mirror of https://github.com/Mai-with-u/MaiBot.git
feat:将记忆配置项添加到配置文件
SengokuCola
parent
e0a5cd5922
commit
16ae212adc
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@ -1,6 +1,7 @@
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import argparse
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import json
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import random
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import re
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import sys
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import os
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from datetime import datetime
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@ -13,12 +14,33 @@ if PROJECT_ROOT not in sys.path:
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from src.common.data_models.database_data_model import DatabaseMessages
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from src.common.message_repository import find_messages
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from src.chat.utils.chat_message_builder import build_readable_messages, build_readable_messages_anonymized
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from src.chat.utils.chat_message_builder import build_readable_messages
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SECONDS_5_MINUTES = 5 * 60
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def clean_output_text(text: str) -> str:
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"""
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清理输出文本，移除表情包和回复内容
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- 移除 [表情包：...] 格式的内容
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- 移除 [回复...] 格式的内容
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"""
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if not text:
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return text
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# 移除表情包内容：[表情包：...]
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text = re.sub(r'\[表情包：[^\]]*\]', '', text)
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# 移除回复内容：[回复...]，说：... 的完整模式
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text = re.sub(r'\[回复[^\]]*\]，说：[^@]*@[^:]*:', '', text)
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# 清理多余的空格和换行
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text = re.sub(r'\s+', ' ', text).strip()
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return text
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def parse_datetime_to_timestamp(value: str) -> float:
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"""
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接受多种常见格式并转换为时间戳（秒）
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@ -162,37 +184,70 @@ def merge_adjacent_same_user(messages: List[DatabaseMessages]) -> List[DatabaseM
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def build_pairs_for_chat(
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original_messages: List[DatabaseMessages],
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merged_messages: List[DatabaseMessages],
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min_ctx: int,
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max_ctx: int,
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target_user_id: Optional[str] = None,
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) -> List[Tuple[str, str, str]]:
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"""
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对每条消息作为 output，从其前面取 20-30 条（可配置）的消息作为 input。
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input 使用 chat_message_builder.build_readable_messages 构建为字符串。
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output 使用该消息的 processed_plain_text。
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对每条合并后的消息作为 output，从其前面取 20-30 条（可配置）的原始消息作为 input。
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input 使用原始未合并的消息构建上下文。
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output 使用合并后消息的 processed_plain_text。
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如果指定了 target_user_id，则只处理该用户的消息作为 output。
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"""
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pairs: List[Tuple[str, str, str]] = []
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n = len(merged_messages)
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if n == 0:
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n_merged = len(merged_messages)
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n_original = len(original_messages)
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if n_merged == 0 or n_original == 0:
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return pairs
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for i in range(n):
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# 为每个合并后的消息找到对应的原始消息位置
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merged_to_original_map = {}
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original_idx = 0
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for merged_idx, merged_msg in enumerate(merged_messages):
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# 找到这个合并消息对应的第一个原始消息
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while (original_idx < n_original and
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original_messages[original_idx].time < merged_msg.time):
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original_idx += 1
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# 如果找到了时间匹配的原始消息，建立映射
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if (original_idx < n_original and
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original_messages[original_idx].time == merged_msg.time):
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merged_to_original_map[merged_idx] = original_idx
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for merged_idx in range(n_merged):
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merged_msg = merged_messages[merged_idx]
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# 如果指定了 target_user_id，只处理该用户的消息作为 output
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if target_user_id and merged_msg.user_info.user_id != target_user_id:
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continue
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# 找到对应的原始消息位置
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if merged_idx not in merged_to_original_map:
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continue
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original_idx = merged_to_original_map[merged_idx]
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# 选择上下文窗口大小
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window = random.randint(min_ctx, max_ctx) if max_ctx > min_ctx else min_ctx
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start = max(0, i - window)
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context_msgs = merged_messages[start:i]
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start = max(0, original_idx - window)
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context_msgs = original_messages[start:original_idx]
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# 使用匿名化构建 input，并拿到原始显示名 -> 匿名名的映射
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input_str, name_mapping = build_readable_messages_anonymized(
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# 使用原始未合并消息构建 input
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input_str = build_readable_messages(
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messages=context_msgs,
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timestamp_mode="relative",
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timestamp_mode="normal_no_YMD",
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show_actions=False,
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show_pic=True,
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)
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# 输出取 processed_plain_text（不再额外替换）
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output_text = merged_messages[i].processed_plain_text or ""
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output_id = merged_messages[i].message_id or ""
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# 输出取合并后消息的 processed_plain_text 并清理表情包和回复内容
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output_text = merged_msg.processed_plain_text or ""
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output_text = clean_output_text(output_text)
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output_id = merged_msg.message_id or ""
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pairs.append((input_str, output_text, output_id))
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return pairs
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@ -202,16 +257,20 @@ def build_pairs(
