_call}"
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if msg.tool_call_id:
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msg_info += f"\n 工具调用ID: {msg.tool_call_id}"
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log_lines.append(msg_info)
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total_count = len(conversation_messages) + (1 if head_prompt else 0)
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log_text = f"消息列表 (共{total_count}条):{''.join(log_lines)}"
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if final_status:
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log_text += f"\n\n[最终结果] {final_status}"
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logger.info(log_text)
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async def _react_agent_solve_question(
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question: str,
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chat_id: str,
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max_iterations: int = 5,
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timeout: float = 30.0,
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initial_info: str = "",
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initial_jargon_concepts: Optional[List[str]] = None,
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) -> Tuple[bool, str, List[Dict[str, Any]], bool]:
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"""使用ReAct架构的Agent来解决问题
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Args:
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question: 要回答的问题
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chat_id: 聊天ID
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max_iterations: 最大迭代次数
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timeout: 超时时间（秒）
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initial_info: 初始信息（如概念检索结果），将作为collected_info的初始值
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initial_jargon_concepts: 预先已解析过的黑话列表，避免重复解释
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Returns:
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Tuple[bool, str, List[Dict[str, Any]], bool]: (是否找到答案, 答案内容, 思考步骤列表, 是否超时)
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"""
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start_time = time.time()
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collected_info = initial_info if initial_info else ""
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enable_jargon_detection = global_config.memory.enable_jargon_detection
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seen_jargon_concepts: Set[str] = set()
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if enable_jargon_detection and initial_jargon_concepts:
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for concept in initial_jargon_concepts:
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concept = (concept or "").strip()
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if concept:
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seen_jargon_concepts.add(concept)
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thinking_steps = []
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is_timeout = False
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conversation_messages: List[Message] = []
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first_head_prompt: Optional[str] = None # 保存第一次使用的head_prompt（用于日志显示）
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for iteration in range(max_iterations):
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# 检查超时
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if time.time() - start_time > timeout:
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logger.warning(f"ReAct Agent超时，已迭代{iteration}次")
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is_timeout = True
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break
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# 获取工具注册器
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tool_registry = get_tool_registry()
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||
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# 获取bot_name
