refactor(core): 优化类型提示与代码风格
本次提交对项目代码进行了广泛的重构，主要集中在以下几个方面：
1. **类型提示现代化**:
- 将 `typing` 模块中的 `Optional[T]`、`List[T]`、`Dict[K, V]` 等旧式类型提示更新为现代的 `T | None`、`list[T]`、`dict[K, V]` 语法。
- 这提高了代码的可读性，并与较新 Python 版本的风格保持一致。
2. **代码风格统一**:
- 移除了多余的空行和不必要的空格，使代码更加紧凑和规范。
- 统一了部分日志输出的格式，增强了日志的可读性。
3. **导入语句优化**:
- 调整了部分模块的 `import` 语句顺序，使其符合 PEP 8 规范。
这些更改不涉及任何功能性变动，旨在提升代码库的整体质量、可维护性和开发体验。
This commit is contained in:
minecraft1024a
@@ -5,7 +5,6 @@
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"""
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import json
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import time
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from typing import Any
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from sqlalchemy import select
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@@ -22,7 +21,7 @@ logger = get_logger("chat_stream_impression_tool")
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class ChatStreamImpressionTool(BaseTool):
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"""聊天流印象更新工具
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使用二步调用机制：
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1. LLM决定是否调用工具并传入初步参数（stream_id会自动传入）
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2. 工具内部调用LLM，结合现有数据和传入参数，决定最终更新内容
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@@ -31,27 +30,52 @@ class ChatStreamImpressionTool(BaseTool):
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name = "update_chat_stream_impression"
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description = "当你通过观察聊天记录对当前聊天环境（群聊或私聊）产生了整体印象或认识时使用此工具，更新对这个聊天流的看法。包括：环境氛围、聊天风格、常见话题、你的兴趣程度。调用时机：当你发现这个聊天环境有明显的氛围特点（如很活跃、很专业、很闲聊）、群成员经常讨论某类话题、或者你对这个环境的感受发生变化时。注意：这是对整个聊天环境的印象，而非对单个用户。"
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parameters = [
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("impression_description", ToolParamType.STRING, "你对这个聊天环境的整体感受和印象，例如'这是个技术氛围浓厚的群'、'大家都很友好热情'。当你通过聊天记录感受到环境特点时填写（可选）", False, None),
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("chat_style", ToolParamType.STRING, "这个聊天环境的风格特征，如'活跃热闹,互帮互助'、'严肃专业,深度讨论'、'轻松闲聊,段子频出'等。当你发现聊天方式有明显特点时填写（可选）", False, None),
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("topic_keywords", ToolParamType.STRING, "这个聊天环境中经常出现的话题，如'编程,AI,技术分享'或'游戏,动漫,娱乐'。当你观察到群里反复讨论某些主题时填写，多个关键词用逗号分隔（可选）", False, None),
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("interest_score", ToolParamType.FLOAT, "你对这个聊天环境的兴趣和喜欢程度，0.0(无聊/不喜欢)到1.0(很有趣/很喜欢)。当你对这个环境的感觉发生变化时更新（可选）", False, None),
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(
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"impression_description",
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ToolParamType.STRING,
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"你对这个聊天环境的整体感受和印象，例如'这是个技术氛围浓厚的群'、'大家都很友好热情'。当你通过聊天记录感受到环境特点时填写（可选）",
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False,
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None,
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),
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(
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"chat_style",
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ToolParamType.STRING,
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"这个聊天环境的风格特征，如'活跃热闹,互帮互助'、'严肃专业,深度讨论'、'轻松闲聊,段子频出'等。当你发现聊天方式有明显特点时填写（可选）",
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False,
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None,
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),
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(
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"topic_keywords",
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ToolParamType.STRING,
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"这个聊天环境中经常出现的话题，如'编程,AI,技术分享'或'游戏,动漫,娱乐'。当你观察到群里反复讨论某些主题时填写，多个关键词用逗号分隔（可选）",
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False,
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None,
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),
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(
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"interest_score",
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ToolParamType.FLOAT,
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"你对这个聊天环境的兴趣和喜欢程度，0.0(无聊/不喜欢)到1.0(很有趣/很喜欢)。当你对这个环境的感觉发生变化时更新（可选）",
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False,
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None,
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),
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]
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available_for_llm = True
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history_ttl = 5
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def __init__(self, plugin_config: dict | None = None, chat_stream: Any = None):
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super().__init__(plugin_config, chat_stream)
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# 初始化用于二步调用的LLM
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try:
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self.impression_llm = LLMRequest(
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model_set=model_config.model_task_config.relationship_tracker,
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request_type="chat_stream_impression_update"
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request_type="chat_stream_impression_update",
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)
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except AttributeError:
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# 降级处理
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available_models = [
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attr for attr in dir(model_config.model_task_config)
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attr
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for attr in dir(model_config.model_task_config)
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if not attr.startswith("_") and attr != "model_dump"
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]
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if available_models:
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@@ -59,7 +83,7 @@ class ChatStreamImpressionTool(BaseTool):
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logger.warning(f"relationship_tracker配置不存在，使用降级模型: {fallback_model}")
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self.impression_llm = LLMRequest(
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model_set=getattr(model_config.model_task_config, fallback_model),
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request_type="chat_stream_impression_update"
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request_type="chat_stream_impression_update",
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)
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else:
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logger.error("无可用的模型配置")
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@@ -67,17 +91,17 @@ class ChatStreamImpressionTool(BaseTool):
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async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
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"""执行聊天流印象更新
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Args:
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function_args: 工具参数
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Returns:
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dict: 执行结果
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"""
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try:
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# 优先从 function_args 获取 stream_id
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stream_id = function_args.get("stream_id")
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# 如果没有，从 chat_stream 对象获取
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if not stream_id and self.chat_stream:
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try:
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@@ -85,61 +109,49 @@ class ChatStreamImpressionTool(BaseTool):
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logger.debug(f"从 chat_stream 获取到 stream_id: {stream_id}")
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except AttributeError:
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logger.warning("chat_stream 对象没有 stream_id 属性")
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# 如果还是没有，返回错误
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if not stream_id:
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logger.error("无法获取 stream_id：function_args 和 chat_stream 都没有提供")
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return {
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"type": "error",
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"id": "chat_stream_impression",
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"content": "错误：无法获取当前聊天流ID"
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}
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return {"type": "error", "id": "chat_stream_impression", "content": "错误：无法获取当前聊天流ID"}
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# 从LLM传入的参数
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new_impression = function_args.get("impression_description", "")
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new_style = function_args.get("chat_style", "")
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new_topics = function_args.get("topic_keywords", "")
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new_score = function_args.get("interest_score")
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# 从数据库获取现有聊天流印象
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existing_impression = await self._get_stream_impression(stream_id)
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# 如果LLM没有传入任何有效参数，返回提示
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if not any([new_impression, new_style, new_topics, new_score is not None]):
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return {
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"type": "info",
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"id": stream_id,
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"content": "提示：需要提供至少一项更新内容（印象描述、聊天风格、话题关键词或兴趣分数）"
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"content": "提示：需要提供至少一项更新内容（印象描述、聊天风格、话题关键词或兴趣分数）",
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}
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# 调用LLM进行二步决策
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if self.impression_llm is None:
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logger.error("LLM未正确初始化，无法执行二步调用")
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return {
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"type": "error",
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"id": stream_id,
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"content": "系统错误：LLM未正确初始化"
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}
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return {"type": "error", "id": stream_id, "content": "系统错误：LLM未正确初始化"}
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final_impression = await self._llm_decide_final_impression(
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stream_id=stream_id,
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existing_impression=existing_impression,
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new_impression=new_impression,
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new_style=new_style,
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new_topics=new_topics,
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new_score=new_score
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new_score=new_score,
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)
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if not final_impression:
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return {
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"type": "error",
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"id": stream_id,
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"content": "LLM决策失败，无法更新聊天流印象"
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}
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return {"type": "error", "id": stream_id, "content": "LLM决策失败，无法更新聊天流印象"}
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# 更新数据库
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await self._update_stream_impression_in_db(stream_id, final_impression)
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# 构建返回信息
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updates = []
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if final_impression.get("stream_impression_text"):
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@@ -150,30 +162,26 @@ class ChatStreamImpressionTool(BaseTool):
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updates.append(f"话题: {final_impression['stream_topic_keywords']}")
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if final_impression.get("stream_interest_score") is not None:
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updates.append(f"兴趣分: {final_impression['stream_interest_score']:.2f}")
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result_text = f"已更新聊天流 {stream_id} 的印象：\n" + "\n".join(updates)
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logger.info(f"聊天流印象更新成功: {stream_id}")
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return {
