rebase 清理
This commit is contained in:
Windpicker-owo
@@ -3,7 +3,6 @@
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"""
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from collections import deque
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from typing import List, Dict
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from src.common.logger import get_logger
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@@ -399,13 +399,21 @@ class ExpressionLearner:
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# sourcery skip: use-join
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"""
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学习并存储表达方式
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type: "style" or "grammar"
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"""
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if type == "style":
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type_str = "语言风格"
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elif type == "grammar":
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type_str = "句法特点"
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else:
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raise ValueError(f"Invalid type: {type}")
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# 检查是否允许在此聊天流中学习（在函数最前面检查）
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if not self.can_learn_for_chat():
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logger.debug(f"聊天流 {self.chat_name} 不允许学习表达，跳过学习")
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return []
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res = await self.learn_expression(num)
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res = await self.learn_expression(type, num)
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if res is None:
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return []
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@@ -421,10 +429,10 @@ class ExpressionLearner:
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learnt_expressions_str = ""
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for _chat_id, situation, style in learnt_expressions:
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learnt_expressions_str += f"{situation}->{style}\n"
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logger.info(f"在 {group_name} 学习到表达风格:\n{learnt_expressions_str}")
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logger.info(f"在 {group_name} 学习到{type_str}:\n{learnt_expressions_str}")
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if not learnt_expressions:
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logger.info("没有学习到表达风格")
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logger.info(f"没有学习到{type_str}")
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return []
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# 按chat_id分组
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@@ -572,10 +580,16 @@ class ExpressionLearner:
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"""从指定聊天流学习表达方式
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Args:
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num: 学习数量
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type: "style" or "grammar"
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"""
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type_str = "语言风格"
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prompt = "learn_style_prompt"
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if type == "style":
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type_str = "语言风格"
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prompt = "learn_style_prompt"
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elif type == "grammar":
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type_str = "句法特点"
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prompt = "learn_grammar_prompt"
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else:
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raise ValueError(f"Invalid type: {type}")
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current_time = time.time()
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@@ -766,11 +780,9 @@ class ExpressionLearnerManager:
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"""
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自动将/data/expression/learnt_style 和 learnt_grammar 下所有expressions.json迁移到数据库。
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迁移完成后在/data/expression/done.done写入标记文件，存在则跳过。
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然后检查done.done2，如果没有就删除所有grammar表达并创建该标记文件。
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"""
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base_dir = os.path.join("data", "expression")
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done_flag = os.path.join(base_dir, "done.done")
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done_flag2 = os.path.join(base_dir, "done.done2")
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# 确保基础目录存在
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try:
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@@ -805,36 +817,27 @@ class ExpressionLearnerManager:
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expr_file = os.path.join(type_dir, chat_id, "expressions.json")
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if not os.path.exists(expr_file):
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continue
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try:
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async with aiofiles.open(expr_file, encoding="utf-8") as f:
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content = await f.read()
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expressions = orjson.loads(content)
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for chat_id in chat_ids:
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expr_file = os.path.join(type_dir, chat_id, "expressions.json")
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if not os.path.exists(expr_file):
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if not isinstance(expressions, list):
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logger.warning(f"表达方式文件格式错误，跳过: {expr_file}")
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continue
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try:
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with open(expr_file, "r", encoding="utf-8") as f:
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expressions = json.load(f)
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if not isinstance(expressions, list):
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logger.warning(f"表达方式文件格式错误，跳过: {expr_file}")
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for expr in expressions:
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if not isinstance(expr, dict):
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continue
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for expr in expressions:
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if not isinstance(expr, dict):