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||
bot_name = global_config.bot.nickname
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||
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# 获取当前时间
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||
time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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# 计算剩余迭代次数
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current_iteration = iteration + 1
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remaining_iterations = max_iterations - current_iteration
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is_final_iteration = current_iteration >= max_iterations
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# 提取函数调用中参数的值，支持单引号和双引号
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def extract_quoted_content(text, func_name, param_name):
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"""从文本中提取函数调用中参数的值，支持单引号和双引号
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||
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Args:
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text: 要搜索的文本
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func_name: 函数名，如 'found_answer'
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param_name: 参数名，如 'answer'
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Returns:
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提取的参数值，如果未找到则返回None
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||
"""
|
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if not text:
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return None
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||
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# 查找函数调用位置（不区分大小写）
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||
func_pattern = func_name.lower()
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text_lower = text.lower()
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func_pos = text_lower.find(func_pattern)
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if func_pos == -1:
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return None
|
||
|
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# 查找参数名和等号
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||
param_pattern = f"{param_name}="
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param_pos = text_lower.find(param_pattern, func_pos)
|
||
if param_pos == -1:
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return None
|
||
|
||
# 跳过参数名、等号和空白
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||
start_pos = param_pos + len(param_pattern)
|
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while start_pos < len(text) and text[start_pos] in " \t\n":
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start_pos += 1
|
||
|
||
if start_pos >= len(text):
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return None
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||
|
||
# 确定引号类型
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||
quote_char = text[start_pos]
|
||
if quote_char not in ['"', "'"]:
|
||
return None
|
||
|
||
# 查找匹配的结束引号（考虑转义）
|
||
end_pos = start_pos + 1
|
||
while end_pos < len(text):
|
||
if text[end_pos] == quote_char:
|
||
# 检查是否是转义的引号
|
||
if end_pos > start_pos + 1 and text[end_pos - 1] == "\\":
|
||
end_pos += 1
|
||
continue
|
||
# 找到匹配的引号
|
||
content = text[start_pos + 1 : end_pos]
|
||
# 处理转义字符
|
||
content = content.replace('\\"', '"').replace("\\'", "'").replace("\\\\", "\\")
|
||
return content
|
||
end_pos += 1
|
||
|
||
return None
|
||
|
||
# 如果是最后一次迭代，使用final_prompt进行总结
|
||
if is_final_iteration:
|
||
evaluation_prompt = await global_prompt_manager.format_prompt(
|
||
"memory_retrieval_react_final_prompt",
|
||
bot_name=bot_name,
|
||
time_now=time_now,
|
||
question=question,
|
||
collected_info=collected_info if collected_info else "暂无信息",
|
||
current_iteration=current_iteration,
|
||
remaining_iterations=remaining_iterations,
|
||
max_iterations=max_iterations,
|
||
)
|
||
|
||
if global_config.debug.show_memory_prompt:
|
||
logger.info(f"ReAct Agent 最终评估Prompt: {evaluation_prompt}")
|
||
|
||