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"type": "chat_stream_impression_update",
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"id": stream_id,
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"content": result_text
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}
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return {"type": "chat_stream_impression_update", "id": stream_id, "content": result_text}
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except Exception as e:
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logger.error(f"聊天流印象更新失败: {e}", exc_info=True)
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return {
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"type": "error",
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"id": function_args.get("stream_id", "unknown"),
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"content": f"聊天流印象更新失败: {str(e)}"
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"content": f"聊天流印象更新失败: {e!s}",
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}
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async def _get_stream_impression(self, stream_id: str) -> dict[str, Any]:
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"""从数据库获取聊天流现有印象
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Args:
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stream_id: 聊天流ID
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Returns:
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dict: 聊天流印象数据
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"""
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@@ -182,13 +190,15 @@ class ChatStreamImpressionTool(BaseTool):
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stmt = select(ChatStreams).where(ChatStreams.stream_id == stream_id)
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result = await session.execute(stmt)
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stream = result.scalar_one_or_none()
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if stream:
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return {
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"stream_impression_text": stream.stream_impression_text or "",
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"stream_chat_style": stream.stream_chat_style or "",
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"stream_topic_keywords": stream.stream_topic_keywords or "",
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"stream_interest_score": float(stream.stream_interest_score) if stream.stream_interest_score is not None else 0.5,
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"stream_interest_score": float(stream.stream_interest_score)
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if stream.stream_interest_score is not None
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else 0.5,
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"group_name": stream.group_name or "私聊",
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}
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else:
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@@ -217,10 +227,10 @@ class ChatStreamImpressionTool(BaseTool):
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new_impression: str,
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new_style: str,
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new_topics: str,
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new_score: float | None
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new_score: float | None,
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) -> dict[str, Any] | None:
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"""使用LLM决策最终的聊天流印象内容
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Args:
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stream_id: 聊天流ID
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existing_impression: 现有印象数据
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@@ -228,33 +238,34 @@ class ChatStreamImpressionTool(BaseTool):
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new_style: LLM传入的新风格
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new_topics: LLM传入的新话题
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new_score: LLM传入的新分数
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Returns:
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dict: 最终决定的印象数据，如果失败返回None
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"""
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try:
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# 获取bot人设
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from src.individuality.individuality import Individuality
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individuality = Individuality()
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bot_personality = await individuality.get_personality_block()
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prompt = f"""
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你现在是一个有着特定性格和身份的AI助手。你的人设是：{bot_personality}
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你正在更新对聊天流 {stream_id} 的整体印象。
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【当前聊天流信息】
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- 聊天环境: {existing_impression.get('group_name', '未知')}
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- 当前印象: {existing_impression.get('stream_impression_text', '暂无印象')}
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- 聊天风格: {existing_impression.get('stream_chat_style', '未知')}
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- 常见话题: {existing_impression.get('stream_topic_keywords', '未知')}
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- 当前兴趣分: {existing_impression.get('stream_interest_score', 0.5):.2f}
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- 聊天环境: {existing_impression.get("group_name", "未知")}
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- 当前印象: {existing_impression.get("stream_impression_text", "暂无印象")}
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- 聊天风格: {existing_impression.get("stream_chat_style", "未知")}
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- 常见话题: {existing_impression.get("stream_topic_keywords", "未知")}
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- 当前兴趣分: {existing_impression.get("stream_interest_score", 0.5):.2f}
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【本次想要更新的内容】
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- 新的印象描述: {new_impression if new_impression else '不更新'}
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- 新的聊天风格: {new_style if new_style else '不更新'}
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- 新的话题关键词: {new_topics if new_topics else '不更新'}
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- 新的兴趣分数: {new_score if new_score is not None else '不更新'}
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- 新的印象描述: {new_impression if new_impression else "不更新"}
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- 新的聊天风格: {new_style if new_style else "不更新"}
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- 新的话题关键词: {new_topics if new_topics else "不更新"}
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- 新的兴趣分数: {new_score if new_score is not None else "不更新"}
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请综合考虑现有信息和新信息，决定最终的聊天流印象内容。注意：
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1. 印象描述：如果提供了新印象，应该综合现有印象和新印象，形成对这个聊天环境的整体认知（100-200字）
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@@ -271,31 +282,47 @@ class ChatStreamImpressionTool(BaseTool):
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"reasoning": "你的决策理由"
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}}
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"""
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# 调用LLM
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llm_response, _ = await self.impression_llm.generate_response_async(prompt=prompt)
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if not llm_response:
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logger.warning("LLM未返回有效响应")
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return None
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# 清理并解析响应
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cleaned_response = self._clean_llm_json_response(llm_response)
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response_data = json.loads(cleaned_response)
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# 提取最终决定的数据
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final_impression = {
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"stream_impression_text": response_data.get("stream_impression_text", existing_impression.get("stream_impression_text", "")),
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"stream_chat_style": response_data.get("stream_chat_style", existing_impression.get("stream_chat_style", "")),
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"stream_topic_keywords": response_data.get("stream_topic_keywords", existing_impression.get("stream_topic_keywords", "")),
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"stream_interest_score": max(0.0, min(1.0, float(response_data.get("stream_interest_score", existing_impression.get("stream_interest_score", 0.5))))),
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"stream_impression_text": response_data.get(
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"stream_impression_text", existing_impression.get("stream_impression_text", "")
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),
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"stream_chat_style": response_data.get(
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"stream_chat_style", existing_impression.get("stream_chat_style", "")
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),
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"stream_topic_keywords": response_data.get(
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"stream_topic_keywords", existing_impression.get("stream_topic_keywords", "")
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),
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"stream_interest_score": max(
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0.0,
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min(
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1.0,
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float(
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response_data.get(
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"stream_interest_score", existing_impression.get("stream_interest_score", 0.5)
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)
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),
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),
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),
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}
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logger.info(f"LLM决策完成: {stream_id}")
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logger.debug(f"决策理由: {response_data.get('reasoning', '无')}")
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return final_impression
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except json.JSONDecodeError as e:
|
||||
logger.error(f"LLM响应JSON解析失败: {e}")
|
||||
logger.debug(f"LLM原始响应: {llm_response if 'llm_response' in locals() else 'N/A'}")
|
||||
@@ -306,7 +333,7 @@ class ChatStreamImpressionTool(BaseTool):
|
||||
|
||||
async def _update_stream_impression_in_db(self, stream_id: str, impression: dict[str, Any]):
|
||||
"""更新数据库中的聊天流印象
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
impression: 印象数据
|
||||
@@ -316,14 +343,14 @@ class ChatStreamImpressionTool(BaseTool):
|
||||
stmt = select(ChatStreams).where(ChatStreams.stream_id == stream_id)
|
||||
result = await session.execute(stmt)
|
||||
existing = result.scalar_one_or_none()
|
||||
|
||||
|
||||
if existing:
|
||||
# 更新现有记录
|
||||
existing.stream_impression_text = impression.get("stream_impression_text", "")
|
||||
existing.stream_chat_style = impression.get("stream_chat_style", "")
|
||||
existing.stream_topic_keywords = impression.get("stream_topic_keywords", "")
|
||||
existing.stream_interest_score = impression.get("stream_interest_score", 0.5)
|
||||
|
||||
|
||||
await session.commit()
|
||||
logger.info(f"聊天流印象已更新到数据库: {stream_id}")
|
||||
else:
|
||||
@@ -331,40 +358,40 @@ class ChatStreamImpressionTool(BaseTool):
|
||||
logger.error(error_msg)
|
||||
# 注意：通常聊天流应该在消息处理时就已创建，这里不创建新记录
|
||||
raise ValueError(error_msg)
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"更新聊天流印象到数据库失败: {e}", exc_info=True)
|
||||
raise
|
||||
|
||||
def _clean_llm_json_response(self, response: str) -> str:
|
||||
"""清理LLM响应，移除可能的JSON格式标记
|
||||
|
||||
|
||||
Args:
|
||||
response: LLM原始响应
|
||||
|
||||
|
||||
Returns:
|
||||
str: 清理后的JSON字符串
|
||||
"""
|
||||
try:
|
||||
import re
|
||||
|
||||
|
||||
cleaned = response.strip()
|
||||
|
||||
|
||||
# 移除 ```json 或 ``` 等标记
|
||||
cleaned = re.sub(r"^```(?:json)?\s*", "", cleaned, flags=re.MULTILINE | re.IGNORECASE)
|
||||
cleaned = re.sub(r"\s*```$", "", cleaned, flags=re.MULTILINE)
|
||||
|
||||
|
||||
# 尝试找到JSON对象的开始和结束
|
||||
json_start = cleaned.find("{")
|
||||
json_end = cleaned.rfind("}")
|
||||
|
||||
|
||||
if json_start != -1 and json_end != -1 and json_end > json_start:
|
||||
cleaned = cleaned[json_start:json_end + 1]
|
||||
|
||||
cleaned = cleaned[json_start : json_end + 1]
|
||||
|
||||
cleaned = cleaned.strip()
|
||||
|
||||
|
||||
return cleaned
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"清理LLM响应失败: {e}")
|
||||
return response
|
||||
|
||||
@@ -231,11 +231,11 @@ class ChatterPlanExecutor:
