feat：修改统计，分离emoji动作
This commit is contained in:
SengokuCola
@@ -42,7 +42,6 @@ class HFCPerformanceLogger:
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"total_time": cycle_data.get("total_time", 0),
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"step_times": cycle_data.get("step_times", {}),
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"processor_time_costs": cycle_data.get("processor_time_costs", {}), # 前处理器时间
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"post_processor_time_costs": cycle_data.get("post_processor_time_costs", {}), # 后处理器时间
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"reasoning": cycle_data.get("reasoning", ""),
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"success": cycle_data.get("success", False),
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}
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@@ -60,12 +59,7 @@ class HFCPerformanceLogger:
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f"time={record['total_time']:.2f}s",
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]
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# 添加后处理器时间信息到日志
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if record["post_processor_time_costs"]:
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post_processor_stats = ", ".join(
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[f"{name}: {time_cost:.3f}s" for name, time_cost in record["post_processor_time_costs"].items()]
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)
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log_parts.append(f"post_processors=({post_processor_stats})")
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logger.debug(f"记录HFC循环数据: {', '.join(log_parts)}")
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@@ -20,7 +20,7 @@ class HFCVersionManager:
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"""HFC版本号管理器"""
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# 默认版本号
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DEFAULT_VERSION = "v4.0.0"
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DEFAULT_VERSION = "v5.0.0"
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# 当前运行时版本号
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_current_version: Optional[str] = None
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@@ -46,9 +46,12 @@ def init_prompt():
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# --- Group Chat Prompt ---
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memory_activator_prompt = """
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你是一个记忆分析器，你需要根据以下信息来进行回忆
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以下是一场聊天中的信息，请根据这些信息，总结出几个关键词作为记忆回忆的触发词
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以下是一段聊天记录，请根据这些信息，总结出几个关键词作为记忆回忆的触发词
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聊天记录:
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{obs_info_text}
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你想要回复的消息:
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{target_message}
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历史关键词（请避免重复提取这些关键词）：
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{cached_keywords}
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@@ -69,12 +72,12 @@ class MemoryActivator:
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self.summary_model = LLMRequest(
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model=global_config.model.memory_summary,
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temperature=0.7,
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request_type="focus.memory_activator",
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request_type="memory_activator",
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)
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self.running_memory = []
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self.cached_keywords = set() # 用于缓存历史关键词
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async def activate_memory_with_chat_history(self, chat_id, target_message, chat_history_prompt) -> List[Dict]:
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async def activate_memory_with_chat_history(self, target_message, chat_history_prompt) -> List[Dict]:
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"""
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激活记忆
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@@ -88,23 +91,13 @@ class MemoryActivator:
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if not global_config.memory.enable_memory:
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return []
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# obs_info_text = ""
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# for observation in observations:
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# if isinstance(observation, ChattingObservation):
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# obs_info_text += observation.talking_message_str_truncate_short
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# elif isinstance(observation, StructureObservation):
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# working_info = observation.get_observe_info()
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# for working_info_item in working_info:
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# obs_info_text += f"{working_info_item['type']}: {working_info_item['content']}\n"
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# logger.info(f"回忆待检索内容：obs_info_text: {obs_info_text}")
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# 将缓存的关键词转换为字符串，用于prompt
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cached_keywords_str = ", ".join(self.cached_keywords) if self.cached_keywords else "暂无历史关键词"
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prompt = await global_prompt_manager.format_prompt(
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"memory_activator_prompt",
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obs_info_text=chat_history_prompt,
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target_message=target_message,
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cached_keywords=cached_keywords_str,
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)
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@@ -130,9 +123,6 @@ class MemoryActivator:
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related_memory = await hippocampus_manager.get_memory_from_topic(
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valid_keywords=keywords, max_memory_num=3, max_memory_length=2, max_depth=3
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)
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# related_memory = await hippocampus_manager.get_memory_from_text(
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# text=obs_info_text, max_memory_num=5, max_memory_length=2, max_depth=3, fast_retrieval=False
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# )
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logger.info(f"获取到的记忆: {related_memory}")
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@@ -23,7 +23,7 @@ def init_real_time_info_prompts():
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{name_block}
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现在，你想要回复{person_name}的消息，消息内容是：{target_message}。请根据聊天记录和你要回复的消息，从你对{person_name}的了解中提取有关的信息：
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1.你需要提供你想要提取的信息具体是哪方面的信息，例如：年龄，性别，对ta的印象，最近发生的事等等。
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1.你需要提供你想要提取的信息具体是哪方面的信息，例如：年龄，性别，你们之间的交流方式，最近发生的事等等。
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2.请注意，请不要重复调取相同的信息，已经调取的信息如下：
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{info_cache_block}
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3.如果当前聊天记录中没有需要查询的信息，或者现有信息已经足够回复，请返回{{"none": "不需要查询"}}
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@@ -71,13 +71,13 @@ class RelationshipFetcher:
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# LLM模型配置
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self.llm_model = LLMRequest(
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model=global_config.model.relation,
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request_type="focus.real_time_info",
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request_type="relation",
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)
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# 小模型用于即时信息提取
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self.instant_llm_model = LLMRequest(
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model=global_config.model.utils_small,
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request_type="focus.real_time_info.instant",
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request_type="relation.instant",
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)
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name = get_chat_manager().get_stream_name(self.chat_id)
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81
src/plugins/built_in/core_actions/emoji.py
Normal file
81
src/plugins/built_in/core_actions/emoji.py
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@@ -0,0 +1,81 @@
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from typing import Tuple
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# 导入新插件系统
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from src.plugin_system import BaseAction, ActionActivationType, ChatMode
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# 导入依赖的系统组件
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from src.common.logger import get_logger
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# 导入API模块 - 标准Python包方式