eval_success, eval_response, eval_reasoning_content, eval_model_name, eval_tool_calls = await llm_api.generate_with_model_with_tools(
|
||
evaluation_prompt,
|
||
model_config=model_config.model_task_config.tool_use,
|
||
tool_options=[], # 最终评估阶段不提供工具
|
||
request_type="memory.react.final",
|
||
)
|
||
|
||
if not eval_success:
|
||
logger.error(f"ReAct Agent 第 {iteration + 1} 次迭代 最终评估阶段 LLM调用失败: {eval_response}")
|
||
_log_conversation_messages(
|
||
conversation_messages,
|
||
head_prompt=first_head_prompt,
|
||
final_status="未找到答案：最终评估阶段LLM调用失败",
|
||
)
|
||
return False, "最终评估阶段LLM调用失败", thinking_steps, False
|
||
|
||
logger.info(
|
||
f"ReAct Agent 第 {iteration + 1} 次迭代 最终评估响应: {eval_response}"
|
||
)
|
||
|
||
# 从最终评估响应中提取found_answer或not_enough_info
|
||
found_answer_content = None
|
||
not_enough_info_reason = None
|
||
|
||
if eval_response:
|
||
found_answer_content = extract_quoted_content(eval_response, "found_answer", "answer")
|
||
if not found_answer_content:
|
||
not_enough_info_reason = extract_quoted_content(eval_response, "not_enough_info", "reason")
|
||
|
||
# 如果找到答案，返回
|
||
if found_answer_content:
|
||
eval_step = {
|
||
"iteration": iteration + 1,
|
||
"thought": f"[最终评估] {eval_response}",
|
||
"actions": [{"action_type": "found_answer", "action_params": {"answer": found_answer_content}}],
|
||
"observations": ["最终评估阶段检测到found_answer"]
|
||
}
|
||
thinking_steps.append(eval_step)
|
||
logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代 最终评估阶段找到关于问题{question}的答案: {found_answer_content}")
|
||
|
||
_log_conversation_messages(
|
||
conversation_messages,
|
||
head_prompt=first_head_prompt,
|
||
final_status=f"找到答案：{found_answer_content}",
|
||
)
|
||
|
||
return True, found_answer_content, thinking_steps, False
|
||
|
||
# 如果评估为not_enough_info，返回空字符串（不返回任何信息）
|
||
if not_enough_info_reason:
|
||
eval_step = {
|
||
"iteration": iteration + 1,
|
||
"thought": f"[最终评估] {eval_response}",
|
||
"actions": [{"action_type": "not_enough_info", "action_params": {"reason": not_enough_info_reason}}],
|
||
"observations": ["最终评估阶段检测到not_enough_info"]
|
||
}
|
||
thinking_steps.append(eval_step)
|
||
logger.info(
|
||
f"ReAct Agent 第 {iteration + 1} 次迭代 最终评估阶段判断信息不足: {not_enough_info_reason}"
|
||
)
|
||
|
||
_log_conversation_messages(
|
||
conversation_messages,
|
||
head_prompt=first_head_prompt,
|
||
final_status=f"未找到答案：{not_enough_info_reason}",
|
||
)
|
||
|
||
return False, "", thinking_steps, False
|
||
|
||
# 如果没有明确判断，视为not_enough_info，返回空字符串（不返回任何信息）
|
||
eval_step = {
|
||
"iteration": iteration + 1,
|
||
"thought": f"[最终评估] {eval_response}",
|
||
"actions": [{"action_type": "not_enough_info", "action_params": {"reason": "已到达最后一次迭代，无法找到答案"}}],
|
||
"observations": ["已到达最后一次迭代，无法找到答案"]
|
||
}
|
||
thinking_steps.append(eval_step)
|
||
logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代 已到达最后一次迭代，无法找到答案")
|
||
|
||
_log_conversation_messages(
|
||
conversation_messages,
|
||
head_prompt=first_head_prompt,
|
||
final_status="未找到答案：已到达最后一次迭代，无法找到答案",
|
||
)
|
||
|
||
return False, "", thinking_steps, False
|
||
|
||
# 前n-1次迭代，使用head_prompt决定调用哪些工具（包含found_answer工具）
|
||
tool_definitions = tool_registry.get_tool_definitions()
|
||
logger.info(
|
||
f"ReAct Agent 第 {iteration + 1} 次迭代，问题: {question}|可用工具数量: {len(tool_definitions)}"
|
||
)
|
||
|
||
# head_prompt应该只构建一次，使用初始的collected_info，后续迭代都复用同一个
|
||
if first_head_prompt is None:
|
||
# 第一次构建，使用初始的collected_info（即initial_info）
|
||
initial_collected_info = initial_info if initial_info else ""
|
||
first_head_prompt = await global_prompt_manager.format_prompt(
|
||
"memory_retrieval_react_prompt_head",
|
||
bot_name=bot_name,
|
||
time_now=time_now,
|
||
question=question,
|
||
collected_info=initial_collected_info,
|
||
current_iteration=current_iteration,
|
||
remaining_iterations=remaining_iterations,
|
||
max_iterations=max_iterations,
|
||
)
|
||
|
||
# 后续迭代都复用第一次构建的head_prompt
|
||
head_prompt = first_head_prompt
|
||
|
||
def message_factory(
|
||
_client,
|
||
*,
|
||
_head_prompt: str = head_prompt,
|
||
_conversation_messages: List[Message] = conversation_messages,
|
||
) -> List[Message]:
|
||
messages: List[Message] = []
|
||
|
||
system_builder = MessageBuilder()
