feat(action): 重构 Action 激活机制并添加 go_activate() 方法
引入新的 Action 激活机制，允许通过重写 go_activate() 方法来自定义激活逻辑。提供了三个工具函数： - _random_activation(): 随机概率激活 - _keyword_match(): 关键词匹配激活 - _llm_judge_activation(): LLM 智能判断激活 主要变更： - 在 BaseAction 中添加 go_activate() 抽象方法和相关工具函数 - 更新 ActionModifier 使用新的激活判断逻辑 - 在 hello_world_plugin 中添加新的激活方式示例 - 更新文档说明新的激活机制 - 保持向后兼容，旧的激活类型配置仍然可用 BREAKING CHANGE: Action 激活判断现在通过 go_activate() 方法进行，旧的激活类型字段已标记为废弃但仍然兼容
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Windpicker-owo
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docs/plugins/action-activation-guide.md
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docs/plugins/action-activation-guide.md
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# Action 激活机制重构指南
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## 📋 概述
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本文档介绍 MoFox-Bot Action 组件的新激活机制。新机制通过 `go_activate()` 方法提供更灵活、更强大的激活判断能力。
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## 🎯 为什么要重构？
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### 旧的激活机制的问题
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1. **不够灵活**：只能使用预定义的激活类型（`ALWAYS`、`NEVER`、`RANDOM`、`KEYWORD`、`LLM_JUDGE`）
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2. **难以组合**：无法轻松组合多种激活条件
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3. **配置复杂**：需要在类属性中配置多个字段
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4. **扩展困难**：添加新的激活逻辑需要修改核心代码
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### 新的激活机制的优势
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1. **完全自定义**：通过重写 `go_activate()` 方法实现任意激活逻辑
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2. **灵活组合**：可以轻松组合多种激活条件
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3. **简洁明了**：激活逻辑集中在一个方法中
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4. **易于扩展**：可以实现任何复杂的激活判断
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## 🚀 快速开始
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### 基本结构
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```python
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from src.plugin_system import BaseAction
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class MyAction(BaseAction):
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"""我的自定义 Action"""
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action_name = "my_action"
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action_description = "这是一个示例 Action"
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async def go_activate(self, llm_judge_model=None) -> bool:
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"""判断此 Action 是否应该被激活
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Args:
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chat_content: 聊天内容
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llm_judge_model: LLM 判断模型（可选）
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Returns:
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bool: True 表示激活，False 表示不激活
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"""
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# 在这里实现你的激活逻辑
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return True
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async def execute(self) -> tuple[bool, str]:
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"""执行 Action 的具体逻辑"""
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await self.send_text("Hello, World!")
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return True, "发送成功"
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```
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## 🛠️ 工具函数
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BaseAction 提供了三个便捷的工具函数来简化常见的激活判断：
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### 1. `_random_activation(probability)` - 随机激活
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```python
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async def go_activate(self, llm_judge_model=None) -> bool:
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"""30% 概率激活"""
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return await self._random_activation(0.3)
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```
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**参数：**
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- `probability`: 激活概率，范围 0.0 到 1.0
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### 2. `_keyword_match(keywords, case_sensitive)` - 关键词匹配
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```python
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async def go_activate(self, llm_judge_model=None) -> bool:
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"""当消息包含特定关键词时激活"""
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return await self._keyword_match(
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keywords=["你好", "hello", "hi"],
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case_sensitive=False # 不区分大小写
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)
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```
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**参数：**
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- `keywords`: 关键词列表
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- `case_sensitive`: 是否区分大小写（默认 False）
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### 3. `_llm_judge_activation(...)` - LLM 智能判断
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```python
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async def go_activate(self, llm_judge_model=None) -> bool:
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"""使用 LLM 判断是否激活"""
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return await self._llm_judge_activation(
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judge_prompt="当用户询问天气信息时激活",
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llm_judge_model=llm_judge_model
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)
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```
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**参数：**
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- `judge_prompt`: 判断提示词（核心判断逻辑）
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- `llm_judge_model`: LLM 模型实例（可选，会自动创建）
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- `action_description`: Action 描述（可选，默认使用类属性）
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- `action_require`: 使用场景列表（可选，默认使用类属性）
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## 📚 示例
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### 示例 1：简单的关键词激活
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```python
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class GreetingAction(BaseAction):
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"""问候 Action - 当检测到问候语时激活"""
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action_name = "greeting"
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action_description = "回应用户的问候"
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async def go_activate(self, llm_judge_model=None) -> bool:
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"""检测到问候语时激活"""
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return await self._keyword_match(
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keywords=["你好", "hello", "hi", "嗨"],
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case_sensitive=False
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)
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async def execute(self) -> tuple[bool, str]:
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await self.send_text("你好！很高兴见到你！👋")
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return True, "发送了问候"
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```
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### 示例 2：LLM 智能判断激活
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```python
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class ComfortAction(BaseAction):
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"""安慰 Action - 当用户情绪低落时激活"""
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action_name = "comfort"
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action_description = "提供情感支持和安慰"
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action_require = ["用户情绪低落", "需要安慰"]
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async def go_activate(self, llm_judge_model=None) -> bool:
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"""使用 LLM 判断用户是否需要安慰"""
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return await self._llm_judge_activation(
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judge_prompt="""
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判断用户是否表达了以下情绪或需求：
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1. 感到难过、沮丧或失落
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2. 表达了负面情绪
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3. 需要安慰或鼓励
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如果满足上述条件，回答"是"，否则回答"否"。
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""",
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llm_judge_model=llm_judge_model
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)
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async def execute(self) -> tuple[bool, str]:
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await self.send_text("看起来你心情不太好，希望能让你开心一点！🤗💕")
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return True, "发送了安慰"
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```
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### 示例 3：随机激活
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```python
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class RandomEmojiAction(BaseAction):
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"""随机表情 Action - 10% 概率激活"""
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action_name = "random_emoji"
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action_description = "随机发送表情增加趣味性"
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async def go_activate(self, llm_judge_model=None) -> bool:
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"""10% 概率激活"""
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return await self._random_activation(0.1)
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async def execute(self) -> tuple[bool, str]:
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import random
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emojis = ["😊", "😂", "👍", "🎉", "🤔", "🤖"]
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await self.send_text(random.choice(emojis))
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return True, "发送了表情"
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```
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### 示例 4：组合多种激活条件
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```python
