remove：移除tool_use模型，修复Jargon提取问题，修改统计为tool统计

2026-03-29 16:26:34 +08:00
parent 868438e3c1
commit 82bbf0fd52
25 changed files with 906 additions and 311 deletions
--- a/mai_knowledge/knowledge.json
+++ b/mai_knowledge/knowledge.json
@@ -1,5 +1,69 @@
 {
-  "1": [],
+  "1": [
+    {
+      "id": "know_1_1774770946.623486",
+      "content": "备战中考",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:55:46.623486"
+    },
+    {
+      "id": "know_1_1774771765.051286",
+      "content": "性别为女性",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:09:25.051286"
+    },
+    {
+      "id": "know_1_1774771851.333504",
+      "content": "用户是I人（内向型人格）",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:10:51.333504"
+    },
+    {
+      "id": "know_1_1774771894.517183",
+      "content": "用户名为小千，被他人称为“宝宝”，结合语境推测为女性",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:11:34.517183"
+    },
+    {
+      "id": "know_1_1774771923.859455",
+      "content": "小千是I人（内向型人格）",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:12:03.859455"
+    },
+    {
+      "id": "know_1_1774771993.479732",
+      "content": "小千是女性",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:13:13.479732"
+    },
+    {
+      "id": "know_1_1774772079.496335",
+      "content": "用户名为小千，被他人称为“宝宝”，推测为女性或处于亲密社交语境中（注：性别非明确陈述，但基于昵称高频使用及语境，高置信度归纳为女性或女性化称呼偏好，若严格遵循“明确表达”则此项存疑。鉴于指令要求“高置信度可归纳”，且群内互动模式符合典型女性向昵称习惯，此处提取为倾向性事实）",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:14:39.496335"
+    }
+  ],
  "2": [
    {
      "id": "know_2_1774768612.298128",
@@ -18,6 +82,78 @@
        "source": "maisaka_learning"
      },
      "created_at": "2026-03-29T15:17:25.029561"
+    },
+    {
+      "id": "know_2_1774771068.355999",
+      "content": "喜欢用夸张、幽默或古风修辞表达观点",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:57:48.355999"
+    },
+    {
+      "id": "know_2_1774771397.764996",
+      "content": "性格幽默，喜欢使用夸张比喻和古风表达",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:03:17.764996"
+    },
+    {
+      "id": "know_2_1774771471.03367",
+      "content": "幽默风趣，喜欢使用夸张比喻和玩梗",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:04:31.033670"
+    },
+    {
+      "id": "know_2_1774771765.052285",
+      "content": "性格不孤僻，社交圈较广",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:09:25.052285"
+    },
+    {
+      "id": "know_2_1774771851.33601",
+      "content": "用户表现出社恐倾向，喜欢回避社交互动",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:10:51.336010"
+    },
+    {
+      "id": "know_2_1774771894.520185",
+      "content": "性格偏向内向（I人），有社恐倾向，喜欢回避社交压力",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:11:34.520185"
+    },
+    {
+      "id": "know_2_1774771958.585244",
+      "content": "小千是内向型人格（I人）",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:12:38.585244"
+    },
+    {
+      "id": "know_2_1774771993.481732",
+      "content": "小千性格内向（I人）",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:13:13.481732"
    }
  ],
  "3": [],
@@ -41,6 +177,213 @@
        "source": "maisaka_learning"
      },
      "created_at": "2026-03-29T15:15:17.122405"
+    },
+    {
+      "id": "know_6_1774769406.247087",
+      "content": "喜欢动漫风格插画",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:30:06.247087"
+    },
+    {
+      "id": "know_6_1774770487.207364",
+      "content": "关注显卡硬件参数（如显存、型号）及深度学习/炼丹应用",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:48:07.207364"
+    },
+    {
+      "id": "know_6_1774770487.209372",
+      "content": "对游戏光影效果感兴趣",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:48:07.209372"
+    },
+    {
+      "id": "know_6_1774770603.063873",
+      "content": "喜欢玩《我的世界》和VRChat",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:50:03.063873"
+    },
+    {
+      "id": "know_6_1774770654.654349",
+      "content": "关注显卡硬件参数（如4090、48G显存、5090）",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:50:54.654349"
+    },
+    {
+      "id": "know_6_1774770654.655356",
+      "content": "使用VRChat进行社交娱乐",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:50:54.655356"
+    },
+    {
+      "id": "know_6_1774770734.287947",
+      "content": "关注显卡硬件（如4090、3050）及AI炼丹技术",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:52:14.287947"
+    },
+    {
+      "id": "know_6_1774770734.289944",
+      "content": "玩《我的世界》并配置光影效果",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:52:14.289944"
+    },
+    {
+      "id": "know_6_1774770734.291944",
+      "content": "计划游玩VRChat",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:52:14.291944"
+    },
+    {
+      "id": "know_6_1774771033.111011",
+      "content": "喜欢玩VRChat",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:57:13.111011"
+    },
+    {
+      "id": "know_6_1774771068.358999",
+      "content": "关注VRChat等虚拟现实游戏及硬件性能话题",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:57:48.358999"
+    },
+    {
+      "id": "know_6_1774771233.980219",
+      "content": "使用VRChat（VRC）",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:00:33.980219"
+    },
+    {
+      "id": "know_6_1774771397.766996",
+      "content": "对VRChat（VRC）及虚拟形象社交感兴趣",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:03:17.766996"
+    },
+    {
+      "id": "know_6_1774771471.03567",
+      "content": "对VRChat等虚拟社交游戏感兴趣",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:04:31.035670"
+    },
+    {
+      "id": "know_6_1774771894.521183",
+      "content": "熟悉二次元文化、动漫角色及互联网流行梗（Meme）",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:11:34.521183"
+    },
+    {
+      "id": "know_6_1774771923.861534",
+      "content": "小千玩CS:GO游戏",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:12:03.861534"
+    },
+    {
+      "id": "know_6_1774771958.587243",
+      "content": "回声者_Echoderd喜欢玩CS:GO游戏",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:12:38.587243"
+    },
+    {
+      "id": "know_6_1774771993.483732",
+      "content": "小千喜欢二次元文化及动漫游戏圈梗",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:13:13.483732"
+    },
+    {
+      "id": "know_6_1774772079.499335",
+      "content": "熟悉并喜爱二次元文化、动漫角色及互联网梗图（如阴间美学、病娇系、黑长直萌妹等风格）",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:14:39.499335"
+    },
+    {
+      "id": "know_6_1774772112.716455",
+      "content": "小千关注CS:GO游戏及中考备考话题",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:15:12.716455"
+    },
+    {
+      "id": "know_6_1774772154.873237",
+      "content": "用户玩CS:GO游戏",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:15:54.873237"
+    },
+    {
+      "id": "know_6_1774772186.438797",
+      "content": "玩CS:GO游戏",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:16:26.438797"
+    },
+    {
+      "id": "know_6_1774772730.867535",
+      "content": "熟悉《我的青春恋爱物语果然有问题》及二次元表情包文化",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:25:30.867535"
    }
  ],
  "7": [
@@ -61,9 +404,127 @@
        "source": "maisaka_learning"
      },
      "created_at": "2026-03-29T15:16:13.741823"
+    },
+    {
+      "id": "know_7_1774770603.062873",
+      "content": "备战中考",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:50:03.062873"
+    },
+    {
+      "id": "know_7_1774771471.036668",
+      "content": "正在备战中考的学生",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:04:31.036668"
+    },
+    {
+      "id": "know_7_1774771923.862535",
+      "content": "小千正在备战中考",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:12:03.862535"
+    },
+    {
+      "id": "know_7_1774771958.588749",
+      "content": "回声者_Echoderd正在备战中考",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:12:38.588749"
+    },
+    {
+      "id": "know_7_1774772112.714455",
+      "content": "小千使用AI模型进行对话",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:15:12.714455"
+    },
+    {
+      "id": "know_7_1774772154.870238",
+      "content": "用户正在备战中考",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:15:54.870238"
+    }
+  ],
+  "8": [
+    {
+      "id": "know_8_1774770946.624486",
+      "content": "日常逛游戏地图",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:55:46.624486"
+    },
+    {
+      "id": "know_8_1774771397.769034",
+      "content": "备考中考期间仍保持日常游戏娱乐习惯",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:03:17.769034"
+    },
+    {
+      "id": "know_8_1774771851.338018",
+      "content": "用户有备考中考的学习任务",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:10:51.338018"
+    },
+    {
+      "id": "know_8_1774771894.523189",
+      "content": "备考中（备战中考）",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:11:34.523189"
+    },
+    {
+      "id": "know_8_1774771993.484733",
+      "content": "小千有打CS:GO的游戏习惯",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:13:13.484733"
+    },
+    {
+      "id": "know_8_1774772079.501334",
+      "content": "有在高压环境下（如中考前）进行游戏娱乐（CS:GO）的习惯，自称或认同“摆烂”的生活态度",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:14:39.501334"
+    },
+    {
+      "id": "know_8_1774772154.875743",
+      "content": "用户在备考期间有打游戏摸鱼的习惯",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:15:54.875743"
    }
  ],
-  "8": [],
  "9": [],
  "10": [
    {
@@ -119,8 +580,82 @@
        "source": "maisaka_learning"
      },
      "created_at": "2026-03-29T15:17:25.028561"
+    },
+    {
+      "id": "know_10_1774769406.249584",
+      "content": "沟通中常使用文言文或半文言表达",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:30:06.249584"
+    },
+    {
+      "id": "know_10_1774769406.251097",
+      "content": "习惯用反问句和夸张语气进行互动",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:30:06.251097"
+    },
+    {
+      "id": "know_10_1774770487.211056",
+      "content": "沟通风格幽默，常使用网络梗和夸张表达",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:48:07.211056"
+    },
+    {
+      "id": "know_10_1774771471.038677",
+      "content": "沟通风格轻松随意，善于接话和调侃",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:04:31.038677"
+    },
+    {
+      "id": "know_10_1774771765.053285",
+      "content": "沟通风格活泼，喜欢使用语气词和表情符号撒娇",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:09:25.053285"
+    },
+    {
+      "id": "know_10_1774772079.503333",
+      "content": "沟通风格幽默调侃，擅长用反话（如“烦到了”）和夸张修辞（如“耳朵起茧子”、“要报警了”）表达情绪",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T16:14:39.503333"
    }
  ],
-  "11": [],
-  "12": []
+  "11": [
+    {
+      "id": "know_11_1774771068.360999",
+      "content": "乐于接受并学习新的技术技巧（如加速器用法）",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:57:48.360999"
+    }
+  ],
+  "12": [
+    {
+      "id": "know_12_1774770654.657355",
+      "content": "面对网络延迟问题倾向于寻找加速器解决方案",
+      "metadata": {
+        "session_id": "628336b082552269377e9d0648e26c60",
+        "source": "maisaka_learning"
+      },
+      "created_at": "2026-03-29T15:50:54.657355"
+    }
+  ]
 }
--- a/src/chat/heart_flow/heartFC_chat.py
+++ b/src/chat/heart_flow/heartFC_chat.py
@@ -198,7 +198,6 @@ class HeartFChatting:
        """判定和生成回复"""
        asyncio.create_task(self._trigger_expression_learning(self.message_cache))
        # TODO: 完成反思器之后的逻辑
-        start_time = time.time()
        current_cycle_detail = self._start_cycle()
        logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考")