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start_ts: float,
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end_ts: float,
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platform: Optional[str],
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user_id: Optional[str],
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min_ctx: int,
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max_ctx: int,
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) -> List[Tuple[str, str, str]]:
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# 获取所有消息（不按user_id过滤），这样input上下文可以包含所有用户的消息
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messages = fetch_messages_between(start_ts, end_ts, platform)
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groups = group_by_chat(messages)
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all_pairs: List[Tuple[str, str, str]] = []
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for chat_id, msgs in groups.items(): # noqa: F841 - chat_id 未直接使用
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# 对消息进行合并，用于output
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merged = merge_adjacent_same_user(msgs)
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pairs = build_pairs_for_chat(merged, min_ctx, max_ctx)
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# 传递原始消息和合并后消息，input使用原始消息，output使用合并后消息
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pairs = build_pairs_for_chat(msgs, merged, min_ctx, max_ctx, user_id)
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all_pairs.extend(pairs)
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return all_pairs
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@ -225,10 +284,11 @@ def main(argv: Optional[List[str]] = None) -> int:
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if len(argv) == 0:
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return run_interactive()
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parser = argparse.ArgumentParser(description="构建 (input_str, output_str, message_id) 列表")
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parser = argparse.ArgumentParser(description="构建 (input_str, output_str, message_id) 列表，支持按用户ID筛选消息")
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parser.add_argument("start", help="起始时间，如 2025-09-28 00:00:00")
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parser.add_argument("end", help="结束时间，如 2025-09-29 00:00:00")
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parser.add_argument("--platform", default=None, help="仅选择 chat_info_platform 为该值的消息")
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parser.add_argument("--user_id", default=None, help="仅选择指定 user_id 的消息")
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parser.add_argument("--min_ctx", type=int, default=20, help="输入上下文的最少条数，默认20")
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parser.add_argument("--max_ctx", type=int, default=30, help="输入上下文的最多条数，默认30")
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parser.add_argument(
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@ -247,7 +307,7 @@ def main(argv: Optional[List[str]] = None) -> int:
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if args.max_ctx < args.min_ctx:
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raise ValueError("max_ctx 不能小于 min_ctx")
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pairs = build_pairs(start_ts, end_ts, args.platform, args.min_ctx, args.max_ctx)
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pairs = build_pairs(start_ts, end_ts, args.platform, args.user_id, args.min_ctx, args.max_ctx)
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if args.output:
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# 保存为 JSONL，每行一个 {input, output, message_id}
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@ -277,6 +337,7 @@ def run_interactive() -> int:
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start_str = _prompt_with_default("请输入起始时间", None)
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end_str = _prompt_with_default("请输入结束时间", None)
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platform = _prompt_with_default("平台（可留空表示不限）", "")
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user_id = _prompt_with_default("用户ID（可留空表示不限）", "")
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try:
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min_ctx = int(_prompt_with_default("输入上下文最少条数", "20"))
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max_ctx = int(_prompt_with_default("输入上下文最多条数", "30"))
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@ -305,7 +366,8 @@ def run_interactive() -> int:
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return 2
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platform_val = platform if platform != "" else None
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pairs = build_pairs(start_ts, end_ts, platform_val, min_ctx, max_ctx)
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user_id_val = user_id if user_id != "" else None
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pairs = build_pairs(start_ts, end_ts, platform_val, user_id_val, min_ctx, max_ctx)
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if output_path:
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with open(output_path, "w", encoding="utf-8") as f:
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@ -184,16 +184,16 @@ class HeartFChatting:
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)
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question_probability = 0
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if time.time() - self.last_active_time > 1200:
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question_probability = 0.04
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elif time.time() - self.last_active_time > 600:
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question_probability = 0.02
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elif time.time() - self.last_active_time > 300:
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if time.time() - self.last_active_time > 3600:
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question_probability = 0.01
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elif time.time() - self.last_active_time > 1200:
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question_probability = 0.005
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else:
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elif time.time() - self.last_active_time > 600:
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question_probability = 0.001
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else:
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question_probability = 0.0003
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question_probability = question_probability * global_config.chat.auto_chat_value
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question_probability = question_probability * global_config.chat.get_auto_chat_value(self.stream_id)
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# print(f"{self.log_prefix} questioned: {self.questioned},len: {len(global_conflict_tracker.get_questions_by_chat_id(self.stream_id))}")
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if question_probability > 0 and not self.questioned and len(global_conflict_tracker.get_questions_by_chat_id(self.stream_id)) == 0: #长久没有回复，可以试试主动发言，提问概率随着时间增加
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@ -335,8 +335,6 @@ class HeartFChatting:
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await global_memory_chest.build_running_content(chat_id=self.stream_id)
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cycle_timers, thinking_id = self.start_cycle()
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logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考")
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@ -926,202 +926,6 @@ async def build_anonymous_messages(messages: List[DatabaseMessages]) -> str:
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return formatted_string
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def build_readable_messages_anonymized(
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messages: List[DatabaseMessages],
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timestamp_mode: str = "relative",
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show_actions: bool = False,
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show_pic: bool = True,
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replace_bot_name: bool = True,
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remove_emoji_stickers: bool = False,
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) -> Tuple[str, Dict[str, str]]:
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"""
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仿照 build_readable_messages，构建匿名化的可读消息：
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- 所有用户名替换为 用户A、用户B、...、用户Z、用户AA、用户AB ...