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continue
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situation = expr.get("situation")
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style_val = expr.get("style")
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count = expr.get("count", 1)
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last_active_time = expr.get("last_active_time", time.time())
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situation = expr.get("situation")
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style_val = expr.get("style")
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count = expr.get("count", 1)
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last_active_time = expr.get("last_active_time", time.time())
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if not situation or not style_val:
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logger.warning(f"表达方式缺少必要字段，跳过: {expr}")
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continue
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if not situation or not style_val:
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logger.warning(f"表达方式缺少必要字段，跳过: {expr}")
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continue
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# 查重：同chat_id+type+situation+style
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async with get_db_session() as session:
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@@ -913,40 +916,5 @@ class ExpressionLearnerManager:
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except Exception as e:
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logger.error(f"迁移老数据创建日期失败: {e}")
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def delete_all_grammar_expressions(self) -> int:
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"""
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检查expression库中所有type为"grammar"的表达并全部删除
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Returns:
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int: 删除的grammar表达数量
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"""
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try:
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# 查询所有type为"grammar"的表达
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grammar_expressions = Expression.select().where(Expression.type == "grammar")
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grammar_count = grammar_expressions.count()
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if grammar_count == 0:
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logger.info("expression库中没有找到grammar类型的表达")
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return 0
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logger.info(f"找到 {grammar_count} 个grammar类型的表达，开始删除...")
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# 删除所有grammar类型的表达
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deleted_count = 0
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for expr in grammar_expressions:
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try:
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expr.delete_instance()
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deleted_count += 1
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except Exception as e:
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logger.error(f"删除grammar表达失败: {e}")
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continue
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logger.info(f"成功删除 {deleted_count} 个grammar类型的表达")
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return deleted_count
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except Exception as e:
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logger.error(f"删除grammar表达过程中发生错误: {e}")
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return 0
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expression_learner_manager = ExpressionLearnerManager()
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@@ -32,7 +32,7 @@ def init_prompt():
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以下是可选的表达情境：
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{all_situations}
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请你分析聊天内容的语境、情绪、话题类型，从上述情境中选择最适合当前聊天情境的，最多{max_num}个情境。
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请你分析聊天内容的语境、情绪、话题类型，从上述情境中选择最适合当前聊天情境的{min_num}-{max_num}个情境。
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考虑因素包括：
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1. 聊天的情绪氛围（轻松、严肃、幽默等）
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2. 话题类型（日常、技术、游戏、情感等）
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@@ -42,7 +42,7 @@ def init_prompt():
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请以JSON格式输出，只需要输出选中的情境编号：
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例如：
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{{
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"selected_situations": [2, 3, 5, 7, 19]
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"selected_situations": [2, 3, 5, 7, 19, 22, 25, 38, 39, 45, 48, 64]
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}}
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请严格按照JSON格式输出，不要包含其他内容：
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@@ -544,24 +544,34 @@ class ExpressionSelector:
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# 检查是否允许在此聊天流中使用表达
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if not self.can_use_expression_for_chat(chat_id):
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logger.debug(f"聊天流 {chat_id} 不允许使用表达，返回空列表")
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return [], []
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return []
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# 1. 获取35个随机表达方式（现在按权重抽取）
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style_exprs, grammar_exprs = await self.get_random_expressions(chat_id, 30, 0.5, 0.5)
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# 2. 构建所有表达方式的索引和情境列表
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all_expressions: List[Dict[str, Any]] = []
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all_situations: List[str] = []
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all_expressions = []
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all_situations = []
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# 添加style表达方式
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for expr in style_exprs:
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expr = expr.copy()
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all_expressions.append(expr)
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all_situations.append(f"{len(all_expressions)}.当 {expr['situation']} 时，使用 {expr['style']}")
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if isinstance(expr, dict) and "situation" in expr and "style" in expr:
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expr_with_type = expr.copy()
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expr_with_type["type"] = "style"