|
||||
except Exception as e:
|
||||
error_message = str(e)
|
||||
logger.error(f"执行回复动作失败: {action_info.action_type}, 错误: {error_message}")
|
||||
|
||||
|
||||
# 将机器人回复添加到已读消息中
|
||||
if success and action_info.action_message:
|
||||
await self._add_bot_reply_to_read_messages(action_info, plan, reply_content)
|
||||
|
||||
|
||||
execution_time = time.time() - start_time
|
||||
self.execution_stats["execution_times"].append(execution_time)
|
||||
|
||||
@@ -381,13 +381,11 @@ class ChatterPlanExecutor:
|
||||
is_picid=False,
|
||||
is_command=False,
|
||||
is_notify=False,
|
||||
|
||||
# 用户信息
|
||||
user_id=bot_user_id,
|
||||
user_nickname=bot_nickname,
|
||||
user_cardname=bot_nickname,
|
||||
user_platform="qq",
|
||||
|
||||
# 聊天上下文信息
|
||||
chat_info_user_id=chat_stream.user_info.user_id if chat_stream.user_info else bot_user_id,
|
||||
chat_info_user_nickname=chat_stream.user_info.user_nickname if chat_stream.user_info else bot_nickname,
|
||||
@@ -397,23 +395,22 @@ class ChatterPlanExecutor:
|
||||
chat_info_platform=chat_stream.platform,
|
||||
chat_info_create_time=chat_stream.create_time,
|
||||
chat_info_last_active_time=chat_stream.last_active_time,
|
||||
|
||||
# 群组信息（如果是群聊）
|
||||
chat_info_group_id=chat_stream.group_info.group_id if chat_stream.group_info else None,
|
||||
chat_info_group_name=chat_stream.group_info.group_name if chat_stream.group_info else None,
|
||||
chat_info_group_platform=getattr(chat_stream.group_info, "platform", None) if chat_stream.group_info else None,
|
||||
|
||||
chat_info_group_platform=getattr(chat_stream.group_info, "platform", None)
|
||||
if chat_stream.group_info
|
||||
else None,
|
||||
# 动作信息
|
||||
actions=["bot_reply"],
|
||||
should_reply=False,
|
||||
should_act=False
|
||||
should_act=False,
|
||||
)
|
||||
|
||||
# 添加到chat_stream的已读消息中
|
||||
chat_stream.context_manager.context.history_messages.append(bot_message)
|
||||
logger.debug(f"机器人回复已添加到已读消息: {reply_content[:50]}...")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"添加机器人回复到已读消息时出错: {e}")
|
||||
logger.debug(f"plan.chat_id: {plan.chat_id}")
|
||||
|
||||
@@ -60,7 +60,7 @@ class ChatterPlanFilter:
|
||||
prompt, used_message_id_list = await self._build_prompt(plan)
|
||||
plan.llm_prompt = prompt
|
||||
if global_config.debug.show_prompt:
|
||||
logger.info(f"规划器原始提示词:{prompt}") #叫你不要改你耳朵聋吗😡😡😡😡😡
|
||||
logger.info(f"规划器原始提示词:{prompt}") # 叫你不要改你耳朵聋吗😡😡😡😡😡
|
||||
|
||||
llm_content, _ = await self.planner_llm.generate_response_async(prompt=prompt)
|
||||
|
||||
@@ -104,24 +104,26 @@ class ChatterPlanFilter:
|
||||
# 预解析 action_type 来进行判断
|
||||
thinking = item.get("thinking", "未提供思考过程")
|
||||
actions_obj = item.get("actions", {})
|
||||
|
||||
|
||||
# 记录决策历史
|
||||
if hasattr(global_config.chat, "enable_decision_history") and global_config.chat.enable_decision_history:
|
||||
if (
|
||||
hasattr(global_config.chat, "enable_decision_history")
|
||||
and global_config.chat.enable_decision_history
|
||||
):
|
||||
action_types_to_log = []
|
||||
actions_to_process_for_log = []
|
||||
if isinstance(actions_obj, dict):
|
||||
actions_to_process_for_log.append(actions_obj)
|
||||
elif isinstance(actions_obj, list):
|
||||
actions_to_process_for_log.extend(actions_obj)
|
||||
|
||||
|
||||
for single_action in actions_to_process_for_log:
|
||||
if isinstance(single_action, dict):
|
||||
action_types_to_log.append(single_action.get("action_type", "no_action"))
|
||||
|
||||
|
||||
if thinking != "未提供思考过程" and action_types_to_log:
|
||||
await self._add_decision_to_history(plan, thinking, ", ".join(action_types_to_log))
|
||||
|
||||
|
||||
# 处理actions字段可能是字典或列表的情况
|
||||
if isinstance(actions_obj, dict):
|
||||
action_type = actions_obj.get("action_type", "no_action")
|
||||
@@ -579,15 +581,15 @@ class ChatterPlanFilter:
|
||||
):
|
||||
reasoning = f"LLM 返回了当前不可用的动作 '{action}'。原始理由: {reasoning}"
|
||||
action = "no_action"
|
||||
#TODO:把逻辑迁移到DatabaseMessages(如果没人做下个星期我自己来)
|
||||
#from src.common.data_models.database_data_model import DatabaseMessages
|
||||
# TODO:把逻辑迁移到DatabaseMessages(如果没人做下个星期我自己来)
|
||||
# from src.common.data_models.database_data_model import DatabaseMessages
|
||||
|
||||
#action_message_obj = None
|
||||
#if target_message_obj:
|
||||
#try:
|
||||
#action_message_obj = DatabaseMessages(**target_message_obj)
|
||||
#except Exception:
|
||||
#logger.warning("无法将目标消息转换为DatabaseMessages对象")
|
||||
# action_message_obj = None
|
||||
# if target_message_obj:
|
||||
# try:
|
||||
# action_message_obj = DatabaseMessages(**target_message_obj)
|
||||
# except Exception:
|
||||
# logger.warning("无法将目标消息转换为DatabaseMessages对象")
|
||||
|
||||
parsed_actions.append(
|
||||
ActionPlannerInfo(
|
||||
|
||||
@@ -17,7 +17,6 @@ from src.plugins.built_in.affinity_flow_chatter.plan_generator import ChatterPla
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from src.chat.planner_actions.action_manager import ChatterActionManager
|
||||
from src.common.data_models.database_data_model import DatabaseMessages
|
||||
from src.common.data_models.info_data_model import Plan
|
||||
from src.common.data_models.message_manager_data_model import StreamContext
|
||||
|
||||
@@ -100,11 +99,11 @@ class ChatterActionPlanner:
|
||||
if context:
|
||||
context.chat_mode = ChatMode.FOCUS
|
||||
await self._sync_chat_mode_to_stream(context)
|
||||
|
||||
|
||||
# Normal模式下使用简化流程
|
||||
if chat_mode == ChatMode.NORMAL:
|
||||
return await self._normal_mode_flow(context)
|
||||
|
||||
|
||||
# 在规划前，先进行动作修改
|
||||
from src.chat.planner_actions.action_modifier import ActionModifier
|
||||
action_modifier = ActionModifier(self.action_manager, self.chat_id)
|
||||
@@ -184,12 +183,12 @@ class ChatterActionPlanner:
|
||||
for action in filtered_plan.decided_actions:
|
||||
if action.action_type in ["reply", "proactive_reply"] and action.action_message:
|
||||
# 提取目标消息ID
|
||||
if hasattr(action.action_message, 'message_id'):
|
||||
if hasattr(action.action_message, "message_id"):
|
||||
target_message_id = action.action_message.message_id
|
||||
elif isinstance(action.action_message, dict):
|
||||
target_message_id = action.action_message.get('message_id')
|
||||
target_message_id = action.action_message.get("message_id")
|
||||
break
|
||||
|
||||
|
||||
# 如果找到目标消息ID，检查是否已经在处理中
|
||||
if target_message_id and context:
|
||||
if context.processing_message_id == target_message_id:
|
||||
@@ -215,7 +214,7 @@ class ChatterActionPlanner:
|
||||
|
||||
# 6. 根据执行结果更新统计信息
|
||||
self._update_stats_from_execution_result(execution_result)
|
||||
|
||||
|
||||
# 7. Focus模式下如果执行了reply动作，切换到Normal模式
|
||||
if chat_mode == ChatMode.FOCUS and context:
|
||||
if filtered_plan.decided_actions:
|
||||
@@ -233,7 +232,7 @@ class ChatterActionPlanner:
|
||||
# 8. 清理处理标记
|
||||
if context:
|
||||
context.processing_message_id = None
|
||||
logger.debug(f"已清理处理标记，完成规划流程")
|
||||
logger.debug("已清理处理标记，完成规划流程")
|
||||
|
||||
# 9. 返回结果
|
||||
return self._build_return_result(filtered_plan)
|
||||
@@ -262,7 +261,7 @@ class ChatterActionPlanner:
|
||||
return await self._enhanced_plan_flow(context)
|
||||
try:
|
||||
unread_messages = context.get_unread_messages() if context else []
|
||||
|
||||
|
||||
if not unread_messages:
|
||||
logger.debug("Normal模式: 没有未读消息")
|
||||
from src.common.data_models.info_data_model import ActionPlannerInfo
|
||||
@@ -273,11 +272,11 @@ class ChatterActionPlanner:
|
||||
action_message=None,
|
||||
)
|
||||
return [asdict(no_action)], None
|
||||
|
||||
|
||||
# 检查是否有消息达到reply阈值
|
||||
should_reply = False
|
||||
target_message = None
|
||||
|
||||
|
||||
for message in unread_messages:
|
||||
message_should_reply = getattr(message, "should_reply", False)
|
||||
if message_should_reply:
|
||||
@@ -285,7 +284,7 @@ class ChatterActionPlanner:
|
||||
target_message = message
|
||||
logger.info(f"Normal模式: 消息 {message.message_id} 达到reply阈值")
|
||||
break
|
||||
|
||||
|
||||
if should_reply and target_message:
|
||||
# 检查是否正在处理相同的目标消息，防止重复回复
|
||||
target_message_id = target_message.message_id
|
||||
@@ -302,26 +301,26 @@ class ChatterActionPlanner:
|
||||
action_message=None,
|
||||
)
|
||||
return [asdict(no_action)], None
|
||||
|
||||
|
||||
# 记录当前正在处理的消息ID
|
||||
if context:
|
||||
context.processing_message_id = target_message_id
|
||||
logger.debug(f"Normal模式: 开始处理目标消息: {target_message_id}")
|
||||
|
||||
|
||||
# 达到reply阈值，直接进入回复流程
|
||||
from src.common.data_models.info_data_model import ActionPlannerInfo, Plan
|
||||
from src.plugin_system.base.component_types import ChatType
|
||||
|
||||
|
||||
# 构建目标消息字典 - 使用 flatten() 方法获取扁平化的字典
|
||||
target_message_dict = target_message.flatten()
|
||||
|
||||
|
||||
reply_action = ActionPlannerInfo(
|
||||
action_type="reply",
|
||||
reasoning="Normal模式: 兴趣度达到阈值，直接回复",
|
||||
action_data={"target_message_id": target_message.message_id},
|
||||
action_message=target_message,
|
||||
)
|
||||
|
||||
|
||||
# Normal模式下直接构建最小化的Plan，跳过generator和action_modifier
|
||||
# 这样可以显著降低延迟
|
||||
minimal_plan = Plan(
|
||||
@@ -330,25 +329,25 @@ class ChatterActionPlanner:
|
||||
mode=ChatMode.NORMAL,
|
||||
decided_actions=[reply_action],
|
||||
)
|
||||
|
||||
|
||||
# 执行reply动作
|
||||
execution_result = await self.executor.execute(minimal_plan)
|
||||
self._update_stats_from_execution_result(execution_result)
|
||||
|
||||
|
||||
logger.info("Normal模式: 执行reply动作完成")
|
||||
|
||||
|
||||
# 清理处理标记
|
||||
if context:
|
||||
context.processing_message_id = None
|
||||
logger.debug(f"Normal模式: 已清理处理标记")
|
||||
|
||||
logger.debug("Normal模式: 已清理处理标记")
|
||||
|
||||
# 无论是否回复，都进行退出normal模式的判定
|
||||
await self._check_exit_normal_mode(context)
|
||||
|
||||
|
||||
return [asdict(reply_action)], target_message_dict
|
||||
else:
|
||||
# 未达到reply阈值
|
||||
logger.debug(f"Normal模式: 未达到reply阈值")
|
||||
logger.debug("Normal模式: 未达到reply阈值")
|
||||
from src.common.data_models.info_data_model import ActionPlannerInfo
|
||||
no_action = ActionPlannerInfo(
|
||||
action_type="no_action",
|
||||
@@ -356,12 +355,12 @@ class ChatterActionPlanner:
|
||||
action_data={},
|
||||
action_message=None,
|
||||
)
|
||||
|
||||
|
||||
# 无论是否回复，都进行退出normal模式的判定
|
||||
await self._check_exit_normal_mode(context)
|
||||
|
||||
|
||||
return [asdict(no_action)], None
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Normal模式流程出错: {e}")
|
||||
self.planner_stats["failed_plans"] += 1
|
||||
@@ -378,16 +377,16 @@ class ChatterActionPlanner:
|
||||
"""
|
||||
if not context:
|
||||
return
|
||||
|
||||
|
||||
try:
|
||||
from src.chat.message_receive.chat_stream import get_chat_manager
|
||||
|
||||
|
||||
chat_manager = get_chat_manager()
|
||||
chat_stream = await chat_manager.get_stream(self.chat_id) if chat_manager else None
|
||||
|
||||
|
||||
if not chat_stream:
|
||||
return
|
||||
|
||||
|
||||
focus_energy = chat_stream.focus_energy
|
||||
# focus_energy越低，退出normal模式的概率越高
|
||||
# 使用反比例函数: 退出概率 = 1 - focus_energy
|
||||
@@ -395,7 +394,7 @@ class ChatterActionPlanner:
|
||||
# 当focus_energy = 0.5时，退出概率 = 50%
|
||||
# 当focus_energy = 0.9时，退出概率 = 10%
|
||||
exit_probability = 1.0 - focus_energy
|
||||
|
||||
|
||||
import random
|
||||
if random.random() < exit_probability:
|
||||
logger.info(f"Normal模式: focus_energy={focus_energy:.3f}, 退出概率={exit_probability:.3f}, 切换回focus模式")
|
||||
@@ -404,7 +403,7 @@ class ChatterActionPlanner:
|
||||
await self._sync_chat_mode_to_stream(context)
|
||||
else:
|
||||
logger.debug(f"Normal模式: focus_energy={focus_energy:.3f}, 退出概率={exit_probability:.3f}, 保持normal模式")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"检查退出Normal模式失败: {e}")
|
||||
|
||||
@@ -412,7 +411,7 @@ class ChatterActionPlanner:
|
||||
"""同步chat_mode到ChatStream"""
|
||||
try:
|
||||
from src.chat.message_receive.chat_stream import get_chat_manager
|
||||
|
||||
|
||||
chat_manager = get_chat_manager()
|
||||
if chat_manager:
|
||||
chat_stream = await chat_manager.get_stream(context.stream_id)
|
||||
|
||||
@@ -15,57 +15,57 @@ logger = get_logger("proactive_thinking_event")
|
||||
|
||||
class ProactiveThinkingReplyHandler(BaseEventHandler):
|
||||
"""Reply事件处理器
|
||||
|
||||
|
||||
当bot回复某个聊天流后：
|
||||
1. 如果该聊天流的主动思考被暂停（因为抛出了话题），则恢复它
|
||||
2. 无论是否暂停，都重置定时任务，重新开始计时
|
||||
"""
|
||||
|
||||
|
||||
handler_name: str = "proactive_thinking_reply_handler"
|
||||
handler_description: str = "监听reply事件，重置主动思考定时任务"
|
||||
init_subscribe: list[EventType | str] = [EventType.AFTER_SEND]
|
||||
|
||||
|
||||
async def execute(self, kwargs: dict | None) -> HandlerResult:
|
||||
"""处理reply事件
|
||||
|
||||
|
||||
Args:
|
||||
kwargs: 事件参数，应包含 stream_id
|
||||
|
||||
|
||||
Returns:
|
||||
HandlerResult: 处理结果
|
||||
"""
|
||||
logger.debug("[主动思考事件] ProactiveThinkingReplyHandler 开始执行")
|
||||
logger.debug(f"[主动思考事件] 接收到的参数: {kwargs}")
|
||||
|
||||
|
||||
if not kwargs:
|
||||
logger.debug("[主动思考事件] kwargs 为空，跳过处理")
|
||||
return HandlerResult(success=True, continue_process=True, message=None)
|
||||
|
||||
|
||||
stream_id = kwargs.get("stream_id")
|
||||
if not stream_id:
|
||||
logger.debug(f"[主动思考事件] Reply事件缺少stream_id参数")
|
||||
logger.debug("[主动思考事件] Reply事件缺少stream_id参数")
|
||||
return HandlerResult(success=True, continue_process=True, message=None)
|
||||
|
||||
|
||||
logger.debug(f"[主动思考事件] 收到 AFTER_SEND 事件，stream_id={stream_id}")
|
||||
|
||||
|
||||
try:
|
||||
from src.config.config import global_config
|
||||
|
||||
|
||||
# 检查是否启用reply重置
|
||||
if not global_config.proactive_thinking.reply_reset_enabled:
|
||||
logger.debug(f"[主动思考事件] reply_reset_enabled 为 False，跳过重置")
|
||||
logger.debug("[主动思考事件] reply_reset_enabled 为 False，跳过重置")
|
||||
return HandlerResult(success=True, continue_process=True, message=None)
|
||||
|
||||
|
||||
# 检查是否被暂停
|
||||
was_paused = await proactive_thinking_scheduler.is_paused(stream_id)
|
||||
