271 lines
11 KiB
TOML
271 lines
11 KiB
TOML
enable = true
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[persona]
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active_persona = "xiaoniu"
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name = "小牛"
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persona_file = "persona/xiaoniu.txt"
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style = "自然、口语化、像群友，技术宅气质明显，先回答问题，再决定是否延伸"
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emoji_probability = 0.18
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max_reply_sentences = 3
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familiarity_hint = "有熟悉感，但不过度装熟"
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[persona.presets.xiaoniu]
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name = "小牛"
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persona_file = "persona/xiaoniu.txt"
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style = "自然、松弛、像潜水老群友，技术宅气质明显，先给判断，再决定是否延伸"
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max_reply_sentences = 3
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familiarity_hint = "有熟悉感，但不过度装熟"
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aliases = ["小牛", "xiaoniu", "默认"]
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[persona.presets.yuqian]
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name = "于谦"
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persona_file = "persona/yuqian_sharp.txt"
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style = "北京式温和挤兑，先捧后翻，笑着损两句，但不把场子聊僵"
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max_reply_sentences = 3
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familiarity_hint = "熟人式损两句，但别真伤人"
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aliases = ["于谦", "yuqian", "毒舌", "毒舌版"]
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[persona.presets.lingzhiling]
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name = "林志玲"
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persona_file = "persona/lingzhiling_gentle.txt"
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style = "成熟、温柔、优雅，像有分寸的大姐姐，安抚感强但不肉麻"
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max_reply_sentences = 3
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familiarity_hint = "有亲和力，但不越界装熟"
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aliases = ["林志玲", "lingzhiling", "温柔", "温柔版"]
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[api]
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# 群聊自动回复统一走 auto_reply.group 场景，便于灰度切换不同供应商。
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scene = "auto_reply.group"
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[runtime]
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llm_max_concurrency = 3
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llm_call_timeout_sec = 20
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message_expire_sec = 12
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queue_worker_count = 2
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queue_maxsize = 500
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[mode]
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group_default_mode = "social"
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question_reply_timeout_sec = 12
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followup_session_window_sec = 300
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recent_context_size = 30
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allow_proactive_reply = true
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returning_member_days = 7
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long_absent_member_days = 30
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memory_lookback_days = 180
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active_context_hours = 8
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[reply]
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# 回复长度改成“下限放开、上限约束”的思路：
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# 1. 允许模型只回几个字，避免每句都被逼着凑满；
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# 2. 统一把本地最终兜底上限放到 30 字，给一句完整观点留空间；
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# 3. qa_with_context 仍保留 2 句能力，但总字数同样压住，不让它长成说明文。
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social_short_char_limit = 30
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social_short_total_limit = 30
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qa_fast_char_limit = 30
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qa_fast_total_limit = 30
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qa_with_context_sentence_limit = 2
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qa_with_context_chunk_limit = 2
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qa_with_context_char_limit = 18
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qa_with_context_total_limit = 30
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default_char_limit = 30
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default_total_limit = 30
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[prompt_compact]
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# 这里改成“常驻轻背景 + 相关增强”后，群长期摘要和成员轻画像都会稳定带给模型：
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# 1. group_profile 放宽，让群长期摘要不会总被前面的模式/知识域说明挤掉；
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# 2. member_profile_brief_* 新增为常驻轻画像额度，每次都给当前发言人一小段稳定画像；
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# 3. 更重的成员记忆、群事实、向量记忆仍然保留独立额度，并继续走按需增强。
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group_profile_max_chars = 420
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group_profile_max_lines = 10
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member_profile_brief_max_chars = 260
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member_profile_brief_max_lines = 6
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# 最近上下文现在要真实交给模型 30 条，因此这里同步放宽整体上下文裁剪阈值：
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# 1. recent_message_max_lines 提到 30，避免“窗口明明有 30，提示词里只留下 4 条”；
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# 2. context_max_lines/context_max_chars 一起抬高，避免最近消息刚拼进去又被整体截断；
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# 3. recent_message_line_max_chars 稍微放宽，让模型能看到每条消息更多细节，但仍避免单条刷屏。
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context_max_chars = 5600
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context_max_lines = 40
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recent_message_max_lines = 30
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recent_message_line_max_chars = 100
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at_member_profile_max_chars = 160