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- 内容中的 回复<aaa:bbb> 与 @<aaa:bbb> 也替换为匿名名
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Returns:
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formatted_string: 格式化后的聊天记录字符串
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mapping: 原始显示用户名 -> 匿名名 的映射表
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"""
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if not messages:
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return "", {}
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# 生成匿名标签：A..Z, AA..AZ, BA.. 等
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def alphabet_labels() -> Iterable[str]:
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import string
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letters = string.ascii_uppercase
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# 单字母
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for ch in letters:
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yield ch
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# 多字母（简单生成两位，若需要可继续扩展）
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for a in letters:
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for b in letters:
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yield f"{a}{b}"
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label_iter = alphabet_labels()
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user_to_label: Dict[Tuple[str, str], str] = {}
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name_mapping: Dict[str, str] = {}
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def get_display_name(platform: str, user_id: str, user_nickname: str, user_cardname: Optional[str]) -> str:
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person = Person(platform=platform, user_id=user_id)
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return person.person_name or f"{user_nickname}" or (f"昵称：{user_cardname}" if user_cardname else "某人")
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def get_anon_name(platform: str, user_id: str, user_nickname: str, user_cardname: Optional[str]) -> str:
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key = (platform or "", user_id or "")
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# 机器人处理：若需要替换机器人名称，则直接返回 昵称(你)
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if replace_bot_name and user_id == global_config.bot.qq_account:
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anon = f"{global_config.bot.nickname}(你)"
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original_display = get_display_name(platform, user_id, user_nickname, user_cardname)
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if original_display not in name_mapping:
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name_mapping[original_display] = anon
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return anon
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if key not in user_to_label:
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user_to_label[key] = f"用户{next(label_iter)}"
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anon = user_to_label[key]
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# 记录原始显示名到匿名名（可能重复显示名时后写覆盖）
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original_display = get_display_name(platform, user_id, user_nickname, user_cardname)
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if original_display not in name_mapping:
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name_mapping[original_display] = anon
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return anon
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# 如果启用移除表情包，先过滤消息
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if remove_emoji_stickers:
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filtered_messages = []
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for msg in messages:
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# 获取消息内容
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content = msg.display_message or msg.processed_plain_text or ""
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# 移除表情包
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emoji_pattern = r"\[表情包:[^\]]+\]"
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content = re.sub(emoji_pattern, "", content)
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# 如果移除表情包后内容不为空，则保留消息
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if content.strip():
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filtered_messages.append(msg)
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messages = filtered_messages
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# 将 DatabaseMessages 转换为可处理结构，并可选拼入动作
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copy_messages: List[MessageAndActionModel] = []
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for msg in messages:
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if remove_emoji_stickers:
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# 创建 MessageAndActionModel 但移除表情包
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model = MessageAndActionModel.from_DatabaseMessages(msg)
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# 移除表情包
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if model.display_message:
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model.display_message = re.sub(r"\[表情包:[^\]]+\]", "", model.display_message)
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if model.processed_plain_text:
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model.processed_plain_text = re.sub(r"\[表情包:[^\]]+\]", "", model.processed_plain_text)
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copy_messages.append(model)
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else:
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copy_messages.append(MessageAndActionModel.from_DatabaseMessages(msg))
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if show_actions and copy_messages:
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min_time = min(msg.time or 0 for msg in copy_messages)
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max_time = max(msg.time or 0 for msg in copy_messages)
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chat_id = messages[0].chat_id if messages else None
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actions_in_range = (