logger.debug(f"[主动思考事件] 聊天流 {stream_id} 暂停状态: {was_paused}")
|
||||
|
||||
|
||||
if was_paused:
|
||||
logger.debug(f"[主动思考事件] 检测到reply事件，聊天流 {stream_id} 之前因抛出话题而暂停，现在恢复")
|
||||
|
||||
|
||||
# 重置定时任务（这会自动清除暂停标记并创建新任务）
|
||||
success = await proactive_thinking_scheduler.schedule_proactive_thinking(stream_id)
|
||||
|
||||
|
||||
if success:
|
||||
if was_paused:
|
||||
logger.info(f"✅ 聊天流 {stream_id} 主动思考已恢复并重置")
|
||||
@@ -73,82 +73,82 @@ class ProactiveThinkingReplyHandler(BaseEventHandler):
|
||||
logger.debug(f"✅ 聊天流 {stream_id} 主动思考任务已重置")
|
||||
else:
|
||||
logger.warning(f"❌ 重置聊天流 {stream_id} 主动思考任务失败")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 处理reply事件时出错: {e}", exc_info=True)
|
||||
|
||||
|
||||
# 总是继续处理其他handler
|
||||
return HandlerResult(success=True, continue_process=True, message=None)
|
||||
|
||||
|
||||
class ProactiveThinkingMessageHandler(BaseEventHandler):
|
||||
"""消息事件处理器
|
||||
|
||||
|
||||
当收到消息时，如果该聊天流还没有主动思考任务，则创建一个
|
||||
这样可以确保新的聊天流也能获得主动思考功能
|
||||
"""
|
||||
|
||||
|
||||
handler_name: str = "proactive_thinking_message_handler"
|
||||
handler_description: str = "监听消息事件，为新聊天流创建主动思考任务"
|
||||
init_subscribe: list[EventType | str] = [EventType.ON_MESSAGE]
|
||||
|
||||
|
||||
async def execute(self, kwargs: dict | None) -> HandlerResult:
|
||||
"""处理消息事件
|
||||
|
||||
|
||||
Args:
|
||||
kwargs: 事件参数，格式为 {"message": DatabaseMessages}
|
||||
|
||||
|
||||
Returns:
|
||||
HandlerResult: 处理结果
|
||||
"""
|
||||
if not kwargs:
|
||||
return HandlerResult(success=True, continue_process=True, message=None)
|
||||
|
||||
|
||||
# 从 kwargs 中获取 DatabaseMessages 对象
|
||||
message = kwargs.get("message")
|
||||
if not message or not hasattr(message, "chat_stream"):
|
||||
return HandlerResult(success=True, continue_process=True, message=None)
|
||||
|
||||
|
||||
# 从 chat_stream 获取 stream_id
|
||||
chat_stream = message.chat_stream
|
||||
if not chat_stream or not hasattr(chat_stream, "stream_id"):
|
||||
return HandlerResult(success=True, continue_process=True, message=None)
|
||||
|
||||
|
||||
stream_id = chat_stream.stream_id
|
||||
|
||||
|
||||
try:
|
||||
from src.config.config import global_config
|
||||
|
||||
|
||||
# 检查是否启用主动思考
|
||||
if not global_config.proactive_thinking.enable:
|
||||
return HandlerResult(success=True, continue_process=True, message=None)
|
||||
|
||||
|
||||
# 检查该聊天流是否已经有任务
|
||||
task_info = await proactive_thinking_scheduler.get_task_info(stream_id)
|
||||
if task_info:
|
||||
# 已经有任务，不需要创建
|
||||
return HandlerResult(success=True, continue_process=True, message=None)
|
||||
|
||||
|
||||
# 从 message_info 获取平台和聊天ID信息
|
||||
message_info = message.message_info
|
||||
platform = message_info.platform
|
||||
is_group = message_info.group_info is not None
|
||||
chat_id = message_info.group_info.group_id if is_group else message_info.user_info.user_id # type: ignore
|
||||
|
||||
|
||||
# 构造配置字符串
|
||||
stream_config = f"{platform}:{chat_id}:{'group' if is_group else 'private'}"
|
||||
|
||||
|
||||
# 检查黑白名单
|
||||
if not proactive_thinking_scheduler._check_whitelist_blacklist(stream_config):
|
||||
return HandlerResult(success=True, continue_process=True, message=None)
|
||||
|
||||
|
||||
# 创建主动思考任务
|
||||
success = await proactive_thinking_scheduler.schedule_proactive_thinking(stream_id)
|
||||
if success:
|
||||
logger.info(f"为新聊天流 {stream_id} 创建了主动思考任务")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"处理消息事件时出错: {e}", exc_info=True)
|
||||
|
||||
|
||||
# 总是继续处理其他handler
|
||||
return HandlerResult(success=True, continue_process=True, message=None)
|
||||
|
||||
@@ -5,11 +5,10 @@
|
||||
|
||||
import json
|
||||
from datetime import datetime
|
||||
from typing import Any, Literal, Optional
|
||||
from typing import Any, Literal
|
||||
|
||||
from sqlalchemy import select
|
||||
|
||||
from src.chat.express.expression_learner import expression_learner_manager
|
||||
from src.chat.express.expression_selector import expression_selector
|
||||
from src.common.database.sqlalchemy_database_api import get_db_session
|
||||
from src.common.database.sqlalchemy_models import ChatStreams
|
||||
@@ -17,42 +16,40 @@ from src.common.logger import get_logger
|
||||
from src.config.config import global_config, model_config
|
||||
from src.individuality.individuality import Individuality
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.plugin_system.apis import chat_api, message_api, send_api
|
||||
from src.plugin_system.apis import message_api, send_api
|
||||
|
||||
logger = get_logger("proactive_thinking_executor")
|
||||
|
||||
|
||||
class ProactiveThinkingPlanner:
|
||||
"""主动思考规划器
|
||||
|
||||
|
||||
负责：
|
||||
1. 搜集信息（聊天流印象、话题关键词、历史聊天记录）
|
||||
2. 调用LLM决策：什么都不做/简单冒泡/抛出话题
|
||||
3. 根据决策生成回复内容
|
||||
"""
|
||||
|
||||
|
||||
def __init__(self):
|
||||
"""初始化规划器"""
|
||||
try:
|
||||
self.decision_llm = LLMRequest(
|
||||
model_set=model_config.model_task_config.utils,
|
||||
request_type="proactive_thinking_decision"
|
||||
model_set=model_config.model_task_config.utils, request_type="proactive_thinking_decision"
|
||||
)
|
||||
self.reply_llm = LLMRequest(
|
||||
model_set=model_config.model_task_config.replyer,
|
||||
request_type="proactive_thinking_reply"
|
||||
model_set=model_config.model_task_config.replyer, request_type="proactive_thinking_reply"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"初始化LLM失败: {e}")
|
||||
self.decision_llm = None
|
||||
self.reply_llm = None
|
||||
|
||||
async def gather_context(self, stream_id: str) -> Optional[dict[str, Any]]:
|
||||
|
||||
async def gather_context(self, stream_id: str) -> dict[str, Any] | None:
|
||||
"""搜集聊天流的上下文信息
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
|
||||
|
||||
Returns:
|
||||
dict: 包含所有上下文信息的字典，失败返回None
|
||||
"""
|
||||
@@ -62,27 +59,25 @@ class ProactiveThinkingPlanner:
|
||||
if not stream_data:
|
||||
logger.warning(f"无法获取聊天流 {stream_id} 的印象数据")
|
||||
return None
|
||||
|
||||
|
||||
# 2. 获取最近的聊天记录
|
||||
recent_messages = await message_api.get_recent_messages(
|
||||
chat_id=stream_id,
|
||||
limit=20,
|
||||
limit_mode="latest",
|
||||
hours=24
|
||||
chat_id=stream_id, limit=20, limit_mode="latest", hours=24
|
||||
)
|
||||
|
||||
|
||||
recent_chat_history = ""
|
||||
if recent_messages:
|
||||
recent_chat_history = await message_api.build_readable_messages_to_str(recent_messages)
|
||||
|
||||
|
||||
# 3. 获取bot人设
|
||||
individuality = Individuality()
|
||||
bot_personality = await individuality.get_personality_block()
|
||||
|
||||
|
||||
# 4. 获取当前心情
|
||||
current_mood = "感觉很平静" # 默认心情
|
||||
try:
|
||||
from src.mood.mood_manager import mood_manager
|
||||
|
||||
mood_obj = mood_manager.get_mood_by_chat_id(stream_id)
|
||||
if mood_obj:
|
||||
await mood_obj._initialize() # 确保已初始化
|
||||
@@ -90,19 +85,20 @@ class ProactiveThinkingPlanner:
|
||||
logger.debug(f"获取到聊天流 {stream_id} 的心情: {current_mood}")
|
||||
except Exception as e:
|
||||
logger.warning(f"获取心情失败，使用默认值: {e}")
|
||||
|
||||
|
||||
# 5. 获取上次决策
|
||||
last_decision = None
|
||||
try:
|
||||
from src.plugins.built_in.affinity_flow_chatter.proactive_thinking_scheduler import (
|
||||
proactive_thinking_scheduler,
|
||||
)
|
||||
|
||||
last_decision = proactive_thinking_scheduler.get_last_decision(stream_id)
|
||||
if last_decision:
|
||||
logger.debug(f"获取到聊天流 {stream_id} 的上次决策: {last_decision.get('action')}")
|
||||
except Exception as e:
|
||||
logger.warning(f"获取上次决策失败: {e}")
|
||||
|
||||
|
||||
# 6. 构建上下文
|
||||
context = {
|
||||
"stream_id": stream_id,
|
||||
@@ -117,45 +113,45 @@ class ProactiveThinkingPlanner:
|
||||
"current_mood": current_mood,
|
||||
"last_decision": last_decision,
|
||||
}
|
||||
|
||||
|
||||
logger.debug(f"成功搜集聊天流 {stream_id} 的上下文信息")
|
||||
return context
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"搜集上下文信息失败: {e}", exc_info=True)
|
||||
return None
|
||||
|
||||
async def _get_stream_impression(self, stream_id: str) -> Optional[dict[str, Any]]:
|
||||
|
||||
async def _get_stream_impression(self, stream_id: str) -> dict[str, Any] | None:
|
||||
"""从数据库获取聊天流印象数据"""
|
||||
try:
|
||||
async with get_db_session() as session:
|
||||
stmt = select(ChatStreams).where(ChatStreams.stream_id == stream_id)
|
||||
result = await session.execute(stmt)
|
||||
stream = result.scalar_one_or_none()
|
||||
|
||||
|
||||
if not stream:
|
||||
return None
|
||||
|
||||
|
||||
return {
|
||||
"stream_name": stream.group_name or "私聊",
|
||||
"stream_impression_text": stream.stream_impression_text or "",
|
||||
"stream_chat_style": stream.stream_chat_style or "",
|
||||
"stream_topic_keywords": stream.stream_topic_keywords or "",
|
||||
"stream_interest_score": float(stream.stream_interest_score) if stream.stream_interest_score else 0.5,
|
||||
"stream_interest_score": float(stream.stream_interest_score)
|
||||
if stream.stream_interest_score
|
||||
else 0.5,
|
||||
}
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取聊天流印象失败: {e}")
|
||||
return None
|
||||
|
||||
async def make_decision(
|
||||
self, context: dict[str, Any]
|
||||
) -> Optional[dict[str, Any]]:
|
||||
|
||||
async def make_decision(self, context: dict[str, Any]) -> dict[str, Any] | None:
|
||||
"""使用LLM进行决策
|
||||
|
||||
|
||||
Args:
|
||||
context: 上下文信息
|
||||
|
||||
|
||||
Returns:
|
||||
dict: 决策结果，包含：
|
||||
- action: "do_nothing" | "simple_bubble" | "throw_topic"
|
||||
@@ -165,30 +161,28 @@ class ProactiveThinkingPlanner:
|
||||
if not self.decision_llm:
|
||||
logger.error("决策LLM未初始化")
|
||||
return None
|
||||
|
||||
|
||||
response = None
|
||||
try:
|
||||
decision_prompt = self._build_decision_prompt(context)
|
||||
|
||||
|
||||
if global_config.debug.show_prompt:
|
||||
logger.info(f"决策提示词:\n{decision_prompt}")
|
||||
|
||||
|
||||
response, _ = await self.decision_llm.generate_response_async(prompt=decision_prompt)
|
||||
|
||||
|
||||
if not response:
|
||||
logger.warning("LLM未返回有效响应")
|
||||
return None
|
||||
|
||||
|
||||
# 清理并解析JSON响应
|
||||
cleaned_response = self._clean_json_response(response)
|
||||
decision = json.loads(cleaned_response)
|
||||
|
||||
logger.info(
|
||||
f"决策结果: {decision.get('action', 'unknown')} - {decision.get('reasoning', '无理由')}"
|
||||
)
|
||||
|
||||
|
||||
logger.info(f"决策结果: {decision.get('action', 'unknown')} - {decision.get('reasoning', '无理由')}")
|
||||
|
||||
return decision
|
||||
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(f"解析决策JSON失败: {e}")
|
||||
if response:
|
||||
@@ -197,18 +191,18 @@ class ProactiveThinkingPlanner:
|
||||
except Exception as e:
|
||||
logger.error(f"决策过程失败: {e}", exc_info=True)
|
||||
return None
|
||||
|
||||
|
||||
def _build_decision_prompt(self, context: dict[str, Any]) -> str:
|
||||
"""构建决策提示词"""
|
||||
# 构建上次决策信息
|
||||
last_decision_text = ""
|
||||
if context.get('last_decision'):
|
||||
last_dec = context['last_decision']
|
||||
last_action = last_dec.get('action', '未知')
|
||||
last_reasoning = last_dec.get('reasoning', '无')
|
||||
last_topic = last_dec.get('topic')
|
||||
last_time = last_dec.get('timestamp', '未知')
|
||||
|
||||
if context.get("last_decision"):
|
||||
last_dec = context["last_decision"]
|
||||
last_action = last_dec.get("action", "未知")
|
||||
last_reasoning = last_dec.get("reasoning", "无")
|
||||
last_topic = last_dec.get("topic")
|
||||
last_time = last_dec.get("timestamp", "未知")
|
||||
|
||||
last_decision_text = f"""
|
||||
【上次主动思考的决策】
|
||||
- 时间: {last_time}
|
||||
@@ -216,24 +210,24 @@ class ProactiveThinkingPlanner:
|
||||
- 理由: {last_reasoning}"""
|
||||
if last_topic:
|
||||
last_decision_text += f"\n- 话题: {last_topic}"
|
||||
|
||||
return f"""你是一个有着独特个性的AI助手。你的人设是：
|
||||
{context['bot_personality']}
|
||||
|
||||
现在是 {context['current_time']}，你正在考虑是否要主动在 "{context['stream_name']}" 中说些什么。
|
||||
return f"""你是一个有着独特个性的AI助手。你的人设是：
|
||||
{context["bot_personality"]}
|
||||
|
||||
现在是 {context["current_time"]}，你正在考虑是否要主动在 "{context["stream_name"]}" 中说些什么。
|
||||
|
||||
【你当前的心情】
|
||||
{context.get('current_mood', '感觉很平静')}
|
||||
{context.get("current_mood", "感觉很平静")}
|
||||
|
||||
【聊天环境信息】
|
||||
- 整体印象: {context['stream_impression']}
|
||||
- 聊天风格: {context['chat_style']}
|
||||
- 常见话题: {context['topic_keywords'] or '暂无'}
|
||||
- 你的兴趣程度: {context['interest_score']:.2f}/1.0
|
||||
- 整体印象: {context["stream_impression"]}
|
||||
- 聊天风格: {context["chat_style"]}
|
||||
- 常见话题: {context["topic_keywords"] or "暂无"}
|
||||
- 你的兴趣程度: {context["interest_score"]:.2f}/1.0
|
||||
{last_decision_text}
|
||||
|
||||
【最近的聊天记录】
|
||||
{context['recent_chat_history']}
|
||||
{context["recent_chat_history"]}
|
||||
|
||||
请根据以上信息（包括你的心情和上次决策），决定你现在应该做什么：
|
||||
|
||||
@@ -267,53 +261,50 @@ class ProactiveThinkingPlanner:
|
||||
3. 只有在真的有话题想聊时才选择 throw_topic
|
||||
4. 符合你的人设，不要太过热情或冷淡
|
||||
"""
|
||||
|
||||
|
||||
async def generate_reply(
|
||||
self,
|
||||
context: dict[str, Any],
|
||||
action: Literal["simple_bubble", "throw_topic"],
|
||||
topic: Optional[str] = None
|
||||
) -> Optional[str]:
|
||||
self, context: dict[str, Any], action: Literal["simple_bubble", "throw_topic"], topic: str | None = None
|
||||
) -> str | None:
|
||||
"""生成回复内容
|
||||
|
||||
|
||||
Args:
|
||||
context: 上下文信息
|
||||
action: 动作类型
|
||||
topic: (可选) 话题内容，当action=throw_topic时必须提供
|
||||
|
||||
|
||||
Returns:
|
||||
str: 生成的回复文本，失败返回None
|
||||
"""
|
||||
if not self.reply_llm:
|
||||
logger.error("回复LLM未初始化")
|
||||
return None
|
||||
|
||||
|
||||
try:
|
||||
reply_prompt = await self._build_reply_prompt(context, action, topic)
|
||||
|
||||
|
||||
if global_config.debug.show_prompt:
|
||||
logger.info(f"回复提示词:\n{reply_prompt}")
|
||||
|
||||
|
||||
response, _ = await self.reply_llm.generate_response_async(prompt=reply_prompt)
|
||||
|
||||
|
||||
if not response:
|
||||
logger.warning("LLM未返回有效回复")
|
||||
return None
|
||||
|
||||
|
||||
logger.info(f"生成回复成功: {response[:50]}...")