|
||
system_builder.set_role(RoleType.System)
|
||
system_builder.add_text_content(_head_prompt)
|
||
messages.append(system_builder.build())
|
||
|
||
messages.extend(_conversation_messages)
|
||
|
||
return messages
|
||
|
||
(
|
||
success,
|
||
response,
|
||
reasoning_content,
|
||
model_name,
|
||
tool_calls,
|
||
) = await llm_api.generate_with_model_with_tools_by_message_factory(
|
||
message_factory,
|
||
model_config=model_config.model_task_config.tool_use,
|
||
tool_options=tool_definitions,
|
||
request_type="memory.react",
|
||
)
|
||
|
||
logger.debug(
|
||
f"ReAct Agent 第 {iteration + 1} 次迭代 模型: {model_name} ，调用工具数量: {len(tool_calls) if tool_calls else 0} ，调用工具响应: {response}"
|
||
)
|
||
|
||
if not success:
|
||
logger.error(f"ReAct Agent LLM调用失败: {response}")
|
||
break
|
||
|
||
# 注意：这里会检查found_answer工具调用，如果检测到found_answer工具，会直接返回答案
|
||
|
||
assistant_message: Optional[Message] = None
|
||
if tool_calls:
|
||
assistant_builder = MessageBuilder()
|
||
assistant_builder.set_role(RoleType.Assistant)
|
||
if response and response.strip():
|
||
assistant_builder.add_text_content(response)
|
||
assistant_builder.set_tool_calls(tool_calls)
|
||
assistant_message = assistant_builder.build()
|
||
elif response and response.strip():
|
||
assistant_builder = MessageBuilder()
|
||
assistant_builder.set_role(RoleType.Assistant)
|
||
assistant_builder.add_text_content(response)
|
||
assistant_message = assistant_builder.build()
|
||
|
||
# 记录思考步骤
|
||
step = {"iteration": iteration + 1, "thought": response, "actions": [], "observations": []}
|
||
|
||
if assistant_message:
|
||
conversation_messages.append(assistant_message)
|
||
|
||
# 记录思考过程到collected_info中
|
||
if reasoning_content or response:
|
||
thought_summary = reasoning_content or (response[:200] if response else "")
|
||
if thought_summary:
|
||
collected_info += f"\n[思考] {thought_summary}\n"
|
||
|
||
# 处理工具调用
|
||
if not tool_calls:
|
||
# 如果没有工具调用，记录思考过程，继续下一轮迭代（下一轮会再次评估）
|
||
if response and response.strip():
|
||
step["observations"] = [f"思考完成，但未调用工具。响应: {response}"]
|
||
logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代 思考完成但未调用工具: {response}")
|
||
collected_info += f"思考: {response}"
|
||
else:
|
||
logger.warning(f"ReAct Agent 第 {iteration + 1} 次迭代 无工具调用且无响应")
|
||
step["observations"] = ["无响应且无工具调用"]
|
||
thinking_steps.append(step)
|
||
continue
|
||
|
||
# 处理工具调用
|
||
# 首先检查是否有found_answer工具调用，如果有则立即返回，不再处理其他工具
|
||
found_answer_from_tool = None
|
||
for tool_call in tool_calls:
|
||
tool_name = tool_call.func_name
|
||
tool_args = tool_call.args or {}
|
||
|
||
if tool_name == "found_answer":
|
||
found_answer_from_tool = tool_args.get("answer", "")
|
||
if found_answer_from_tool:
|
||
step["actions"].append({"action_type": "found_answer", "action_params": {"answer": found_answer_from_tool}})
|
||
step["observations"] = ["检测到found_answer工具调用"]
|
||
thinking_steps.append(step)
|
||
logger.debug(f"ReAct Agent 第 {iteration + 1} 次迭代 通过found_answer工具找到关于问题{question}的答案: {found_answer_from_tool}")
|
||
|
||
_log_conversation_messages(
|
||
conversation_messages,
|
||
head_prompt=first_head_prompt,
|
||
final_status=f"找到答案：{found_answer_from_tool}",
|
||
)
|
||
|
||
return True, found_answer_from_tool, thinking_steps, False
|
||
|
||
# 如果没有found_answer工具调用，或者found_answer工具调用没有答案，继续处理其他工具
|
||
tool_tasks = []
|
||
for i, tool_call in enumerate(tool_calls):
|
||
tool_name = tool_call.func_name
|
||
tool_args = tool_call.args or {}
|
||
|
||
logger.debug(
|
||
f"ReAct Agent 第 {iteration + 1} 次迭代 工具调用 {i + 1}/{len(tool_calls)}: {tool_name}({tool_args})"
|
||
)
|
||
|
||
# 跳过found_answer工具调用（已经在上面处理过了）
|
||
if tool_name == "found_answer":
|
||
continue
|
||
|
||
# 普通工具调用
|
||
tool = tool_registry.get_tool(tool_name)
|
||
if tool:
|
||
# 准备工具参数（需要添加chat_id如果工具需要）
|
||
import inspect
|
||
|
||
sig = inspect.signature(tool.execute_func)
|
||
tool_params = tool_args.copy()
|
||
if "chat_id" in sig.parameters:
|
||
tool_params["chat_id"] = chat_id
|
||
|
||
# 创建异步任务
|
||
async def execute_single_tool(tool_instance, params, tool_name_str, iter_num):
|
||
try:
|
||
observation = await tool_instance.execute(**params)
|
||
