init: first commit to Gitea mirror, update README with Docker quick start and new repo URL

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

reclassify_api.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+"""
+用 API 重新打标未分类记忆桶，修正 domain/tags/name，移动到正确目录。
+用法: docker exec ombre-brain python3 /app/reclassify_api.py
+"""
+import asyncio
+import os
+import json
+import glob
+import re
+
+from openai import AsyncOpenAI
+import frontmatter
+
+ANALYZE_PROMPT = (
+    "你是一个内容分析器。请分析以下文本，输出结构化的元数据。\n\n"
+    "分析规则：\n"
+    '1. domain（主题域）：选最精确的 1~2 个，只选真正相关的\n'
+    '   日常: ["饮食", "穿搭", "出行", "居家", "购物"]\n'
+    '   人际: ["家庭", "恋爱", "友谊", "社交"]\n'
+    '   成长: ["工作", "学习", "考试", "求职"]\n'
+    '   身心: ["健康", "心理", "睡眠", "运动"]\n'
+    '   兴趣: ["游戏", "影视", "音乐", "阅读", "创作", "手工"]\n'
+    '   数字: ["编程", "AI", "硬件", "网络"]\n'
+    '   事务: ["财务", "计划", "待办"]\n'
+    '   内心: ["情绪", "回忆", "梦境", "自省"]\n'
+    "2. valence（情感效价）：0.0~1.0，0=极度消极 → 0.5=中性 → 1.0=极度积极\n"
+    "3. arousal（情感唤醒度）：0.0~1.0，0=非常平静 → 0.5=普通 → 1.0=非常激动\n"
+    "4. tags（关键词标签）：3~5 个最能概括内容的关键词\n"
+    "5. suggested_name（建议桶名）：10字以内的简短标题\n\n"
+    "输出格式（纯 JSON，无其他内容）：\n"
+    '{\n'
+    '  "domain": ["主题域1", "主题域2"],\n'
+    '  "valence": 0.7,\n'
+    '  "arousal": 0.4,\n'
+    '  "tags": ["标签1", "标签2", "标签3"],\n'
+    '  "suggested_name": "简短标题"\n'
+    '}'
+)
+
+DATA_DIR = "/data/dynamic"
+UNCLASS_DIR = os.path.join(DATA_DIR, "未分类")
+
+
+def sanitize(name):
+    name = re.sub(r'[<>:"/\\|?*\n\r]', '', name).strip()
+    return name[:20] if name else "未命名"
+
+
+async def reclassify():
+    client = AsyncOpenAI(
+        api_key=os.environ.get("OMBRE_API_KEY", ""),
+        base_url="https://api.siliconflow.cn/v1",
+        timeout=60.0,
+    )
+
+    files = sorted(glob.glob(os.path.join(UNCLASS_DIR, "*.md")))
+    print(f"找到 {len(files)} 个未分类文件\n")
+
+    for fpath in files:
+        basename = os.path.basename(fpath)
+        post = frontmatter.load(fpath)
+        content = post.content.strip()
+        name = post.metadata.get("name", "")
+        full_text = f"{name}\n{content}" if name else content
+
+        try:
+            resp = await client.chat.completions.create(
+                model="deepseek-ai/DeepSeek-V3",
+                messages=[
+                    {"role": "system", "content": ANALYZE_PROMPT},
+                    {"role": "user", "content": full_text[:2000]},
+                ],
+                max_tokens=256,
+                temperature=0.1,
+            )
+            raw = resp.choices[0].message.content.strip()
+            if raw.startswith("```"):
+                raw = raw.split("\n", 1)[-1].rsplit("```", 1)[0]
+            result = json.loads(raw)
+        except Exception as e:
+            print(f"  X API失败 {basename}: {e}")
+            continue
+
+        new_domain = result.get("domain", ["未分类"])[:3]
+        new_tags = result.get("tags", [])[:5]
+        new_name = sanitize(result.get("suggested_name", "") or name)
+        new_valence = max(0.0, min(1.0, float(result.get("valence", 0.5))))
+        new_arousal = max(0.0, min(1.0, float(result.get("arousal", 0.3))))
+
+        post.metadata["domain"] = new_domain
+        post.metadata["tags"] = new_tags
+        post.metadata["valence"] = new_valence
+        post.metadata["arousal"] = new_arousal
+        if new_name:
+            post.metadata["name"] = new_name
+
+        # 写回文件
+        with open(fpath, "w", encoding="utf-8") as f:
+            f.write(frontmatter.dumps(post))
+
+        # 移动到正确目录
+        primary = sanitize(new_domain[0]) if new_domain else "未分类"
+        target_dir = os.path.join(DATA_DIR, primary)
+        os.makedirs(target_dir, exist_ok=True)
+
+        bid = post.metadata.get("id", "")
+        new_filename = f"{new_name}_{bid}.md" if new_name and new_name != bid else basename
+        dest = os.path.join(target_dir, new_filename)
+
+        if dest != fpath:
+            os.rename(fpath, dest)
+
+        print(f"  OK {basename}")
+        print(f"     -> {primary}/{new_filename}")
+        print(f"     domain={new_domain} tags={new_tags} V={new_valence} A={new_arousal}")
+        print()
+
+
+if __name__ == "__main__":
+    asyncio.run(reclassify())