|
||||
return response.strip()
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"生成回复失败: {e}", exc_info=True)
|
||||
return None
|
||||
|
||||
|
||||
async def _get_expression_habits(self, stream_id: str, chat_history: str) -> str:
|
||||
"""获取表达方式参考
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
chat_history: 聊天历史
|
||||
|
||||
|
||||
Returns:
|
||||
str: 格式化的表达方式参考文本
|
||||
"""
|
||||
@@ -324,15 +315,15 @@ class ProactiveThinkingPlanner:
|
||||
chat_history=chat_history,
|
||||
target_message=None, # 主动思考没有target message
|
||||
max_num=6, # 主动思考时使用较少的表达方式
|
||||
min_num=2
|
||||
min_num=2,
|
||||
)
|
||||
|
||||
|
||||
if not selected_expressions:
|
||||
return ""
|
||||
|
||||
|
||||
style_habits = []
|
||||
grammar_habits = []
|
||||
|
||||
|
||||
for expr in selected_expressions:
|
||||
if isinstance(expr, dict) and "situation" in expr and "style" in expr:
|
||||
expr_type = expr.get("type", "style")
|
||||
@@ -340,7 +331,7 @@ class ProactiveThinkingPlanner:
|
||||
grammar_habits.append(f"当{expr['situation']}时，使用 {expr['style']}")
|
||||
else:
|
||||
style_habits.append(f"当{expr['situation']}时，使用 {expr['style']}")
|
||||
|
||||
|
||||
expression_block = ""
|
||||
if style_habits or grammar_habits:
|
||||
expression_block = "\n【表达方式参考】\n"
|
||||
@@ -349,41 +340,37 @@ class ProactiveThinkingPlanner:
|
||||
if grammar_habits:
|
||||
expression_block += "句法特点：\n" + "\n".join(grammar_habits) + "\n"
|
||||
expression_block += "注意：仅在情景合适时自然地使用这些表达，不要生硬套用。\n"
|
||||
|
||||
|
||||
return expression_block
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"获取表达方式失败: {e}")
|
||||
return ""
|
||||
|
||||
|
||||
async def _build_reply_prompt(
|
||||
self,
|
||||
context: dict[str, Any],
|
||||
action: Literal["simple_bubble", "throw_topic"],
|
||||
topic: Optional[str]
|
||||
self, context: dict[str, Any], action: Literal["simple_bubble", "throw_topic"], topic: str | None
|
||||
) -> str:
|
||||
"""构建回复提示词"""
|
||||
# 获取表达方式参考
|
||||
expression_habits = await self._get_expression_habits(
|
||||
stream_id=context.get('stream_id', ''),
|
||||
chat_history=context.get('recent_chat_history', '')
|
||||
stream_id=context.get("stream_id", ""), chat_history=context.get("recent_chat_history", "")
|
||||
)
|
||||
|
||||
|
||||
if action == "simple_bubble":
|
||||
return f"""你是一个有着独特个性的AI助手。你的人设是：
|
||||
{context['bot_personality']}
|
||||
{context["bot_personality"]}
|
||||
|
||||
现在是 {context['current_time']}，你决定在 "{context['stream_name']}" 中简单冒个泡。
|
||||
现在是 {context["current_time"]}，你决定在 "{context["stream_name"]}" 中简单冒个泡。
|
||||
|
||||
【你当前的心情】
|
||||
{context.get('current_mood', '感觉很平静')}
|
||||
{context.get("current_mood", "感觉很平静")}
|
||||
|
||||
【聊天环境】
|
||||
- 整体印象: {context['stream_impression']}
|
||||
- 聊天风格: {context['chat_style']}
|
||||
- 整体印象: {context["stream_impression"]}
|
||||
- 聊天风格: {context["chat_style"]}
|
||||
|
||||
【最近的聊天记录】
|
||||
{context['recent_chat_history']}
|
||||
{context["recent_chat_history"]}
|
||||
{expression_habits}
|
||||
请生成一条简短的消息，用于水群。要求：
|
||||
1. 非常简短（5-15字）
|
||||
@@ -394,23 +381,23 @@ class ProactiveThinkingPlanner:
|
||||
6. 如果有表达方式参考，在合适时自然使用
|
||||
7. 合理参考历史记录
|
||||
直接输出消息内容，不要解释："""
|
||||
|
||||
|
||||
else: # throw_topic
|
||||
return f"""你是一个有着独特个性的AI助手。你的人设是：
|
||||
{context['bot_personality']}
|
||||
{context["bot_personality"]}
|
||||
|
||||
现在是 {context['current_time']}，你决定在 "{context['stream_name']}" 中抛出一个话题。
|
||||
现在是 {context["current_time"]}，你决定在 "{context["stream_name"]}" 中抛出一个话题。
|
||||
|
||||
【你当前的心情】
|
||||
{context.get('current_mood', '感觉很平静')}
|
||||
{context.get("current_mood", "感觉很平静")}
|
||||
|
||||
【聊天环境】
|
||||
- 整体印象: {context['stream_impression']}
|
||||
- 聊天风格: {context['chat_style']}
|
||||
- 常见话题: {context['topic_keywords'] or '暂无'}
|
||||
- 整体印象: {context["stream_impression"]}
|
||||
- 聊天风格: {context["chat_style"]}
|
||||
- 常见话题: {context["topic_keywords"] or "暂无"}
|
||||
|
||||
【最近的聊天记录】
|
||||
{context['recent_chat_history']}
|
||||
{context["recent_chat_history"]}
|
||||
|
||||
【你想抛出的话题】
|
||||
{topic}
|
||||
@@ -425,21 +412,21 @@ class ProactiveThinkingPlanner:
|
||||
7. 如果有表达方式参考，在合适时自然使用
|
||||
|
||||
直接输出消息内容，不要解释："""
|
||||
|
||||
|
||||
def _clean_json_response(self, response: str) -> str:
|
||||
"""清理LLM响应中的JSON格式标记"""
|
||||
import re
|
||||
|
||||
|
||||
cleaned = response.strip()
|
||||
cleaned = re.sub(r"^```(?:json)?\s*", "", cleaned, flags=re.MULTILINE | re.IGNORECASE)
|
||||
cleaned = re.sub(r"\s*```$", "", cleaned, flags=re.MULTILINE)
|
||||
|
||||
|
||||
json_start = cleaned.find("{")
|
||||
json_end = cleaned.rfind("}")
|
||||
|
||||
|
||||
if json_start != -1 and json_end != -1 and json_end > json_start:
|
||||
cleaned = cleaned[json_start:json_end + 1]
|
||||
|
||||
cleaned = cleaned[json_start : json_end + 1]
|
||||
|
||||
return cleaned.strip()
|
||||
|
||||
|
||||
@@ -452,7 +439,7 @@ _statistics: dict[str, dict[str, Any]] = {}
|
||||
|
||||
def _update_statistics(stream_id: str, action: str):
|
||||
"""更新统计数据
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
action: 执行的动作
|
||||
@@ -465,18 +452,18 @@ def _update_statistics(stream_id: str, action: str):
|
||||
"throw_topic_count": 0,
|
||||
"last_execution_time": None,
|
||||
}
|
||||
|
||||
|
||||
_statistics[stream_id]["total_executions"] += 1
|
||||
_statistics[stream_id][f"{action}_count"] += 1
|
||||
_statistics[stream_id]["last_execution_time"] = datetime.now().isoformat()
|
||||
|
||||
|
||||
def get_statistics(stream_id: Optional[str] = None) -> dict[str, Any]:
|
||||
def get_statistics(stream_id: str | None = None) -> dict[str, Any]:
|
||||
"""获取统计数据
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID，None表示获取所有统计
|
||||
|
||||
|
||||
Returns:
|
||||
统计数据字典
|
||||
"""
|
||||
@@ -487,7 +474,7 @@ def get_statistics(stream_id: Optional[str] = None) -> dict[str, Any]:
|
||||
|
||||
async def execute_proactive_thinking(stream_id: str):
|
||||
"""执行主动思考（被调度器调用的回调函数）
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
"""
|
||||
@@ -495,125 +482,125 @@ async def execute_proactive_thinking(stream_id: str):
|
||||
from src.plugins.built_in.affinity_flow_chatter.proactive_thinking_scheduler import (
|
||||
proactive_thinking_scheduler,
|
||||
)
|
||||
|
||||
|
||||
config = global_config.proactive_thinking
|
||||
|
||||
|
||||
logger.debug(f"🤔 开始主动思考 {stream_id}")
|
||||
|
||||
|
||||
try:
|
||||
# 0. 前置检查
|
||||
if proactive_thinking_scheduler._is_in_quiet_hours():
|
||||
logger.debug(f"安静时段，跳过")
|
||||
logger.debug("安静时段，跳过")
|
||||
return
|
||||
|
||||
|
||||
if not proactive_thinking_scheduler._check_daily_limit(stream_id):
|
||||
logger.debug(f"今日发言达上限")
|
||||
logger.debug("今日发言达上限")
|
||||
return
|
||||
|
||||
|
||||
# 1. 搜集信息
|
||||
logger.debug(f"步骤1: 搜集上下文")
|
||||
logger.debug("步骤1: 搜集上下文")
|
||||
context = await _planner.gather_context(stream_id)
|
||||
if not context:
|
||||
logger.warning(f"无法搜集上下文，跳过")
|
||||
logger.warning("无法搜集上下文，跳过")
|
||||
return
|
||||
|
||||
# 检查兴趣分数阈值
|
||||
interest_score = context.get('interest_score', 0.5)
|
||||
interest_score = context.get("interest_score", 0.5)
|
||||
if not proactive_thinking_scheduler._check_interest_score_threshold(interest_score):
|
||||
logger.debug(f"兴趣分数不在阈值范围内")
|
||||
logger.debug("兴趣分数不在阈值范围内")
|
||||
return
|
||||
|
||||
|
||||
# 2. 进行决策
|
||||
logger.debug(f"步骤2: LLM决策")
|
||||
logger.debug("步骤2: LLM决策")
|
||||
decision = await _planner.make_decision(context)
|
||||
if not decision:
|
||||
logger.warning(f"决策失败，跳过")
|
||||
logger.warning("决策失败，跳过")
|
||||
return
|
||||
|
||||
|
||||
action = decision.get("action", "do_nothing")
|
||||
reasoning = decision.get("reasoning", "无")
|
||||
|
||||
|
||||
# 记录决策日志
|
||||
if config.log_decisions:
|
||||
logger.debug(f"决策: action={action}, reasoning={reasoning}")
|
||||
|
||||
|
||||
# 3. 根据决策执行相应动作
|
||||
if action == "do_nothing":
|
||||
logger.debug(f"决策：什么都不做。理由：{reasoning}")
|
||||
proactive_thinking_scheduler.record_decision(stream_id, action, reasoning, None)
|
||||
return
|
||||
|
||||
|
||||
elif action == "simple_bubble":
|
||||
logger.info(f"💬 决策：冒个泡。理由：{reasoning}")
|
||||
|
||||
|
||||
proactive_thinking_scheduler.record_decision(stream_id, action, reasoning, None)
|
||||
|
||||
|
||||
# 生成简单的消息
|
||||
logger.debug(f"步骤3: 生成冒泡回复")
|
||||
logger.debug("步骤3: 生成冒泡回复")
|
||||
reply = await _planner.generate_reply(context, "simple_bubble")
|
||||
if reply:
|
||||
await send_api.text_to_stream(
|
||||
stream_id=stream_id,
|
||||
text=reply,
|
||||
)
|
||||
logger.info(f"✅ 已发送冒泡消息")
|
||||
|
||||
logger.info("✅ 已发送冒泡消息")
|
||||
|
||||
# 增加每日计数
|
||||
proactive_thinking_scheduler._increment_daily_count(stream_id)
|
||||
|
||||
|
||||
# 更新统计
|
||||
if config.enable_statistics:
|
||||
_update_statistics(stream_id, action)
|
||||
|
||||
|
||||
# 冒泡后暂停主动思考，等待用户回复
|
||||
# 使用与 topic_throw 相同的冷却时间配置
|
||||
if config.topic_throw_cooldown > 0:
|
||||
logger.info(f"[主动思考] 步骤5：暂停任务")
|
||||
logger.info("[主动思考] 步骤5：暂停任务")
|
||||
await proactive_thinking_scheduler.pause_proactive_thinking(stream_id, reason="已冒泡")