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class FlexibleAction(BaseAction):
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"""灵活的 Action - 组合多种激活条件"""
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action_name = "flexible"
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action_description = "展示灵活的激活逻辑"
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async def go_activate(self, llm_judge_model=None) -> bool:
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"""组合激活：随机 20% 概率，或者匹配关键词"""
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# 策略 1: 随机激活
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if await self._random_activation(0.2):
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return True
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# 策略 2: 关键词匹配
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if await self._keyword_match(["表情", "emoji"], case_sensitive=False):
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return True
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# 策略 3: 所有条件都不满足
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return False
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async def execute(self) -> tuple[bool, str]:
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await self.send_text("这是一个灵活的激活示例！✨")
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return True, "执行成功"
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```
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### 示例 5：复杂的自定义逻辑
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```python
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class AdvancedAction(BaseAction):
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"""高级 Action - 实现复杂的激活逻辑"""
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action_name = "advanced"
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action_description = "高级激活逻辑示例"
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async def go_activate(self, llm_judge_model=None) -> bool:
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"""实现复杂的激活逻辑"""
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# 1. 检查时间：只在工作时间激活
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from datetime import datetime
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now = datetime.now()
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if now.hour < 9 or now.hour > 18:
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return False
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# 2. 检查消息长度：消息太短不激活
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if len(chat_content) < 10:
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return False
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# 3. 组合关键词和 LLM 判断
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has_keyword = await self._keyword_match(
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["帮助", "help", "求助"],
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case_sensitive=False
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)
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if has_keyword:
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# 如果匹配到关键词，用 LLM 进一步判断
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return await self._llm_judge_activation(
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judge_prompt="用户是否真的需要帮助？",
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llm_judge_model=llm_judge_model
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)
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return False
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async def execute(self) -> tuple[bool, str]:
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await self.send_text("我来帮助你！")
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return True, "提供了帮助"
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```
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### 示例 6：始终激活或从不激活
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```python
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class AlwaysActiveAction(BaseAction):
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"""始终激活的 Action"""
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action_name = "always_active"
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action_description = "这个 Action 总是激活"
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async def go_activate(self, llm_judge_model=None) -> bool:
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"""始终返回 True"""
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return True
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async def execute(self) -> tuple[bool, str]:
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await self.send_text("我总是可用！")
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return True, "执行成功"
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class NeverActiveAction(BaseAction):
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"""从不激活的 Action（可用于测试或临时禁用）"""
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action_name = "never_active"
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action_description = "这个 Action 从不激活"
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async def go_activate(self, llm_judge_model=None) -> bool:
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"""始终返回 False"""
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return False
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async def execute(self) -> tuple[bool, str]:
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# 这个方法不会被调用
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return False, "未执行"
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```
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## 🔄 从旧的激活机制迁移
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### 旧写法（已废弃但仍然兼容）
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```python
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class OldStyleAction(BaseAction):
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action_name = "old_style"
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action_description = "旧风格的 Action"
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# 旧的激活配置
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activation_type = ActionActivationType.KEYWORD
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activation_keywords = ["你好", "hello"]
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keyword_case_sensitive = False
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async def execute(self) -> tuple[bool, str]:
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return True, "执行成功"
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```
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### 新写法（推荐）
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```python
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class NewStyleAction(BaseAction):
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action_name = "new_style"
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action_description = "新风格的 Action"
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async def go_activate(self, llm_judge_model=None) -> bool:
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"""使用新的激活方式"""
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return await self._keyword_match(
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chat_content,
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keywords=["你好", "hello"],
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case_sensitive=False
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)
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async def execute(self) -> tuple[bool, str]:
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return True, "执行成功"
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```
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### 迁移对照表
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| 旧的激活类型 | 新的实现方式 |
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|-------------|-------------|
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| `ActionActivationType.ALWAYS` | `return True` |
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| `ActionActivationType.NEVER` | `return False` |
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| `ActionActivationType.RANDOM` | `return await self._random_activation(probability)` |
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| `ActionActivationType.KEYWORD` | `return await self._keyword_match( keywords)` |
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| `ActionActivationType.LLM_JUDGE` | `return await self._llm_judge_activation(judge_prompt, llm_judge_model)` |
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## ⚠️ 注意事项
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### 1. 向后兼容性
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旧的激活类型配置仍然有效！如果你的 Action 没有重写 `go_activate()` 方法，BaseAction 的默认实现会自动使用旧的配置字段。
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### 2. 性能考虑
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- `_random_activation()` 和 `_keyword_match()` 非常快速
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- `_llm_judge_activation()` 需要调用 LLM，会有延迟
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- ActionModifier 会并行执行所有 Action 的 `go_activate()` 方法以提高性能
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### 3. 日志记录
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工具函数会自动记录调试日志，便于追踪激活决策过程：
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```
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[DEBUG] 随机激活判断: 概率=0.3, 结果=激活
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[DEBUG] 匹配到关键词: ['你好', 'hello']
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[DEBUG] LLM 判断结果: 响应='是', 结果=激活
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```
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### 4. 错误处理
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- 如果 `go_activate()` 抛出异常，Action 会被标记为不激活
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- `_llm_judge_activation()` 在出错时默认返回 False（不激活）
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## 🎨 最佳实践
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### 1. 保持 `go_activate()` 方法简洁
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```python
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# ✅ 好的做法：简洁明了
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async def go_activate(self, llm_judge_model=None) -> bool:
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return await self._keyword_match(["帮助", "help"])
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# ❌ 不好的做法：过于复杂
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async def go_activate(self, llm_judge_model=None) -> bool:
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# 大量复杂的逻辑...