param_str = ", ".join([f"{k}={v}" for k, v in params.items() if k != "chat_id"])
|
||
return f"查询{tool_name_str}({param_str})的结果：{observation}"
|
||
except Exception as e:
|
||
error_msg = f"工具执行失败: {str(e)}"
|
||
logger.error(f"ReAct Agent 第 {iter_num + 1} 次迭代 工具 {tool_name_str} {error_msg}")
|
||
return f"查询{tool_name_str}失败: {error_msg}"
|
||
|
||
tool_tasks.append(execute_single_tool(tool, tool_params, tool_name, iteration))
|
||
step["actions"].append({"action_type": tool_name, "action_params": tool_args})
|
||
else:
|
||
error_msg = f"未知的工具类型: {tool_name}"
|
||
logger.warning(f"ReAct Agent 第 {iteration + 1} 次迭代 工具 {i + 1}/{len(tool_calls)} {error_msg}")
|
||
tool_tasks.append(asyncio.create_task(asyncio.sleep(0, result=f"查询{tool_name}失败: {error_msg}")))
|
||
|
||
# 并行执行所有工具
|
||
if tool_tasks:
|
||
observations = await asyncio.gather(*tool_tasks, return_exceptions=True)
|
||
|
||
# 处理执行结果
|
||
for i, (tool_call_item, observation) in enumerate(zip(tool_calls, observations, strict=False)):
|
||
if isinstance(observation, Exception):
|
||
observation = f"工具执行异常: {str(observation)}"
|
||
logger.error(f"ReAct Agent 第 {iteration + 1} 次迭代 工具 {i + 1} 执行异常: {observation}")
|
||
|
||
observation_text = observation if isinstance(observation, str) else str(observation)
|
||
stripped_observation = observation_text.strip()
|
||
step["observations"].append(observation_text)
|
||
collected_info += f"\n{observation_text}\n"
|
||
if stripped_observation:
|
||
# 检查工具输出中是否有新的jargon，如果有则追加到工具结果中
|
||
if enable_jargon_detection:
|
||
jargon_concepts = match_jargon_from_text(stripped_observation, chat_id)
|
||
if jargon_concepts:
|
||
new_concepts = []
|
||
for concept in jargon_concepts:
|
||
normalized_concept = concept.strip()
|
||
if normalized_concept and normalized_concept not in seen_jargon_concepts:
|
||
new_concepts.append(normalized_concept)
|
||
seen_jargon_concepts.add(normalized_concept)
|
||
if new_concepts:
|
||
jargon_info = await retrieve_concepts_with_jargon(new_concepts, chat_id)
|
||
if jargon_info:
|
||
# 将jargon查询结果追加到工具结果中
|
||
observation_text += f"\n\n{jargon_info}"
|
||
collected_info += f"\n{jargon_info}\n"
|
||
logger.info(f"工具输出触发黑话解析: {new_concepts}")
|
||
|
||
tool_builder = MessageBuilder()
|
||
tool_builder.set_role(RoleType.Tool)
|
||
tool_builder.add_text_content(observation_text)
|
||
tool_builder.add_tool_call(tool_call_item.call_id)
|
||
conversation_messages.append(tool_builder.build())
|
||
|
||
thinking_steps.append(step)
|
||
|
||
# 达到最大迭代次数或超时，但Agent没有明确返回found_answer
|
||
# 迭代超时应该直接视为not_enough_info，而不是使用已有信息
|
||
# 只有Agent明确返回found_answer时，才认为找到了答案
|
||
if collected_info:
|
||
logger.warning(
|
||
f"ReAct Agent达到最大迭代次数或超时，但未明确返回found_answer。已收集信息: {collected_info[:100]}..."
|
||
)
|
||
if is_timeout:
|
||
logger.warning("ReAct Agent超时，直接视为not_enough_info")
|
||
else:
|
||
logger.warning("ReAct Agent达到最大迭代次数，直接视为not_enough_info")
|
||
|
||
# React完成时输出消息列表
|
||
timeout_reason = "超时" if is_timeout else "达到最大迭代次数"
|
||
_log_conversation_messages(
|
||
conversation_messages,
|
||
head_prompt=first_head_prompt,
|
||
final_status=f"未找到答案：{timeout_reason}",
|
||
)
|
||
|
||
return False, "", thinking_steps, is_timeout
|
||
|
||
|
||
def _get_recent_query_history(chat_id: str, time_window_seconds: float = 600.0) -> str:
|
||
"""获取最近一段时间内的查询历史（用于避免重复查询）
|
||
|
||
Args:
|
||
chat_id: 聊天ID
|
||
time_window_seconds: 时间窗口（秒），默认10分钟
|
||
|
||
Returns:
|
||
str: 格式化的查询历史字符串
|
||
"""
|
||
try:
|
||
current_time = time.time()
|
||
start_time = current_time - time_window_seconds
|
||
|
||
# 查询最近时间窗口内的记录，按更新时间倒序
|
||
records = (
|
||
ThinkingBack.select()
|
||
.where((ThinkingBack.chat_id == chat_id) & (ThinkingBack.update_time >= start_time))
|
||
.order_by(ThinkingBack.update_time.desc())
|
||
.limit(5) # 最多返回5条最近的记录
|
||
)
|
||
|
||
if not records.exists():
|
||
return ""
|
||
|
||
history_lines = []
|
||
history_lines.append("最近已查询的问题和结果：")
|
||
|
||
for record in records:
|
||
status = "✓ 已找到答案" if record.found_answer else "✗ 未找到答案"
|
||
answer_preview = ""
|
||
# 只有找到答案时才显示答案内容
|
||
if record.found_answer and record.answer:
|
||
# 截取答案前100字符
|
||
answer_preview = record.answer[:100]
|
||
if len(record.answer) > 100:
|
||
answer_preview += "..."