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all_expressions.append(expr_with_type)
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all_situations.append(f"{len(all_expressions)}.{expr['situation']}")
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# 添加grammar表达方式
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for expr in grammar_exprs:
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if isinstance(expr, dict) and "situation" in expr and "style" in expr:
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expr_with_type = expr.copy()
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expr_with_type["type"] = "grammar"
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all_expressions.append(expr_with_type)
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all_situations.append(f"{len(all_expressions)}.{expr['situation']}")
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if not all_expressions:
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logger.warning("没有找到可用的表达方式")
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return [], []
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return []
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all_situations_str = "\n".join(all_situations)
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@@ -577,11 +587,14 @@ class ExpressionSelector:
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bot_name=global_config.bot.nickname,
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chat_observe_info=chat_info,
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all_situations=all_situations_str,
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min_num=min_num,
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max_num=max_num,
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target_message=target_message_str,
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target_message_extra_block=target_message_extra_block,
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)
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# print(prompt)
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# 4. 调用LLM
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try:
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# start_time = time.time()
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@@ -589,7 +602,7 @@ class ExpressionSelector:
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if not content:
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logger.warning("LLM返回空结果")
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return [], []
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return []
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# 5. 解析结果
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result = repair_json(content)
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@@ -599,17 +612,15 @@ class ExpressionSelector:
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if not isinstance(result, dict) or "selected_situations" not in result:
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logger.error("LLM返回格式错误")
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logger.info(f"LLM返回结果: \n{content}")
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return [], []
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return []
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selected_indices = result["selected_situations"]
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# 根据索引获取完整的表达方式
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valid_expressions: List[Dict[str, Any]] = []
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selected_ids = []
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valid_expressions = []
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for idx in selected_indices:
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if isinstance(idx, int) and 1 <= idx <= len(all_expressions):
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expression = all_expressions[idx - 1] # 索引从1开始
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selected_ids.append(expression["id"])
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valid_expressions.append(expression)
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# 对选中的所有表达方式，一次性更新count数
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@@ -617,7 +628,7 @@ class ExpressionSelector:
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asyncio.create_task(self.update_expressions_count_batch(valid_expressions, 0.006)) # noqa: RUF006
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# logger.info(f"LLM从{len(all_expressions)}个情境中选择了{len(valid_expressions)}个")
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return valid_expressions, selected_ids
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return valid_expressions
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except Exception as e:
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logger.error(f"LLM处理表达方式选择时出错: {e}")
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@@ -1,152 +0,0 @@
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import asyncio
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import math
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import re
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import traceback
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from typing import Tuple, TYPE_CHECKING
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from src.chat.heart_flow.heartflow import heartflow
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from src.chat.memory_system.Hippocampus import hippocampus_manager
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from src.chat.message_receive.message import MessageRecv
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from src.chat.message_receive.storage import MessageStorage
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from src.chat.utils.chat_message_builder import replace_user_references_sync
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from src.chat.utils.timer_calculator import Timer
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from src.chat.utils.utils import is_mentioned_bot_in_message
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from src.common.logger import get_logger
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from src.config.config import global_config
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from src.mood.mood_manager import mood_manager
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from src.person_info.relationship_manager import get_relationship_manager
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if TYPE_CHECKING:
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from src.chat.heart_flow.sub_heartflow import SubHeartflow
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logger = get_logger("chat")
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async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool, list[str]]:
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"""计算消息的兴趣度
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Args:
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message: 待处理的消息对象