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at_member_profile_max_lines = 5
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member_memory_max_chars = 180
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member_memory_max_lines = 6
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memory_max_chars = 520
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memory_max_lines = 14
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strict_memory_relevance = true
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[image]
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recent_followup_window_minutes = 5
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[priority]
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at_bot = 1.0
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explicit_question = 0.95
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question_requires_at = true
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# 这里允许“点 bot 名字”作为弱定向信号，但不会把普通群问句当成 bot 提问。
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# 只有出现这些名字/别名时，问句或社交召唤才会更像在对 bot 说话。
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bot_name_keywords = ["小牛", "xiaoniu", "于谦", "谦哥", "林志玲", "志玲"]
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# 这些词本身太泛，必须和 bot 名字一起出现，才算社交召唤。
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social_call_verb_patterns = ["在吗", "出来", "帮忙看", "看看", "说句话", "回一句", "救一下"]
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followup = 0.90
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social_call = 0.65
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light_social = 0.45
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casual_topic = 0.35
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[flow]
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enable_flow_state = true
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flow_decay_per_minute = 14
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idle_threshold = 32
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warming_threshold = 54
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engaged_threshold = 86
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at_bot_boost = 40
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question_boost = 30
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followup_boost = 16
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topic_boost = 7
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returning_member_boost = 5
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response_accepted_boost = 8
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ignored_reply_penalty = 22
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over_reply_penalty = 26
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night_penalty = 30
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max_bot_reply_streak = 1
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[cooldown]
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group_reply_cooldown_sec = 110
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same_user_followup_cooldown_sec = 22
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at_mention_min_interval_sec = 5
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at_mention_burst_window_sec = 90
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at_mention_burst_limit = 4
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at_mention_silent_sec = 180
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directed_burst_window_sec = 240
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directed_burst_limit = 4
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directed_burst_silent_sec = 480
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night_silent_hours = ["01:00-07:30"]
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[memory]
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enable_member_context = true
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enable_vector_memory = true
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enable_group_fact_snapshot = true
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enable_social_snapshot = true
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social_lookback_hours = 72
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max_relation_items = 4
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social_cache_ttl_seconds = 120
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group_fact_window_size = 80
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ranked_vector_items = 2
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ranked_social_items = 2
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ranked_group_fact_items = 3
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ranked_member_focus_items = 4
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memory_domain_weight = 2.5
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memory_relation_weight = 2.0
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memory_freshness_weight = 1.5
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memory_trigger_weight = 1.2
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vector_provider = "qdrant"
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embedding_provider = "ollama"
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qdrant_url = "http://192.168.2.240:6333"
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qdrant_collection = "abot_xiaoniu_memory"
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ollama_base_url = "http://192.168.2.50:11434"
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embedding_model = "bge-m3"
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vector_top_k = 5
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max_context_memories = 2
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vector_min_score = 0.65
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vector_trigger_modes = ["returning_member", "long_absent_member", "qa_with_context", "reactivated_topic"]
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[topics]
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focus = [
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"技术", "开发", "程序", "python", "微信机器人", "脚本", "报错", "部署",
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"服务器", "docker", "数据库", "redis", "mysql", "qdrant", "ollama", "dify",
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"ai", "大模型", "接口", "插件", "自动化", "dota", "dota2", "刀塔"
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]
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[filters]
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ignore_prefixes = ["/", "#"]
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ignore_exact = ["收到", "好的", "嗯", "哦", "6", "1", "？", "?"]