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ActionRecords.select()
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.where((ActionRecords.time >= min_time) & (ActionRecords.time <= max_time) & (ActionRecords.chat_id == chat_id))
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.order_by(ActionRecords.time)
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)
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action_after_latest = (
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ActionRecords.select()
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.where((ActionRecords.time > max_time) & (ActionRecords.chat_id == chat_id))
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.order_by(ActionRecords.time)
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.limit(1)
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)
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actions: List[ActionRecords] = list(actions_in_range) + list(action_after_latest)
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for action in actions:
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if action.action_build_into_prompt:
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action_msg = MessageAndActionModel(
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time=float(action.time), # type: ignore
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user_id=global_config.bot.qq_account,
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user_platform=global_config.bot.platform,
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user_nickname=global_config.bot.nickname,
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user_cardname="",
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processed_plain_text=f"{action.action_prompt_display}",
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display_message=f"{action.action_prompt_display}",
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chat_info_platform=str(action.chat_info_platform),
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is_action_record=True,
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action_name=str(action.action_name),
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)
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copy_messages.append(action_msg)
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copy_messages.sort(key=lambda x: x.time or 0)
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# 图片替换帮助
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def process_pic_ids(content: Optional[str]) -> str:
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if content is None:
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return ""
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pic_pattern = r"\[picid:([^\]]+)\]"
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def replace_pic_id(_m: re.Match) -> str:
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return "[图片]" if show_pic else ""
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return re.sub(pic_pattern, replace_pic_id, content)
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# 内容引用替换的 resolver：将 <aaa:bbb> / @<aaa:bbb> 中的 bbb 映射为匿名名
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def anon_name_resolver(platform: str, user_id: str) -> str:
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try:
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# 与主流程一致处理机器人名字
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if replace_bot_name and user_id == global_config.bot.qq_account:
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return f"{global_config.bot.nickname}(你)"
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return get_anon_name(platform, user_id, "", None)
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except Exception:
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return "用户?"
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# 构建结果
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detailed: List[Tuple[float, str, str, bool]] = []
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for m in copy_messages:
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if m.is_action_record:
|
||||
content = process_pic_ids(m.display_message)
|
||||
detailed.append((m.time or 0.0, "", content, True))
|
||||
continue
|
||||
|
||||
platform = m.user_platform
|
||||
user_id = m.user_id
|
||||
user_nickname = m.user_nickname
|
||||
user_cardname = m.user_cardname
|
||||
content = m.display_message or m.processed_plain_text or ""
|
||||
|
||||
content = process_pic_ids(content)
|
||||
anon_name = get_anon_name(platform, user_id, user_nickname, user_cardname)
|
||||
try:
|
||||
content = replace_user_references(content, platform, anon_name_resolver, replace_bot_name=False)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
detailed.append((m.time or 0.0, anon_name, content, False))
|
||||
|
||||
if not detailed:
|
||||
return "", name_mapping
|
||||
|
||||
detailed.sort(key=lambda x: x[0])
|
||||
|
||||
output_lines: List[str] = []
|
||||
for ts, name, content, is_action in detailed:
|
||||
readable_time = translate_timestamp_to_human_readable(ts, mode=timestamp_mode)
|
||||
if is_action:
|
||||
output_lines.append(f"{readable_time}, {content}")
|
||||
else:
|
||||
output_lines.append(f"{readable_time}, {name}: {content}")
|
||||
output_lines.append("\n")
|
||||
|
||||
formatted_string = "".join(output_lines).strip()
|
||||
|
||||
# 最后对完整字符串再按映射表做一次替换，处理正文里直接出现的原始昵称
|
||||
if name_mapping:
|
||||
for original_name, anon_name in sorted(name_mapping.items(), key=lambda x: len(x[0]), reverse=True):
|
||||
if original_name:
|
||||
formatted_string = formatted_string.replace(original_name, anon_name)
|
||||
|
||||
return formatted_string, name_mapping
|
||||
|
||||
|
||||
async def get_person_id_list(messages: List[Dict[str, Any]]) -> List[str]:
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -108,6 +108,23 @@ class ChatConfig(ConfigBase):
|
|||
时间区间支持跨夜，例如 "23:00-02:00"。
|
||||
"""
|
||||
|
||||
auto_chat_value_rules: list[dict] = field(default_factory=lambda: [])
|
||||
"""
|
||||
自动聊天频率规则列表，支持按聊天流/按日内时段配置。
|
||||
规则格式：{ target="platform:id:type" 或 "", time="HH:MM-HH:MM", value=0.5 }
|
||||
|
||||
示例:
|
||||
[
|
||||
["", "00:00-08:59", 0.2], # 全局规则：凌晨到早上更安静
|
||||
["", "09:00-22:59", 1.0], # 全局规则：白天正常
|
||||
["qq:1919810:group", "20:00-23:59", 0.6], # 指定群在晚高峰降低发言
|
||||
["qq:114514:private", "00:00-23:59", 0.3],# 指定私聊全时段较安静
|
||||
]
|
||||
|
||||
匹配优先级: 先匹配指定 chat 流规则，再匹配全局规则(\"\").