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# 应该拆分成辅助方法
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```
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### 2. 合理使用 LLM 判断
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```python
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# ✅ 好的做法：需要语义理解时使用 LLM
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async def go_activate(self, llm_judge_model=None) -> bool:
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# 判断用户情绪需要 LLM
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return await self._llm_judge_activation(
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"用户是否情绪低落？",
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llm_judge_model
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)
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# ❌ 不好的做法：简单匹配也用 LLM（浪费资源）
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async def go_activate(self, llm_judge_model=None) -> bool:
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# 简单的关键词匹配不需要 LLM
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return await self._llm_judge_activation(
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"消息是否包含'你好'？",
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llm_judge_model
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)
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```
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### 3. 组合条件时使用清晰的逻辑结构
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||||
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```python
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# ✅ 好的做法：清晰的条件组合
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async def go_activate(self, llm_judge_model=None) -> bool:
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# 策略 1: 快速路径 - 关键词匹配
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if await self._keyword_match(["紧急", "urgent"]):
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return True
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# 策略 2: 随机激活
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if await self._random_activation(0.1):
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return True
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||||
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# 策略 3: LLM 判断（最耗时，放最后）
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return await self._llm_judge_activation(
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"是否需要特别关注？",
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||||
llm_judge_model
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||||
)
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||||
```
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||||
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||||
## 📖 完整示例项目
|
||||
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||||
查看 `plugins/hello_world_plugin/plugin.py` 获取更多实际示例。
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||||
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||||
## 🤝 贡献
|
||||
|
||||
如果你有更好的激活逻辑实现，欢迎分享！
|
||||
|
||||
@@ -1,5 +1,9 @@
|
||||
# ⚡ Action组件详解
|
||||
|
||||
> **🎉 新功能：更灵活的激活机制！**
|
||||
> MoFox-Bot 现在支持通过 `go_activate()` 方法自定义 Action 激活逻辑！
|
||||
> 详见：[Action 激活机制重构指南](./action-activation-guide.md)
|
||||
|
||||
## 📖 什么是Action
|
||||
|
||||
Action是给MoFox_Bot在回复之外提供额外功能的智能组件，**由MoFox_Bot的决策系统自主选择是否使用**，具有随机性和拟人化的调用特点。Action不是直接响应用户命令，而是让MoFox_Bot根据聊天情境智能地选择合适的动作，使其行为更加自然和真实。
|
||||
@@ -72,11 +76,55 @@ Action采用**两层决策机制**来优化性能和决策质量：
|
||||
|
||||
**第一层：激活控制（Activation Control）**
|
||||
|
||||
激活决定MoFox-Bot是否 **“知道”** 这个Action的存在，即这个Action是否进入决策候选池。不被激活的ActionMoFox-Bot永远不会选择。
|
||||
激活决定MoFox-Bot是否 **"知道"** 这个Action的存在，即这个Action是否进入决策候选池。不被激活的ActionMoFox-Bot永远不会选择。
|
||||
|
||||
**第二层：使用决策（Usage Decision）**
|
||||
|
||||
在Action被激活后，使用条件决定MoFox-Bot什么时候会 **“选择”** 使用这个Action。
|
||||
在Action被激活后，使用条件决定MoFox-Bot什么时候会 **"选择"** 使用这个Action。
|
||||
|
||||
---
|
||||
|
||||
## 🆕 新的激活机制（推荐）
|
||||
|
||||
从现在开始，推荐使用 **`go_activate()` 方法** 来自定义 Action 的激活逻辑。这种方式更灵活、更强大！
|
||||
|
||||
### 快速示例
|
||||
|
||||
```python
|
||||
class MyAction(BaseAction):
|
||||
action_name = "my_action"
|
||||
action_description = "我的自定义 Action"
|
||||
|
||||
async def go_activate(self, llm_judge_model=None) -> bool:
|
||||
"""判断是否激活此 Action
|
||||
|
||||
注意：聊天内容会自动从实例属性中获取，无需手动传入
|
||||
"""
|
||||
# 关键词激活
|
||||
if await self._keyword_match(["你好", "hello"]):
|
||||
return True
|
||||
|
||||
# 或者随机 10% 概率激活
|
||||
return await self._random_activation(0.1)
|
||||
|
||||
async def execute(self) -> tuple[bool, str]:
|
||||
await self.send_text("Hello!")