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from src.plugin_system.apis import emoji_api
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from src.plugins.built_in.core_actions.no_reply import NoReplyAction
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logger = get_logger("core_actions")
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class EmojiAction(BaseAction):
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"""表情动作 - 发送表情包"""
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# 激活设置
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focus_activation_type = ActionActivationType.LLM_JUDGE
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normal_activation_type = ActionActivationType.RANDOM
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mode_enable = ChatMode.ALL
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parallel_action = True
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random_activation_probability = 0.2 # 默认值，可通过配置覆盖
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# 动作基本信息
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action_name = "emoji"
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action_description = "发送表情包辅助表达情绪"
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# LLM判断提示词
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llm_judge_prompt = """
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判定是否需要使用表情动作的条件：
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1. 用户明确要求使用表情包
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2. 这是一个适合表达强烈情绪的场合
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3. 不要发送太多表情包，如果你已经发送过多个表情包则回答"否"
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请回答"是"或"否"。
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"""
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# 动作参数定义
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action_parameters = {"description": "文字描述你想要发送的表情包内容"}
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# 动作使用场景
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action_require = ["发送表情包辅助表达情绪","表达情绪时可以选择使用", "不要连续发送，如果你已经发过[表情包]，就不要选择此动作"]
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# 关联类型
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associated_types = ["emoji"]
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async def execute(self) -> Tuple[bool, str]:
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"""执行表情动作"""
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logger.info(f"{self.log_prefix} 决定发送表情")
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try:
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# 1. 根据描述选择表情包
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description = self.action_data.get("description", "")
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emoji_result = await emoji_api.get_by_description(description)
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if not emoji_result:
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logger.warning(f"{self.log_prefix} 未找到匹配描述 '{description}' 的表情包")
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return False, f"未找到匹配 '{description}' 的表情包"
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emoji_base64, emoji_description, matched_emotion = emoji_result
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logger.info(f"{self.log_prefix} 找到表情包: {emoji_description}, 匹配情感: {matched_emotion}")
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# 使用BaseAction的便捷方法发送表情包
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success = await self.send_emoji(emoji_base64)
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if not success:
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logger.error(f"{self.log_prefix} 表情包发送失败")
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return False, "表情包发送失败"
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# 重置NoReplyAction的连续计数器
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NoReplyAction.reset_consecutive_count()
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return True, f"发送表情包: {emoji_description}"
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except Exception as e:
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logger.error(f"{self.log_prefix} 表情动作执行失败: {e}")
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return False, f"表情发送失败: {str(e)}"
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@@ -18,8 +18,9 @@ from src.config.config import global_config
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from src.common.logger import get_logger
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# 导入API模块 - 标准Python包方式
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from src.plugin_system.apis import emoji_api, generator_api, message_api
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from src.plugin_system.apis import generator_api, message_api
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from src.plugins.built_in.core_actions.no_reply import NoReplyAction
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from src.plugins.built_in.core_actions.emoji import EmojiAction
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logger = get_logger("core_actions")
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@@ -112,72 +113,6 @@ class ReplyAction(BaseAction):
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return False, f"回复失败: {str(e)}"
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class EmojiAction(BaseAction):
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"""表情动作 - 发送表情包"""
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# 激活设置
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focus_activation_type = ActionActivationType.LLM_JUDGE
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normal_activation_type = ActionActivationType.RANDOM
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mode_enable = ChatMode.ALL
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parallel_action = True
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random_activation_probability = 0.2 # 默认值，可通过配置覆盖
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# 动作基本信息
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action_name = "emoji"
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action_description = "发送表情包辅助表达情绪"
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# LLM判断提示词
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llm_judge_prompt = """
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判定是否需要使用表情动作的条件：
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1. 用户明确要求使用表情包
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2. 这是一个适合表达强烈情绪的场合
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3. 不要发送太多表情包，如果你已经发送过多个表情包则回答"否"
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请回答"是"或"否"。
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"""
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# 动作参数定义
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action_parameters = {"description": "文字描述你想要发送的表情包内容"}
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# 动作使用场景
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action_require = ["表达情绪时可以选择使用", "重点：不要连续发，如果你已经发过[表情包]，就不要选择此动作"]
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# 关联类型
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associated_types = ["emoji"]
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async def execute(self) -> Tuple[bool, str]:
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"""执行表情动作"""
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logger.info(f"{self.log_prefix} 决定发送表情")
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try:
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# 1. 根据描述选择表情包
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description = self.action_data.get("description", "")
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emoji_result = await emoji_api.get_by_description(description)
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if not emoji_result:
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logger.warning(f"{self.log_prefix} 未找到匹配描述 '{description}' 的表情包")
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return False, f"未找到匹配 '{description}' 的表情包"
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emoji_base64, emoji_description, matched_emotion = emoji_result
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logger.info(f"{self.log_prefix} 找到表情包: {emoji_description}, 匹配情感: {matched_emotion}")
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# 使用BaseAction的便捷方法发送表情包
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success = await self.send_emoji(emoji_base64)
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if not success:
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logger.error(f"{self.log_prefix} 表情包发送失败")
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return False, "表情包发送失败"
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# 重置NoReplyAction的连续计数器
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NoReplyAction.reset_consecutive_count()
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return True, f"发送表情包: {emoji_description}"
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except Exception as e:
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logger.error(f"{self.log_prefix} 表情动作执行失败: {e}")
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return False, f"表情发送失败: {str(e)}"
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@register_plugin
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class CoreActionsPlugin(BasePlugin):
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"""核心动作插件
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