|
||
|
||
history_lines.append(f"- 问题：{record.question}")
|
||
history_lines.append(f" 状态：{status}")
|
||
if answer_preview:
|
||
history_lines.append(f" 答案：{answer_preview}")
|
||
history_lines.append("") # 空行分隔
|
||
|
||
return "\n".join(history_lines)
|
||
|
||
except Exception as e:
|
||
logger.error(f"获取查询历史失败: {e}")
|
||
return ""
|
||
|
||
|
||
def _get_recent_found_answers(chat_id: str, time_window_seconds: float = 600.0) -> List[str]:
|
||
"""获取最近一段时间内已找到答案的查询记录（用于返回给 replyer）
|
||
|
||
Args:
|
||
chat_id: 聊天ID
|
||
time_window_seconds: 时间窗口（秒），默认10分钟
|
||
|
||
Returns:
|
||
List[str]: 格式化的答案列表，每个元素格式为 "问题：xxx\n答案：xxx"
|
||
"""
|
||
try:
|
||
current_time = time.time()
|
||
start_time = current_time - time_window_seconds
|
||
|
||
# 查询最近时间窗口内已找到答案的记录，按更新时间倒序
|
||
records = (
|
||
ThinkingBack.select()
|
||
.where(
|
||
(ThinkingBack.chat_id == chat_id)
|
||
& (ThinkingBack.update_time >= start_time)
|
||
& (ThinkingBack.found_answer == 1)
|
||
& (ThinkingBack.answer.is_null(False))
|
||
& (ThinkingBack.answer != "")
|
||
)
|
||
.order_by(ThinkingBack.update_time.desc())
|
||
.limit(3) # 最多返回5条最近的记录
|
||
)
|
||
|
||
if not records.exists():
|
||
return []
|
||
|
||
found_answers = []
|
||
for record in records:
|
||
if record.answer:
|
||
found_answers.append(f"问题：{record.question}\n答案：{record.answer}")
|
||
|
||
return found_answers
|
||
|
||
except Exception as e:
|
||
logger.error(f"获取最近已找到答案的记录失败: {e}")
|
||
return []
|
||
|
||
|
||
def _store_thinking_back(
|
||
chat_id: str, question: str, context: str, found_answer: bool, answer: str, thinking_steps: List[Dict[str, Any]]
|
||
) -> None:
|
||
"""存储或更新思考过程到数据库（如果已存在则更新，否则创建）
|
||
|
||
Args:
|
||
chat_id: 聊天ID
|
||
question: 问题
|
||
context: 上下文信息
|
||
found_answer: 是否找到答案
|
||
answer: 答案内容
|
||
thinking_steps: 思考步骤列表
|
||
"""
|
||
try:
|
||
now = time.time()
|
||
|
||
# 先查询是否已存在相同chat_id和问题的记录
|
||
existing = (
|
||
ThinkingBack.select()
|
||
.where((ThinkingBack.chat_id == chat_id) & (ThinkingBack.question == question))
|
||
.order_by(ThinkingBack.update_time.desc())
|
||
.limit(1)
|
||
)
|
||
|
||
if existing.exists():
|
||
# 更新现有记录
|
||
record = existing.get()
|
||
record.context = context
|
||
record.found_answer = found_answer
|
||
record.answer = answer
|
||
record.thinking_steps = json.dumps(thinking_steps, ensure_ascii=False)
|
||
record.update_time = now
|
||
record.save()
|
||
logger.info(f"已更新思考过程到数据库，问题: {question[:50]}...")
|
||
else:
|
||
# 创建新记录
|
||
ThinkingBack.create(
|
||
chat_id=chat_id,
|
||
question=question,
|
||
context=context,
|
||
found_answer=found_answer,
|
||
answer=answer,
|
||
thinking_steps=json.dumps(thinking_steps, ensure_ascii=False),
|
||
create_time=now,
|
||
update_time=now,
|
||
)