@@ -207,10 +206,7 @@ class HeartFChatting:
        # TODO: 动作执行逻辑

        cycle_detail = self._end_cycle(current_cycle_detail)
-        if wait_time := global_config.chat.planner_smooth - (time.time() - start_time) > 0:
-            await asyncio.sleep(wait_time)
-        else:
-            await asyncio.sleep(0.1)  # 最小等待时间，避免过快循环
+        await asyncio.sleep(0.1)  # 最小等待时间，避免过快循环
        return True

    def _handle_loop_completion(self, task: asyncio.Task):
--- a/src/chat/replyer/group_generator.py
+++ b/src/chat/replyer/group_generator.py
@@ -577,29 +577,6 @@ class DefaultReplyer:
        duration = end_time - start_time
        return name, result, duration

-    async def _build_disabled_jargon_explanation(self) -> str:
-        """当关闭黑话解释时使用的占位协程，避免额外的LLM调用"""
-        return ""
-
-    async def _build_unknown_words_jargon(self, unknown_words: Optional[List[str]], chat_id: str) -> str:
-        """针对 Planner 提供的未知词语列表执行黑话检索"""
-        if not unknown_words:
-            return ""
-        # 清洗未知词语列表，只保留非空字符串
-        concepts: List[str] = []
-        for item in unknown_words:
-            if isinstance(item, str):
-                s = item.strip()
-                if s:
-                    concepts.append(s)
-        if not concepts:
-            return ""
-        try:
-            return await retrieve_concepts_with_jargon(concepts, chat_id)
-        except Exception as e:
-            logger.error(f"未知词语黑话检索失败: {e}")
-            return ""
-
    async def _build_jargon_explanation(
        self,
        chat_id: str,
@@ -609,19 +586,14 @@ class DefaultReplyer:
    ) -> str:
        """
        统一的黑话解释构建函数：
-        - 根据 enable_jargon_explanation / jargon_mode 决定具体策略
+        - 根据 enable_jargon_explanation 决定是否启用
        """
+        del unknown_words
        enable_jargon_explanation = getattr(global_config.expression, "enable_jargon_explanation", True)
        if not enable_jargon_explanation:
            return ""

-        jargon_mode = getattr(global_config.expression, "jargon_mode", "context")
-
-        # planner 模式：仅使用 Planner 的 unknown_words
-        if jargon_mode == "planner":
-            return await self._build_unknown_words_jargon(unknown_words, chat_id)
-
-        # 默认 / context 模式：使用上下文自动匹配黑话
+        # 使用上下文自动匹配黑话
        try:
            return await explain_jargon_in_context(chat_id, messages_short, chat_talking_prompt_short) or ""
        except Exception as e:
@@ -1209,7 +1181,7 @@ class DefaultReplyer:
            prompt = await prompt_manager.render_prompt(template_prompt)
            generation_result = await llm_api.generate(
                llm_api.LLMServiceRequest(
-                    task_name="tool_use",
+                    task_name="utils",
                    request_type="replyer.lpmm_knowledge",
                    prompt=prompt,
                    tool_options=[search_knowledge_tool.get_tool_definition()],
--- a/src/chat/replyer/maisaka_generator.py
+++ b/src/chat/replyer/maisaka_generator.py
@@ -20,8 +20,8 @@ from src.services.llm_service import LLMServiceClient
 from src.maisaka.message_adapter import (
    get_message_kind,
    get_message_role,
+    get_message_source,
    get_message_text,
-    is_perception_message,
    parse_speaker_content,
 )

@@ -121,6 +121,9 @@ class MaisakaReplyGenerator:
            role = get_message_role(message)
            timestamp = self._format_message_time(message)

