mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'dev' of github.com:MaiM-with-u/MaiBot into dev
UnCLAS-Prommer
commit
a196c940bf
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@ -199,7 +199,7 @@ class HeartFChatting:
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# !处理no_reply_until_call逻辑
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if self.no_reply_until_call:
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for message in recent_messages_list:
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if message.is_mentioned or message.is_at:
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if message.is_mentioned or message.is_at or len(recent_messages_list) >= 8 or time.time() - self.last_read_time > 600:
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self.no_reply_until_call = False
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break
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# 没有提到，继续保持沉默
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@ -333,6 +333,31 @@ class HeartFChatting:
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loop_start_time=self.last_read_time,
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available_actions=available_actions,
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)
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# !此处使at或者提及必定回复
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metioned_message = None
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for message in recent_messages_list:
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if (message.is_mentioned or message.is_at) and global_config.chat.mentioned_bot_reply:
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metioned_message = message
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has_reply = False
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for action in action_to_use_info:
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if action.action_type == "reply":
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has_reply =True
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break
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if not has_reply and metioned_message:
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action_to_use_info.append(
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ActionPlannerInfo(
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action_type="reply",
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reasoning="有人提到了你，进行回复",
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action_data={},
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action_message=metioned_message,
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available_actions=available_actions,
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)
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)
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# 3. 并行执行所有动作
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action_tasks = [
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@ -350,18 +375,15 @@ class HeartFChatting:
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reply_text_from_reply = ""
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action_success = False
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action_reply_text = ""
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action_command = ""
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for i, result in enumerate(results):
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if isinstance(result, BaseException):
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logger.error(f"{self.log_prefix} 动作执行异常: {result}")
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continue
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_cur_action = action_to_use_info[i]
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if result["action_type"] != "reply":
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action_success = result["success"]
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action_reply_text = result["reply_text"]
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action_command = result.get("command", "")
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elif result["action_type"] == "reply":
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if result["success"]:
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reply_loop_info = result["loop_info"]
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@ -377,7 +399,6 @@ class HeartFChatting:
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loop_info["loop_action_info"].update(
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{
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"action_taken": action_success,
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"command": action_command,
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"taken_time": time.time(),
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}
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)
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@ -391,7 +412,6 @@ class HeartFChatting:
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"loop_action_info": {
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"action_taken": action_success,
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"reply_text": action_reply_text,
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"command": action_command,
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"taken_time": time.time(),
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},
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}
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@ -39,6 +39,7 @@ def init_prompt():
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"""
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{time_block}
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{name_block}
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你的兴趣是：{interest}
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{chat_context_description}，以下是具体的聊天内容
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**聊天内容**
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{chat_content_block}
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@ -60,6 +61,7 @@ reply
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no_reply
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动作描述：
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保持沉默，不回复直到有新消息
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控制聊天频率，不要太过频繁的发言
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{{
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"action": "no_reply",
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}}
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@ -67,30 +69,18 @@ no_reply
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no_reply_until_call
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动作描述：
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保持沉默，直到有人直接叫你的名字
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当前话题不感兴趣时使用，或有人不喜欢你的发言时使用
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{{
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"action": "no_reply_until_call",
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}}
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wait_time