|
||||
logger.info(f"[主动思考] 已暂停聊天流 {stream_id} 的主动思考，等待用户回复")
|
||||
|
||||
logger.info(f"[主动思考] simple_bubble 执行完成")
|
||||
|
||||
logger.info("[主动思考] simple_bubble 执行完成")
|
||||
|
||||
elif action == "throw_topic":
|
||||
topic = decision.get("topic", "")
|
||||
logger.info(f"[主动思考] 决策：抛出话题。理由：{reasoning}，话题：{topic}")
|
||||
|
||||
|
||||
# 记录决策
|
||||
proactive_thinking_scheduler.record_decision(stream_id, action, reasoning, topic)
|
||||
|
||||
|
||||
if not topic:
|
||||
logger.warning("[主动思考] 选择了抛出话题但未提供话题内容，降级为冒泡")
|
||||
logger.info(f"[主动思考] 步骤3：生成降级冒泡回复")
|
||||
logger.info("[主动思考] 步骤3：生成降级冒泡回复")
|
||||
reply = await _planner.generate_reply(context, "simple_bubble")
|
||||
else:
|
||||
# 生成基于话题的消息
|
||||
logger.info(f"[主动思考] 步骤3：生成话题回复")
|
||||
logger.info("[主动思考] 步骤3：生成话题回复")
|
||||
reply = await _planner.generate_reply(context, "throw_topic", topic)
|
||||
|
||||
|
||||
if reply:
|
||||
logger.info(f"[主动思考] 步骤4：发送消息")
|
||||
logger.info("[主动思考] 步骤4：发送消息")
|
||||
await send_api.text_to_stream(
|
||||
stream_id=stream_id,
|
||||
text=reply,
|
||||
)
|
||||
logger.info(f"[主动思考] 已发送话题消息到 {stream_id}")
|
||||
|
||||
|
||||
# 增加每日计数
|
||||
proactive_thinking_scheduler._increment_daily_count(stream_id)
|
||||
|
||||
|
||||
# 更新统计
|
||||
if config.enable_statistics:
|
||||
_update_statistics(stream_id, action)
|
||||
|
||||
|
||||
# 抛出话题后暂停主动思考（如果配置了冷却时间）
|
||||
if config.topic_throw_cooldown > 0:
|
||||
logger.info(f"[主动思考] 步骤5：暂停任务")
|
||||
logger.info("[主动思考] 步骤5：暂停任务")
|
||||
await proactive_thinking_scheduler.pause_proactive_thinking(stream_id, reason="已抛出话题")
|
||||
logger.info(f"[主动思考] 已暂停聊天流 {stream_id} 的主动思考，等待用户回复")
|
||||
|
||||
logger.info(f"[主动思考] throw_topic 执行完成")
|
||||
logger.info("[主动思考] throw_topic 执行完成")
|
||||
|
||||
logger.info(f"[主动思考] 聊天流 {stream_id} 的主动思考执行完成")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[主动思考] 执行主动思考失败: {e}", exc_info=True)
|
||||
|
||||
@@ -6,20 +6,17 @@
|
||||
|
||||
import asyncio
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Any, Optional
|
||||
from typing import Any
|
||||
|
||||
from src.common.database.sqlalchemy_database_api import get_db_session
|
||||
from src.common.database.sqlalchemy_models import ChatStreams
|
||||
from src.common.logger import get_logger
|
||||
from src.schedule.unified_scheduler import TriggerType, unified_scheduler
|
||||
from sqlalchemy import select
|
||||
|
||||
logger = get_logger("proactive_thinking_scheduler")
|
||||
|
||||
|
||||
class ProactiveThinkingScheduler:
|
||||
"""主动思考调度器
|
||||
|
||||
|
||||
负责为每个聊天流创建和管理主动思考任务。
|
||||
特点：
|
||||
1. 根据聊天流的兴趣分数动态计算触发间隔
|
||||
@@ -32,27 +29,28 @@ class ProactiveThinkingScheduler:
|
||||
self._stream_schedules: dict[str, str] = {} # stream_id -> schedule_id
|
||||
self._paused_streams: set[str] = set() # 因抛出话题而暂停的聊天流
|
||||
self._lock = asyncio.Lock()
|
||||
|
||||
|
||||
# 统计数据
|
||||
self._statistics: dict[str, dict[str, Any]] = {} # stream_id -> 统计信息
|
||||
self._daily_counts: dict[str, dict[str, int]] = {} # stream_id -> {date: count}
|
||||
|
||||
|
||||
# 历史决策记录：stream_id -> 上次决策信息
|
||||
self._last_decisions: dict[str, dict[str, Any]] = {}
|
||||
|
||||
|
||||
# 从全局配置加载（延迟导入避免循环依赖）
|
||||
from src.config.config import global_config
|
||||
|
||||
self.config = global_config.proactive_thinking
|
||||
|
||||
|
||||
def _calculate_interval(self, focus_energy: float) -> int:
|
||||
"""根据 focus_energy 计算触发间隔
|
||||
|
||||
|
||||
Args:
|
||||
focus_energy: 聊天流的 focus_energy 值 (0.0-1.0)
|
||||
|
||||
|
||||
Returns:
|
||||
int: 触发间隔（秒）
|
||||
|
||||
|
||||
公式：
|
||||
- focus_energy 越高，间隔越短（更频繁思考）
|
||||
- interval = base_interval * (factor - focus_energy)
|
||||
@@ -63,26 +61,26 @@ class ProactiveThinkingScheduler:
|
||||
# 如果不使用 focus_energy，直接返回基础间隔
|
||||
if not self.config.use_interest_score:
|
||||
return self.config.base_interval
|
||||
|
||||
|
||||
# 确保值在有效范围内
|
||||
focus_energy = max(0.0, min(1.0, focus_energy))
|
||||
|
||||
|
||||
# 计算间隔：focus_energy 越高，系数越小，间隔越短
|
||||
factor = self.config.interest_score_factor - focus_energy
|
||||
interval = int(self.config.base_interval * factor)
|
||||
|
||||
|
||||
# 限制在最小和最大间隔之间
|
||||
interval = max(self.config.min_interval, min(self.config.max_interval, interval))
|
||||
|
||||
logger.debug(f"Focus Energy {focus_energy:.3f} -> 触发间隔 {interval}秒 ({interval/60:.1f}分钟)")
|
||||
|
||||
logger.debug(f"Focus Energy {focus_energy:.3f} -> 触发间隔 {interval}秒 ({interval / 60:.1f}分钟)")
|
||||
return interval
|
||||
|
||||
|
||||
def _check_whitelist_blacklist(self, stream_config: str) -> bool:
|
||||
"""检查聊天流是否通过黑白名单验证
|
||||
|
||||
|
||||
Args:
|
||||
stream_config: 聊天流配置字符串，格式: "platform:id:type"
|
||||
|
||||
|
||||
Returns:
|
||||
bool: True表示允许主动思考，False表示拒绝
|
||||
"""
|
||||
@@ -91,148 +89,148 @@ class ProactiveThinkingScheduler:
|
||||
if len(parts) != 3:
|
||||
logger.warning(f"无效的stream_config格式: {stream_config}")
|
||||
return False
|
||||
|
||||
|
||||
is_private = parts[2] == "private"
|
||||
|
||||
|
||||
# 检查基础开关
|
||||
if is_private and not self.config.enable_in_private:
|
||||
return False
|
||||
if not is_private and not self.config.enable_in_group:
|
||||
return False
|
||||
|
||||
|
||||
# 黑名单检查（优先级高）
|
||||
if self.config.blacklist_mode:
|
||||
blacklist = self.config.blacklist_private if is_private else self.config.blacklist_group
|
||||
if stream_config in blacklist:
|
||||
logger.debug(f"聊天流 {stream_config} 在黑名单中，拒绝主动思考")
|
||||
return False
|
||||
|
||||
|
||||
# 白名单检查
|
||||
if self.config.whitelist_mode:
|
||||
whitelist = self.config.whitelist_private if is_private else self.config.whitelist_group
|
||||
if stream_config not in whitelist:
|
||||
logger.debug(f"聊天流 {stream_config} 不在白名单中，拒绝主动思考")
|
||||
return False
|
||||
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def _check_interest_score_threshold(self, interest_score: float) -> bool:
|
||||
"""检查兴趣分数是否在阈值范围内
|
||||
|
||||
|
||||
Args:
|
||||
interest_score: 兴趣分数
|
||||
|
||||
|
||||
Returns:
|
||||
bool: True表示在范围内
|
||||
"""
|
||||
if interest_score < self.config.min_interest_score:
|
||||
logger.debug(f"兴趣分数 {interest_score:.2f} 低于最低阈值 {self.config.min_interest_score}")
|
||||
return False
|
||||
|
||||
|
||||
if interest_score > self.config.max_interest_score:
|
||||
logger.debug(f"兴趣分数 {interest_score:.2f} 高于最高阈值 {self.config.max_interest_score}")
|
||||
return False
|
||||
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def _check_daily_limit(self, stream_id: str) -> bool:
|
||||
"""检查今日主动发言次数是否超限
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
|
||||
|
||||
Returns:
|
||||
bool: True表示未超限
|
||||
"""
|
||||
if self.config.max_daily_proactive == 0:
|
||||
return True # 不限制
|
||||
|
||||
|
||||
today = datetime.now().strftime("%Y-%m-%d")
|
||||
|
||||
|
||||
if stream_id not in self._daily_counts:
|
||||
self._daily_counts[stream_id] = {}
|
||||
|
||||
|
||||
# 清理过期日期的数据
|
||||
for date in list(self._daily_counts[stream_id].keys()):
|
||||
if date != today:
|
||||
del self._daily_counts[stream_id][date]
|
||||
|
||||
|
||||
count = self._daily_counts[stream_id].get(today, 0)
|
||||
|
||||
|
||||
if count >= self.config.max_daily_proactive:
|
||||
logger.debug(f"聊天流 {stream_id} 今日主动发言次数已达上限 ({count}/{self.config.max_daily_proactive})")
|
||||
return False
|
||||
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def _increment_daily_count(self, stream_id: str):
|
||||
"""增加今日主动发言计数"""
|
||||
today = datetime.now().strftime("%Y-%m-%d")
|
||||
|
||||
|
||||
if stream_id not in self._daily_counts:
|
||||
self._daily_counts[stream_id] = {}
|
||||
|
||||
|
||||
self._daily_counts[stream_id][today] = self._daily_counts[stream_id].get(today, 0) + 1
|
||||
|
||||
|
||||
def _is_in_quiet_hours(self) -> bool:
|
||||
"""检查当前是否在安静时段
|
||||
|
||||
|
||||
Returns:
|
||||
bool: True表示在安静时段
|
||||
"""
|
||||
if not self.config.enable_time_strategy:
|
||||
return False
|
||||
|
||||
|
||||
now = datetime.now()
|
||||
current_time = now.strftime("%H:%M")
|
||||
|
||||
|
||||
start = self.config.quiet_hours_start
|
||||
end = self.config.quiet_hours_end
|
||||
|
||||
|
||||
# 处理跨日的情况（如23:00-07:00）
|
||||
if start <= end:
|
||||
return start <= current_time <= end
|
||||
else:
|
||||
return current_time >= start or current_time <= end
|
||||
|
||||
|
||||
async def _get_stream_focus_energy(self, stream_id: str) -> float:
|
||||
"""获取聊天流的 focus_energy
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
|
||||
|
||||
Returns:
|
||||
float: focus_energy 值，默认0.5
|
||||
"""
|
||||
try:
|
||||
# 从聊天管理器获取聊天流
|
||||
from src.chat.message_receive.chat_stream import get_chat_manager
|
||||
|
||||
logger.debug(f"[调度器] 获取聊天管理器")
|
||||
|
||||
logger.debug("[调度器] 获取聊天管理器")
|
||||
chat_manager = get_chat_manager()
|
||||
logger.debug(f"[调度器] 从聊天管理器获取聊天流 {stream_id}")
|
||||
chat_stream = await chat_manager.get_stream(stream_id)
|
||||
|
||||
|
||||
if chat_stream:
|
||||
# 计算并获取最新的 focus_energy
|
||||
logger.debug(f"[调度器] 找到聊天流，开始计算 focus_energy")