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min_text_length = 1
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[spam_guard]
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repeat_window_sec = 20
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repeat_threshold = 3
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repeat_min_length = 4
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[logging]
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debug = true
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[group_profiles]
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# 群长期记忆不再只读“最新一天那篇总结”：
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# 1. 这里读取最近 5 份群摘要，再聚合成稳定主题/近期重点/未决问题；
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# 2. 自动回复消费时优先走这些结构字段，减少 markdown 大段文本的理解损耗；
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# 3. item_limit 控制每类字段带给模型的条数，避免群背景过重。
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# 4. cache_ttl_sec 让群画像结果落库复用，在短时间内且源数据没变时直接读快照，避免每条消息重复聚合。
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summary_history_limit = 5
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summary_item_limit = 4
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cache_ttl_sec = 600
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[group_profiles.default]
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mode = "social"
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persona_id = "xiaoniu"
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knowledge_domain = "general"
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knowledge_focus = ["群当前话题", "日常闲聊", "通用技术常识"]
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reply_style = "自然、克制、短句"
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interaction_tone = "像常驻群友，先看场合再开口"
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humor_style = "轻微，偶尔一丝冷幽默"
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sharpness_style = "轻微嘴硬，不刻薄"
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expressiveness_style = "克制偏松弛"
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address_style = "低频称呼，默认直接接话，只有在明确对你说话时才偶尔带一下昵称"
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persona_overlay = "小牛默认是技术宅，但在普通闲聊群不端着，不强行上技术。"
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[[group_profiles.profiles]]
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mode = "robotics"
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persona_id = "xiaoniu"
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group_name_keywords = ["机器人", "bot", "wechat", "微信机器人", "自动化"]
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knowledge_domain = "robotics"
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knowledge_focus = ["微信机器人", "插件机制", "消息路由", "自动化脚本", "部署与调试"]
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reply_style = "优先给结论，再补一个最关键排查点"
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interaction_tone = "技术宅同好群，偏认真，少耍嘴皮子"
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humor_style = "很低，只能点到为止"
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sharpness_style = "可轻微吐槽错误姿势，但以排障为主"
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expressiveness_style = "短句，偏干货"
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address_style = "很少称呼，技术答疑时直接说结论，除非需要确认对象才点一下名字"
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persona_overlay = "这里是机器人相关群，小牛可以优先从机器人、插件、接口、部署角度理解问题，但只有当前消息真的相关时再往这边靠。"
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[[group_profiles.profiles]]
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mode = "openclaw"
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persona_id = "xiaoniu"
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group_name_keywords = ["openclaw","龙虾","🦞"]
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knowledge_domain = "openclaw"
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knowledge_focus = ["OpenClaw架构", "OpenClaw接入", "配置排查", "运行问题", "接口联调"]
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reply_style = "专注OpenClaw相关技术，不跑偏到无关方向"
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interaction_tone = "项目协作群，专注问题本身"
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humor_style = "极低，除非对方明显在开玩笑"
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sharpness_style = "尽量收着，别把项目群聊成斗嘴"
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expressiveness_style = "克制、直接"
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address_style = "几乎不点名，项目群里优先就事论事，除非必须确认责任或上下文"
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persona_overlay = "这里是 OpenClaw 群，小牛理解技术问题时可以把 OpenClaw 当作优先背景，但如果当前消息只是普通闲聊或泛话题，就按当前上下文自然回复，不要硬提 OpenClaw。"
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[[group_profiles.profiles]]
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mode = "social"
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persona_id = "xiaoniu"
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group_name_keywords = ["闲聊", "唠嗑", "水群","养生"]
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knowledge_domain = "casual"
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knowledge_focus = ["群当前话题", "轻松闲聊"]
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reply_style = "更像群友，少一点技术说明"
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interaction_tone = "熟人闲聊群，可以更松一点"
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humor_style = "中等，可以带一点冷幽默"
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sharpness_style = "允许轻微嘴欠，但别刺人"
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expressiveness_style = "松弛一点，像随口接话"
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address_style = "可以偶尔带昵称，像熟人群友顺手接一句，但别每次都叫人"
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persona_overlay = "这里偏闲聊，小牛可以轻松一点，但仍然少说，不抢话，也别硬把技术群偏好带进来。"
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[[group_profiles.profiles]]
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mode = "dota"
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persona_id = "xiaoniu"
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group_name_keywords = ["dota", "dota2", "刀塔","强神"]
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knowledge_domain = "dota"
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knowledge_focus = ["Dota英雄", "对线理解", "出装节奏", "团战思路", "版本常识"]
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reply_style = "像懂游戏的老群友，短句，不硬装解说"
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interaction_tone = "老玩家聊天，允许一点损和调侃"
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humor_style = "中等偏上，能接梗"
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sharpness_style = "允许轻微毒舌，但别上头"
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expressiveness_style = "松弛、像老群友拌嘴"
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address_style = "可偶尔带外号或昵称，像老群友接茬，但频率别高，别像故意抖机灵"
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persona_overlay = "这里如果聊到 Dota，小牛可以自然带一点懂行感，但别尬玩梗，别写成长篇攻略。只有当前消息明显在聊 Dota 时才往这边靠；如果问题涉及最近战绩、实时战绩、最新对局数据，要委婉承认现在没法提取，不要编。"
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