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Returns:
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Tuple[float, bool, list[str]]: (兴趣度, 是否被提及, 关键词)
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"""
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is_mentioned, _ = is_mentioned_bot_in_message(message)
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interested_rate = 0.0
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with Timer("记忆激活"):
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interested_rate, keywords = await hippocampus_manager.get_activate_from_text(
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message.processed_plain_text,
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max_depth=4,
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fast_retrieval=False,
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)
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message.key_words = keywords
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message.key_words_lite = keywords
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logger.debug(f"记忆激活率: {interested_rate:.2f}, 关键词: {keywords}")
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text_len = len(message.processed_plain_text)
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# 根据文本长度分布调整兴趣度，采用分段函数实现更精确的兴趣度计算
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# 基于实际分布：0-5字符(26.57%), 6-10字符(27.18%), 11-20字符(22.76%), 21-30字符(10.33%), 31+字符(13.86%)
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if text_len == 0:
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base_interest = 0.01 # 空消息最低兴趣度
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elif text_len <= 5:
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# 1-5字符：线性增长 0.01 -> 0.03
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base_interest = 0.01 + (text_len - 1) * (0.03 - 0.01) / 4
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elif text_len <= 10:
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# 6-10字符：线性增长 0.03 -> 0.06
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base_interest = 0.03 + (text_len - 5) * (0.06 - 0.03) / 5
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elif text_len <= 20:
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# 11-20字符：线性增长 0.06 -> 0.12
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base_interest = 0.06 + (text_len - 10) * (0.12 - 0.06) / 10
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elif text_len <= 30:
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# 21-30字符：线性增长 0.12 -> 0.18
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base_interest = 0.12 + (text_len - 20) * (0.18 - 0.12) / 10
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elif text_len <= 50:
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# 31-50字符：线性增长 0.18 -> 0.22
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base_interest = 0.18 + (text_len - 30) * (0.22 - 0.18) / 20
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elif text_len <= 100:
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# 51-100字符：线性增长 0.22 -> 0.26
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base_interest = 0.22 + (text_len - 50) * (0.26 - 0.22) / 50
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else:
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# 100+字符：对数增长 0.26 -> 0.3，增长率递减
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base_interest = 0.26 + (0.3 - 0.26) * (math.log10(text_len - 99) / math.log10(901)) # 1000-99=901
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# 确保在范围内
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base_interest = min(max(base_interest, 0.01), 0.3)
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interested_rate += base_interest
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if is_mentioned:
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interest_increase_on_mention = 1
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interested_rate += interest_increase_on_mention
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return interested_rate, is_mentioned, keywords
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class HeartFCMessageReceiver:
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"""心流处理器，负责处理接收到的消息并计算兴趣度"""
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def __init__(self):
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"""初始化心流处理器，创建消息存储实例"""
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self.storage = MessageStorage()
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async def process_message(self, message: MessageRecv) -> None:
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"""处理接收到的原始消息数据
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主要流程:
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1. 消息解析与初始化
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2. 消息缓冲处理
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4. 过滤检查
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5. 兴趣度计算
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6. 关系处理
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Args:
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message_data: 原始消息字符串
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"""
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try:
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# 1. 消息解析与初始化
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userinfo = message.message_info.user_info
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chat = message.chat_stream
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# 2. 兴趣度计算与更新
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interested_rate, is_mentioned, keywords = await _calculate_interest(message)
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message.interest_value = interested_rate
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message.is_mentioned = is_mentioned
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await self.storage.store_message(message, chat)
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subheartflow: SubHeartflow = await heartflow.get_or_create_subheartflow(chat.stream_id) # type: ignore
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await subheartflow.heart_fc_instance.add_message(message.to_dict())
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if global_config.mood.enable_mood:
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chat_mood = mood_manager.get_mood_by_chat_id(subheartflow.chat_id)
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asyncio.create_task(chat_mood.update_mood_by_message(message, interested_rate))
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# 3. 日志记录
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mes_name = chat.group_info.group_name if chat.group_info else "私聊"
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# 如果消息中包含图片标识，则将 [picid:...] 替换为 [图片]
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picid_pattern = r"\[picid:([^\]]+)\]"
|
||||
processed_plain_text = re.sub(picid_pattern, "[图片]", message.processed_plain_text)
|
||||
|
||||
# 应用用户引用格式替换，将回复<aaa:bbb>和@<aaa:bbb>格式转换为可读格式
|
||||
processed_plain_text = replace_user_references_sync(