|
||||
时间区间支持跨夜，例如 "23:00-02:00"。
|
||||
"""
|
||||
|
||||
def _parse_stream_config_to_chat_id(self, stream_config_str: str) -> Optional[str]:
|
||||
"""与 ChatStream.get_stream_id 一致地从 "platform:id:type" 生成 chat_id。"""
|
||||
try:
|
||||
|
|
@ -213,6 +230,61 @@ class ChatConfig(ConfigBase):
|
|||
# 3) 未命中规则返回基础值
|
||||
return self.talk_value
|
||||
|
||||
def get_auto_chat_value(self, chat_id: Optional[str]) -> float:
|
||||
"""根据规则返回当前 chat 的动态 auto_chat_value，未匹配则回退到基础值。"""
|
||||
if not self.auto_chat_value_rules:
|
||||
return self.auto_chat_value
|
||||
|
||||
now_min = self._now_minutes()
|
||||
|
||||
# 1) 先尝试匹配指定 chat 的规则
|
||||
if chat_id:
|
||||
for rule in self.auto_chat_value_rules:
|
||||
if not isinstance(rule, dict):
|
||||
continue
|
||||
target = rule.get("target", "")
|
||||
time_range = rule.get("time", "")
|
||||
value = rule.get("value", None)
|
||||
if not isinstance(time_range, str):
|
||||
continue
|
||||
# 跳过全局
|
||||
if target == "":
|
||||
continue
|
||||
config_chat_id = self._parse_stream_config_to_chat_id(str(target))
|
||||
if config_chat_id is None or config_chat_id != chat_id:
|
||||
continue
|
||||
parsed = self._parse_range(time_range)
|
||||
if not parsed:
|
||||
continue
|
||||
start_min, end_min = parsed
|
||||
if self._in_range(now_min, start_min, end_min):
|
||||
try:
|
||||
return float(value)
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
# 2) 再匹配全局规则("")
|
||||
for rule in self.auto_chat_value_rules:
|
||||
if not isinstance(rule, dict):
|
||||
continue
|
||||
target = rule.get("target", None)
|
||||
time_range = rule.get("time", "")
|
||||
value = rule.get("value", None)
|
||||
if target != "" or not isinstance(time_range, str):
|
||||
continue
|
||||
parsed = self._parse_range(time_range)
|
||||
if not parsed:
|
||||
continue
|
||||
start_min, end_min = parsed
|
||||
if self._in_range(now_min, start_min, end_min):
|
||||
try:
|
||||
return float(value)
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
# 3) 未命中规则返回基础值
|
||||
return self.auto_chat_value
|
||||
|
||||
|
||||
@dataclass
|
||||
class MessageReceiveConfig(ConfigBase):
|
||||
|
|
@ -231,8 +303,8 @@ class MemoryConfig(ConfigBase):
|
|||
max_memory_number: int = 100
|
||||
"""记忆最大数量"""
|
||||
|
||||
max_memory_size: int = 2048
|
||||
"""记忆最大大小"""
|
||||
memory_build_frequency: int = 1
|
||||
"""记忆构建频率"""
|
||||
|
||||
@dataclass
|
||||
class ExpressionConfig(ConfigBase):
|
||||
|
|
|
|||
|
|
@ -37,8 +37,6 @@ class MemoryChest:
|
|||
request_type="memory_chest_build",
|
||||
)
|
||||
|
||||
self.memory_build_threshold = 20
|
||||
self.memory_size_limit = global_config.memory.max_memory_size
|
||||
|
||||
self.running_content_list = {} # {chat_id: {"content": running_content, "last_update_time": timestamp, "create_time": timestamp}}
|
||||
self.fetched_memory_list = [] # [(chat_id, (question, answer, timestamp)), ...]