+            if get_message_source(message) == "user_reference":
+                continue
+
            if role == "user":
                guided_reply = self._extract_guided_bot_reply(message)
                if guided_reply:
@@ -148,7 +151,6 @@ class MaisakaReplyGenerator:
        chat_history: List[SessionMessage],
        reply_reason: str,
        expression_habits: str = "",
-        jargon_explanation: str = "",
    ) -> str:
        """构建 Maisaka replyer 提示词。"""
        current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
@@ -167,8 +169,6 @@ class MaisakaReplyGenerator:
        extra_sections: List[str] = []
        if expression_habits.strip():
            extra_sections.append(expression_habits.strip())
-        if jargon_explanation.strip():
-            extra_sections.append(jargon_explanation.strip())

        user_sections = [
            f"当前时间：{current_time}",
@@ -198,7 +198,6 @@ class MaisakaReplyGenerator:
        log_reply: bool = True,
        chat_history: Optional[List[SessionMessage]] = None,
        expression_habits: str = "",
-        jargon_explanation: str = "",
        selected_expression_ids: Optional[List[int]] = None,
    ) -> Tuple[bool, ReplyGenerationResult]:
        """结合上下文生成 Maisaka 的最终可见回复。"""
@@ -223,20 +222,20 @@ class MaisakaReplyGenerator:
            f"Maisaka replyer start: stream_id={stream_id} reply_reason={reply_reason!r} "
            f"history_size={len(chat_history)} target_message_id="
            f"{reply_message.message_id if reply_message else None} "
-            f"expression_count={len(result.selected_expression_ids)} "
-            f"jargon_enabled={bool(jargon_explanation.strip())}"
+            f"expression_count={len(result.selected_expression_ids)}"
        )

        filtered_history = [
            message
            for message in chat_history
-            if get_message_role(message) != "system" and get_message_kind(message) != "perception"
+            if get_message_role(message) != "system"
+            and get_message_kind(message) != "perception"
+            and get_message_source(message) != "user_reference"
        ]
        prompt = self._build_prompt(
            chat_history=filtered_history,
            reply_reason=reply_reason or "",
            expression_habits=expression_habits,
-            jargon_explanation=jargon_explanation,
        )
        result.completion.request_prompt = prompt

--- a/src/chat/utils/statistic.py
+++ b/src/chat/utils/statistic.py
@@ -12,7 +12,7 @@ from sqlmodel import col, select

 from src.common.logger import get_logger
 from src.common.database.database import get_db_session
-from src.common.database.database_model import OnlineTime, ModelUsage, Messages, ActionRecord
+from src.common.database.database_model import Messages, ModelUsage, OnlineTime, ToolRecord
 from src.manager.async_task_manager import AsyncTask
 from src.manager.local_store_manager import local_storage
 from src.config.config import global_config
@@ -648,7 +648,7 @@ class StatisticOutputTask(AsyncTask):
    def _collect_message_count_for_period(
        self,
        collect_period: list[tuple[str, datetime]],
-    ) -> StatPeriodMapping:
+    ) -> dict[str, dict[str, object]]:
        """
        收集指定时间段的消息统计数据

@@ -659,8 +659,13 @@ class StatisticOutputTask(AsyncTask):

        collect_period.sort(key=lambda x: x[1], reverse=True)

-        stats: StatPeriodMapping = {
-            period_key: StatisticOutputTask._build_stat_period_data() for period_key, _ in collect_period
+        stats: dict[str, dict[str, object]] = {
+            period_key: {
+                TOTAL_MSG_CNT: 0,
+                MSG_CNT_BY_CHAT: defaultdict(int),
+                TOTAL_REPLY_CNT: 0,
+            }
+            for period_key, _ in collect_period
        }

        query_start_timestamp = collect_period[-1][1]
@@ -710,24 +715,24 @@ class StatisticOutputTask(AsyncTask):
                        StatisticOutputTask._add_defaultdict_int(stats[period_key], MSG_CNT_BY_CHAT, chat_id, 1)
                    break

-        # 使用 ActionRecords 中的 reply 动作次数作为回复数基准
+        # 使用 ToolRecord 中的 reply 工具次数作为回复数基准
        try:
-            action_query_start_timestamp = collect_period[-1][1]
+            tool_query_start_timestamp = collect_period[-1][1]
            with get_db_session(auto_commit=False) as session:
-                statement = select(ActionRecord).where(col(ActionRecord.timestamp) >= action_query_start_timestamp)
-                actions = session.exec(statement).all()
-            for action in actions:
-                if action.action_name != "reply":
+                statement = select(ToolRecord).where(col(ToolRecord.timestamp) >= tool_query_start_timestamp)
+                tool_records = session.exec(statement).all()
+            for tool_record in tool_records:
+                if tool_record.tool_name != "reply":
                    continue

-                action_time_ts = action.timestamp.timestamp()
+                action_time_ts = tool_record.timestamp.timestamp()
                for idx, (_, period_start_dt) in enumerate(collect_period):
                    if action_time_ts >= period_start_dt.timestamp():
                        for period_key, _ in collect_period[idx:]:
                            StatisticOutputTask._add_int_stat(stats[period_key], TOTAL_REPLY_CNT, 1)
                        break
        except Exception as e:
-            logger.warning(f"统计 reply 动作次数失败，将回复数视为 0，错误信息：{e}")
+            logger.warning(f"统计 reply 工具次数失败，将回复数视为 0，错误信息：{e}")

        return stats

--- a/src/common/data_models/message_component_data_model.py
+++ b/src/common/data_models/message_component_data_model.py
@@ -348,17 +348,11 @@ class MessageSequence:
        if isinstance(item, TextComponent):
            return {"type": "text", "data": item.text}
        elif isinstance(item, ImageComponent):
-            if not item.content:
-                raise RuntimeError("ImageComponent content 未初始化")
-            return {"type": "image", "data": item.content, "hash": item.binary_hash}
+            return {"type": "image", "data": self._ensure_binary_component_content(item, "[图片]"), "hash": item.binary_hash}
        elif isinstance(item, EmojiComponent):
-            if not item.content:
-                raise RuntimeError("EmojiComponent content 未初始化")
-            return {"type": "emoji", "data": item.content, "hash": item.binary_hash}
+            return {"type": "emoji", "data": self._ensure_binary_component_content(item, "[表情包]"), "hash": item.binary_hash}
        elif isinstance(item, VoiceComponent):
-            if not item.content:
-                raise RuntimeError("VoiceComponent content 未初始化")
-            return {"type": "voice", "data": item.content, "hash": item.binary_hash}
+            return {"type": "voice", "data": self._ensure_binary_component_content(item, "[语音消息]"), "hash": item.binary_hash}
        elif isinstance(item, AtComponent):
            return {
                "type": "at",
@@ -388,6 +382,14 @@ class MessageSequence:
            logger.warning(f"Unofficial component type: {type(item)}, defaulting to DictComponent")
            return {"type": "dict", "data": item.data}

+    @staticmethod
+    def _ensure_binary_component_content(item: ByteComponent, fallback_text: str) -> str:
+        """确保二进制组件在序列化时带有稳定的文本占位。"""
+        if item.content:
+            return item.content
+        item.content = fallback_text
+        return item.content
+
    @classmethod
    def _dict_2_item(cls, item: Dict[str, Any]) -> StandardMessageComponents:
        """内部方法：将单个消息组件的字典格式转换回组件对象"""
--- a/src/common/data_models/tool_record_data_model.py
+++ b/src/common/data_models/tool_record_data_model.py
@@ -0,0 +1,59 @@
+from datetime import datetime
+from typing import Dict, Optional
+
+import json
+
+from src.common.database.database_model import ToolRecord
+
+from . import BaseDatabaseDataModel
+
+
+class MaiToolRecord(BaseDatabaseDataModel[ToolRecord]):
+    """工具调用记录数据模型。"""
+
+    def __init__(
+        self,
+        tool_id: str,
+        timestamp: datetime,
+        session_id: str,
+        tool_name: str,
+        tool_reasoning: Optional[str] = None,
+        tool_data: Optional[Dict] = None,
+        tool_builtin_prompt: Optional[str] = None,
+        tool_display_prompt: Optional[str] = None,
+    ):
+        self.tool_id = tool_id
+        self.timestamp = timestamp
+        self.session_id = session_id
+        self.tool_name = tool_name
+        self.tool_reasoning = tool_reasoning
+        self.tool_data = tool_data or {}
+        self.tool_builtin_prompt = tool_builtin_prompt
+        self.tool_display_prompt = tool_display_prompt
+
+    @classmethod
+    def from_db_instance(cls, db_record: ToolRecord):
+        """从数据库实例创建数据模型对象。"""
+        return cls(
+            tool_id=db_record.tool_id,
+            timestamp=db_record.timestamp,
+            session_id=db_record.session_id,
+            tool_name=db_record.tool_name,
+            tool_reasoning=db_record.tool_reasoning,
+            tool_data=json.loads(db_record.tool_data) if db_record.tool_data else None,
+            tool_builtin_prompt=db_record.tool_builtin_prompt,
+            tool_display_prompt=db_record.tool_display_prompt,
+        )
+
+    def to_db_instance(self):
+        """将数据模型对象转换为数据库实例。"""
+        return ToolRecord(
+            tool_id=self.tool_id,
+            timestamp=self.timestamp,
+            session_id=self.session_id,
+            tool_name=self.tool_name,
+            tool_reasoning=self.tool_reasoning,
+            tool_data=json.dumps(self.tool_data) if self.tool_data else None,
+            tool_builtin_prompt=self.tool_builtin_prompt,
+            tool_display_prompt=self.tool_display_prompt,
+        )
--- a/src/common/database/database.py
+++ b/src/common/database/database.py
@@ -3,7 +3,7 @@ from contextlib import contextmanager
 from pathlib import Path
 from typing import Generator, TYPE_CHECKING