|
||
# logger.info(f"已创建思考过程到数据库，问题: {question[:50]}...")
|
||
except Exception as e:
|
||
logger.error(f"存储思考过程失败: {e}")
|
||
|
||
|
||
async def _process_single_question(
|
||
question: str,
|
||
chat_id: str,
|
||
context: str,
|
||
initial_info: str = "",
|
||
initial_jargon_concepts: Optional[List[str]] = None,
|
||
) -> Optional[str]:
|
||
"""处理单个问题的查询
|
||
|
||
Args:
|
||
question: 要查询的问题
|
||
chat_id: 聊天ID
|
||
context: 上下文信息
|
||
initial_info: 初始信息（如概念检索结果），将传递给ReAct Agent
|
||
initial_jargon_concepts: 已经处理过的黑话概念列表，用于ReAct阶段的去重
|
||
|
||
Returns:
|
||
Optional[str]: 如果找到答案，返回格式化的结果字符串，否则返回None
|
||
"""
|
||
# logger.info(f"开始处理问题: {question}")
|
||
|
||
_cleanup_stale_not_found_thinking_back()
|
||
|
||
question_initial_info = initial_info or ""
|
||
|
||
# 直接使用ReAct Agent查询（不再从thinking_back获取缓存）
|
||
# logger.info(f"使用ReAct Agent查询，问题: {question[:50]}...")
|
||
|
||
jargon_concepts_for_agent = initial_jargon_concepts if global_config.memory.enable_jargon_detection else None
|
||
|
||
found_answer, answer, thinking_steps, is_timeout = await _react_agent_solve_question(
|
||
question=question,
|
||
chat_id=chat_id,
|
||
max_iterations=global_config.memory.max_agent_iterations,
|
||
timeout=120.0,
|
||
initial_info=question_initial_info,
|
||
initial_jargon_concepts=jargon_concepts_for_agent,
|
||
)
|
||
|
||
# 存储查询历史到数据库（超时时不存储）
|
||
if not is_timeout:
|
||
_store_thinking_back(
|
||
chat_id=chat_id,
|
||
question=question,
|
||
context=context,
|
||
found_answer=found_answer,
|
||
answer=answer,
|
||
thinking_steps=thinking_steps,
|
||
)
|
||
else:
|
||
logger.info(f"ReAct Agent超时，不存储到数据库，问题: {question[:50]}...")
|
||
|
||
if found_answer and answer:
|
||
return f"问题：{question}\n答案：{answer}"
|
||
|
||
return None
|
||
|
||
|
||
async def build_memory_retrieval_prompt(
|
||
message: str,
|
||
sender: str,
|
||
target: str,
|
||
chat_stream,
|
||
tool_executor,
|
||
) -> str:
|
||
"""构建记忆检索提示
|
||
使用两段式查询：第一步生成问题，第二步使用ReAct Agent查询答案
|
||
|
||
Args:
|
||
message: 聊天历史记录
|
||
sender: 发送者名称
|
||
target: 目标消息内容
|
||
chat_stream: 聊天流对象
|
||
tool_executor: 工具执行器（保留参数以兼容接口）
|
||
|
||
Returns:
|
||
str: 记忆检索结果字符串
|
||
"""
|
||
start_time = time.time()
|
||
|
||
logger.info(f"检测是否需要回忆，元消息：{message[:30]}...，消息长度: {len(message)}")
|
||
try:
|
||
time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
|
||
bot_name = global_config.bot.nickname
|
||
chat_id = chat_stream.stream_id
|
||
|
||
# 获取最近查询历史（最近10分钟内的查询，用于避免重复查询）
|
||
recent_query_history = _get_recent_query_history(chat_id, time_window_seconds=600.0)
|
||
if not recent_query_history:
|
||
recent_query_history = "最近没有查询记录。"
|
||
|
||
# 第一步：生成问题
|
||
question_prompt = await global_prompt_manager.format_prompt(
|
||
"memory_retrieval_question_prompt",
|
||
bot_name=bot_name,
|
||
time_now=time_now,
|
||
chat_history=message,
|
||
recent_query_history=recent_query_history,
|
||
sender=sender,
|
||
target_message=target,
|
||
)
|
||
|
||
success, response, reasoning_content, model_name = await llm_api.generate_with_model(
|
||
question_prompt,
|
||
model_config=model_config.model_task_config.tool_use,
|
||
request_type="memory.question",
|
||
)
|
||
|
||
if global_config.debug.show_memory_prompt:
|
||
logger.info(f"记忆检索问题生成提示词: {question_prompt}")
|
||
# logger.info(f"记忆检索问题生成响应: {response}")
|
||
|
||
if not success:
|
||
logger.error(f"LLM生成问题失败: {response}")
|
||