|
||||
return True, "发送成功"
|
||||
```
|
||||
|
||||
**提供的工具函数：**
|
||||
- `_random_activation(probability)` - 随机激活
|
||||
- `_keyword_match(keywords)` - 关键词匹配（自动获取聊天内容）
|
||||
- `_llm_judge_activation(judge_prompt, llm_judge_model)` - LLM 智能判断（自动获取聊天内容）
|
||||
|
||||
**📚 完整指南：** 查看 [Action 激活机制重构指南](./action-activation-guide.md) 了解详情和更多示例。
|
||||
|
||||
---
|
||||
|
||||
## 📜 旧的激活机制（已废弃但仍然兼容）
|
||||
|
||||
> ⚠️ **注意：** 以下激活类型配置方式已废弃，但仍然兼容。
|
||||
> 推荐使用新的 `go_activate()` 方法来实现更灵活的激活逻辑。
|
||||
|
||||
### 决策参数详解 🔧
|
||||
|
||||
|
||||
@@ -57,16 +57,113 @@ class HelloCommand(PlusCommand):
|
||||
return True, "成功发送问候", True
|
||||
|
||||
|
||||
# ==================================================================================
|
||||
# 新的激活方式示例 Actions
|
||||
# ==================================================================================
|
||||
|
||||
|
||||
class KeywordActivationExampleAction(BaseAction):
|
||||
"""关键词激活示例
|
||||
|
||||
此示例展示如何使用关键词匹配来激活 Action。
|
||||
"""
|
||||
|
||||
action_name = "keyword_example"
|
||||
action_description = "当检测到特定关键词时发送回应"
|
||||
action_require = ["用户提到了问候语"]
|
||||
associated_types = ["text"]
|
||||
|
||||
async def go_activate(self, chat_content: str = "", llm_judge_model=None) -> bool:
|
||||
"""关键词激活：检测到"你好"、"hello"或"hi"时激活"""
|
||||
return await self._keyword_match(
|
||||
chat_content,
|
||||
keywords=["你好", "hello", "hi", "嗨"],
|
||||
case_sensitive=False # 不区分大小写
|
||||
)
|
||||
|
||||
async def execute(self) -> tuple[bool, str]:
|
||||
await self.send_text("检测到问候语，我也向你问好！👋")
|
||||
return True, "发送了问候回应"
|
||||
|
||||
|
||||
class LLMJudgeExampleAction(BaseAction):
|
||||
"""LLM 判断激活示例
|
||||
|
||||
此示例展示如何使用 LLM 来智能判断是否激活 Action。
|
||||
"""
|
||||
|
||||
action_name = "llm_judge_example"
|
||||
action_description = "当用户表达情绪低落时提供安慰"
|
||||
action_require = ["用户情绪低落", "需要情感支持"]
|
||||
associated_types = ["text"]
|
||||
|
||||
async def go_activate(self, chat_content: str = "", llm_judge_model=None) -> bool:
|
||||
"""LLM 判断激活：判断用户是否情绪低落"""
|
||||
return await self._llm_judge_activation(
|
||||
chat_content=chat_content,
|
||||
judge_prompt="""
|
||||
判断用户是否表达了以下情绪或需求：
|
||||
1. 感到难过、沮丧或失落
|
||||
2. 表达了负面情绪
|
||||
3. 需要安慰或鼓励
|
||||
|
||||
如果用户表达了上述情绪或需求，回答"是"，否则回答"否"。
|
||||
""",
|
||||
llm_judge_model=llm_judge_model
|
||||
)
|
||||
|
||||
async def execute(self) -> tuple[bool, str]:
|
||||
await self.send_text("看起来你心情不太好，希望能让你开心一点！🤗💕")
|
||||
return True, "发送了安慰消息"
|
||||
|
||||
|
||||
class CombinedActivationExampleAction(BaseAction):
|
||||
"""组合激活条件示例
|
||||
|
||||
此示例展示如何组合多种激活条件。
|
||||
"""
|
||||
|
||||
action_name = "combined_example"
|
||||
action_description = "展示如何组合多种激活条件"
|
||||
action_require = ["展示灵活的激活逻辑"]
|
||||
associated_types = ["text"]
|
||||
|
||||
async def go_activate(self, chat_content: str = "", llm_judge_model=None) -> bool:
|
||||
"""组合激活：随机 20% 概率，或者匹配特定关键词"""
|
||||
# 先尝试随机激活
|
||||
if await self._random_activation(0.2):
|
||||
return True
|
||||
|
||||
# 如果随机未激活，尝试关键词匹配
|
||||
if await self._keyword_match(chat_content, ["表情", "emoji", "😊"], case_sensitive=False):
|
||||
return True
|
||||
|
||||
# 都不满足则不激活
|
||||
return False
|
||||
|
||||
async def execute(self) -> tuple[bool, str]:
|
||||
await self.send_text("这是一个组合激活条件的示例！✨")
|
||||
return True, "发送了示例消息"
|
||||
|
||||
|
||||
class RandomEmojiAction(BaseAction):
|
||||
"""一个随机发送表情的动作。"""
|
||||
"""一个随机发送表情的动作。
|
||||
|
||||
此示例展示了如何使用新的 go_activate() 方法来实现随机激活。
|
||||
"""
|
||||
|
||||
action_name = "random_emoji"
|
||||
action_description = "随机发送一个表情符号，增加聊天的趣味性。"
|
||||
activation_type = ActionActivationType.RANDOM
|
||||
random_activation_probability = 0.1
|
||||
action_require = ["当对话气氛轻松时", "可以用来回应简单的情感表达"]
|
||||
associated_types = ["text"]
|
||||
|
||||
async def go_activate(self, llm_judge_model=None) -> bool:
|
||||
"""使用新的激活方式：10% 的概率激活
|
||||
|
||||
注意：不需要传入 chat_content，会自动从实例属性中获取
|
||||
"""
|
||||
return await self._random_activation(0.1)
|
||||
|
||||
async def execute(self) -> tuple[bool, str]:
|
||||
emojis = ["😊", "😂", "👍", "🎉", "🤔", "🤖"]
|
||||
await self.send_text(random.choice(emojis))
|
||||
|
||||
@@ -197,6 +197,8 @@ class ActionModifier:
|
||||
"""
|
||||
根据激活类型过滤，返回需要停用的动作列表及原因
|
||||
|
||||
新的实现：调用每个 Action 类的 go_activate 方法来判断是否激活
|
||||
|
||||
Args:
|
||||
actions_with_info: 带完整信息的动作字典
|
||||
chat_content: 聊天内容
|
||||
@@ -206,55 +208,71 @@ class ActionModifier:
|
||||
"""
|
||||
deactivated_actions = []
|
||||
|
||||
# 分类处理不同激活类型的actions
|
||||
llm_judge_actions = {}
