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