return ""
|
||
|
||
# 解析概念列表和问题列表
|
||
_, questions = parse_questions_json(response)
|
||
if questions:
|
||
logger.info(f"解析到 {len(questions)} 个问题: {questions}")
|
||
|
||
enable_jargon_detection = global_config.memory.enable_jargon_detection
|
||
concepts: List[str] = []
|
||
|
||
if enable_jargon_detection:
|
||
# 使用匹配逻辑自动识别聊天中的黑话概念
|
||
concepts = match_jargon_from_text(message, chat_id)
|
||
if concepts:
|
||
logger.info(f"黑话匹配命中 {len(concepts)} 个概念: {concepts}")
|
||
else:
|
||
logger.debug("黑话匹配未命中任何概念")
|
||
else:
|
||
logger.debug("已禁用记忆检索中的黑话识别")
|
||
|
||
# 对匹配到的概念进行jargon检索，作为初始信息
|
||
initial_info = ""
|
||
if enable_jargon_detection and concepts:
|
||
concept_info = await retrieve_concepts_with_jargon(concepts, chat_id)
|
||
if concept_info:
|
||
initial_info += concept_info
|
||
logger.debug(f"概念检索完成，结果: {concept_info}")
|
||
else:
|
||
logger.debug("概念检索未找到任何结果")
|
||
|
||
if not questions:
|
||
logger.debug("模型认为不需要检索记忆或解析失败，不返回任何查询结果")
|
||
end_time = time.time()
|
||
logger.info(f"无当次查询，不返回任何结果，耗时: {(end_time - start_time):.3f}秒")
|
||
return ""
|
||
|
||
# 第二步：并行处理所有问题（使用配置的最大迭代次数/120秒超时）
|
||
max_iterations = global_config.memory.max_agent_iterations
|
||
logger.debug(f"问题数量: {len(questions)}，设置最大迭代次数: {max_iterations}，超时时间: 120秒")
|
||
|
||
# 并行处理所有问题，将概念检索结果作为初始信息传递
|
||
question_tasks = [
|
||
_process_single_question(
|
||
question=question,
|
||
chat_id=chat_id,
|
||
context=message,
|
||
initial_info=initial_info,
|
||
initial_jargon_concepts=concepts if enable_jargon_detection else None,
|
||
)
|
||
for question in questions
|
||
]
|
||
|
||
# 并行执行所有查询任务
|
||
results = await asyncio.gather(*question_tasks, return_exceptions=True)
|
||
|
||
# 收集所有有效结果
|
||
question_results: List[str] = []
|
||
for i, result in enumerate(results):
|
||
if isinstance(result, Exception):
|
||
logger.error(f"处理问题 '{questions[i]}' 时发生异常: {result}")
|
||
elif result is not None:
|
||
question_results.append(result)
|
||
|
||
# 获取最近10分钟内已找到答案的缓存记录
|
||
cached_answers = _get_recent_found_answers(chat_id, time_window_seconds=600.0)
|
||
|
||
# 合并当前查询结果和缓存答案（去重：如果当前查询的问题在缓存中已存在，优先使用当前结果）
|
||
all_results = []
|
||
|
||
# 先添加当前查询的结果
|
||
current_questions = set()
|
||
for result in question_results:
|
||
# 提取问题（格式为 "问题：xxx\n答案：xxx"）
|
||
if result.startswith("问题："):
|
||
question_end = result.find("\n答案：")
|
||
if question_end != -1:
|
||
current_questions.add(result[4:question_end])
|
||
all_results.append(result)
|
||
|
||
# 添加缓存答案（排除当前查询中已存在的问题）
|
||
for cached_answer in cached_answers:
|
||
if cached_answer.startswith("问题："):
|
||
question_end = cached_answer.find("\n答案：")
|
||
if question_end != -1:
|
||
cached_question = cached_answer[4:question_end]
|
||
if cached_question not in current_questions:
|
||
all_results.append(cached_answer)
|
||
|
||
end_time = time.time()
|
||
|
||
if all_results:
|
||
retrieved_memory = "\n\n".join(all_results)
|
||
current_count = len(question_results)
|
||
cached_count = len(all_results) - current_count
|
||
logger.info(
|
||
f"记忆检索成功，耗时: {(end_time - start_time):.3f}秒，"
|
||
f"当前查询 {current_count} 条记忆，缓存 {cached_count} 条记忆，共 {len(all_results)} 条记忆"
|
||
)
|
||
return f"你回忆起了以下信息：\n{retrieved_memory}\n如果与回复内容相关，可以参考这些回忆的信息。\n"
|
||
else:
|
||
logger.debug("所有问题均未找到答案，且无缓存答案")
|
||
return ""
|
||
|
||
except Exception as e:
|
||
logger.error(f"记忆检索时发生异常: {str(e)}")
|
||
return ""
|
||
|