|
||||
# 获取 Action 类注册表
|
||||
from src.plugin_system.core.component_registry import component_registry
|
||||
from src.plugin_system.base.component_types import ComponentType
|
||||
|
||||
actions_to_check = list(actions_with_info.items())
|
||||
random.shuffle(actions_to_check)
|
||||
|
||||
# 创建并行任务列表
|
||||
activation_tasks = []
|
||||
task_action_names = []
|
||||
|
||||
for action_name, action_info in actions_to_check:
|
||||
activation_type = action_info.activation_type or action_info.focus_activation_type
|
||||
# 获取 Action 类
|
||||
action_class = component_registry.get_component_class(action_name, ComponentType.ACTION)
|
||||
if not action_class:
|
||||
logger.warning(f"{self.log_prefix}未找到 Action 类: {action_name}，默认不激活")
|
||||
deactivated_actions.append((action_name, "未找到 Action 类"))
|
||||
continue
|
||||
|
||||
if activation_type == ActionActivationType.ALWAYS:
|
||||
continue # 总是激活，无需处理
|
||||
# 创建一个临时实例来调用 go_activate 方法
|
||||
# 注意：这里只是为了调用 go_activate，不需要完整的初始化
|
||||
try:
|
||||
# 创建一个最小化的实例
|
||||
action_instance = object.__new__(action_class)
|
||||
# 设置必要的属性
|
||||
action_instance.action_name = action_name
|
||||
action_instance.log_prefix = self.log_prefix
|
||||
# 设置聊天内容，用于激活判断
|
||||
action_instance._activation_chat_content = chat_content
|
||||
|
||||
elif activation_type == ActionActivationType.RANDOM:
|
||||
probability = action_info.random_activation_probability
|
||||
probability = action_info.random_activation_probability
|
||||
if random.random() >= probability:
|
||||
reason = f"RANDOM类型未触发（概率{probability}）"
|
||||
deactivated_actions.append((action_name, reason))
|
||||
logger.debug(f"{self.log_prefix}未激活动作: {action_name}，原因: {reason}")
|
||||
# 调用 go_activate 方法（不再需要传入 chat_content）
|
||||
task = action_instance.go_activate(
|
||||
llm_judge_model=self.llm_judge,
|
||||
)
|
||||
activation_tasks.append(task)
|
||||
task_action_names.append(action_name)
|
||||
|
||||
elif activation_type == ActionActivationType.KEYWORD:
|
||||
if not self._check_keyword_activation(action_name, action_info, chat_content):
|
||||
keywords = action_info.activation_keywords
|
||||
reason = f"关键词未匹配（关键词: {keywords}）"
|
||||
deactivated_actions.append((action_name, reason))
|
||||
logger.debug(f"{self.log_prefix}未激活动作: {action_name}，原因: {reason}")
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix}创建 Action 实例 {action_name} 失败: {e}")
|
||||
deactivated_actions.append((action_name, f"创建实例失败: {e}"))
|
||||
|
||||
elif activation_type == ActionActivationType.LLM_JUDGE:
|
||||
llm_judge_actions[action_name] = action_info
|
||||
# 并行执行所有激活判断
|
||||
if activation_tasks:
|
||||
logger.debug(f"{self.log_prefix}并行执行激活判断，任务数: {len(activation_tasks)}")
|
||||
try:
|
||||
task_results = await asyncio.gather(*activation_tasks, return_exceptions=True)
|
||||
|
||||
elif activation_type == ActionActivationType.NEVER:
|
||||
reason = "激活类型为never"
|
||||
deactivated_actions.append((action_name, reason))
|
||||
logger.debug(f"{self.log_prefix}未激活动作: {action_name}，原因: 激活类型为never")
|
||||
# 处理结果
|
||||
for action_name, result in zip(task_action_names, task_results, strict=False):
|
||||
if isinstance(result, Exception):
|
||||
logger.error(f"{self.log_prefix}激活判断 {action_name} 时出错: {result}")
|
||||
deactivated_actions.append((action_name, f"激活判断出错: {result}"))
|
||||
elif not result:
|
||||
# go_activate 返回 False，不激活
|
||||
deactivated_actions.append((action_name, "go_activate 返回 False"))
|
||||
logger.debug(f"{self.log_prefix}未激活动作: {action_name}，原因: go_activate 返回 False")
|
||||
else:
|
||||
# go_activate 返回 True，激活
|
||||
logger.debug(f"{self.log_prefix}激活动作: {action_name}")
|
||||
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix}未知的激活类型: {activation_type}，跳过处理")
|
||||
|
||||
# 并行处理LLM_JUDGE类型
|
||||
if llm_judge_actions:
|
||||
llm_results = await self._process_llm_judge_actions_parallel(
|
||||
llm_judge_actions,
|
||||
chat_content,
|
||||
)
|
||||
for action_name, should_activate in llm_results.items():
|
||||
if not should_activate:
|
||||
reason = "LLM判定未激活"
|
||||
deactivated_actions.append((action_name, reason))
|
||||
logger.debug(f"{self.log_prefix}未激活动作: {action_name}，原因: {reason}")
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix}并行激活判断失败: {e}")
|
||||
# 如果并行执行失败，为所有任务默认不激活
|
||||
for action_name in task_action_names:
|
||||
deactivated_actions.append((action_name, f"并行判断失败: {e}"))
|
||||
|
||||
return deactivated_actions
|
||||
|
||||
|
||||
@@ -1,13 +1,18 @@
|
||||
# Todo: 重构Action,这里现在只剩下了报错。
|