|
||||
|
|
@ -54,7 +52,19 @@ class MemoryChest:
|
|||
Returns:
|
||||
str: 构建后的运行内容
|
||||
"""
|
||||
# 检查是否需要更新：上次更新时间和现在时间的消息数量大于30
|
||||
# 检查是否需要更新：基于消息数量和最新消息时间差的智能更新机制
|
||||
#
|
||||
# 更新机制说明：
|
||||
# 1. 消息数量 > 100：直接触发更新（高频消息场景）
|
||||
# 2. 消息数量 > 70 且最新消息时间差 > 30秒：触发更新（中高频消息场景）
|
||||
# 3. 消息数量 > 50 且最新消息时间差 > 60秒：触发更新（中频消息场景）
|
||||
# 4. 消息数量 > 30 且最新消息时间差 > 300秒：触发更新（低频消息场景）
|
||||
#
|
||||
# 设计理念：
|
||||
# - 消息越密集，时间阈值越短，确保及时更新记忆
|
||||
# - 消息越稀疏，时间阈值越长，避免频繁无意义的更新
|
||||
# - 通过最新消息时间差判断消息活跃度，而非简单的总时间差
|
||||
# - 平衡更新频率与性能，在保证记忆及时性的同时减少计算开销
|
||||
if chat_id not in self.running_content_list:
|
||||
self.running_content_list[chat_id] = {
|
||||
"content": "",
|
||||
|
|
@ -75,16 +85,51 @@ class MemoryChest:
|
|||
)
|
||||
|
||||
new_messages_count = len(message_list)
|
||||
time_diff_minutes = (current_time - last_update_time) / 60
|
||||
|
||||
# 检查是否满足强制构建条件：超过15分钟且至少有5条新消息
|
||||
forced_update = time_diff_minutes > 15 and new_messages_count >= 5
|
||||
should_update = new_messages_count > self.memory_build_threshold or forced_update
|
||||
|
||||
if forced_update:
|
||||
logger.debug(f"chat_id {chat_id} 距离上次更新已 {time_diff_minutes:.1f} 分钟，有 {new_messages_count} 条新消息，强制构建")
|
||||
else:
|
||||
logger.debug(f"chat_id {chat_id} 自上次更新后有 {new_messages_count} 条新消息，{'需要' if should_update else '不需要'}更新")
|
||||
|
||||
# 获取最新消息的时间戳
|
||||
latest_message_time = last_update_time
|
||||
if message_list:
|
||||
# 假设消息列表按时间排序，取最后一条消息的时间戳
|
||||
latest_message = message_list[-1]
|
||||
if hasattr(latest_message, 'timestamp'):
|
||||
latest_message_time = latest_message.timestamp
|
||||
elif isinstance(latest_message, dict) and 'timestamp' in latest_message:
|
||||
latest_message_time = latest_message['timestamp']
|
||||
|
||||
# 计算最新消息时间与现在时间的差（秒）
|
||||
latest_message_time_diff = current_time - latest_message_time
|
||||
|
||||
# 智能更新条件判断 - 按优先级从高到低检查
|
||||
should_update = False
|
||||
update_reason = ""
|
||||
|
||||
if global_config.memory.memory_build_frequency > 0:
|
||||
if new_messages_count > 100/global_config.memory.memory_build_frequency:
|
||||
# 条件1：消息数量 > 100，直接触发更新
|
||||
# 适用场景：群聊刷屏、高频讨论等消息密集场景
|
||||
# 无需时间限制，确保重要信息不被遗漏
|
||||
should_update = True
|
||||
update_reason = f"消息数量 {new_messages_count} > 100，直接触发更新"
|
||||
elif new_messages_count > 70/global_config.memory.memory_build_frequency and latest_message_time_diff > 30:
|
||||
# 条件2：消息数量 > 70 且最新消息时间差 > 30秒
|
||||
# 适用场景：中高频讨论，但需要确保消息流已稳定
|
||||
# 30秒的时间差确保不是正在进行的实时对话
|
||||
should_update = True
|
||||
update_reason = f"消息数量 {new_messages_count} > 70 且最新消息时间差 {latest_message_time_diff:.1f}s > 30s"
|
||||
elif new_messages_count > 50/global_config.memory.memory_build_frequency and latest_message_time_diff > 60:
|
||||
# 条件3：消息数量 > 50 且最新消息时间差 > 60秒
|
||||
# 适用场景：中等频率讨论，等待1分钟确保对话告一段落
|
||||
# 平衡及时性与稳定性
|
||||
should_update = True
|
||||
