mirror of https://github.com/Mai-with-u/MaiBot.git
fix：修复麦麦好奇和message_api的一个错误
SengokuCola
parent
e2310de6b5
commit
b2eec7d3b1
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@ -124,6 +124,7 @@ def get_raw_msg_by_timestamp_with_chat(
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# 只有当 limit 为 0 时才应用外部 sort
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sort_order = [("time", 1)] if limit == 0 else None
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# 直接将 limit_mode 传递给 find_messages
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# print(f"get_raw_msg_by_timestamp_with_chat: {chat_id}, {timestamp_start}, {timestamp_end}, {limit}, {limit_mode}, {filter_bot}, {filter_command}")
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return find_messages(
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message_filter=filter_query,
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sort=sort_order,
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@ -681,10 +682,9 @@ def build_readable_messages(
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filtered_messages = []
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for msg in messages:
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# 获取消息内容
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content = msg.display_message or msg.processed_plain_text or ""
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content = msg.processed_plain_text
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# 移除表情包
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emoji_pattern = r"\[表情包:[^\]]+\]"
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emoji_pattern = r"\[表情包：[^\]]+\]"
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content = re.sub(emoji_pattern, "", content)
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# 如果移除表情包后内容不为空，则保留消息
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@ -699,10 +699,8 @@ def build_readable_messages(
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# 创建 MessageAndActionModel 但移除表情包
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model = MessageAndActionModel.from_DatabaseMessages(msg)
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# 移除表情包
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if model.display_message:
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model.display_message = re.sub(r"\[表情包:[^\]]+\]", "", model.display_message)
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if model.processed_plain_text:
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model.processed_plain_text = re.sub(r"\[表情包:[^\]]+\]", "", model.processed_plain_text)
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model.processed_plain_text = re.sub(r"\[表情包：[^\]]+\]", "", model.processed_plain_text)
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copy_messages.append(model)
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else:
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copy_messages.append(MessageAndActionModel.from_DatabaseMessages(msg))
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@ -81,7 +81,8 @@ def find_messages(
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query = query.where(Messages.user_id != global_config.bot.qq_account)
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if filter_command:
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query = query.where(not Messages.is_command)
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# 使用按位取反构造 Peewee 的 NOT 条件，避免直接与 False 比较
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query = query.where(~Messages.is_command)
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if limit > 0:
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if limit_mode == "earliest":
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@ -1,6 +1,5 @@
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import time
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import asyncio
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from rich.traceback import install
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from src.common.logger import get_logger
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from src.common.database.database_model import MemoryConflict
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from src.chat.utils.chat_message_builder import (
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@ -12,10 +11,6 @@ from src.config.config import model_config, global_config
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logger = get_logger("conflict_tracker")
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logger = get_logger("conflict_tracker")
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install(extra_lines=3)
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class QuestionTracker:
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"""
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用于跟踪一个问题在后续聊天中的解答情况
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@ -41,26 +36,25 @@ class QuestionTracker:
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"""
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prompt = (
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"你是一个严谨的判定器。下面给出聊天记录以及一个问题。\n"
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"任务：判断在这段聊天中，该问题是否已经得到明确解答。或从聊天内容中可以整理出答案\n"
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"任务：判断在这段聊天中，该问题是否已经得到明确解答。\n"
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"如果已解答，请只输出：YES: <简短答案>\n"
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"如果没有，请只输出：NO\n\n"
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f"问题：{self.question}\n"
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"聊天记录如下：\n"
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f"{conversation_text}"
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)
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if global_config.debug.show_prompt:
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logger.info(f"判定提示词: {prompt}")
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else:
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logger.debug("已发送判定提示词")
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result_text, _ = await self.llm_request.generate_response_async(prompt, temperature=0.2)
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result_text, _ = await self.llm_request.generate_response_async(prompt, temperature=0.5)
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logger.info(f"判定结果: {prompt}\n{result_text}")
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if not result_text:
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return False, ""
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logger.info(f"判定提示词: {prompt},问题: {self.question},result: {result_text}")
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text = result_text.strip()
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if text.upper().startswith("YES:"):
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@ -123,7 +117,7 @@ class ConflictTracker:
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max_duration = 30 * 60 # 30 分钟
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max_messages = 100 # 最多 100 条消息
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poll_interval = 2.0 # 秒
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logger.info(f"开始跟踪问题: {original_question}")
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while tracker.active:
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now_ts = time.time()
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# 终止条件：时长达到上限
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@ -136,7 +130,7 @@ class ConflictTracker:
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chat_id=tracker.chat_id,
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timestamp_start=tracker.last_read_time,
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timestamp_end=now_ts,
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limit=0,
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limit=30,
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limit_mode="latest",
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filter_bot=False,
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filter_command=True,
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@ -98,6 +98,31 @@ class MemoryChest:
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if chat_id and chat_id in self.running_content_list:
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current_running_content = self.running_content_list[chat_id]["content"]
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# 随机从格式示例列表中选取若干行用于提示
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format_candidates = [
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"[概念] 是 [概念的含义(简短描述，不超过十个字)]",
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"[概念] 不是 [对概念的负面含义(简短描述，不超过十个字)]",
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"[概念1] 与 [概念2] 是 [概念1和概念2的关联(简短描述，不超过二十个字)]",