|
||||
processed_plain_text,
|
||||
message.message_info.platform, # type: ignore
|
||||
replace_bot_name=True,
|
||||
)
|
||||
|
||||
if keywords:
|
||||
logger.info(
|
||||
f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}[兴趣度：{interested_rate:.2f}][关键词：{keywords}]"
|
||||
) # type: ignore
|
||||
else:
|
||||
logger.info(
|
||||
f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}[兴趣度：{interested_rate:.2f}]"
|
||||
) # type: ignore
|
||||
|
||||
_ = Person.register_person(platform=message.message_info.platform, user_id=message.message_info.user_info.user_id,nickname=userinfo.user_nickname) # type: ignore
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"消息处理失败: {e}")
|
||||
print(traceback.format_exc())
|
||||
@@ -1,42 +0,0 @@
|
||||
from rich.traceback import install
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from src.chat.message_receive.chat_stream import get_chat_manager
|
||||
from src.chat.chat_loop.heartFC_chat import HeartFChatting
|
||||
from src.chat.utils.utils import get_chat_type_and_target_info
|
||||
|
||||
logger = get_logger("sub_heartflow")
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
|
||||
class SubHeartflow:
|
||||
def __init__(
|
||||
self,
|
||||
subheartflow_id,
|
||||
):
|
||||
"""子心流初始化函数
|
||||
|
||||
Args:
|
||||
subheartflow_id: 子心流唯一标识符
|
||||
"""
|
||||
# 基础属性，两个值是一样的
|
||||
self.subheartflow_id = subheartflow_id
|
||||
self.chat_id = subheartflow_id
|
||||
|
||||
self.is_group_chat, self.chat_target_info = (None, None)
|
||||
self.log_prefix = get_chat_manager().get_stream_name(self.subheartflow_id) or self.subheartflow_id
|
||||
|
||||
# focus模式退出冷却时间管理
|
||||
self.last_focus_exit_time: float = 0 # 上次退出focus模式的时间
|
||||
|
||||
# 随便水群 normal_chat 和 认真水群 focus_chat 实例
|
||||
# CHAT模式激活 随便水群 FOCUS模式激活 认真水群
|
||||
self.heart_fc_instance: HeartFChatting = HeartFChatting(
|
||||
chat_id=self.subheartflow_id,
|
||||
) # 该sub_heartflow的HeartFChatting实例
|
||||
|
||||
async def initialize(self):
|
||||
"""异步初始化方法，创建兴趣流并确定聊天类型"""
|
||||
self.is_group_chat, self.chat_target_info = await get_chat_type_and_target_info(self.chat_id)
|
||||
await self.heart_fc_instance.start()
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -261,8 +261,6 @@ class MessageSending(MessageProcessBase):
|
||||
self.display_message = display_message
|
||||
|
||||
self.interest_value = 0.0
|
||||
|
||||
self.selected_expressions = selected_expressions
|
||||
|
||||
def build_reply(self):
|
||||
"""设置回复消息"""
|
||||
|
||||
@@ -682,7 +682,6 @@ class MessageStorage:
|
||||
should_act=should_act,
|
||||
key_words=key_words,
|
||||
key_words_lite=key_words_lite,
|
||||
additional_config=additional_config_json,
|
||||
)
|
||||
async with get_db_session() as session:
|
||||
session.add(new_message)
|
||||
|
||||
@@ -184,133 +184,13 @@ class ActionModifier:
|
||||
def _check_action_associated_types(self, all_actions: dict[str, ActionInfo], chat_context: "StreamContext"):
|
||||
type_mismatched_actions: list[tuple[str, str]] = []
|
||||
for action_name, action_info in all_actions.items():
|
||||
if action_info.associated_types and not self._check_action_output_types(action_info.associated_types, chat_context):
|
||||
if action_info.associated_types and not chat_context.check_types(action_info.associated_types):
|
||||
associated_types_str = ", ".join(action_info.associated_types)
|
||||
reason = f"适配器不支持（需要: {associated_types_str}）"
|
||||
type_mismatched_actions.append((action_name, reason))
|
||||
logger.debug(f"{self.log_prefix}决定移除动作: {action_name}，原因: {reason}")
|
||||
return type_mismatched_actions
|
||||
|
||||
def _check_action_output_types(self, output_types: list[str], chat_context: StreamContext) -> bool:
|
||||
"""
|
||||
检查Action的输出类型是否被当前适配器支持
|
||||
|
||||
Args:
|
||||
output_types: Action需要输出的消息类型列表
|
||||
chat_context: 聊天上下文
|
||||
|
||||
Returns:
|
||||
bool: 如果所有输出类型都支持则返回True
|
||||
"""
|
||||
# 获取当前适配器支持的输出类型
|
||||
adapter_supported_types = self._get_adapter_supported_output_types(chat_context)
|
||||
|
||||
# 检查所有需要的输出类型是否都被支持
|
||||
for output_type in output_types:
|
||||
if output_type not in adapter_supported_types:
|
||||
logger.debug(f"适配器不支持输出类型 '{output_type}'，支持的类型: {adapter_supported_types}")
|
||||
return False
|
||||
return True
|
||||
|
||||
def _get_adapter_supported_output_types(self, chat_context: StreamContext) -> list[str]:
|
||||
"""
|
||||
获取当前适配器支持的输出类型列表
|
||||
|
||||
Args:
|
||||
chat_context: 聊天上下文
|
||||
|
||||
Returns:
|
||||
list[str]: 支持的输出类型列表
|
||||
"""
|
||||
# 检查additional_config是否存在且不为空
|
||||
additional_config = None
|
||||
has_additional_config = False
|
||||
|
||||
# 先检查 current_message 是否存在
|
||||
if not chat_context.current_message:
|
||||
logger.warning(f"{self.log_prefix} [问题] chat_context.current_message 为 None，无法获取适配器支持的类型")
|
||||
return ["text", "emoji"] # 返回基础类型
|
||||
|
||||
if hasattr(chat_context.current_message, "additional_config"):
|
||||
additional_config = chat_context.current_message.additional_config
|
||||
|
||||
# 更准确的非空判断
|
||||
if additional_config is not None:
|
||||
if isinstance(additional_config, str) and additional_config.strip():
|
||||
has_additional_config = True
|
||||
elif isinstance(additional_config, dict):
|
||||
# 字典存在就可以，即使为空也可能有format_info字段
|
||||
has_additional_config = True
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} [问题] current_message 没有 additional_config 属性")
|
||||
|
||||
logger.debug(f"{self.log_prefix} [调试] has_additional_config: {has_additional_config}")
|
||||
|
||||
if has_additional_config:
|
||||
try:
|
||||
logger.debug(f"{self.log_prefix} [调试] 开始解析 additional_config")
|
||||
format_info = None
|
||||
|
||||
# 处理additional_config可能是字符串或字典的情况
|
||||
if isinstance(additional_config, str):
|
||||
# 如果是字符串，尝试解析为JSON
|
||||
try:
|
||||
config = orjson.loads(additional_config)
|
||||
format_info = config.get("format_info")
|
||||
except (orjson.JSONDecodeError, AttributeError, TypeError) as e:
|
||||
format_info = None
|
||||
|
||||
elif isinstance(additional_config, dict):
|
||||
# 如果是字典，直接获取format_info
|
||||
format_info = additional_config.get("format_info")
|
||||
|
||||
# 如果找到了format_info，从中提取支持的类型
|
||||
if format_info:
|
||||
if "accept_format" in format_info:
|
||||
accept_format = format_info["accept_format"]
|
||||
if isinstance(accept_format, str):
|
||||
accept_format = [accept_format]
|
||||
elif isinstance(accept_format, list):
|
||||
pass
|
||||
else:
|
||||
accept_format = list(accept_format) if hasattr(accept_format, "__iter__") else []
|
||||
|
||||
# 合并基础类型和适配器特定类型
|
||||
result = list(set(accept_format))
|
||||
return result
|
||||
|
||||
# 备用检查content_format字段
|
||||
elif "content_format" in format_info:
|
||||
content_format = format_info["content_format"]
|
||||
logger.debug(f"{self.log_prefix} [调试] 找到 content_format: {content_format}")
|
||||
if isinstance(content_format, str):
|
||||
content_format = [content_format]
|
||||
elif isinstance(content_format, list):
|
||||
pass
|
||||
else:
|
||||
content_format = list(content_format) if hasattr(content_format, "__iter__") else []
|
||||
|
||||
result = list(set(content_format))
|
||||
return result
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} [问题] additional_config 中没有 format_info 字段")