|
||||
logger.debug("[调度器] 找到聊天流，开始计算 focus_energy")
|
||||
focus_energy = await chat_stream.calculate_focus_energy()
|
||||
logger.info(f"[调度器] 聊天流 {stream_id} 的 focus_energy: {focus_energy:.3f}")
|
||||
return focus_energy
|
||||
else:
|
||||
logger.warning(f"[调度器] ⚠️ 未找到聊天流 {stream_id}，使用默认 focus_energy=0.5")
|
||||
return 0.5
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[调度器] ❌ 获取聊天流 {stream_id} 的 focus_energy 失败: {e}", exc_info=True)
|
||||
return 0.5
|
||||
|
||||
|
||||
async def schedule_proactive_thinking(self, stream_id: str) -> bool:
|
||||
"""为聊天流创建或重置主动思考任务
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
|
||||
|
||||
Returns:
|
||||
bool: 是否成功创建/重置任务
|
||||
"""
|
||||
@@ -243,25 +241,25 @@ class ProactiveThinkingScheduler:
|
||||
if stream_id in self._paused_streams:
|
||||
logger.debug(f"[调度器] 清除聊天流 {stream_id} 的暂停标记")
|
||||
self._paused_streams.discard(stream_id)
|
||||
|
||||
|
||||
# 如果已经有任务，先移除
|
||||
if stream_id in self._stream_schedules:
|
||||
old_schedule_id = self._stream_schedules[stream_id]
|
||||
logger.debug(f"[调度器] 移除聊天流 {stream_id} 的旧任务")
|
||||
await unified_scheduler.remove_schedule(old_schedule_id)
|
||||
|
||||
|
||||
# 获取 focus_energy 并计算间隔
|
||||
focus_energy = await self._get_stream_focus_energy(stream_id)
|
||||
logger.debug(f"[调度器] focus_energy={focus_energy:.3f}")
|
||||
|
||||
|
||||
interval_seconds = self._calculate_interval(focus_energy)
|
||||
logger.debug(f"[调度器] 触发间隔={interval_seconds}秒 ({interval_seconds/60:.1f}分钟)")
|
||||
|
||||
logger.debug(f"[调度器] 触发间隔={interval_seconds}秒 ({interval_seconds / 60:.1f}分钟)")
|
||||
|
||||
# 导入回调函数（延迟导入避免循环依赖）
|
||||
from src.plugins.built_in.affinity_flow_chatter.proactive_thinking_executor import (
|
||||
execute_proactive_thinking,
|
||||
)
|
||||
|
||||
|
||||
# 创建新任务
|
||||
schedule_id = await unified_scheduler.create_schedule(
|
||||
callback=execute_proactive_thinking,
|
||||
@@ -273,34 +271,34 @@ class ProactiveThinkingScheduler:
|
||||
task_name=f"ProactiveThinking-{stream_id}",
|
||||
callback_args=(stream_id,),
|
||||
)
|
||||
|
||||
|
||||
self._stream_schedules[stream_id] = schedule_id
|
||||
|
||||
|
||||
# 计算下次触发时间
|
||||
next_run_time = datetime.now() + timedelta(seconds=interval_seconds)
|
||||
|
||||
|
||||
logger.info(
|
||||
f"✅ 聊天流 {stream_id} 主动思考任务已创建 | "
|
||||
f"Focus: {focus_energy:.3f} | "
|
||||
f"间隔: {interval_seconds/60:.1f}分钟 | "
|
||||
f"间隔: {interval_seconds / 60:.1f}分钟 | "
|
||||
f"下次: {next_run_time.strftime('%H:%M:%S')}"
|
||||
)
|
||||
return True
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 创建主动思考任务失败 {stream_id}: {e}", exc_info=True)
|
||||
return False
|
||||
|
||||
|
||||
async def pause_proactive_thinking(self, stream_id: str, reason: str = "抛出话题") -> bool:
|
||||
"""暂停聊天流的主动思考任务
|
||||
|
||||
|
||||
当选择"抛出话题"后，应该暂停该聊天流的主动思考，
|
||||
直到bot至少执行过一次reply后才恢复。
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
reason: 暂停原因
|
||||
|
||||
|
||||
Returns:
|
||||
bool: 是否成功暂停
|
||||
"""
|
||||
@@ -309,26 +307,26 @@ class ProactiveThinkingScheduler:
|
||||
if stream_id not in self._stream_schedules:
|
||||
logger.warning(f"尝试暂停不存在的任务: {stream_id}")
|
||||
return False
|
||||
|
||||
|
||||
schedule_id = self._stream_schedules[stream_id]
|
||||
success = await unified_scheduler.pause_schedule(schedule_id)
|
||||
|
||||
|
||||
if success:
|
||||
self._paused_streams.add(stream_id)
|
||||
logger.info(f"⏸️ 暂停主动思考 {stream_id}，原因: {reason}")
|
||||
|
||||
|
||||
return success
|
||||
|
||||
except Exception as e:
|
||||
|
||||
except Exception:
|
||||
# 错误日志已在上面记录
|
||||
return False
|
||||
|
||||
|
||||
async def resume_proactive_thinking(self, stream_id: str) -> bool:
|
||||
"""恢复聊天流的主动思考任务
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
|
||||
|
||||
Returns:
|
||||
bool: 是否成功恢复
|
||||
"""
|
||||
@@ -337,26 +335,26 @@ class ProactiveThinkingScheduler:
|
||||
if stream_id not in self._stream_schedules:
|
||||
logger.warning(f"尝试恢复不存在的任务: {stream_id}")
|
||||
return False
|
||||
|
||||
|
||||
schedule_id = self._stream_schedules[stream_id]
|
||||
success = await unified_scheduler.resume_schedule(schedule_id)
|
||||
|
||||
|
||||
if success:
|
||||
self._paused_streams.discard(stream_id)
|
||||
logger.info(f"▶️ 恢复主动思考 {stream_id}")
|
||||
|
||||
|
||||
return success
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 恢复主动思考失败 {stream_id}: {e}", exc_info=True)
|
||||
return False
|
||||
|
||||
|
||||
async def cancel_proactive_thinking(self, stream_id: str) -> bool:
|
||||
"""取消聊天流的主动思考任务
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
|
||||
|
||||
Returns:
|
||||
bool: 是否成功取消
|
||||
"""
|
||||
@@ -364,55 +362,55 @@ class ProactiveThinkingScheduler:
|
||||
async with self._lock:
|
||||
if stream_id not in self._stream_schedules:
|
||||
return True # 已经不存在，视为成功
|
||||
|
||||
|
||||
schedule_id = self._stream_schedules.pop(stream_id)
|
||||
self._paused_streams.discard(stream_id)
|
||||
|
||||
|
||||
success = await unified_scheduler.remove_schedule(schedule_id)
|
||||
logger.debug(f"⏹️ 取消主动思考 {stream_id}")
|
||||
|
||||
|
||||
return success
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 取消主动思考失败 {stream_id}: {e}", exc_info=True)
|
||||
return False
|
||||
|
||||
|
||||
async def is_paused(self, stream_id: str) -> bool:
|
||||
"""检查聊天流的主动思考是否被暂停
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
|
||||
|
||||
Returns:
|
||||
bool: 是否暂停中
|
||||
"""
|
||||
async with self._lock:
|
||||
return stream_id in self._paused_streams
|
||||
|
||||
async def get_task_info(self, stream_id: str) -> Optional[dict[str, Any]]:
|
||||
|
||||
async def get_task_info(self, stream_id: str) -> dict[str, Any] | None:
|
||||
"""获取聊天流的主动思考任务信息
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
|
||||
|
||||
Returns:
|
||||
dict: 任务信息，如果不存在返回None
|
||||
"""
|
||||
async with self._lock:
|
||||
if stream_id not in self._stream_schedules:
|
||||
return None
|
||||
|
||||
|
||||
schedule_id = self._stream_schedules[stream_id]
|
||||
task_info = await unified_scheduler.get_task_info(schedule_id)
|
||||
|
||||
|
||||
if task_info:
|
||||
task_info["is_paused_for_topic"] = stream_id in self._paused_streams
|
||||
|
||||
|
||||
return task_info
|
||||
|
||||
|
||||
async def list_all_tasks(self) -> list[dict[str, Any]]:
|
||||
"""列出所有主动思考任务
|
||||
|
||||
|
||||
Returns:
|
||||
list: 任务信息列表
|
||||
"""
|
||||
@@ -425,10 +423,10 @@ class ProactiveThinkingScheduler:
|
||||
task_info["is_paused_for_topic"] = stream_id in self._paused_streams
|
||||
tasks.append(task_info)
|
||||
return tasks
|
||||
|
||||
|
||||
def get_statistics(self) -> dict[str, Any]:
|
||||
"""获取调度器统计信息
|
||||
|
||||
|
||||
Returns:
|
||||
dict: 统计信息
|
||||
"""
|
||||
@@ -437,51 +435,48 @@ class ProactiveThinkingScheduler:
|
||||
"paused_for_topic": len(self._paused_streams),
|
||||
"active_tasks": len(self._stream_schedules) - len(self._paused_streams),
|
||||
}
|
||||
|
||||
|
||||
async def log_next_trigger_times(self, max_streams: int = 10):
|
||||
"""在日志中输出聊天流的下次触发时间
|
||||
|
||||
|
||||
Args:
|
||||
max_streams: 最多显示多少个聊天流，0表示全部
|
||||
"""
|
||||
logger.info("=" * 60)
|
||||
logger.info("主动思考任务状态")
|
||||
logger.info("=" * 60)
|
||||
|
||||
|
||||
tasks = await self.list_all_tasks()
|
||||
|
||||
|
||||
if not tasks:
|
||||
logger.info("当前没有活跃的主动思考任务")
|
||||
logger.info("=" * 60)
|
||||
return
|
||||
|
||||
|
||||
# 按下次触发时间排序
|
||||
tasks_sorted = sorted(
|
||||
tasks,
|
||||
key=lambda x: x.get("next_run_time", datetime.max) or datetime.max
|
||||
)
|
||||
|
||||
tasks_sorted = sorted(tasks, key=lambda x: x.get("next_run_time", datetime.max) or datetime.max)
|
||||
|
||||
# 限制显示数量
|
||||
if max_streams > 0:
|
||||
tasks_sorted = tasks_sorted[:max_streams]
|
||||
|
||||
|
||||
logger.info(f"共有 {len(self._stream_schedules)} 个任务，显示前 {len(tasks_sorted)} 个")
|
||||
logger.info("")
|
||||
|
||||
|
||||
for i, task in enumerate(tasks_sorted, 1):
|
||||
stream_id = task.get("stream_id", "Unknown")
|
||||
next_run = task.get("next_run_time")
|
||||
is_paused = task.get("is_paused_for_topic", False)
|
||||
|
||||
|
||||
# 获取聊天流名称（如果可能）
|
||||
stream_name = stream_id[:16] + "..." if len(stream_id) > 16 else stream_id
|
||||
|
||||
|
||||
if next_run:
|
||||
# 计算剩余时间
|
||||
now = datetime.now()
|
||||
remaining = next_run - now
|
||||
remaining_seconds = int(remaining.total_seconds())
|
||||
|
||||
|
||||
if remaining_seconds < 0:
|
||||
time_str = "已过期（待执行）"
|
||||
elif remaining_seconds < 60:
|
||||
@@ -492,28 +487,25 @@ class ProactiveThinkingScheduler:
|
||||
hours = remaining_seconds // 3600
|
||||
minutes = (remaining_seconds % 3600) // 60
|
||||
time_str = f"{hours}小时{minutes}分钟后"
|
||||
|
||||
|
||||
status = "⏸️ 暂停中" if is_paused else "✅ 活跃"
|
||||
|
||||
|
||||
logger.info(
|
||||
f"[{i:2d}] {status} | {stream_name}\n"
|
||||
f" 下次触发: {next_run.strftime('%Y-%m-%d %H:%M:%S')} ({time_str})"
|
||||
)
|
||||
else:
|
||||
logger.info(
|
||||
f"[{i:2d}] ⚠️ 未知 | {stream_name}\n"
|
||||
f" 下次触发: 未设置"
|
||||
)
|
||||
|
||||