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"[概念1] 包含 [概念2] 和 [概念3]",
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"[概念1] 属于 [概念2]",
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"[概念1] 的例子是 [例子1] 和 [例子2]",
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"[概念] 的特征是 [特征1]、[特征2]",
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"[概念1] 导致 [概念2]",
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"[概念1] 需要 [条件1] 和 [条件2]",
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"[概念1] 的用途是 [用途1] 和 [用途2]",
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"[概念1] 与 [概念2] 的区别是 [区别点]",
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"[概念] 的别名是 [别名]",
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"[概念1] 包括但不限于 [概念2]、[概念3]",
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"[概念] 的反义是 [反义概念]",
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"[概念] 的组成有 [部分1]、[部分2]",
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"[概念] 出现于 [时间或场景]",
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"[概念] 的方法有 [方法1]、[方法2]",
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]
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selected_count = random.randint(5, len(format_candidates))
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selected_lines = random.sample(format_candidates, selected_count)
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format_section = "\n".join(selected_lines) + "\n......(不要包含中括号)"
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prompt = f"""
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以下是你的记忆内容和新的聊天记录，请你将他们整合和修改：
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记忆内容：
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@ -117,13 +142,8 @@ class MemoryChest:
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3.如果有图片，请只关注图片和文本结合的知识和概念性内容
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4.记忆为一段纯文本，逻辑清晰，指出概念的含义，并说明关系
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记忆内容的格式，你必须仿照下面的格式，但不一定全部使用:
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[概念] 是 [概念的含义(简短描述，不超过十个字)]
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[概念] 不是 [对概念的负面含义(简短描述，不超过十个字)]
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[概念1] 与 [概念2] 是 [概念1和概念2的关联(简短描述，不超过二十个字)]
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[概念1] 包含 [概念2] 和 [概念3]
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[概念1] 属于 [概念2]
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......(不要包含中括号)
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记忆内容的格式，你必须仿照下面的格式，但不一定全部使用:
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{format_section}
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请仿照上述格式输出，每个知识点一句话。输出成一段平文本
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现在请你输出,不要输出其他内容，注意一定要直白，白话，口语化不要浮夸，修辞。：
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@ -136,7 +156,7 @@ class MemoryChest:
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running_content, (reasoning_content, model_name, tool_calls) = await self.LLMRequest_build.generate_response_async(prompt)
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print(f"记忆仓库构建运行内容: {running_content}")
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print(f"prompt: {prompt}\n记忆仓库构建运行内容: {running_content}")
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# 如果有chat_id，更新对应的running_content
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if chat_id and running_content:
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@ -98,11 +98,19 @@ def get_messages_by_time_in_chat(
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raise ValueError("chat_id 不能为空")
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if not isinstance(chat_id, str):
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raise ValueError("chat_id 必须是字符串类型")
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if filter_mai:
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return filter_mai_messages(
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get_raw_msg_by_timestamp_with_chat(chat_id, start_time, end_time, limit, limit_mode, filter_command)
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)
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return get_raw_msg_by_timestamp_with_chat(chat_id, start_time, end_time, limit, limit_mode, filter_command)
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# if filter_mai:
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# return filter_mai_messages(
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# get_raw_msg_by_timestamp_with_chat(chat_id, start_time, end_time, limit, limit_mode, filter_command)
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# )
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return get_raw_msg_by_timestamp_with_chat(
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chat_id=chat_id,
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timestamp_start=start_time,
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timestamp_end=end_time,
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limit=limit,
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limit_mode=limit_mode,
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filter_bot=filter_mai,
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filter_command=filter_command
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)
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def get_messages_by_time_in_chat_inclusive(
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@ -18,7 +18,7 @@ from src.plugin_system.apis import frequency_api
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from src.plugin_system.apis import generator_api
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from src.curiousity.questions import global_conflict_tracker
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logger = get_logger("relation_actions")
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logger = get_logger("question_actions")
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@ -26,16 +26,16 @@ class CuriousAction(BaseAction):
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"""频率调节动作 - 调整聊天发言频率"""
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activation_type = ActionActivationType.LLM_JUDGE
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parallel_action = False
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parallel_action = True
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# 动作基本信息
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action_name = "question"
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action_name = "make_question"
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action_description = "对现有的存在疑问的信息生成一个问题"
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action_description = "提出一个问题，当有人反驳你的观点，或其他人之间有观点冲突时使用"
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# 动作参数定义
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action_parameters = {
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"question": "存在疑问的信息，提出一个问题",
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"question": "对存在疑问的信息提出一个问题，描述全面，使用陈述句",
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}
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# 动作使用场景
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@ -46,6 +46,8 @@ class CuriousAction(BaseAction):
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f"当聊天记录中的信息存在逻辑上的矛盾时使用",
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f"当有人反对或否定你提出的信息时使用",
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f"或当你对现有的信息存在疑问时使用",
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f"有人认为你的观点是错误的，请选择question动作",
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f"有人与你观点不一致，请选择question动作",
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]
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# 关联类型
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@ -59,10 +61,10 @@ class CuriousAction(BaseAction):
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# 存储问题到冲突追踪器
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if question:
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await global_conflict_tracker.record_conflict(conflict_content=question, start_following=True,chat_id=self.chat_id)
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logger.info(f"已存储问题到冲突追踪器: {len(question)} 字符")
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logger.info(f"已存储问题到冲突追踪器: {question}")
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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"你产生了一个问题，原因：{question}，尝试向其他人提问或回忆吧",
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action_prompt_display=f"你产生了一个问题：{question}，尝试向其他人提问或回忆吧",
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action_done=True,
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)
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return True, "问题已记录"
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