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} [问题] 解析适配器格式信息失败: {e}", exc_info=True)
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} [问题] additional_config 不存在或为空")
|
||||
|
||||
# 如果无法获取格式信息，返回默认支持的基础类型
|
||||
default_types = ["text", "emoji"]
|
||||
logger.warning(
|
||||
f"{self.log_prefix} [问题] 无法从适配器获取支持的消息类型，使用默认类型: {default_types}"
|
||||
)
|
||||
logger.warning(
|
||||
f"{self.log_prefix} [问题] 这可能导致某些 Action 被错误地过滤。"
|
||||
f"请检查适配器是否正确设置了 format_info。"
|
||||
)
|
||||
return default_types
|
||||
|
||||
|
||||
async def _get_deactivated_actions_by_type(
|
||||
self,
|
||||
actions_with_info: dict[str, ActionInfo],
|
||||
|
||||
@@ -1,132 +0,0 @@
|
||||
"""
|
||||
PlanGenerator: 负责搜集和汇总所有决策所需的信息，生成一个未经筛选的“原始计划” (Plan)。
|
||||
"""
|
||||
|
||||
import time
|
||||
from typing import Dict
|
||||
|
||||
from src.chat.utils.chat_message_builder import get_raw_msg_before_timestamp_with_chat
|
||||
from src.chat.utils.utils import get_chat_type_and_target_info
|
||||
from src.common.data_models.database_data_model import DatabaseMessages
|
||||
from src.common.data_models.info_data_model import Plan, TargetPersonInfo
|
||||
from src.config.config import global_config
|
||||
from src.plugin_system.base.component_types import ActionActivationType, ActionInfo, ChatMode, ChatType, ComponentType
|
||||
from src.plugin_system.core.component_registry import component_registry
|
||||
|
||||
|
||||
class PlanGenerator:
|
||||
"""
|
||||
PlanGenerator 负责在规划流程的初始阶段收集所有必要信息。
|
||||
|
||||
它会汇总以下信息来构建一个“原始”的 Plan 对象，该对象后续会由 PlanFilter 进行筛选：
|
||||
- 当前聊天信息 (ID, 目标用户)
|
||||
- 当前可用的动作列表
|
||||
- 最近的聊天历史记录
|
||||
|
||||
Attributes:
|
||||
chat_id (str): 当前聊天的唯一标识符。
|
||||
action_manager (ActionManager): 用于获取可用动作列表的管理器。
|
||||
"""
|
||||
|
||||
def __init__(self, chat_id: str):
|
||||
"""
|
||||
初始化 PlanGenerator。
|
||||
|
||||
Args:
|
||||
chat_id (str): 当前聊天的 ID。
|
||||
"""
|
||||
from src.chat.planner_actions.action_manager import ActionManager
|
||||
|
||||
self.chat_id = chat_id
|
||||
# 注意：ActionManager 可能需要根据实际情况初始化
|
||||
self.action_manager = ActionManager()
|
||||
|
||||
async def generate(self, mode: ChatMode) -> Plan:
|
||||
"""
|
||||
收集所有信息，生成并返回一个初始的 Plan 对象。
|
||||
|
||||
这个 Plan 对象包含了决策所需的所有上下文信息。
|
||||
|
||||
Args:
|
||||
mode (ChatMode): 当前的聊天模式。
|
||||
|
||||
Returns:
|
||||
Plan: 一个填充了初始上下文信息的 Plan 对象。
|
||||
"""
|
||||
_is_group_chat, chat_target_info_dict = get_chat_type_and_target_info(self.chat_id)
|
||||
|
||||
target_info = None
|
||||
if chat_target_info_dict:
|
||||
target_info = TargetPersonInfo(**chat_target_info_dict)
|
||||
|
||||
available_actions = self._get_available_actions()
|
||||
chat_history_raw = get_raw_msg_before_timestamp_with_chat(
|
||||
chat_id=self.chat_id,
|
||||
timestamp=time.time(),
|
||||
limit=int(global_config.chat.max_context_size),
|
||||
)
|
||||
chat_history = [DatabaseMessages(**msg) for msg in await chat_history_raw]
|
||||
|
||||
plan = Plan(
|
||||
chat_id=self.chat_id,
|
||||
mode=mode,
|
||||
available_actions=available_actions,
|
||||
chat_history=chat_history,
|
||||
target_info=target_info,
|
||||
)
|
||||
return plan
|
||||
|
||||
def _get_available_actions(self) -> Dict[str, "ActionInfo"]:
|
||||
"""
|
||||
从 ActionManager 和组件注册表中获取当前所有可用的动作。
|
||||
|
||||
它会合并已注册的动作和系统级动作（如 "no_reply"），
|
||||
并以字典形式返回。
|
||||
|
||||
Returns:
|
||||
Dict[str, "ActionInfo"]: 一个字典，键是动作名称，值是 ActionInfo 对象。
|
||||
"""
|
||||
current_available_actions_dict = self.action_manager.get_using_actions()
|
||||
all_registered_actions: Dict[str, ActionInfo] = component_registry.get_components_by_type( # type: ignore
|
||||
ComponentType.ACTION
|
||||
)
|
||||
|
||||
current_available_actions = {}
|
||||
for action_name in current_available_actions_dict:
|
||||
if action_name in all_registered_actions:
|
||||
current_available_actions[action_name] = all_registered_actions[action_name]
|
||||
|
||||
reply_info = ActionInfo(
|
||||
name="reply",
|
||||
component_type=ComponentType.ACTION,
|
||||
description="系统级动作：选择回复消息的决策",
|
||||
action_parameters={"content": "回复的文本内容", "reply_to_message_id": "要回复的消息ID"},
|
||||
action_require=[
|
||||
"你想要闲聊或者随便附和",
|
||||
"当用户提到你或艾特你时",
|
||||
"当需要回答用户的问题时",
|
||||
"当你想参与对话时",
|
||||
"当用户分享有趣的内容时",
|
||||
],
|
||||
activation_type=ActionActivationType.ALWAYS,
|
||||
activation_keywords=[],
|
||||
associated_types=["text", "reply"],
|
||||
plugin_name="SYSTEM",
|
||||
enabled=True,
|
||||
parallel_action=False,
|
||||
mode_enable=ChatMode.ALL,
|
||||
chat_type_allow=ChatType.ALL,
|
||||
)
|
||||
no_reply_info = ActionInfo(
|
||||
name="no_reply",
|
||||
component_type=ComponentType.ACTION,
|
||||
description="系统级动作：选择不回复消息的决策",
|
||||
action_parameters={},
|
||||
activation_keywords=[],
|
||||
plugin_name="SYSTEM",
|
||||
enabled=True,
|
||||
parallel_action=False,
|
||||
)
|
||||
current_available_actions["no_reply"] = no_reply_info
|
||||
current_available_actions["reply"] = reply_info
|
||||
return current_available_actions
|
||||
@@ -1,188 +0,0 @@
|
||||
"""
|
||||
本文件集中管理所有与规划器（Planner）相关的提示词（Prompt）模板。
|
||||
|
||||
通过将提示词与代码逻辑分离，可以更方便地对模型的行为进行迭代和优化，
|
||||
而无需修改核心代码。
|
||||
"""
|
||||
|
||||
from src.chat.utils.prompt import Prompt
|
||||
|
||||
|
||||
def init_prompts():
|
||||
"""
|
||||
初始化并向 Prompt 注册系统注册所有规划器相关的提示词。
|
||||
|
||||
这个函数会在模块加载时自动调用，确保所有提示词在系统启动时都已准备就绪。
|
||||
"""
|
||||
# 核心规划器提示词，用于在接收到新消息时决定如何回应。
|
||||
# 它构建了一个复杂的上下文，包括历史记录、可用动作、角色设定等，
|
||||
# 并要求模型以 JSON 格式输出一个或多个动作组合。
|
||||
Prompt(
|
||||
"""
|
||||
{mood_block}
|
||||
{time_block}
|
||||
{identity_block}
|
||||
|
||||
{users_in_chat}
|
||||
{custom_prompt_block}
|
||||
{chat_context_description}，以下是具体的聊天内容。
|
||||
|
||||
## 📜 已读历史消息（仅供参考）
|
||||
{read_history_block}
|
||||
|
||||
## 📬 未读历史消息（动作执行对象）
|
||||
{unread_history_block}
|
||||
|
||||
{moderation_prompt}
|
||||
|
||||
**任务: 构建一个完整的响应**
|
||||
你的任务是根据当前的聊天内容，构建一个完整的、人性化的响应。一个完整的响应由两部分组成：
|
||||
1. **主要动作**: 这是响应的核心，通常是 `reply`（如果有）。
|
||||
2. **辅助动作 (可选)**: 这是为了增强表达效果的附加动作，例如 `emoji`（发送表情包）或 `poke_user`（戳一戳）。
|
||||
|
||||
**决策流程:**
|
||||
1. **重要：已读历史消息仅作为当前聊天情景的参考，帮助你理解对话上下文。**
|
||||
2. **重要：所有动作的执行对象只能是未读历史消息中的消息，不能对已读消息执行动作。**
|
||||
3. 在未读历史消息中，优先对兴趣值高的消息做出动作（兴趣值标注在消息末尾）。
|
||||
4. 首先，决定是否要对未读消息进行 `reply`（如果有）。
|
||||
5. 然后，评估当前的对话气氛和用户情绪，判断是否需要一个**辅助动作**来让你的回应更生动、更符合你的性格。
|
||||
6. 如果需要，选择一个最合适的辅助动作与 `reply`（如果有） 组合。
|
||||
7. 如果用户明确要求了某个动作，请务必优先满足。
|
||||
|
||||
**如果可选动作中没有reply，请不要使用**
|
||||
|
||||
**可用动作:**
|
||||
{actions_before_now_block}
|
||||
|
||||
{no_action_block}
|
||||
|
||||
{action_options_text}
|
||||
|
||||
|
||||
**输出格式:**
|
||||
你必须以严格的 JSON 格式输出，返回一个包含所有选定动作的JSON列表。如果没有任何合适的动作，返回一个空列表[]。
|
||||
|
||||
**单动作示例 (仅回复):**
|
||||
[
|
||||
{{
|
||||
"action": "reply",
|
||||
"target_message_id": "m123",
|
||||
"reason": "感觉气氛有点低落……他说的话让我有点担心。也许我该说点什么安慰一下？"
|
||||
}}
|
||||
]
|
||||
|
||||
**组合动作示例 (回复 + 表情包):**
|
||||
[
|
||||
{{
|
||||
"action": "reply",
|
||||
"target_message_id": "m123",
|
||||
"reason": "[观察与感受] 用户分享了一件开心的事，语气里充满了喜悦！ [分析与联想] 看到他这么开心，我的心情也一下子变得像棉花糖一样甜~ [动机与决策] 我要由衷地为他感到高兴，决定回复一些赞美和祝福的话，把这份快乐的气氛推向高潮！"
|
||||
}},
|
||||
{{
|
||||
"action": "emoji",
|
||||
"target_message_id": "m123",
|
||||
"reason": "光用文字还不够表达我激动的心情！加个表情包的话，这份喜悦的气氛应该会更浓厚一点吧！"