update_reason = f"消息数量 {new_messages_count} > 50 且最新消息时间差 {latest_message_time_diff:.1f}s > 60s"
|
||||
elif new_messages_count > 30/global_config.memory.memory_build_frequency and latest_message_time_diff > 300:
|
||||
# 条件4：消息数量 > 30 且最新消息时间差 > 300秒（5分钟）
|
||||
# 适用场景：低频但有一定信息量的讨论
|
||||
# 5分钟的时间差确保对话完全结束，避免频繁更新
|
||||
should_update = True
|
||||
update_reason = f"消息数量 {new_messages_count} > 30 且最新消息时间差 {latest_message_time_diff:.1f}s > 300s"
|
||||
|
||||
logger.debug(f"chat_id {chat_id} 更新检查: {update_reason if should_update else f'消息数量 {new_messages_count}，最新消息时间差 {latest_message_time_diff:.1f}s，不满足更新条件'}")
|
||||
|
||||
|
||||
if should_update:
|
||||
|
|
@ -98,11 +143,6 @@ class MemoryChest:
|
|||
remove_emoji_stickers=True,
|
||||
)
|
||||
|
||||
|
||||
current_running_content = ""
|
||||
if chat_id and chat_id in self.running_content_list:
|
||||
current_running_content = self.running_content_list[chat_id]["content"]
|
||||
|
||||
# 随机从格式示例列表中选取若干行用于提示
|
||||
format_candidates = [
|
||||
"[概念] 是 [概念的含义(简短描述，不超过十个字)]",
|
||||
|
|
@ -129,18 +169,13 @@ class MemoryChest:
|
|||
format_section = "\n".join(selected_lines) + "\n......(不要包含中括号)"
|
||||
|
||||
prompt = f"""
|
||||
以下是你的记忆内容和新的聊天记录，请你将他们整合和修改：
|
||||
记忆内容：
|
||||
<memory_content>
|
||||
{current_running_content}
|
||||
</memory_content>
|
||||
以下是一段你参与的聊天记录，请你在其中总结出记忆：
|
||||
|
||||
<聊天记录>
|
||||
{message_str}
|
||||
</聊天记录>
|
||||
聊天记录中可能包含有效信息，也可能信息密度很低，请你根据聊天记录中的信息，修改<part1>中的内容与<part2>中的内容
|
||||
聊天记录中可能包含有效信息，也可能信息密度很低，请你根据聊天记录中的信息，总结出记忆内容
|
||||
--------------------------------
|
||||
请将上面的新聊天记录内的有用的信息进行整合到现有的记忆中
|
||||
对[图片]的处理：
|
||||
1.除非与文本有关，不要将[图片]的内容整合到记忆中
|
||||
2.如果图片与某个概念相关，将图片中的关键内容也整合到记忆中，不要写入图片原文，例如：
|
||||
|
|
@ -178,29 +213,9 @@ class MemoryChest:
|
|||
|
||||
print(f"prompt: {prompt}\n记忆仓库构建运行内容: {running_content}")
|
||||
|
||||
# 如果有chat_id，更新对应的running_content
|
||||
# 直接保存：每次构建后立即入库，并刷新时间戳窗口
|
||||
if chat_id and running_content:
|
||||
current_time = time.time()
|
||||
|
||||
# 保留原有的create_time，如果没有则使用当前时间
|
||||
create_time = self.running_content_list[chat_id].get("create_time", current_time)
|
||||
|
||||
self.running_content_list[chat_id] = {
|
||||
"content": running_content,
|
||||
"last_update_time": current_time,
|
||||
"create_time": create_time
|
||||
}
|
||||
|
||||
# 检查running_content长度是否大于限制
|
||||
if len(running_content) > self.memory_size_limit:
|
||||
await self._save_to_database_and_clear(chat_id, running_content)
|
||||
|
||||
# 检查是否需要强制保存：create_time超过1800秒且内容大小达到max_memory_size的30%
|
||||
elif (current_time - create_time > 1800 and
|
||||
len(running_content) >= self.memory_size_limit * 0.3):
|
||||
logger.info(f"chat_id {chat_id} 内容创建时间已超过 {(current_time - create_time)/60:.1f} 分钟，"
|
||||
f"内容大小 {len(running_content)} 达到限制的 {int(self.memory_size_limit * 0.3)} 字符，强制保存")
|
||||
await self._save_to_database_and_clear(chat_id, running_content)
|
||||