||||
import asyncio
|
||||
import random
|
||||
import time
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from src.chat.message_receive.chat_stream import ChatStream
|
||||
from src.common.logger import get_logger
|
||||
from src.plugin_system.apis import database_api, message_api, send_api
|
||||
from src.plugin_system.base.component_types import ActionActivationType, ActionInfo, ChatMode, ChatType, ComponentType
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
|
||||
logger = get_logger("base_action")
|
||||
|
||||
|
||||
@@ -16,16 +21,53 @@ class BaseAction(ABC):
|
||||
|
||||
Action是插件的一种组件类型，用于处理聊天中的动作逻辑
|
||||
|
||||
子类可以通过类属性定义激活条件，这些会在实例化时转换为实例属性：
|
||||
==================================================================================
|
||||
新的激活机制 (推荐使用)
|
||||
==================================================================================
|
||||
推荐通过重写 go_activate() 方法来自定义激活逻辑：
|
||||
|
||||
示例 1 - 关键词激活：
|
||||
async def go_activate(self, llm_judge_model=None) -> bool:
|
||||
return await self._keyword_match(["你好", "hello"])
|
||||
|
||||
示例 2 - LLM 判断激活：
|
||||
async def go_activate(self, llm_judge_model=None) -> bool:
|
||||
return await self._llm_judge_activation(
|
||||
"当用户询问天气信息时激活",
|
||||
llm_judge_model
|
||||
)
|
||||
|
||||
示例 3 - 组合多种条件：
|
||||
async def go_activate(self, llm_judge_model=None) -> bool:
|
||||
# 30% 随机概率，或者匹配关键词
|
||||
if await self._random_activation(0.3):
|
||||
return True
|
||||
return await self._keyword_match(["表情", "emoji"])
|
||||
|
||||
提供的工具函数：
|
||||
- _random_activation(probability): 随机激活
|
||||
- _keyword_match(keywords, case_sensitive): 关键词匹配（自动获取聊天内容）
|
||||
- _llm_judge_activation(judge_prompt, llm_judge_model): LLM 判断（自动获取聊天内容）
|
||||
|
||||
注意：聊天内容会自动从实例属性中获取，无需手动传入。
|
||||
|
||||
==================================================================================
|
||||
旧的激活机制 (已废弃，但仍然兼容)
|
||||
==================================================================================
|
||||
子类可以通过类属性定义激活条件（已废弃，但 go_activate() 的默认实现会使用这些）：
|
||||
- focus_activation_type: 专注模式激活类型
|
||||
- normal_activation_type: 普通模式激活类型
|
||||
- activation_keywords: 激活关键词列表
|
||||
- keyword_case_sensitive: 关键词是否区分大小写
|
||||
- mode_enable: 启用的聊天模式
|
||||
- parallel_action: 是否允许并行执行
|
||||
- random_activation_probability: 随机激活概率
|
||||
- llm_judge_prompt: LLM判断提示词
|
||||
|
||||
==================================================================================
|
||||
其他类属性
|
||||
==================================================================================
|
||||
- mode_enable: 启用的聊天模式
|
||||
- parallel_action: 是否允许并行执行
|
||||
|
||||
二步Action相关属性：
|
||||
- is_two_step_action: 是否为二步Action
|
||||
- step_one_description: 第一步的描述
|
||||
@@ -559,6 +601,247 @@ class BaseAction(ABC):
|
||||
# 子类需要重写此方法来实现具体的第二步逻辑
|
||||
return False, f"二步Action必须实现execute_step_two方法来处理操作: {sub_action_name}"
|
||||
|
||||
# =============================================================================
|
||||
# 新的激活机制 - go_activate 和工具函数
|
||||
# =============================================================================
|
||||
|
||||
def _get_chat_content(self) -> str:
|
||||
"""获取聊天内容用于激活判断
|
||||
|
||||
从实例属性中获取聊天内容。子类可以重写此方法来自定义获取逻辑。
|
||||
|
||||
Returns:
|
||||
str: 聊天内容
|
||||
"""
|
||||
# 尝试从不同的实例属性中获取聊天内容
|
||||
# 优先级：_activation_chat_content > action_data['chat_content'] > ""
|
||||
|
||||
# 1. 如果有专门设置的激活用聊天内容（由 ActionModifier 设置）
|
||||
if hasattr(self, '_activation_chat_content'):
|
||||
return getattr(self, '_activation_chat_content', "")
|
||||
|
||||
# 2. 尝试从 action_data 中获取
|
||||
if hasattr(self, 'action_data') and isinstance(self.action_data, dict):
|
||||
return self.action_data.get('chat_content', "")
|
||||
|
||||
# 3. 默认返回空字符串
|
||||
return ""
|
||||
|
||||
async def go_activate(
|
||||
self,
|
||||
llm_judge_model: "LLMRequest | None" = None,
|
||||
) -> bool:
|
||||
"""判断此 Action 是否应该被激活
|
||||
|
||||
这是新的激活机制的核心方法。子类可以重写此方法来实现自定义的激活逻辑，
|
||||
也可以使用提供的工具函数来简化常见的激活判断。
|
||||
|
||||
默认实现会检查类属性中的激活类型配置，提供向后兼容支持。
|
||||
|
||||
聊天内容会自动从实例属性中获取，不需要手动传入。
|
||||
|
||||
Args:
|
||||
llm_judge_model: LLM 判断模型，如果需要使用 LLM 判断
|
||||
|
||||
Returns:
|
||||
bool: True 表示应该激活，False 表示不激活
|
||||
|
||||
Example:
|
||||
>>> # 简单的关键词激活
|
||||
>>> async def go_activate(self, llm_judge_model=None) -> bool:
|
||||
>>> return await self._keyword_match(["你好", "hello"])
|
||||
>>>
|
||||
>>> # LLM 判断激活
|
||||
>>> async def go_activate(self, llm_judge_model=None) -> bool:
|
||||