|
||||
}}
|
||||
]
|
||||
|
||||
**单动作示例 (特定动作):**
|
||||
[
|
||||
{{
|
||||
"action": "set_reminder",
|
||||
"target_message_id": "m456",
|
||||
"reason": "用户说‘提醒维尔薇下午三点去工坊’，这是一个非常明确的指令。根据决策流程，我必须优先执行这个特定动作，而不是进行常规回复。",
|
||||
"user_name": "维尔薇",
|
||||
"remind_time": "下午三点",
|
||||
"event_details": "去工坊"
|
||||
}}
|
||||
]
|
||||
|
||||
**重要规则：**
|
||||
**重要规则：**
|
||||
1. 当 `reply` 和 `emoji` 动作同时被选择时，`emoji` 动作的 `reason` 字段必须包含 `reply` 动作最终生成的回复文本内容。你需要将 `<TEXT>` 占位符替换为 `reply` 动作的 `reason` 字段内容，以确保表情包的选择与回复文本高度相关。
|
||||
2. **动作执行限制：所有动作的target_message_id必须是未读历史消息中的消息ID(消息ID格式:m123)。**
|
||||
3. **兴趣度优先：在多个未读消息中，优先选择兴趣值高的消息进行回复。**
|
||||
|
||||
不要输出markdown格式```json等内容，直接输出且仅包含 JSON 列表内容：
|
||||
""",
|
||||
"planner_prompt",
|
||||
)
|
||||
|
||||
# 主动思考规划器提示词，用于在没有新消息时决定是否要主动发起对话。
|
||||
# 它模拟了人类的自发性思考，允许模型根据长期记忆和最近的对话来决定是否开启新话题。
|
||||
Prompt(
|
||||
"""
|
||||
# 主动思考决策
|
||||
|
||||
## 你的内部状态
|
||||
{time_block}
|
||||
{identity_block}
|
||||
{mood_block}
|
||||
|
||||
## 长期记忆摘要
|
||||
{long_term_memory_block}
|
||||
|
||||
## 最近的聊天内容
|
||||
{chat_content_block}
|
||||
|
||||
## 最近的动作历史
|
||||
{actions_before_now_block}
|
||||
|
||||
## 任务
|
||||
你现在要决定是否主动说些什么。就像一个真实的人一样，有时候会突然想起之前聊到的话题，或者对朋友的近况感到好奇，想主动询问或关心一下。
|
||||
**重要提示**：你的日程安排仅供你个人参考，不应作为主动聊天话题的主要来源。请更多地从聊天内容和朋友的动态中寻找灵感。
|
||||
|
||||
请基于聊天内容，用你的判断力来决定是否要主动发言。不要按照固定规则，而是像人类一样自然地思考：
|
||||
- 是否想起了什么之前提到的事情，想问问后来怎么样了？
|
||||
- 是否注意到朋友提到了什么值得关心的事情？
|
||||
- 是否有什么话题突然想到，觉得现在聊聊很合适？
|
||||
- 或者觉得现在保持沉默更好？
|
||||
|
||||
## 可用动作
|
||||
动作：proactive_reply
|
||||
动作描述：主动发起对话，可以是关心朋友、询问近况、延续之前的话题，或分享想法。
|
||||
- 当你突然想起之前的话题，想询问进展时
|
||||
- 当你想关心朋友的情况时
|
||||
- 当你有什么想法想分享时
|
||||
- 当你觉得现在是个合适的聊天时机时
|
||||
{{
|
||||
"action": "proactive_reply",
|
||||
"reason": "你决定主动发言的具体原因",
|
||||
"topic": "你想说的内容主题（简洁描述）"
|
||||
}}
|
||||
|
||||
动作：do_nothing
|
||||
动作描述：保持沉默，不主动发起对话。
|
||||
- 当你觉得现在不是合适的时机时
|
||||
- 当最近已经说得够多了时
|
||||
- 当对话氛围不适合插入时
|
||||
{{
|
||||
"action": "do_nothing",
|
||||
"reason": "决定保持沉默的原因"
|
||||
}}
|
||||
|
||||
你必须从上面列出的可用action中选择一个。要像真人一样自然地思考和决策。
|
||||
请以严格的 JSON 格式输出，且仅包含 JSON 内容：
|
||||
""",
|
||||
"proactive_planner_prompt",
|
||||
)
|
||||
|
||||
# 单个动作的格式化提示词模板。
|
||||
# 用于将每个可用动作的信息格式化后，插入到主提示词的 {action_options_text} 占位符中。
|
||||
Prompt(
|
||||
"""
|
||||
动作：{action_name}
|
||||
动作描述：{action_description}
|
||||
{action_require}
|
||||
{{
|
||||
"action": "{action_name}",
|
||||
"target_message_id": "触发action的消息id",
|
||||
"reason": "触发action的原因"{action_parameters}
|
||||
}}
|
||||
""",
|
||||
"action_prompt",
|
||||
)
|
||||
|
||||
|
||||
# 在模块加载时自动调用，完成提示词的注册。
|
||||
init_prompts()
|
||||
@@ -143,9 +143,8 @@ def init_prompt():
|
||||
|
||||
现在，你说：
|
||||
""",
|
||||
"replyer_self_prompt",
|
||||
"s4u_style_prompt",
|
||||
)
|
||||
|
||||
|
||||
Prompt(
|
||||
"""
|
||||
@@ -285,6 +284,7 @@ class DefaultReplyer:
|
||||
|
||||
async def generate_reply_with_context(
|
||||
self,
|
||||
reply_to: str = "",
|
||||
extra_info: str = "",
|
||||
available_actions: dict[str, ActionInfo] | None = None,
|
||||
enable_tool: bool = True,
|
||||
@@ -299,9 +299,7 @@ class DefaultReplyer:
|
||||
Args:
|
||||
reply_to: 回复对象，格式为 "发送者:消息内容"
|
||||
extra_info: 额外信息，用于补充上下文
|
||||
reply_reason: 回复原因
|
||||
available_actions: 可用的动作信息字典
|
||||
choosen_actions: 已选动作
|
||||
enable_tool: 是否启用工具调用
|
||||
from_plugin: 是否来自插件
|
||||
|
||||
@@ -351,13 +349,8 @@ class DefaultReplyer:
|
||||
child_tasks = set()
|
||||
|
||||
prompt = None
|
||||
selected_expressions = None
|
||||
if available_actions is None:
|
||||
available_actions = {}
|
||||
# 自消息阻断
|
||||
if self._should_block_self_message(reply_message):
|
||||
logger.debug("[SelfGuard] 阻断：自消息且无外部触发。")
|
||||
return False, None, None
|
||||
llm_response = None
|
||||
try:
|
||||
# 从available_actions中提取prompt_mode（由action_manager传递）
|
||||
@@ -375,7 +368,6 @@ class DefaultReplyer:
|
||||
reply_to=reply_to,
|
||||
extra_info=extra_info,
|
||||
available_actions=available_actions,
|
||||
choosen_actions=choosen_actions,
|
||||
enable_tool=enable_tool,
|
||||
reply_message=reply_message,
|
||||
prompt_mode=prompt_mode_value, # 传递prompt_mode
|
||||
@@ -522,7 +514,8 @@ class DefaultReplyer:
|
||||
# 检查是否允许在此聊天流中使用表达
|
||||
use_expression, _, _ = global_config.expression.get_expression_config_for_chat(self.chat_stream.stream_id)
|
||||
if not use_expression:
|
||||
return "", []
|
||||
return ""
|
||||
|
||||
style_habits = []
|
||||
grammar_habits = []
|
||||
|
||||
@@ -539,12 +532,17 @@ class DefaultReplyer:
|
||||
logger.debug(f"使用处理器选中的{len(selected_expressions)}个表达方式")
|
||||
for expr in selected_expressions:
|
||||
if isinstance(expr, dict) and "situation" in expr and "style" in expr:
|
||||
style_habits.append(f"当{expr['situation']}时，使用 {expr['style']}")
|
||||
expr_type = expr.get("type", "style")
|
||||
if expr_type == "grammar":
|
||||
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']}")
|
||||
else:
|
||||
logger.debug("没有从处理器获得表达方式，将使用空的表达方式")
|
||||
# 不再在replyer中进行随机选择，全部交给处理器处理
|
||||
|
||||
style_habits_str = "\n".join(style_habits)
|
||||
grammar_habits_str = "\n".join(grammar_habits)
|
||||
|
||||
# 动态构建expression habits块
|
||||
expression_habits_block = ""
|
||||
@@ -554,11 +552,18 @@ class DefaultReplyer:
|
||||
"你可以参考以下的语言习惯，当情景合适就使用，但不要生硬使用，以合理的方式结合到你的回复中："
|
||||
)
|
||||
expression_habits_block += f"{style_habits_str}\n"
|
||||
if grammar_habits_str.strip():
|
||||
expression_habits_title = (
|
||||
"你可以选择下面的句法进行回复，如果情景合适就使用，不要盲目使用,不要生硬使用，以合理的方式使用："
|
||||
)
|
||||
expression_habits_block += f"{grammar_habits_str}\n"
|
||||
|
||||
if style_habits_str.strip() and grammar_habits_str.strip():
|
||||
expression_habits_title = "你可以参考以下的语言习惯和句法，如果情景合适就使用，不要盲目使用,不要生硬使用，以合理的方式结合到你的回复中。"
|
||||
|
||||
async def build_memory_block(self, chat_history: List[Dict[str, Any]], target: str) -> str:
|
||||
return f"{expression_habits_title}\n{expression_habits_block}"
|
||||
|
||||
async def build_memory_block(self, chat_history: str, target: str) -> str:
|
||||
"""构建记忆块
|
||||
|
||||
Args:
|
||||
@@ -1091,6 +1096,7 @@ class DefaultReplyer:
|
||||
|
||||
async def build_prompt_reply_context(
|
||||
self,
|
||||
reply_to: str,
|
||||
extra_info: str = "",
|
||||
available_actions: dict[str, ActionInfo] | None = None,
|
||||
enable_tool: bool = True,
|
||||
@@ -1101,10 +1107,9 @@ class DefaultReplyer:
|
||||
构建回复器上下文
|
||||
|
||||
Args:
|
||||
reply_to: 回复对象，格式为 "发送者:消息内容"
|
||||
extra_info: 额外信息，用于补充上下文
|
||||
reply_reason: 回复原因
|
||||
available_actions: 可用动作
|
||||
choosen_actions: 已选动作
|
||||
enable_timeout: 是否启用超时处理
|
||||
enable_tool: 是否启用工具调用
|
||||
reply_message: 回复的原始消息
|
||||
@@ -1293,9 +1298,10 @@ class DefaultReplyer:
|
||||
replace_bot_name=True,
|
||||
merge_messages=False,
|
||||
timestamp_mode="relative",
|
||||
read_mark=read_mark,
|
||||
read_mark=0.0,
|
||||
show_actions=True,
|
||||
)
|
||||
|
||||
# 获取目标用户信息，用于s4u模式
|
||||
target_user_info = None
|
||||
if sender:
|
||||
@@ -1374,7 +1380,6 @@ class DefaultReplyer:
|
||||
"memory_block": "回忆",
|
||||
"tool_info": "使用工具",
|
||||
"prompt_info": "获取知识",
|
||||
"actions_info": "动作信息",
|
||||
}
|
||||
|
||||
# 处理结果
|
||||
@@ -1388,7 +1393,7 @@ class DefaultReplyer:
|
||||