-from sqlalchemy import event
+from sqlalchemy import event, text
 from sqlalchemy.engine import Engine
 from sqlalchemy.orm import sessionmaker
 from sqlmodel import SQLModel, Session, create_engine
@@ -57,6 +57,41 @@ SessionLocal = sessionmaker(
 _db_initialized = False


+def _migrate_action_records_to_tool_records() -> None:
+    """将旧的 ``action_records`` 历史数据迁移到 ``tool_records``。"""
+    migration_sql = text(
+        """
+        INSERT INTO tool_records (
+            tool_id,
+            timestamp,
+            session_id,
+            tool_name,
+            tool_reasoning,
+            tool_data,
+            tool_builtin_prompt,
+            tool_display_prompt
+        )
+        SELECT
+            action_id,
+            timestamp,
+            session_id,
+            action_name,
+            action_reasoning,
+            action_data,
+            action_builtin_prompt,
+            action_display_prompt
+        FROM action_records
+        WHERE NOT EXISTS (
+            SELECT 1
+            FROM tool_records
+            WHERE tool_records.tool_id = action_records.action_id
+        )
+        """
+    )
+    with engine.begin() as connection:
+        connection.execute(migration_sql)
+
+
 def initialize_database() -> None:
    global _db_initialized
    if _db_initialized:
@@ -65,6 +100,7 @@ def initialize_database() -> None:
    import src.common.database.database_model  # noqa: F401

    SQLModel.metadata.create_all(engine)
+    _migrate_action_records_to_tool_records()
    _db_initialized = True


--- a/src/common/database/database_model.py
+++ b/src/common/database/database_model.py
@@ -134,6 +134,27 @@ class ActionRecord(SQLModel, table=True):
    action_display_prompt: Optional[str] = Field(default=None)  # 最终输入到Prompt的内容


+class ToolRecord(SQLModel, table=True):
+    """存储工具调用记录"""
+
+    __tablename__ = "tool_records"  # type: ignore
+
+    id: Optional[int] = Field(default=None, primary_key=True)  # 自增主键
+
+    # 元信息
+    tool_id: str = Field(index=True, max_length=255)  # 工具调用ID
+    timestamp: datetime = Field(default_factory=datetime.now, sa_column=Column(DateTime, index=True))  # 记录时间戳
+    session_id: str = Field(index=True, max_length=255)  # 对应的 ChatSession session_id
+
+    # 调用信息
+    tool_name: str = Field(index=True, max_length=255)  # 工具名称
+    tool_reasoning: Optional[str] = Field(default=None)  # 工具调用推理过程
+    tool_data: Optional[str] = Field(default=None)  # 工具数据，JSON格式存储
+
+    tool_builtin_prompt: Optional[str] = Field(default=None)  # 内置工具提示
+    tool_display_prompt: Optional[str] = Field(default=None)  # 最终输入到 Prompt 的内容
+
+
 class CommandRecord(SQLModel, table=True):
    """记录命令执行情况"""

--- a/src/common/utils/utils_action.py
+++ b/src/common/utils/utils_action.py
@@ -3,12 +3,12 @@ from typing import TYPE_CHECKING, List
 from src.common.utils.math_utils import translate_timestamp_to_human_readable, TimestampMode

 if TYPE_CHECKING:
-    from src.common.data_models.action_record_data_model import MaiActionRecord
+    from src.common.data_models.tool_record_data_model import MaiToolRecord


 class ActionUtils:
    @staticmethod
-    def build_readable_action_records(action_records: List["MaiActionRecord"], timestamp_mode: str | TimestampMode):
+    def build_readable_action_records(action_records: List["MaiToolRecord"], timestamp_mode: str | TimestampMode):
        """
        将动作列表转换为可读的文本格式。

@@ -27,6 +27,6 @@ class ActionUtils:
        output_lines = []
        for record in action_records:
            timestamp_str = translate_timestamp_to_human_readable(record.timestamp.timestamp(), mode=timestamp_mode)
-            line = f"在{timestamp_str}，你使用了{record.action_name}，具体内容是：{record.action_display_prompt}"
+            line = f"在{timestamp_str}，你使用了{record.tool_name}，具体内容是：{record.tool_display_prompt}"
            output_lines.append(line)
        return "\n".join(output_lines)
--- a/src/common/utils/utils_message.py
+++ b/src/common/utils/utils_message.py
@@ -579,26 +579,26 @@ class MessageUtils:
            List[Tuple[float, str]]: 按时间排序的动作文本列表，每个元素为 (timestamp, action_text)
        """
        from src.common.database.database import get_db_session
-        from src.common.database.database_model import ActionRecord
+        from src.common.database.database_model import ToolRecord

        # 获取这个时间范围内的动作记录，并匹配session_id
        try:
            with get_db_session() as session:
                actions_in_range = session.exec(
-                    select(ActionRecord)
-                    .where(col(ActionRecord.timestamp) >= datetime.fromtimestamp(min_time))
-                    .where(col(ActionRecord.timestamp) <= datetime.fromtimestamp(max_time))
-                    .where(col(ActionRecord.session_id) == session_id)
-                    .order_by(col(ActionRecord.timestamp))
+                    select(ToolRecord)
+                    .where(col(ToolRecord.timestamp) >= datetime.fromtimestamp(min_time))
+                    .where(col(ToolRecord.timestamp) <= datetime.fromtimestamp(max_time))
+                    .where(col(ToolRecord.session_id) == session_id)
+                    .order_by(col(ToolRecord.timestamp))
                ).all()

            # 获取最新消息之后的第一个动作记录
            with get_db_session() as session:
                action_after_latest = session.exec(
-                    select(ActionRecord)
-                    .where(col(ActionRecord.timestamp) > datetime.fromtimestamp(max_time))
-                    .where(col(ActionRecord.session_id) == session_id)
-                    .order_by(col(ActionRecord.timestamp))
+                    select(ToolRecord)
+                    .where(col(ToolRecord.timestamp) > datetime.fromtimestamp(max_time))
+                    .where(col(ToolRecord.session_id) == session_id)
+                    .order_by(col(ToolRecord.timestamp))
                    .limit(1)
                ).all()
        except Exception as e:
@@ -611,7 +611,7 @@ class MessageUtils:
        # 构建动作文本列表
        action_messages: List[Tuple[float, str]] = []
        for action in actions:
-            if action_display_prompt := action.action_display_prompt or "":
+            if action_display_prompt := action.tool_display_prompt or "":
                action_time = action.timestamp.timestamp()
                action_messages.append((action_time, action_display_prompt))

--- a/src/config/config.py
+++ b/src/config/config.py
@@ -56,8 +56,8 @@ CONFIG_DIR: Path = PROJECT_ROOT / "config"
 BOT_CONFIG_PATH: Path = (CONFIG_DIR / "bot_config.toml").resolve().absolute()
 MODEL_CONFIG_PATH: Path = (CONFIG_DIR / "model_config.toml").resolve().absolute()
 MMC_VERSION: str = "1.0.0"
-CONFIG_VERSION: str = "8.1.10"
-MODEL_CONFIG_VERSION: str = "1.12.0"
+CONFIG_VERSION: str = "8.1.11"
+MODEL_CONFIG_VERSION: str = "1.13.1"

 logger = get_logger("config")