await self._save_to_database_and_clear(chat_id, running_content)
|
||||
|
||||
|
||||
return running_content
|
||||
|
|
@ -400,10 +415,15 @@ class MemoryChest:
|
|||
)
|
||||
logger.info(f"已保存记忆仓库内容，标题: {title.strip()}, chat_id: {chat_id}")
|
||||
|
||||
# 清空对应chat_id的running_content
|
||||
# 清空内容并刷新时间戳，但保留条目用于增量计算
|
||||
if chat_id in self.running_content_list:
|
||||
del self.running_content_list[chat_id]
|
||||
logger.info(f"已清空chat_id {chat_id} 的running_content")
|
||||
current_time = time.time()
|
||||
self.running_content_list[chat_id] = {
|
||||
"content": "",
|
||||
"last_update_time": current_time,
|
||||
"create_time": current_time
|
||||
}
|
||||
logger.info(f"已保存并刷新chat_id {chat_id} 的时间戳，准备下一次增量构建")
|
||||
else:
|
||||
logger.warning(f"生成标题失败，chat_id: {chat_id}")
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
[inner]
|
||||
version = "6.18.2"
|
||||
version = "6.18.3"
|
||||
|
||||
#----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
|
||||
#如果你想要修改配置文件，请递增version的值
|
||||
|
|
@ -38,7 +38,6 @@ plan_style = """
|
|||
# 麦麦识图规则，不建议修改
|
||||
visual_style = "请用中文描述这张图片的内容。如果有文字，请把文字描述概括出来，请留意其主题，直观感受，输出为一段平文本，最多30字，请注意不要分点，就输出一段文本"
|
||||
|
||||
|
||||
# 麦麦私聊的说话规则，行为风格:
|
||||
private_plan_style = """
|
||||
1.思考**所有**的可用的action中的**每个动作**是否符合当下条件，如果动作使用条件符合聊天内容就使用
|
||||
|
|
@ -46,9 +45,9 @@ private_plan_style = """
|
|||
3.某句话如果已经被回复过，不要重复回复"""
|
||||
|
||||
[expression]
|
||||
# 表达方式模式
|
||||
# 表达方式模式（此选项暂未使用）
|
||||
mode = "context"
|
||||
# 可选：llm模式，context上下文模式，full-context 完整上下文嵌入模式
|
||||
# 可选：llm模式，context上下文模式
|
||||
|
||||
# 表达学习配置
|
||||
learning_list = [ # 表达学习配置列表，支持按聊天流配置
|
||||
|
|
@ -92,12 +91,23 @@ talk_value_rules = [
|
|||
{ target = "qq:114514:private", time = "00:00-23:59", value = 0.3 },
|
||||
]
|
||||
|
||||
# 动态自动聊天频率规则：按时段/按chat_id调整 auto_chat_value（优先匹配具体chat，再匹配全局）
|
||||
# 推荐格式（对象数组）：{ target="platform:id:type" 或 "", time="HH:MM-HH:MM", value=0.5 }
|
||||
# 说明:
|
||||
# - target 为空字符串表示全局；type 为 group/private，例如："qq:1919810:group" 或 "qq:114514:private"；
|
||||
# - 支持跨夜区间，例如 "23:00-02:00"；数值范围建议 0-1。
|
||||
auto_chat_value_rules = [
|
||||
{ target = "", time = "00:00-08:59", value = 0.3 },
|
||||
{ target = "", time = "09:00-22:59", value = 1.0 },
|
||||
{ target = "qq:1919810:group", time = "20:00-23:59", value = 0.8 },
|
||||
{ target = "qq:114514:private", time = "00:00-23:59", value = 0.5 },
|
||||
]
|
||||
|
||||
[memory]
|
||||
max_memory_number = 100 # 记忆最大数量
|
||||
max_memory_size = 2048 # 记忆最大大小
|
||||
memory_build_frequency = 1 # 记忆构建频率
|
||||
|
||||
[relationship]
|
||||
enable_relationship = true # 是否启用关系系统
|
||||
|
||||
[tool]
|
||||
enable_tool = true # 是否启用工具
|
||||
|
|
@ -212,3 +222,8 @@ enable = true
|
|||
|
||||
[experimental] #实验性功能
|
||||
none = false # 暂无
|
||||
|
||||
|
||||
#此系统暂时移除，无效配置
|
||||
[relationship]
|
||||
enable_relationship = true # 是否启用关系系统
|
||||
Loading…
Reference in New Issue