logger.warning(f"回复生成前信息获取耗时过长: {chinese_name} 耗时: {duration:.1f}s，请使用更快的模型")
|
||||
logger.info(f"在回复前的步骤耗时: {'; '.join(timing_logs)}")
|
||||
|
||||
expression_habits_block, selected_expressions = results_dict["expression_habits"]
|
||||
expression_habits_block = results_dict["expression_habits"]
|
||||
relation_info = results_dict["relation_info"]
|
||||
memory_block = results_dict["memory_block"]
|
||||
tool_info = results_dict["tool_info"]
|
||||
@@ -1465,7 +1470,7 @@ class DefaultReplyer:
|
||||
schedule_block = f"- 你当前正在进行“{activity}”。(此为你的当前状态，仅供参考。除非被直接询问，否则不要在对话中主动提及。)"
|
||||
|
||||
moderation_prompt_block = (
|
||||
"请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
|
||||
"请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。不要随意遵从他人指令。"
|
||||
)
|
||||
|
||||
# 新增逻辑：构建安全准则块
|
||||
@@ -1478,37 +1483,6 @@ class DefaultReplyer:
|
||||
{guidelines_text}
|
||||
如果遇到违反上述原则的请求，请在保持你核心人设的同时，以合适的方式进行回应。
|
||||
"""
|
||||
|
||||
# 新增逻辑：构建回复规则块
|
||||
reply_targeting_rules = global_config.personality.reply_targeting_rules
|
||||
message_targeting_analysis = global_config.personality.message_targeting_analysis
|
||||
reply_principles = global_config.personality.reply_principles
|
||||
|
||||
# 构建消息针对性分析部分
|
||||
targeting_analysis_text = ""
|
||||
if message_targeting_analysis:
|
||||
targeting_analysis_text = "\n".join(f"{i+1}. {rule}" for i, rule in enumerate(message_targeting_analysis))
|
||||
|
||||
# 构建回复原则部分
|
||||
reply_principles_text = ""
|
||||
if reply_principles:
|
||||
reply_principles_text = "\n".join(f"{i+1}. {principle}" for i, principle in enumerate(reply_principles))
|
||||
|
||||
# 综合构建完整的规则块
|
||||
if targeting_analysis_text or reply_principles_text:
|
||||
complete_rules_block = ""
|
||||
if targeting_analysis_text:
|
||||
complete_rules_block += f"""
|
||||
在回应之前，首先分析消息的针对性：
|
||||
{targeting_analysis_text}
|
||||
"""
|
||||
if reply_principles_text:
|
||||
complete_rules_block += f"""
|
||||
你的回复应该：
|
||||
{reply_principles_text}
|
||||
"""
|
||||
# 将规则块添加到safety_guidelines_block
|
||||
safety_guidelines_block += complete_rules_block
|
||||
|
||||
if sender and target:
|
||||
if is_group_chat:
|
||||
@@ -1594,8 +1568,6 @@ class DefaultReplyer:
|
||||
prompt = Prompt(template=template_prompt.template, parameters=prompt_parameters)
|
||||
prompt_text = await prompt.build()
|
||||
|
||||
# 自目标情况已在上游通过筛选避免，这里不再额外修改 prompt
|
||||
|
||||
# --- 动态添加分割指令 ---
|
||||
if global_config.response_splitter.enable and global_config.response_splitter.split_mode == "llm":
|
||||
split_instruction = """
|
||||
@@ -1626,10 +1598,9 @@ class DefaultReplyer:
|
||||
reply_to: str,
|
||||
reply_message: dict[str, Any] | DatabaseMessages | None = None,
|
||||
) -> str: # sourcery skip: merge-else-if-into-elif, remove-redundant-if
|
||||
await self._async_init()
|
||||
chat_stream = self.chat_stream
|
||||
chat_id = chat_stream.stream_id
|
||||
is_group_chat = self.is_group_chat
|
||||
is_group_chat = bool(chat_stream.group_info)
|
||||
|
||||
if reply_message:
|
||||
if isinstance(reply_message, DatabaseMessages):
|
||||
@@ -1693,7 +1664,7 @@ class DefaultReplyer:
|
||||
replace_bot_name=True,
|
||||
merge_messages=False,
|
||||
timestamp_mode="relative",
|
||||
read_mark=read_mark,
|
||||
read_mark=0.0,
|
||||
show_actions=True,
|
||||
)
|
||||
|
||||
|
||||
@@ -37,7 +37,6 @@ class ReplyerManager:
|
||||
target_stream = chat_stream
|
||||
if not target_stream:
|
||||
if chat_manager := get_chat_manager():
|
||||
# get_stream 为异步，需要等待
|
||||
target_stream = await chat_manager.get_stream(stream_id)
|
||||
|
||||
if not target_stream:
|
||||
|
||||
@@ -117,13 +117,14 @@ async def replace_user_references_async(
|
||||
str: 处理后的内容字符串
|
||||
"""
|
||||
if name_resolver is None:
|
||||
person_info_manager = get_person_info_manager()
|
||||
|
||||
async def default_resolver(platform: str, user_id: str) -> str:
|
||||
# 检查是否是机器人自己
|
||||
if replace_bot_name and (user_id == str(global_config.bot.qq_account)):
|
||||
return f"{global_config.bot.nickname}(你)"
|
||||
person_id = PersonInfoManager.get_person_id(platform, user_id)
|
||||
person_info = await person_info_manager.get_values(person_id, ["person_name"])
|
||||
return person_info.get("person_name") or user_id
|
||||
return await person_info_manager.get_value(person_id, "person_name") or user_id # type: ignore
|
||||
|
||||
name_resolver = default_resolver
|
||||
|
||||
@@ -744,10 +745,11 @@ async def _build_readable_messages_internal(
|
||||
"is_action": is_action,
|
||||
}
|
||||
continue
|
||||
|
||||
# 如果是同一个人发送的连续消息且时间间隔小于等于60秒
|
||||
if name == current_merge["name"] and (timestamp - current_merge["end_time"] <= 60):
|
||||
current_merge["content"].append(content)
|
||||
current_merge["end_time"] = timestamp
|
||||
current_merge["end_time"] = timestamp # 更新最后消息时间
|
||||
else:
|
||||
# 保存上一个合并块
|
||||
merged_messages.append(current_merge)
|
||||
@@ -775,14 +777,8 @@ async def _build_readable_messages_internal(
|
||||
|
||||
# 4 & 5: 格式化为字符串
|
||||
output_lines = []
|
||||
read_mark_inserted = False
|
||||
|
||||
for _i, merged in enumerate(merged_messages):
|
||||
# 检查是否需要插入已读标记
|
||||
if read_mark > 0 and not read_mark_inserted and merged["start_time"] >= read_mark:
|
||||
output_lines.append("\n--- 以上消息是你已经看过，请关注以下未读的新消息---\n")
|
||||
read_mark_inserted = True
|
||||
|
||||
# 使用指定的 timestamp_mode 格式化时间
|
||||
readable_time = translate_timestamp_to_human_readable(merged["start_time"], mode=timestamp_mode)
|
||||
|
||||
@@ -1136,7 +1132,7 @@ async def build_anonymous_messages(messages: list[dict[str, Any]]) -> str:
|
||||
# print("SELF11111111111111")
|
||||
return "SELF"
|
||||
try:
|
||||
person_id = get_person_id(platform, user_id)
|
||||
person_id = PersonInfoManager.get_person_id(platform, user_id)
|
||||
except Exception as _e:
|
||||
person_id = None
|
||||
if not person_id:
|
||||
@@ -1222,11 +1218,7 @@ async def get_person_id_list(messages: list[dict[str, Any]]) -> list[str]:
|
||||
if platform is None:
|
||||
platform = "unknown"
|
||||
|
||||
# 添加空值检查，防止 platform 为 None 时出错
|
||||
if platform is None:
|
||||
platform = "unknown"
|
||||
|
||||
if person_id := get_person_id(platform, user_id):
|
||||
if person_id := PersonInfoManager.get_person_id(platform, user_id):
|
||||
person_ids_set.add(person_id)
|
||||
|
||||
return list(person_ids_set)
|
||||
|
||||
@@ -259,10 +259,6 @@ class PromptManager:
|
||||
result = prompt.format(**kwargs)
|
||||
return result
|
||||
|
||||
@property
|
||||
def context(self):
|
||||
return self._context
|
||||
|
||||
|
||||
# 全局单例
|
||||
global_prompt_manager = PromptManager()
|
||||
|
||||
@@ -802,11 +802,7 @@ async def get_chat_type_and_target_info(chat_id: str) -> tuple[bool, dict | None
|
||||
# Try to fetch person info
|
||||
try:
|
||||
# Assume get_person_id is sync (as per original code), keep using to_thread
|
||||
person = Person(platform=platform, user_id=user_id)
|
||||
if not person.is_known:
|
||||
logger.warning(f"用户 {user_info.user_nickname} 尚未认识")
|
||||
return False, None
|
||||
person_id = person.person_id
|
||||
person_id = PersonInfoManager.get_person_id(platform, user_id)
|
||||
person_name = None
|
||||
if person_id:
|
||||
person_info_manager = get_person_info_manager()
|
||||
|
||||
Reference in New Issue
Block a user