--- a/src/config/model_configs.py
+++ b/src/config/model_configs.py
@@ -402,6 +402,15 @@ class ModelTaskConfig(ConfigBase):
        },
    )
    """首要回复模型配置, 还用于表达器和表达方式学习"""
+    
+    planner: TaskConfig = Field(
+        default_factory=TaskConfig,
+        json_schema_extra={
+            "x-widget": "custom",
+            "x-icon": "map",
+        },
+    )
+    """规划模型配置"""

    vlm: TaskConfig = Field(
        default_factory=TaskConfig,
@@ -421,24 +430,6 @@ class ModelTaskConfig(ConfigBase):
    )
    """语音识别模型配置"""

-    tool_use: TaskConfig = Field(
-        default_factory=TaskConfig,
-        json_schema_extra={
-            "x-widget": "custom",
-            "x-icon": "tools",
-        },
-    )
-    """工具使用模型配置, 需要使用支持工具调用的模型"""
-
-    planner: TaskConfig = Field(
-        default_factory=TaskConfig,
-        json_schema_extra={
-            "x-widget": "custom",
-            "x-icon": "map",
-        },
-    )
-    """规划模型配置"""
-
    embedding: TaskConfig = Field(
        default_factory=TaskConfig,
        json_schema_extra={
@@ -446,22 +437,4 @@ class ModelTaskConfig(ConfigBase):
            "x-icon": "database",
        },
    )
-    """嵌入模型配置"""
-
-    lpmm_entity_extract: TaskConfig = Field(
-        default_factory=TaskConfig,
-        json_schema_extra={
-            "x-widget": "custom",
-            "x-icon": "filter",
-        },
-    )
-    """LPMM实体提取模型配置"""
-
-    lpmm_rdf_build: TaskConfig = Field(
-        default_factory=TaskConfig,
-        json_schema_extra={
-            "x-widget": "custom",
-            "x-icon": "network",
-        },
-    )
-    """LPMM RDF构建模型配置"""
+    """嵌入模型配置"""
--- a/src/config/official_configs.py
+++ b/src/config/official_configs.py
@@ -234,17 +234,6 @@ class ChatConfig(ConfigBase):
    )
    """上下文长度"""

-    planner_smooth: float = Field(
-        default=3,
-        ge=0,
-        json_schema_extra={
-            "x-widget": "slider",
-            "x-icon": "gauge",
-            "step": 0.5,
-        },
-    )
-    """规划器平滑，增大数值会减小planner负荷，略微降低反应速度，推荐1-5，0为关闭，必须大于等于0"""
-
    think_mode: Literal["classic", "deep", "dynamic"] = Field(
        default="dynamic",
        json_schema_extra={
@@ -659,21 +648,6 @@ class ExpressionConfig(ConfigBase):
    )
    """是否在回复前尝试对上下文中的黑话进行解释（关闭可减少一次LLM调用，仅影响回复前的黑话匹配与解释，不影响黑话学习）"""

-    jargon_mode: Literal["context", "planner"] = Field(
-        default="planner",
-        json_schema_extra={
-            "x-widget": "select",
-            "x-icon": "settings",
-        },
-    )
-    """
-    黑话解释来源模式
-    
-    可选：
-    - "context"：使用上下文自动匹配黑话
-    - "planner"：仅使用Planner在reply动作中给出的unknown_words列表
-    """
-

 class ToolConfig(ConfigBase):
    """工具配置类"""
@@ -1544,7 +1518,7 @@ class MaiSakaConfig(ConfigBase):
            "x-icon": "brain",
        },
    )
-    """鏄惁鍦?CLI 涓樉绀哄唴蹇冩€濊€冨拰瀹屾暣 Prompt"""
+    """是否显示MaiSaka思考过程"""

    user_name: str = Field(
        default="用户",
@@ -1553,7 +1527,7 @@ class MaiSakaConfig(ConfigBase):
            "x-icon": "user",
        },
    )
-    """MaiSaka 涓敤鎴风殑鏄剧ず鍚嶇О"""
+    """MaiSaka 使用的用户名称"""

    direct_image_input: bool = Field(
        default=True,
@@ -1562,7 +1536,7 @@ class MaiSakaConfig(ConfigBase):
            "x-icon": "image",
        },
    )
-    """æ˜¯å¦å°†å›¾ç‰‡ç›´æŽ¥ä½œä¸ºå¤šæ¨¡æ€æ¶ˆæ¯ä¼ å…¥ Maisaka ä¸»å¾ªçŽ¯ï¼Œè€Œä¸æ˜¯ä»…ä½¿ç”¨è½¬è¯‘æ–‡æœ¬"""
+    """是否直接输入图片"""

    merge_user_messages: bool = Field(
        default=True,
@@ -1571,7 +1545,7 @@ class MaiSakaConfig(ConfigBase):
            "x-icon": "merge",
        },
    )
-    """Whether Maisaka should merge newly received user utterances into a single user message per round"""
+    """是否将新接收的用户发言合并为单个用户消息"""

    max_internal_rounds: int = Field(
        default=6,
@@ -1581,7 +1555,7 @@ class MaiSakaConfig(ConfigBase):
            "x-icon": "repeat",
        },
    )
-    """Maximum number of internal planning rounds per inbound message."""
+    """每个入站消息的最大内部规划轮数"""

    terminal_image_preview: bool = Field(
        default=False,
@@ -1590,7 +1564,7 @@ class MaiSakaConfig(ConfigBase):
            "x-icon": "image",
        },
    )
-    """Whether Maisaka should render a low-resolution terminal preview for images in prompt display"""
+    """是否渲染低分辨率终端预览图片"""

    terminal_image_preview_width: int = Field(
        default=24,
@@ -1600,16 +1574,8 @@ class MaiSakaConfig(ConfigBase):
            "x-icon": "columns",
        },
    )
-    """Character width for Maisaka terminal image previews"""
+    """Maisaka终端图片预览的字符宽度"""

-    take_over_hfc: bool = Field(
-        default=False,
-        json_schema_extra={
-            "x-widget": "switch",
-            "x-icon": "git-branch",
-        },
-    )
-    """Enable Maisaka takeover for the Heart Flow Chat planner and reply pipeline"""

 class PluginRuntimeConfig(ConfigBase):
    """插件运行时配置类"""
--- a/src/learners/expression_auto_check_task.py
+++ b/src/learners/expression_auto_check_task.py
@@ -76,7 +76,7 @@ def create_evaluation_prompt(situation: str, style: str) -> str:
    return prompt


-judge_llm = LLMServiceClient(task_name="tool_use", request_type="expression_check")
+judge_llm = LLMServiceClient(task_name="utils", request_type="expression_check")


 async def single_expression_check(situation: str, style: str) -> tuple[bool, str, str | None]:
--- a/src/learners/expression_learner.py
+++ b/src/learners/expression_learner.py
@@ -30,8 +30,8 @@ logger = get_logger("expressor")
 express_learn_model = LLMServiceClient(
    task_name="utils", request_type="expression.learner"
 )
-summary_model = LLMServiceClient(task_name="tool_use", request_type="expression.summary")
-check_model = LLMServiceClient(task_name="tool_use", request_type="expression.check")
+summary_model = LLMServiceClient(task_name="utils", request_type="expression.summary")
+check_model = LLMServiceClient(task_name="utils", request_type="expression.check")


 class ExpressionLearner:
--- a/src/learners/expression_selector.py
+++ b/src/learners/expression_selector.py
@@ -19,7 +19,7 @@ logger = get_logger("expression_selector")
 class ExpressionSelector:
    def __init__(self):
        self.llm_model = LLMServiceClient(
-            task_name="tool_use", request_type="expression.selector"
+            task_name="utils", request_type="expression.selector"
        )

    def can_use_expression_for_chat(self, chat_id: str) -> bool:
--- a/src/learners/expression_utils.py
+++ b/src/learners/expression_utils.py
@@ -12,7 +12,7 @@ from src.common.logger import get_logger

 logger = get_logger("expression_utils")

