mirror of
https://github.com/jie65535/JChatGPT.git
synced 2026-09-15 02:56:10 +08:00
chore: ignore local research files
Keep design documents and experiment scripts local while removing their previously tracked files from version control.
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@@ -137,3 +137,5 @@ __pycache__/
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*.py[cod]
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/nul
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/.playwright-cli/
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/docs/
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/scripts/
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@@ -1,65 +0,0 @@
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# 好感度与主观印象系统
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## 功能定位
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好感度系统保存 Bot 对群友的主观认识,与证据驱动的长期用户画像互补:
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- `FavorabilityInfo` 保存好感度、代号、标签、印象和最近调整原因;
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- `user-profile.sqlite` 保存从历史聊天归纳出的长期画像;
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- 两套数据独立持久化,在普通对话上下文中按用户合并渲染;
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- 自动历史画像不会修改主观好感度。
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## 数据结构
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```kotlin
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@Serializable
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data class FavorabilityInfo(
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val userId: Long,
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val value: Int = 0,
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val reasons: List<String> = emptyList(),
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val impression: String = "",
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val name: String = "",
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val tags: List<String> = emptyList(),
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)
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```
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字段约束:
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- `value`:限制在 -100 到 100;
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- `reasons`:只在好感度发生变化且提供原因时追加,保留最近 10 条;
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- `impression`:最多 200 字符;
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- `name`:最多 20 字符;
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- `tags`:最多 5 项,每项最多 20 字符。
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数据继续保存在 Mirai `data.yml` 中,已有字段保持兼容。
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## AI 更新工具
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`adjustUserFavorability` 是唯一的好感度和主观印象维护入口。模型可以在一次调用中:
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- 通过 `change` 增减好感度;
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- 覆盖 `impression` 或 `name`;
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- 通过 `tags_add`、`tags_remove` 调整标签。
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只更新印象或标签时,`change` 默认为 0。系统不会因为好感度为 0 而删除用户记录。
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## 回复门控
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启用 `enableFavorabilitySystem` 后,负好感度会降低对应用户触发 Bot 回复的概率:
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```text
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忽略概率 = abs(value) / 100
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```
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好感度不再随时间自动向 0 偏移,也没有管理员手动修改或清空好感度的命令。它只会在模型明确调用工具时变化。
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## 上下文注入
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普通聊天会把当前相关用户的主观认识和长期画像合并为紧凑文本:
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- 群聊优先选择触发者和最近发言者;
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- 私聊只注入当前联系人;
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- 仅有数值、没有代号/标签/印象的空记录不会制造提示词噪声;
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- 好感度为 0 但仍有代号、标签或印象的记录继续正常注入。
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长期画像的生成、证据校验和自动维护流程参见 `ProfileSystemDesign-v3.md`。
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@@ -1,72 +0,0 @@
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#!/usr/bin/env python3
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"""
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恢复 data.yml:把 tokenUsageDailyRecords 抽出成 token_usage.json,
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顺手清理 tokenUsageRecords,把 data.yml 重写成合法的、yamlkt 能读回的 JSON。
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用法(在 data.yml 所在目录运行):
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python3 recover_data_yml.py /path/to/top.jie65535.mirai.JChatGPT/
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会做:
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1. 备份原 data.yml -> data.yml.bak-<timestamp>
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2. 读 data.yml(按 JSON 解析,目前文件就是 JSON-flow YAML)
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3. 把 tokenUsageDailyRecords 写到 token_usage.json
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4. 删除 tokenUsageRecords 和 tokenUsageDailyRecords 字段
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5. 重写 data.yml(保留 contactMemory / userFavorability 等)
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"""
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import json
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import os
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import sys
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import time
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def main(target_dir: str) -> int:
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data_path = os.path.join(target_dir, "data.yml")
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if not os.path.exists(data_path):
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print(f"NOT FOUND: {data_path}", file=sys.stderr)
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return 1
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with open(data_path, "r", encoding="utf-8") as f:
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text = f.read()
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try:
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data = json.loads(text)
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except json.JSONDecodeError as e:
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print(f"data.yml 不是合法 JSON:{e}", file=sys.stderr)
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print("如果文件其实是 block-style YAML,请先用 yq/python yaml 转换", file=sys.stderr)
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return 2
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if not isinstance(data, dict):
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print(f"顶层不是 map,是 {type(data).__name__}", file=sys.stderr)
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return 3
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ts = int(time.time())
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backup_path = os.path.join(target_dir, f"data.yml.bak-{ts}")
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with open(backup_path, "w", encoding="utf-8") as f:
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f.write(text)
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print(f"已备份 -> {backup_path}")
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daily_records = data.pop("tokenUsageDailyRecords", [])
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raw_records = data.pop("tokenUsageRecords", [])
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print(f"提取 tokenUsageDailyRecords: {len(daily_records)} 条")
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print(f"丢弃 tokenUsageRecords (legacy): {len(raw_records)} 条")
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token_path = os.path.join(target_dir, "token_usage.json")
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if os.path.exists(token_path):
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token_backup = os.path.join(target_dir, f"token_usage.json.bak-{ts}")
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os.rename(token_path, token_backup)
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print(f"已备份现有 token_usage.json -> {token_backup}")
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with open(token_path, "w", encoding="utf-8") as f:
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json.dump(daily_records, f, ensure_ascii=False, indent=2)
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print(f"写入 -> {token_path} ({len(daily_records)} 条)")
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with open(data_path, "w", encoding="utf-8") as f:
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json.dump(data, f, ensure_ascii=False, indent=4)
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print(f"重写 -> {data_path}(剩余字段: {list(data.keys())})")
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return 0
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if __name__ == "__main__":
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if len(sys.argv) != 2:
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print(__doc__, file=sys.stderr)
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sys.exit(1)
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sys.exit(main(sys.argv[1]))
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