>>> return await self._llm_judge_activation(
|
||||
>>> "当用户询问天气信息时激活",
|
||||
>>> llm_judge_model
|
||||
>>> )
|
||||
>>>
|
||||
>>> # 组合多种条件
|
||||
>>> async def go_activate(self, llm_judge_model=None) -> bool:
|
||||
>>> # 随机 30% 概率，或者匹配关键词
|
||||
>>> if await self._random_activation(0.3):
|
||||
>>> return True
|
||||
>>> return await self._keyword_match(["天气"])
|
||||
"""
|
||||
# 默认实现：向后兼容旧的激活类型系统
|
||||
activation_type = getattr(self, "activation_type", ActionActivationType.ALWAYS)
|
||||
|
||||
if activation_type == ActionActivationType.ALWAYS:
|
||||
return True
|
||||
|
||||
elif activation_type == ActionActivationType.NEVER:
|
||||
return False
|
||||
|
||||
elif activation_type == ActionActivationType.RANDOM:
|
||||
probability = getattr(self, "random_activation_probability", 0.0)
|
||||
return await self._random_activation(probability)
|
||||
|
||||
elif activation_type == ActionActivationType.KEYWORD:
|
||||
keywords = getattr(self, "activation_keywords", [])
|
||||
case_sensitive = getattr(self, "keyword_case_sensitive", False)
|
||||
return await self._keyword_match(keywords, case_sensitive)
|
||||
|
||||
elif activation_type == ActionActivationType.LLM_JUDGE:
|
||||
prompt = getattr(self, "llm_judge_prompt", "")
|
||||
return await self._llm_judge_activation(
|
||||
judge_prompt=prompt,
|
||||
llm_judge_model=llm_judge_model,
|
||||
)
|
||||
|
||||
# 未知类型，默认不激活
|
||||
logger.warning(f"{self.log_prefix} 未知的激活类型: {activation_type}")
|
||||
return False
|
||||
|
||||
async def _random_activation(self, probability: float) -> bool:
|
||||
"""随机激活工具函数
|
||||
|
||||
Args:
|
||||
probability: 激活概率，范围 0.0 到 1.0
|
||||
|
||||
Returns:
|
||||
bool: 是否激活
|
||||
"""
|
||||
result = random.random() < probability
|
||||
logger.debug(f"{self.log_prefix} 随机激活判断: 概率={probability}, 结果={'激活' if result else '不激活'}")
|
||||
return result
|
||||
|
||||
async def _keyword_match(
|
||||
self,
|
||||
keywords: list[str],
|
||||
case_sensitive: bool = False,
|
||||
) -> bool:
|
||||
"""关键词匹配工具函数
|
||||
|
||||
聊天内容会自动从实例属性中获取。
|
||||
|
||||
Args:
|
||||
keywords: 关键词列表
|
||||
case_sensitive: 是否区分大小写
|
||||
|
||||
Returns:
|
||||
bool: 是否匹配到关键词
|
||||
"""
|
||||
if not keywords:
|
||||
logger.warning(f"{self.log_prefix} 关键词列表为空，默认不激活")
|
||||
return False
|
||||
|
||||
# 自动获取聊天内容
|
||||
chat_content = self._get_chat_content()
|
||||
|
||||
search_text = chat_content
|
||||
if not case_sensitive:
|
||||
search_text = search_text.lower()
|
||||
|
||||
matched_keywords = []
|
||||
for keyword in keywords:
|
||||
check_keyword = keyword if case_sensitive else keyword.lower()
|
||||
if check_keyword in search_text:
|
||||
matched_keywords.append(keyword)
|
||||
|
||||
if matched_keywords:
|
||||
logger.debug(f"{self.log_prefix} 匹配到关键词: {matched_keywords}")
|
||||
return True
|
||||
else:
|
||||
logger.debug(f"{self.log_prefix} 未匹配到任何关键词: {keywords}")
|
||||
return False
|
||||
|
||||
async def _llm_judge_activation(
|
||||
self,
|
||||
judge_prompt: str = "",
|
||||
llm_judge_model: "LLMRequest | None" = None,
|
||||
action_description: str = "",
|
||||
action_require: list[str] | None = None,
|
||||
) -> bool:
|
||||
"""LLM 判断激活工具函数
|
||||
|
||||
使用 LLM 来判断是否应该激活此 Action。
|
||||
会自动构建完整的判断提示词，只需要提供核心判断逻辑即可。
|
||||
|
||||
聊天内容会自动从实例属性中获取。
|
||||
|
||||
Args:
|
||||
judge_prompt: 自定义判断提示词（核心判断逻辑）
|
||||
llm_judge_model: LLM 判断模型实例，如果为 None 则会创建默认的小模型
|
||||
action_description: Action 描述，如果不提供则使用类属性
|
||||
action_require: Action 使用场景，如果不提供则使用类属性
|
||||
|
||||
Returns:
|
||||
bool: 是否应该激活
|
||||
|
||||
Example:
|
||||
>>> # 最简单的用法
|
||||
>>> result = await self._llm_judge_activation(
|
||||
>>> "当用户询问天气信息时激活"
|
||||
>>> )
|
||||
>>>
|
||||
>>> # 提供详细信息
|
||||
>>> result = await self._llm_judge_activation(
|
||||
>>> judge_prompt="当用户表达情绪或需要情感支持时激活",
|
||||
>>> action_description="发送安慰表情包",
|
||||
>>> action_require=["用户情绪低落", "需要情感支持"]
|
||||
>>> )
|
||||
"""
|
||||
try:
|
||||
# 自动获取聊天内容
|
||||
chat_content = self._get_chat_content()
|
||||
|
||||
# 如果没有提供 LLM 模型，创建一个默认的
|
||||
if llm_judge_model is None:
|
||||
from src.config.config import model_config
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
|
||||
llm_judge_model = LLMRequest(
|
||||
model_set=model_config.model_task_config.utils_small,
|
||||
request_type="action.judge",
|
||||
)
|
||||
|
||||
# 使用类属性作为默认值
|
||||
if not action_description:
|
||||
action_description = getattr(self, "action_description", "Action 动作")
|
||||
|
||||
if action_require is None:
|
||||