-judge_llm = LLMServiceClient(task_name="tool_use", request_type="expression_check")
+judge_llm = LLMServiceClient(task_name="utils", request_type="expression_check")


 def _normalize_repair_json_result(repaired_result: Any) -> str:
--- a/src/learners/jargon_explainer_old.py
+++ b/src/learners/jargon_explainer_old.py
@@ -25,7 +25,7 @@ class JargonExplainer:
    def __init__(self, chat_id: str) -> None:
        self.chat_id = chat_id
        self.llm = LLMServiceClient(
-            task_name="tool_use",
+            task_name="utils",
            request_type="jargon.explain",
        )

--- a/src/maisaka/reasoning_engine.py
+++ b/src/maisaka/reasoning_engine.py
@@ -1,13 +1,14 @@
 """Maisaka 推理引擎。"""

-import difflib
-import json
-import asyncio
-import re
-import time
 from datetime import datetime
 from typing import TYPE_CHECKING, Optional

+import asyncio
+import difflib
+import json
+import re
+import time
+
 from sqlmodel import select

 from src.chat.heart_flow.heartFC_utils import CycleDetail
@@ -21,13 +22,14 @@ from src.common.logger import get_logger
 from src.config.config import global_config
 from src.learners.jargon_explainer import search_jargon
 from src.llm_models.payload_content.tool_option import ToolCall
-from src.services import send_service
+from src.services import database_service as database_api, send_service

 from .message_adapter import (
    build_message,
    build_visible_text_from_sequence,
    clone_message_sequence,
    format_speaker_content,
+    get_message_source,
    get_message_text,
    get_message_role,
 )
@@ -69,6 +71,8 @@ class MaisakaReasoningEngine:
                        cycle_detail = self._start_cycle()
                        self._runtime._log_cycle_started(cycle_detail, round_index)
                        try:
+                            # 每次LLM生成前，动态添加参考消息到最新位置
+                            self._append_jargon_reference_message()
                            planner_started_at = time.time()
                            response = await self._runtime._chat_loop_service.chat_loop_step(self._runtime._chat_history)
                            cycle_detail.time_records["planner"] = time.time() - planner_started_at
@@ -134,10 +138,7 @@ class MaisakaReasoningEngine:
                raw_message=user_sequence,
                display_text=visible_text,
            )
-            insert_index = self._insert_chat_history_message(history_message)
-            reference_message = await self._build_jargon_reference_message(message)
-            if reference_message is not None:
-                self._runtime._chat_history.insert(insert_index + 1, reference_message)
+            self._insert_chat_history_message(history_message)
            self._trim_chat_history()

    async def _build_message_sequence(self, message: SessionMessage) -> tuple[MessageSequence, str]:
@@ -217,65 +218,84 @@ class MaisakaReasoningEngine:
        self._runtime._chat_history.insert(insert_at, message)
        return insert_at

-    async def _build_jargon_reference_message(self, message: SessionMessage) -> Optional[SessionMessage]:
-        """如果命中了黑话词条，则构建一条额外的参考信息消息。"""
-        content = (get_message_text(message) or "").strip()
+    def _append_jargon_reference_message(self) -> None:
+        """每次LLM生成前，如果命中了黑话词条，则添加一条参考信息消息到聊天历史末尾。"""
+        content = self._build_user_history_corpus()
        if not content:
-            if not message.processed_plain_text:
-                await message.process()
-            content = (message.processed_plain_text or "").strip()
-        if not content:
-            return None
+            return

        matched_words = self._find_jargon_words_in_text(content)
        if not matched_words:
-            return None
+            return

        reference_text = (
            "[参考信息]\n"
            f"{','.join(matched_words)}可能是jargon，可以使用query_jargon来查看其含义"
        )
        reference_sequence = MessageSequence([TextComponent(reference_text)])
-        return build_message(
+
+        # 使用当前时间作为时间戳
+        reference_message = build_message(
            role="user",
            content="",
            source="user_reference",
-            timestamp=message.timestamp,
-            platform=message.platform,
+            timestamp=datetime.now(),
+            platform=self._runtime.chat_stream.platform,
            session_id=self._runtime.session_id,
-            group_info=self._runtime._build_group_info(message),
+            group_info=self._runtime._build_group_info(),
            user_info=self._runtime._build_runtime_user_info(),
            raw_message=reference_sequence,
            display_text=reference_text,
        )
+        self._runtime._chat_history.append(reference_message)
+
+    def _build_user_history_corpus(self) -> str:
+        """拼接当前聊天记录内所有用户消息的正文，用于统一匹配黑话。"""
+        parts: list[str] = []
+        for history_message in self._runtime._chat_history:
+            if get_message_role(history_message) != "user":
+                continue
+            if get_message_source(history_message) != "user":
+                continue
+            text = (get_message_text(history_message) or "").strip()
+            if not text:
+                continue
+            parts.append(text)
+
+        return "\n".join(parts)

    def _find_jargon_words_in_text(self, content: str) -> list[str]:
        """匹配正文中出现的 jargon 词条。"""
        lowered_content = content.lower()
-        matches: list[str] = []
+        matched_entries: list[tuple[int, int, int, str]] = []
        seen_words: set[str] = set()

        with get_db_session(auto_commit=False) as session:
-            query = select(Jargon).where(Jargon.is_jargon.is_(True)).order_by(Jargon.count.desc()).limit(200)  # type: ignore[attr-defined]
+            query = (
+                select(Jargon)
+                .where(Jargon.is_jargon.is_(True))
+                .order_by(Jargon.count.desc())  # type: ignore[attr-defined]
+            )
            jargons = session.exec(query).all()

            for jargon in jargons:
                jargon_content = str(jargon.content or "").strip()
                if not jargon_content:
                    continue
-                if jargon_content in seen_words:
+                normalized_content = jargon_content.lower()
+                if normalized_content in seen_words:
                    continue
                if not self._is_visible_jargon(jargon):
                    continue
-                if not self._jargon_matches_text(jargon_content, lowered_content, content):
+                match_position = self._get_jargon_match_position(jargon_content, lowered_content, content)
+                if match_position is None:
                    continue

-                seen_words.add(jargon_content)
-                matches.append(jargon_content)
-                if len(matches) >= 8:
-                    break
+                seen_words.add(normalized_content)
+                matched_entries.append((match_position, -len(jargon_content), -int(jargon.count or 0), jargon_content))

-        return matches
+        matched_entries.sort()
+        return [matched_content for _, _, _, matched_content in matched_entries[:8]]

    def _is_visible_jargon(self, jargon: Jargon) -> bool:
        """判断当前会话是否可见该 jargon。"""
@@ -290,13 +310,17 @@ class MaisakaReasoningEngine:
        return self._runtime.session_id in session_id_dict

    @staticmethod
-    def _jargon_matches_text(jargon_content: str, lowered_content: str, original_content: str) -> bool:
-        """判断词条是否命中消息正文。"""
+    def _get_jargon_match_position(jargon_content: str, lowered_content: str, original_content: str) -> Optional[int]:
+        """返回 jargon 在文本中的首次命中位置，未命中时返回 `None`。"""
        if re.search(r"[\u4e00-\u9fff]", jargon_content):
-            return jargon_content in original_content
+            match_index = original_content.lower().find(jargon_content.lower())
+            return match_index if match_index >= 0 else None

        pattern = rf"\b{re.escape(jargon_content.lower())}\b"
-        return re.search(pattern, lowered_content) is not None
+        match = re.search(pattern, lowered_content)
+        if match is None:
+            return None
+        return match.start()

    def _start_cycle(self) -> CycleDetail:
        """开始一轮 Maisaka 思考循环。"""
@@ -559,7 +583,6 @@ class MaisakaReasoningEngine:
                chat_history=self._runtime._chat_history,
                reply_message=target_message,
                reply_reason=latest_thought,
-                unknown_words=unknown_words,
            )
        except Exception:
            logger.exception(
@@ -587,7 +610,6 @@ class MaisakaReasoningEngine:
                unknown_words=unknown_words,
                log_reply=False,
                expression_habits=reply_context.expression_habits,
-                jargon_explanation=reply_context.jargon_explanation,
                selected_expression_ids=reply_context.selected_expression_ids,
            )
        except Exception:
@@ -645,6 +667,25 @@ class MaisakaReasoningEngine:
        if not sent:
            return False