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动作描述：
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沉默等待时间，等待一段时间后回复
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{{
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"action": "wait_time",
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"time":"等待时间",
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}}
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{action_options_text}
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请选择合适的action，并说明触发action的消息id和选择该action的原因。消息id格式:m+数字
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先输出你的选择思考理由，再输出你选择的action，理由是一段平文本，不要分点，精简。
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**动作选择要求**
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请你根据聊天内容和用户的最新消息选择合适的动作:
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1.思考**所有**的可用的action中的**每个动作**是否符合当下条件，如果动作使用调节符合当下条件就使用
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2.如果相同的内容已经被执行，请不要重复执行
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3.你的兴趣是：{interest}
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4.请控制你的发言频率，不要太过频繁的发言
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5.如果有人对你感到厌烦，请减少回复
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6.如果有人对你进行攻击，或者情绪激动，请你以合适的方法应对
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请你根据聊天内容,用户的最新消息和以下标准选择合适的动作:
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{plan_style}
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{moderation_prompt}
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请选择所有符合使用要求的action，动作用json格式输出，如果输出多个json，每个json都要单独用```json包裹，你可以重复使用同一个动作或不同动作:
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@ -175,7 +165,7 @@ class ActionPlanner:
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try:
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action = action_json.get("action", "no_action")
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reasoning = action_json.get("reason", "未提供原因")
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action_data = {key: value for key, value in action_json.items() if key not in ["action", "reasoning"]}
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action_data = {key: value for key, value in action_json.items() if key not in ["action", "reason"]}
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# 非no_action动作需要target_message_id
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target_message = None
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@ -357,6 +347,7 @@ class ActionPlanner:
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moderation_prompt=moderation_prompt_block,
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name_block=name_block,
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interest=interest,
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plan_style=global_config.personality.plan_style,
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)
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return prompt, message_id_list
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@ -153,7 +153,7 @@ class ImageManager:
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"这是一个表情包，请详细描述一下表情包所表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
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)
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detailed_description, _ = await self.vlm.generate_response_for_image(
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vlm_prompt, image_base64, image_format, temperature=0.4, max_tokens=300
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vlm_prompt, image_base64, image_format, temperature=0.4
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)
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if detailed_description is None:
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@ -175,7 +175,7 @@ class ImageManager:
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# 使用较低温度确保输出稳定
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emotion_llm = LLMRequest(model_set=model_config.model_task_config.utils, request_type="emoji")
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emotion_result, _ = await emotion_llm.generate_response_async(
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emotion_prompt, temperature=0.3, max_tokens=50
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emotion_prompt, temperature=0.3
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)
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if not emotion_result:
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@ -270,7 +270,7 @@ class ImageManager:
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# 调用AI获取描述
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image_format = Image.open(io.BytesIO(image_bytes)).format.lower() # type: ignore
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prompt = global_config.custom_prompt.image_prompt
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prompt = global_config.personality.visual_style
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logger.info(f"[VLM调用] 为图片生成新描述 (Hash: {image_hash[:8]}...)")
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description, _ = await self.vlm.generate_response_for_image(
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prompt, image_base64, image_format, temperature=0.4, max_tokens=300
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@ -566,7 +566,7 @@ class ImageManager:
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image_format = Image.open(io.BytesIO(image_bytes)).format.lower() # type: ignore
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# 构建prompt
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prompt = global_config.custom_prompt.image_prompt
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prompt = global_config.personality.visual_style
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# 获取VLM描述
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description, _ = await self.vlm.generate_response_for_image(
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@ -117,9 +117,6 @@ class ModelTaskConfig(ConfigBase):
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planner: TaskConfig
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"""规划模型配置"""
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planner_small: TaskConfig
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"""副规划模型配置"""
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embedding: TaskConfig
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"""嵌入模型配置"""
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@ -32,7 +32,6 @@ from src.config.official_configs import (
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ToolConfig,
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VoiceConfig,
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DebugConfig,
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CustomPromptConfig,
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)
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from .api_ada_configs import (
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@ -55,7 +54,7 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template")
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# 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