action_require = getattr(self, "action_require", [])
|
||||
|
||||
# 构建完整的判断提示词
|
||||
prompt = f"""你需要判断在当前聊天情况下，是否应该激活名为"{self.action_name}"的动作。
|
||||
|
||||
动作描述：{action_description}
|
||||
"""
|
||||
|
||||
if action_require:
|
||||
prompt += "\n动作使用场景：\n"
|
||||
for req in action_require:
|
||||
prompt += f"- {req}\n"
|
||||
|
||||
if judge_prompt:
|
||||
prompt += f"\n额外判定条件：\n{judge_prompt}\n"
|
||||
|
||||
if chat_content:
|
||||
prompt += f"\n当前聊天记录：\n{chat_content}\n"
|
||||
|
||||
prompt += """
|
||||
请根据以上信息判断是否应该激活这个动作。
|
||||
只需要回答"是"或"否"，不要有其他内容。
|
||||
"""
|
||||
|
||||
# 调用 LLM 进行判断
|
||||
response, _ = await llm_judge_model.generate_response_async(prompt=prompt)
|
||||
response = response.strip().lower()
|
||||
|
||||
should_activate = "是" in response or "yes" in response or "true" in response
|
||||
|
||||
logger.debug(
|
||||
f"{self.log_prefix} LLM 判断结果: 响应='{response}', 结果={'激活' if should_activate else '不激活'}"
|
||||
)
|
||||
return should_activate
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} LLM 判断激活时出错: {e}")
|
||||
# 出错时默认不激活
|
||||
return False
|
||||
|
||||
@abstractmethod
|
||||
async def execute(self) -> tuple[bool, str]:
|
||||
"""执行Action的抽象方法，子类必须实现
|
||||
|
||||
@@ -132,21 +132,30 @@ class ComponentInfo:
|
||||
|
||||
@dataclass
|
||||
class ActionInfo(ComponentInfo):
|
||||
"""动作组件信息"""
|
||||
"""动作组件信息
|
||||
|
||||
注意：激活类型相关字段已废弃，推荐使用 Action 类的 go_activate() 方法来自定义激活逻辑。
|
||||
这些字段将继续保留以提供向后兼容性，BaseAction.go_activate() 的默认实现会使用这些字段。
|
||||
"""
|
||||
|
||||
action_parameters: dict[str, str] = field(
|
||||
default_factory=dict
|
||||
) # 动作参数与描述，例如 {"param1": "描述1", "param2": "描述2"}
|
||||
action_require: list[str] = field(default_factory=list) # 动作需求说明
|
||||
associated_types: list[str] = field(default_factory=list) # 关联的消息类型
|
||||
# 激活类型相关
|
||||
focus_activation_type: ActionActivationType = ActionActivationType.ALWAYS
|
||||
normal_activation_type: ActionActivationType = ActionActivationType.ALWAYS
|
||||
activation_type: ActionActivationType = ActionActivationType.ALWAYS
|
||||
random_activation_probability: float = 0.0
|
||||
llm_judge_prompt: str = ""
|
||||
activation_keywords: list[str] = field(default_factory=list) # 激活关键词列表
|
||||
keyword_case_sensitive: bool = False
|
||||
|
||||
# ==================================================================================
|
||||
# 激活类型相关字段（已废弃，建议使用 go_activate() 方法）
|
||||
# 保留这些字段是为了向后兼容，BaseAction.go_activate() 的默认实现会使用这些字段
|
||||
# ==================================================================================
|
||||
focus_activation_type: ActionActivationType = ActionActivationType.ALWAYS # 已废弃
|
||||
normal_activation_type: ActionActivationType = ActionActivationType.ALWAYS # 已废弃
|
||||
activation_type: ActionActivationType = ActionActivationType.ALWAYS # 已废弃
|
||||
random_activation_probability: float = 0.0 # 已废弃，建议在 go_activate() 中使用 _random_activation()
|
||||
llm_judge_prompt: str = "" # 已废弃，建议在 go_activate() 中使用 _llm_judge_activation()
|
||||
activation_keywords: list[str] = field(default_factory=list) # 已废弃，建议在 go_activate() 中使用 _keyword_match()
|
||||
keyword_case_sensitive: bool = False # 已废弃
|
||||
|
||||
# 模式和并行设置
|
||||
mode_enable: ChatMode = ChatMode.ALL
|
||||
parallel_action: bool = False
|
||||
|
||||
@@ -18,8 +18,36 @@ logger = get_logger("emoji")
|
||||
|
||||
|
||||
class EmojiAction(BaseAction):
|
||||
"""表情动作 - 发送表情包"""
|
||||
"""表情动作 - 发送表情包
|
||||
|
||||
注意：此 Action 使用旧的激活类型配置方式（已废弃但仍然兼容）。
|
||||
BaseAction.go_activate() 的默认实现会自动处理这些旧配置。
|
||||
|
||||
推荐的新写法（迁移示例）：
|
||||
----------------------------------------
|
||||
# 移除下面的 activation_type 相关配置，改为重写 go_activate 方法：
|
||||
|
||||
async def go_activate(self, chat_content: str = "", llm_judge_model=None) -> bool:
|
||||
# 根据配置选择激活方式
|
||||
if global_config.emoji.emoji_activate_type == "llm":
|
||||
return await self._llm_judge_activation(
|
||||
chat_content=chat_content,
|
||||
judge_prompt=\"""
|
||||
判定是否需要使用表情动作的条件：
|
||||
1. 用户明确要求使用表情包
|
||||
2. 这是一个适合表达情绪的场合
|
||||
3. 发表情包能使当前对话更有趣
|
||||
4. 不要发送太多表情包
|
||||
\""",
|
||||
llm_judge_model=llm_judge_model
|
||||
)
|
||||
else:
|
||||
# 使用随机激活
|
||||
return await self._random_activation(global_config.emoji.emoji_chance)
|
||||
----------------------------------------
|
||||
"""
|
||||
|
||||
# ========== 以下使用旧的激活配置（已废弃但兼容） ==========
|
||||
# 激活设置
|
||||
if global_config.emoji.emoji_activate_type == "llm":
|
||||
activation_type = ActionActivationType.LLM_JUDGE
|
||||
|
||||
Reference in New Issue
Block a user