+        target_user_info = target_message.message_info.user_info
+        target_user_name = (
+            target_user_info.user_cardname
+            or target_user_info.user_nickname
+            or target_user_info.user_id
+        )
+        if self._runtime.chat_stream is not None:
+            await database_api.store_tool_info(
+                chat_stream=self._runtime.chat_stream,
+                display_prompt=f"你对{target_user_name}进行了回复：{reply_text}",
+                tool_data={
+                    "msg_id": target_message_id,
+                    "quote": quote_reply,
+                    "reply_text": reply_text,
+                },
+                tool_name="reply",
+                tool_reasoning=latest_thought,
+            )
+
        bot_name = global_config.bot.nickname.strip() or "MaiSaka"
        self._runtime._chat_history.append(
            build_message(
--- a/src/maisaka/reply_context_builder.py
+++ b/src/maisaka/reply_context_builder.py
@@ -13,9 +13,8 @@ from src.common.database.database import get_db_session
 from src.common.database.database_model import Expression, Jargon
 from src.common.logger import get_logger
 from src.config.config import global_config
-from src.learners.jargon_explainer import search_jargon

-from .message_adapter import get_message_text, parse_speaker_content
+from .message_adapter import get_message_role, get_message_source, get_message_text, parse_speaker_content

 logger = get_logger("maisaka_reply_context")

@@ -40,6 +39,7 @@ class _ExpressionRecord:
 class _JargonRecord:
    jargon_id: Optional[int]
    content: str
+    count: int
    meaning: str
    session_id_dict: str
    is_global: bool
@@ -56,7 +56,6 @@ class MaisakaReplyContextBuilder:
        chat_history: List[SessionMessage],
        reply_message: Optional[SessionMessage],
        reply_reason: str,
-        unknown_words: Optional[List[str]] = None,
    ) -> ReplyContextBuildResult:
        """构建 reply 前置上下文。"""
        expression_habits, selected_expression_ids = self._build_expression_habits(
@@ -67,7 +66,6 @@ class MaisakaReplyContextBuilder:
        jargon_explanation = self._build_jargon_explanation(
            chat_history=chat_history,
            reply_message=reply_message,
-            unknown_words=unknown_words,
        )
        return ReplyContextBuildResult(
            expression_habits=expression_habits,
@@ -129,56 +127,13 @@ class MaisakaReplyContextBuilder:
        self,
        chat_history: List[SessionMessage],
        reply_message: Optional[SessionMessage],
-        unknown_words: Optional[List[str]],
    ) -> str:
        """查询并格式化黑话解释。"""
        if not global_config.expression.enable_jargon_explanation:
            return ""

-        if global_config.expression.jargon_mode == "planner":
-            return self._build_planner_jargon_explanation(unknown_words or [])
-
        return self._build_context_jargon_explanation(chat_history, reply_message)

-    def _build_planner_jargon_explanation(self, unknown_words: List[str]) -> str:
-        """基于 planner 传入的 unknown_words 构建黑话解释。"""
-        normalized_words: List[str] = []
-        seen_words: set[str] = set()
-        for raw_word in unknown_words:
-            word = str(raw_word or "").strip()
-            if not word:
-                continue
-            lowered = word.lower()
-            if lowered in seen_words:
-                continue
-            seen_words.add(lowered)
-            normalized_words.append(word)
-
-        if not normalized_words:
-            return ""
-
-        lines: List[str] = []
-        seen_entries: set[str] = set()
-        for word in normalized_words:
-            matches = search_jargon(word, chat_id=self._session_id, limit=3, fuzzy=False)
-            if not matches:
-                matches = search_jargon(word, chat_id=self._session_id, limit=3, fuzzy=True)
-            for match in matches:
-                content = str(match.get("content") or "").strip()
-                meaning = str(match.get("meaning") or "").strip()
-                if not content or not meaning:
-                    continue
-                entry_key = f"{content}\n{meaning}"
-                if entry_key in seen_entries:
-                    continue
-                seen_entries.add(entry_key)
-                lines.append(f"- {content}: {meaning}")
-
-        if not lines:
-            return ""
-
-        return "【黑话解释】\n" + "\n".join(lines[:8])
-
    def _build_context_jargon_explanation(
        self,
        chat_history: List[SessionMessage],
@@ -190,22 +145,25 @@ class MaisakaReplyContextBuilder:
            return ""

        jargon_records = self._load_jargon_records()
-        lines: List[str] = []
+        matched_records: List[tuple[int, int, int, _JargonRecord]] = []
        seen_contents: set[str] = set()
        for jargon in jargon_records:
            if not jargon.content or not jargon.meaning:
                continue
-            if jargon.content in seen_contents:
+            normalized_content = jargon.content.lower()
+            if normalized_content in seen_contents:
                continue
            if not self._is_visible_jargon(jargon):
                continue
-            if not self._is_jargon_in_corpus(jargon.content, corpus):
+            match_position = self._get_jargon_match_position(jargon.content, corpus)
+            if match_position is None:
                continue

-            seen_contents.add(jargon.content)
-            lines.append(f"- {jargon.content}: {jargon.meaning}")
-            if len(lines) >= 8:
-                break
+            seen_contents.add(normalized_content)
+            matched_records.append((match_position, -len(jargon.content), -jargon.count, jargon))
+
+        matched_records.sort()
+        lines = [f"- {jargon.content}: {jargon.meaning}" for _, _, _, jargon in matched_records[:8]]

        if not lines:
            return ""
@@ -219,13 +177,14 @@ class MaisakaReplyContextBuilder:
    def _load_jargon_records(self) -> List[_JargonRecord]:
        """在 session 内提取黑话的静态数据，避免 detached ORM 对象。"""
        with get_db_session(auto_commit=False) as session:
-            query = select(Jargon).where(Jargon.meaning != "")  # type: ignore[attr-defined]
-            query = query.order_by(Jargon.count.desc()).limit(200)  # type: ignore[attr-defined]
+            query = select(Jargon).where(Jargon.is_jargon.is_(True), Jargon.meaning != "")  # type: ignore[attr-defined]
+            query = query.order_by(Jargon.count.desc())  # type: ignore[attr-defined]
            jargons = session.exec(query).all()
            return [
                _JargonRecord(
                    jargon_id=jargon.id,
                    content=(jargon.content or "").strip(),
+                    count=int(jargon.count or 0),
                    meaning=(jargon.meaning or "").strip(),
                    session_id_dict=jargon.session_id_dict or "{}",
                    is_global=bool(jargon.is_global),
@@ -238,20 +197,26 @@ class MaisakaReplyContextBuilder:
        chat_history: List[SessionMessage],
        reply_message: Optional[SessionMessage],
    ) -> str:
-        """将最近上下文拼成待匹配文本。"""
+        """将当前聊天记录内所有用户消息拼成待匹配文本。"""
        parts: List[str] = []
-        for message in chat_history[-20:]:
+        for message in chat_history:
+            if get_message_role(message) != "user":
+                continue
+            if get_message_source(message) != "user":
+                continue
            text = get_message_text(message).strip()
            if not text:
                continue
            _, body = parse_speaker_content(text)
            parts.append(body.strip() or text)

-        if reply_message is not None:
+        if reply_message is not None and get_message_source(reply_message) == "user":
            reply_text = get_message_text(reply_message).strip()
            if reply_text:
                _, body = parse_speaker_content(reply_text)
-                parts.append(body.strip() or reply_text)
+                normalized_reply_text = body.strip() or reply_text
+                if normalized_reply_text not in parts:
+                    parts.append(normalized_reply_text)

        return "\n".join(parts)