logger.info(f"[{i:2d}] ⚠️ 未知 | {stream_name}\n 下次触发: 未设置")
|
||||
|
||||
logger.info("")
|
||||
logger.info("=" * 60)
|
||||
|
||||
def get_last_decision(self, stream_id: str) -> Optional[dict[str, Any]]:
|
||||
|
||||
def get_last_decision(self, stream_id: str) -> dict[str, Any] | None:
|
||||
"""获取聊天流的上次主动思考决策
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
|
||||
|
||||
Returns:
|
||||
dict: 上次决策信息，包含：
|
||||
- action: "do_nothing" | "simple_bubble" | "throw_topic"
|
||||
@@ -523,16 +515,10 @@ class ProactiveThinkingScheduler:
|
||||
None: 如果没有历史决策
|
||||
"""
|
||||
return self._last_decisions.get(stream_id)
|
||||
|
||||
def record_decision(
|
||||
self,
|
||||
stream_id: str,
|
||||
action: str,
|
||||
reasoning: str,
|
||||
topic: Optional[str] = None
|
||||
) -> None:
|
||||
|
||||
def record_decision(self, stream_id: str, action: str, reasoning: str, topic: str | None = None) -> None:
|
||||
"""记录聊天流的主动思考决策
|
||||
|
||||
|
||||
Args:
|
||||
stream_id: 聊天流ID
|
||||
action: 决策动作
|
||||
|
||||
@@ -4,10 +4,10 @@
|
||||
通过LLM二步调用机制更新用户画像信息，包括别名、主观印象、偏好关键词和好感分数
|
||||
"""
|
||||
|
||||
import orjson
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
import orjson
|
||||
from sqlalchemy import select
|
||||
|
||||
from src.common.database.sqlalchemy_database_api import get_db_session
|
||||
@@ -42,7 +42,7 @@ class UserProfileTool(BaseTool):
|
||||
|
||||
def __init__(self, plugin_config: dict | None = None, chat_stream: Any = None):
|
||||
super().__init__(plugin_config, chat_stream)
|
||||
|
||||
|
||||
# 初始化用于二步调用的LLM
|
||||
try:
|
||||
self.profile_llm = LLMRequest(
|
||||
@@ -84,24 +84,24 @@ class UserProfileTool(BaseTool):
|
||||
"id": "user_profile_update",
|
||||
"content": "错误：必须提供目标用户ID"
|
||||
}
|
||||
|
||||
|
||||
# 从LLM传入的参数
|
||||
new_aliases = function_args.get("user_aliases", "")
|
||||
new_impression = function_args.get("impression_description", "")
|
||||
new_keywords = function_args.get("preference_keywords", "")
|
||||
new_score = function_args.get("affection_score")
|
||||
|
||||
|
||||
# 从数据库获取现有用户画像
|
||||
existing_profile = await self._get_user_profile(target_user_id)
|
||||
|
||||
|
||||
# 如果LLM没有传入任何有效参数，返回提示
|
||||
if not any([new_aliases, new_impression, new_keywords, new_score is not None]):
|
||||
return {
|
||||
"type": "info",
|
||||
"id": target_user_id,
|
||||
"content": f"提示：需要提供至少一项更新内容（别名、印象描述、偏好关键词或好感分数）"
|
||||
"content": "提示：需要提供至少一项更新内容（别名、印象描述、偏好关键词或好感分数）"
|
||||
}
|
||||
|
||||
|
||||
# 调用LLM进行二步决策
|
||||
if self.profile_llm is None:
|
||||
logger.error("LLM未正确初始化，无法执行二步调用")
|
||||
@@ -110,7 +110,7 @@ class UserProfileTool(BaseTool):
|
||||
"id": target_user_id,
|
||||
"content": "系统错误：LLM未正确初始化"
|
||||
}
|
||||
|
||||
|
||||
final_profile = await self._llm_decide_final_profile(
|
||||
target_user_id=target_user_id,
|
||||
existing_profile=existing_profile,
|
||||
@@ -119,17 +119,17 @@ class UserProfileTool(BaseTool):
|
||||
new_keywords=new_keywords,
|
||||
new_score=new_score
|
||||
)
|
||||
|
||||
|
||||
if not final_profile:
|
||||
return {
|
||||
"type": "error",
|
||||
"id": target_user_id,
|
||||
"content": "LLM决策失败，无法更新用户画像"
|
||||
}
|
||||
|
||||
|
||||
# 更新数据库
|
||||
await self._update_user_profile_in_db(target_user_id, final_profile)
|
||||
|
||||
|
||||
# 构建返回信息
|
||||
updates = []
|
||||
if final_profile.get("user_aliases"):
|
||||
@@ -140,22 +140,22 @@ class UserProfileTool(BaseTool):
|
||||
updates.append(f"偏好: {final_profile['preference_keywords']}")
|
||||
if final_profile.get("relationship_score") is not None:
|
||||
updates.append(f"好感分: {final_profile['relationship_score']:.2f}")
|
||||
|
||||
|
||||
result_text = f"已更新用户 {target_user_id} 的画像：\n" + "\n".join(updates)
|
||||
logger.info(f"用户画像更新成功: {target_user_id}")
|
||||
|
||||
|
||||
return {
|
||||
"type": "user_profile_update",
|
||||
"id": target_user_id,
|
||||
"content": result_text
|
||||
}
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"用户画像更新失败: {e}", exc_info=True)
|
||||
return {
|
||||
"type": "error",
|
||||
"id": function_args.get("target_user_id", "unknown"),
|
||||
"content": f"用户画像更新失败: {str(e)}"
|
||||
"content": f"用户画像更新失败: {e!s}"
|
||||
}
|
||||
|
||||
async def _get_user_profile(self, user_id: str) -> dict[str, Any]:
|
||||
@@ -172,7 +172,7 @@ class UserProfileTool(BaseTool):
|
||||
stmt = select(UserRelationships).where(UserRelationships.user_id == user_id)
|
||||
result = await session.execute(stmt)
|
||||
profile = result.scalar_one_or_none()
|
||||
|
||||
|
||||
if profile:
|
||||
return {
|
||||
"user_name": profile.user_name or user_id,
|
||||
@@ -227,7 +227,7 @@ class UserProfileTool(BaseTool):
|
||||
from src.individuality.individuality import Individuality
|
||||
individuality = Individuality()
|
||||
bot_personality = await individuality.get_personality_block()
|
||||
|
||||
|
||||
prompt = f"""
|
||||
你现在是一个有着特定性格和身份的AI助手。你的人设是：{bot_personality}
|
||||
|
||||
@@ -261,18 +261,18 @@ class UserProfileTool(BaseTool):
|
||||
"reasoning": "你的决策理由"
|
||||
}}
|
||||
"""
|
||||
|
||||
|
||||
# 调用LLM
|
||||
llm_response, _ = await self.profile_llm.generate_response_async(prompt=prompt)
|
||||
|
||||
|
||||
if not llm_response:
|
||||
logger.warning("LLM未返回有效响应")
|
||||
return None
|
||||
|
||||
|
||||
# 清理并解析响应
|
||||
cleaned_response = self._clean_llm_json_response(llm_response)
|
||||
response_data = orjson.loads(cleaned_response)
|
||||
|
||||
|
||||
# 提取最终决定的数据
|
||||
final_profile = {
|
||||
"user_aliases": response_data.get("user_aliases", existing_profile.get("user_aliases", "")),
|
||||
@@ -280,12 +280,12 @@ class UserProfileTool(BaseTool):
|
||||
"preference_keywords": response_data.get("preference_keywords", existing_profile.get("preference_keywords", "")),
|
||||
"relationship_score": max(0.0, min(1.0, float(response_data.get("relationship_score", existing_profile.get("relationship_score", 0.3))))),
|
||||
}
|
||||
|
||||
|
||||
logger.info(f"LLM决策完成: {target_user_id}")
|
||||
logger.debug(f"决策理由: {response_data.get('reasoning', '无')}")
|
||||
|
||||
|
||||
return final_profile
|
||||
|
||||
|
||||
except orjson.JSONDecodeError as e:
|
||||
logger.error(f"LLM响应JSON解析失败: {e}")
|
||||
logger.debug(f"LLM原始响应: {llm_response if 'llm_response' in locals() else 'N/A'}")
|
||||
@@ -303,12 +303,12 @@ class UserProfileTool(BaseTool):
|
||||
"""
|
||||
try:
|
||||
current_time = time.time()
|
||||
|
||||
|
||||
async with get_db_session() as session:
|
||||
stmt = select(UserRelationships).where(UserRelationships.user_id == user_id)
|
||||
result = await session.execute(stmt)
|
||||
existing = result.scalar_one_or_none()
|
||||
|
||||
|
||||
if existing:
|
||||
# 更新现有记录
|
||||
existing.user_aliases = profile.get("user_aliases", "")
|
||||
@@ -328,10 +328,10 @@ class UserProfileTool(BaseTool):
|
||||
last_updated=current_time
|
||||
)
|
||||
session.add(new_profile)
|
||||
|
||||
|
||||
await session.commit()
|
||||
logger.info(f"用户画像已更新到数据库: {user_id}")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"更新用户画像到数据库失败: {e}", exc_info=True)
|
||||
raise
|
||||
@@ -347,24 +347,24 @@ class UserProfileTool(BaseTool):
|
||||
"""
|
||||
try:
|
||||
import re
|
||||
|
||||
|
||||
cleaned = response.strip()
|
||||
|
||||
|
||||
# 移除 ```json 或 ``` 等标记
|
||||
cleaned = re.sub(r"^```(?:json)?\s*", "", cleaned, flags=re.MULTILINE | re.IGNORECASE)
|
||||
cleaned = re.sub(r"\s*```$", "", cleaned, flags=re.MULTILINE)
|
||||
|
||||
|
||||
# 尝试找到JSON对象的开始和结束
|
||||
json_start = cleaned.find("{")
|
||||
json_end = cleaned.rfind("}")
|
||||
|
||||
|
||||
if json_start != -1 and json_end != -1 and json_end > json_start:
|
||||
cleaned = cleaned[json_start:json_end + 1]
|
||||
|
||||
|
||||
cleaned = cleaned.strip()
|
||||
|
||||
|
||||
return cleaned
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"清理LLM响应失败: {e}")
|
||||
return response
|
||||
|
||||
@@ -261,7 +261,7 @@ class SetEmojiLikeAction(BaseAction):
|
||||
elif isinstance(self.action_message, dict):
|
||||
message_id = self.action_message.get("message_id")
|
||||
logger.info(f"获取到的消息ID: {message_id}")
|
||||
|
||||
|
||||
if not message_id:
|
||||
logger.error("未提供有效的消息或消息ID")
|
||||
await self.store_action_info(action_prompt_display="贴表情失败: 未提供消息ID", action_done=False)
|
||||
@@ -279,7 +279,7 @@ class SetEmojiLikeAction(BaseAction):
|
||||
context_text = self.action_message.processed_plain_text or ""
|
||||
else:
|
||||
context_text = self.action_message.get("processed_plain_text", "")
|
||||
|
||||
|
||||
if not context_text:
|
||||
logger.error("无法找到动作选择的原始消息文本")
|
||||
return False, "无法找到动作选择的原始消息文本"
|
||||
|
||||
@@ -5,7 +5,7 @@ Web Search Tool Plugin
|
||||
"""
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from src.plugin_system import BasePlugin, ComponentInfo, ConfigField, PythonDependency, register_plugin
|
||||
from src.plugin_system import BasePlugin, ComponentInfo, ConfigField, register_plugin
|
||||
from src.plugin_system.apis import config_api
|
||||
|
||||
from .tools.url_parser import URLParserTool
|
||||
|
||||
Reference in New Issue
Block a user