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# 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/
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MMC_VERSION = "0.10.3-snapshot.2"
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MMC_VERSION = "0.10.3-snapshot.3"
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def get_key_comment(toml_table, key):
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@ -357,7 +356,6 @@ class Config(ConfigBase):
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lpmm_knowledge: LPMMKnowledgeConfig
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tool: ToolConfig
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debug: DebugConfig
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custom_prompt: CustomPromptConfig
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voice: VoiceConfig
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@ -46,6 +46,12 @@ class PersonalityConfig(ConfigBase):
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interest: str = ""
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"""兴趣"""
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plan_style: str = ""
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"""说话规则，行为风格"""
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visual_style: str = ""
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"""图片提示词"""
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@dataclass
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@ -72,6 +78,9 @@ class ChatConfig(ConfigBase):
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planner_size: float = 1.5
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"""副规划器大小，越小，麦麦的动作执行能力越精细，但是消耗更多token，调大可以缓解429类错误"""
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mentioned_bot_reply: bool = True
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"""是否启用提及必回复"""
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at_bot_inevitable_reply: float = 1
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"""@bot 必然回复，1为100%回复，0为不额外增幅"""
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@ -382,14 +391,6 @@ class KeywordReactionConfig(ConfigBase):
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raise ValueError(f"规则必须是KeywordRuleConfig类型，而不是{type(rule).__name__}")
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@dataclass
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class CustomPromptConfig(ConfigBase):
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"""自定义提示词配置类"""
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image_prompt: str = ""
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"""图片提示词"""
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@dataclass
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class ResponsePostProcessConfig(ConfigBase):
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"""回复后处理配置类"""
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@ -1,5 +1,5 @@
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[inner]
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version = "6.10.1"
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version = "6.11.0"
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#----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
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#如果你想要修改配置文件，请递增version的值
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@ -28,6 +28,17 @@ emotion_style = "情绪较为稳定，但遭遇特定事件的时候起伏较大
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# 麦麦的兴趣，会影响麦麦对什么话题进行回复
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interest = "对技术相关话题，游戏和动漫相关话题感兴趣，也对日常话题感兴趣，不喜欢太过沉重严肃的话题"
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# 麦麦的说话规则，行为风格:
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plan_style = """请你根据聊天内容,用户的最新消息和以下标准选择合适的动作:
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1.思考**所有**的可用的action中的**每个动作**是否符合当下条件，如果动作使用条件符合聊天内容就使用
|
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2.如果相同的内容已经被执行，请不要重复执行
|
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3.请控制你的发言频率，不要太过频繁的发言
|
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4.如果有人对你感到厌烦，请减少回复
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5.如果有人对你进行攻击，或者情绪激动，请你以合适的方法应对"""
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# 麦麦识图规则，不建议修改
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visual_style = "请用中文描述这张图片的内容。如果有文字，请把文字描述概括出来，请留意其主题，直观感受，输出为一段平文本，最多30字，请注意不要分点，就输出一段文本"
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[expression]
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# 表达学习配置
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learning_list = [ # 表达学习配置列表，支持按聊天流配置
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@ -50,7 +61,8 @@ expression_groups = [
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[chat] #麦麦的聊天设置
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talk_value = 1.5
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talk_value = 1
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mentioned_bot_reply = true # 是否启用提及必回复
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max_context_size = 20 # 上下文长度
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[relationship]
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@ -120,10 +132,6 @@ regex_rules = [
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{ regex = ["^(?P<n>\\S{1,20})是这样的$"], reaction = "请按照以下模板造句：[n]是这样的，xx只要xx就可以，可是[n]要考虑的事情就很多了，比如什么时候xx，什么时候xx，什么时候xx。（请自由发挥替换xx部分，只需保持句式结构，同时表达一种将[n]过度重视的反讽意味）" }
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]
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# 可以自定义部分提示词
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[custom_prompt]
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image_prompt = "请用中文描述这张图片的内容。如果有文字，请把文字描述概括出来，请留意其主题，直观感受，输出为一段平文本，最多30字，请注意不要分点，就输出一段文本"
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[response_post_process]
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enable_response_post_process = true # 是否启用回复后处理，包括错别字生成器，回复分割器
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@ -1,5 +1,5 @@
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[inner]
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version = "1.5.0"
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version = "1.6.0"
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# 配置文件版本号迭代规则同bot_config.toml
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@ -113,11 +113,6 @@ model_list = ["siliconflow-deepseek-v3"]
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temperature = 0.3
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max_tokens = 800
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[model_task_config.planner_small] #副决策：负责决定麦麦该做什么的模型
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model_list = ["qwen3-30b"]
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temperature = 0.3
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max_tokens = 800
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[model_task_config.emotion] #负责麦麦的情绪变化
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model_list = ["qwen3-30b"]
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temperature = 0.7
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||||
|
|
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Loading…
Reference in New Issue