@@ -268,10 +233,16 @@ class MaisakaReplyContextBuilder:
        return self._session_id in session_id_dict

    @staticmethod
-    def _is_jargon_in_corpus(content: str, corpus: str) -> bool:
-        """判断黑话词条是否出现在上下文中。"""
+    def _get_jargon_match_position(content: str, corpus: str) -> Optional[int]:
+        """返回 jargon 在上下文中的首次命中位置，未命中时返回 `None`。"""
        if re.search(r"[\u4e00-\u9fff]", content):
-            return re.search(re.escape(content), corpus, flags=re.IGNORECASE) is not None
+            match = re.search(re.escape(content), corpus, flags=re.IGNORECASE)
+            if match is None:
+                return None
+            return match.start()

        pattern = rf"\b{re.escape(content)}\b"
-        return re.search(pattern, corpus, flags=re.IGNORECASE) is not None
+        match = re.search(pattern, corpus, flags=re.IGNORECASE)
+        if match is None:
+            return None
+        return match.start()
--- a/src/memory_system/memory_retrieval.py
+++ b/src/memory_system/memory_retrieval.py
@@ -271,7 +271,7 @@ async def _react_agent_solve_question(
        message_factory_fn: Callable[..., List[Message]] = _build_messages  # pyright: ignore[reportGeneralTypeIssues]
        generation_result = await llm_api.generate(
            llm_api.LLMServiceRequest(
-                task_name="tool_use",
+                task_name="utils",
                request_type="memory.react",
                message_factory=message_factory_fn,  # type: ignore[arg-type]
                tool_options=tool_definitions,
@@ -681,7 +681,7 @@ async def _react_agent_solve_question(

        evaluation_result = await llm_api.generate(
            llm_api.LLMServiceRequest(
-                task_name="tool_use",
+                task_name="utils",
                request_type="memory.react.final",
                prompt=evaluation_prompt,
                tool_options=[],
--- a/src/person_info/person_info.py
+++ b/src/person_info/person_info.py
@@ -24,7 +24,7 @@ from src.services.llm_service import LLMServiceClient
 logger = get_logger("person_info")

 relation_selection_model = LLMServiceClient(
-    task_name="tool_use", request_type="relation_selection"
+    task_name="utils", request_type="relation_selection"
 )


--- a/src/services/database_service.py
+++ b/src/services/database_service.py
@@ -11,7 +11,7 @@ from sqlmodel import SQLModel

 from src.chat.message_receive.chat_manager import BotChatSession
 from src.common.database.database import get_db_session
-from src.common.database.database_model import ActionRecord
+from src.common.database.database_model import ToolRecord
 from src.common.logger import get_logger

 logger = get_logger("database_service")
@@ -157,6 +157,39 @@ async def db_count(model_class: type[SQLModel], filters: Optional[dict[str, Any]
        return 0


+async def store_tool_info(
+    chat_stream: BotChatSession,
+    builtin_prompt: Optional[str] = None,
+    display_prompt: str = "",
+    tool_id: str = "",
+    tool_data: Optional[dict[str, Any]] = None,
+    tool_name: str = "",
+    tool_reasoning: str = "",
+) -> Optional[dict[str, Any]]:
+    try:
+        record_data = {
+            "tool_id": tool_id or str(int(time.time() * 1000000)),
+            "timestamp": datetime.now(),
+            "session_id": chat_stream.session_id,
+            "tool_name": tool_name,
+            "tool_data": json.dumps(tool_data or {}, ensure_ascii=False),
+            "tool_reasoning": tool_reasoning,
+            "tool_builtin_prompt": builtin_prompt,
+            "tool_display_prompt": display_prompt,
+        }
+
+        saved_record = await db_save(ToolRecord, data=record_data, key_field="tool_id", key_value=record_data["tool_id"])
+        if saved_record:
+            logger.debug(f"[DatabaseService] 成功存储工具信息: {tool_name} (ID: {record_data['tool_id']})")
+        else:
+            logger.error(f"[DatabaseService] 存储工具信息失败: {tool_name}")
+        return saved_record
+    except Exception as e:
+        logger.error(f"[DatabaseService] 存储工具信息时发生错误: {e}")
+        traceback.print_exc()
+        return None
+
+
 async def store_action_info(
    chat_stream: BotChatSession,
    builtin_prompt: Optional[str] = None,
@@ -166,27 +199,13 @@ async def store_action_info(
    action_name: str = "",
    action_reasoning: str = "",
 ) -> Optional[dict[str, Any]]:
-    try:
-        record_data = {
-            "action_id": thinking_id or str(int(time.time() * 1000000)),
-            "timestamp": datetime.now(),
-            "session_id": chat_stream.session_id,
-            "action_name": action_name,
-            "action_data": json.dumps(action_data or {}, ensure_ascii=False),
-            "action_reasoning": action_reasoning,
-            "action_builtin_prompt": builtin_prompt,
-            "action_display_prompt": display_prompt,
-        }
-
-        saved_record = await db_save(
-            ActionRecord, data=record_data, key_field="action_id", key_value=record_data["action_id"]
-        )
-        if saved_record:
-            logger.debug(f"[DatabaseService] 成功存储动作信息: {action_name} (ID: {record_data['action_id']})")
-        else:
-            logger.error(f"[DatabaseService] 存储动作信息失败: {action_name}")
-        return saved_record
-    except Exception as e:
-        logger.error(f"[DatabaseService] 存储动作信息时发生错误: {e}")
-        traceback.print_exc()
-        return None
+    """兼容旧接口，内部转发到 ``store_tool_info``。"""
+    return await store_tool_info(
+        chat_stream=chat_stream,
+        builtin_prompt=builtin_prompt,
+        display_prompt=display_prompt,
+        tool_id=thinking_id,
+        tool_data=action_data,
+        tool_name=action_name,
+        tool_reasoning=action_reasoning,
+    )
--- a/src/services/message_service.py
+++ b/src/services/message_service.py
@@ -7,9 +7,9 @@ from typing import List, Optional, Tuple
 from sqlmodel import col, select

 from src.chat.message_receive.message import SessionMessage
-from src.common.data_models.action_record_data_model import MaiActionRecord
+from src.common.data_models.tool_record_data_model import MaiToolRecord
 from src.common.database.database import get_db_session
-from src.common.database.database_model import ActionRecord, Images, ImageType
+from src.common.database.database_model import Images, ImageType, ToolRecord
 from src.common.message_repository import count_messages, find_messages
 from src.common.utils.math_utils import translate_timestamp_to_human_readable
 from src.common.utils.utils_action import ActionUtils
@@ -238,18 +238,18 @@ def get_actions_by_timestamp_with_chat(
    timestamp_start: float,
    timestamp_end: float,
    limit: Optional[int] = None,
-) -> List[MaiActionRecord]:
+) -> List[MaiToolRecord]:
    with get_db_session() as session:
        statement = (
-            select(ActionRecord)
-            .where(col(ActionRecord.session_id) == chat_id)
-            .where(col(ActionRecord.timestamp) >= datetime.fromtimestamp(timestamp_start))
-            .where(col(ActionRecord.timestamp) <= datetime.fromtimestamp(timestamp_end))
-            .order_by(col(ActionRecord.timestamp))
+            select(ToolRecord)
+            .where(col(ToolRecord.session_id) == chat_id)
+            .where(col(ToolRecord.timestamp) >= datetime.fromtimestamp(timestamp_start))
+            .where(col(ToolRecord.timestamp) <= datetime.fromtimestamp(timestamp_end))
+            .order_by(col(ToolRecord.timestamp))
        )
        if limit is not None:
            statement = statement.limit(limit)
-        return [MaiActionRecord.from_db_instance(item) for item in session.exec(statement).all()]
+        return [MaiToolRecord.from_db_instance(item) for item in session.exec(statement).all()]


 def replace_user_references(text: str, platform: str, replace_bot_name: bool = False) -> str: