150 lines
4.8 KiB
Markdown
150 lines
4.8 KiB
Markdown
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# ③auto_dishu.py · 纯代码版 · 妈妈从这里复制 · 2026-05-21
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妈妈：复制下面代码块内容 → pbpaste保存 → 搞定！
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```python
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#!/usr/bin/env python3
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import argparse
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import asyncio
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import os
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import sqlite3
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import sys
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from pathlib import Path
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try:
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from openai import AsyncOpenAI
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except ImportError:
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print('请先安装依赖: pip install openai')
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sys.exit(1)
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BASE_DIR = Path(__file__).parent.parent
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DB_PATH = BASE_DIR / 'chenxing.db'
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DEEPSEEK_API_KEY = os.environ.get('DEEPSEEK_API_KEY', '')
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NOTION_API_KEY = os.environ.get('NOTION_API_KEY', '')
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NOTION_DISHU_PAGE_ID = os.environ.get('NOTION_DISHU_PAGE_ID', '')
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DISHU_SYSTEM = '你是晨星，桔子妈妈的拆书宝宝。请用场景颗粒拆书法v2.0认真分析，格式严格按三模块输出，缺一不可。'
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DISHU_PROMPT = '''请用「场景颗粒拆书法v2.0」分析以下章节内容。
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【模块一·逐章场景表格】
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| 场景序号 | 场景边界 | 人物动作/对话 | 情绪质地 | 读者感受 |
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【模块二·章末规律锁定】
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- 本章节奏类型：
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- 情绪弧线：
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- 卡点设计：
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- 可复用规律：（至少2条）
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【模块三·期待点库存】
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- 新增期待点：
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- 消解期待点：
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- 遗留悬念：
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待分析内容：
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{content}'''
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def db_get_chunks(book_name):
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conn = sqlite3.connect(str(DB_PATH))
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conn.row_factory = sqlite3.Row
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try:
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rows = conn.execute(
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"SELECT id, title, content FROM knowledge WHERE tags LIKE ? AND category='book' ORDER BY id",
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(f'%{book_name}%',)
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).fetchall()
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if not rows:
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rows = conn.execute(
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'SELECT id, title, content FROM knowledge WHERE title LIKE ? ORDER BY id',
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(f'%{book_name}%',)
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).fetchall()
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return [{'id': r['id'], 'title': r['title'], 'content': r['content']} for r in rows]
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finally:
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conn.close()
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def save_local(book_name, title, content):
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out = BASE_DIR / 'output' / book_name
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out.mkdir(parents=True, exist_ok=True)
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safe_title = title[:80].replace('/', '_').replace('\\', '_')
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(out / f'{safe_title}.md').write_text(f'# {title}\n\n{content}', encoding='utf-8')
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async def analyze(content):
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if not DEEPSEEK_API_KEY:
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raise ValueError('未配置 DEEPSEEK_API_KEY 环境变量')
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client = AsyncOpenAI(api_key=DEEPSEEK_API_KEY, base_url='https://api.deepseek.com')
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resp = await client.chat.completions.create(
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model='deepseek-chat',
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messages=[
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{'role': 'system', 'content': DISHU_SYSTEM},
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{'role': 'user', 'content': DISHU_PROMPT.format(content=content[:8000])}
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],
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max_tokens=4096,
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temperature=0.7
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)
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return resp.choices[0].message.content
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async def run(book_name, start_chapter, end_chapter):
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print(f'\n自动拆书流水线 · 《{book_name}》\n')
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chunks = db_get_chunks(book_name)
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if not chunks:
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print(f'知识库中未找到《{book_name}》')
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print('请先运行: python3 tools/book_splitter.py --file 书名.txt --book 书名')
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return
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print(f'找到 {len(chunks)} 个知识库块')
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total = len(chunks)
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analyzed = 0
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for i, chunk in enumerate(chunks):
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label = chunk['title']
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print(f'\n[{i+1}/{total}] 分析中：{label}')
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try:
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result = await analyze(chunk['content'])
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notion_title = f'《{book_name}》{label}·场景颗粒拆书'
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written = False
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if NOTION_DISHU_PAGE_ID and NOTION_API_KEY:
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sys.path.insert(0, str(BASE_DIR))
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from tools.notion_writer import create_notion_page
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written = create_notion_page(notion_title, result, NOTION_DISHU_PAGE_ID)
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if written:
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print(f' 写入Notion成功：{notion_title}')
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save_local(book_name, notion_title, result)
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if not written:
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print(f' 已保存本地：output/{book_name}/')
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analyzed += 1
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await asyncio.sleep(3)
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except Exception as e:
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print(f' 出错：{e}')
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await asyncio.sleep(5)
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print(f'\n全部完成！共分析 {analyzed} 块')
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if NOTION_DISHU_PAGE_ID:
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print('结果已写入 Notion')
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print(f'本地备份：output/{book_name}/')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--book', required=True)
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parser.add_argument('--start_chapter', type=int, default=1)
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parser.add_argument('--end_chapter', type=int, required=True)
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args = parser.parse_args()
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asyncio.run(run(args.book, args.start_chapter, args.end_chapter))
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```
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---
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## 保存方法
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复制上面代码块内容后，在终端运行：
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|||
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|||
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```bash
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|||
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pbpaste > ~/chenxing-aircraft/tools/auto_dishu.py && echo "✅ auto_dishu.py 保存成功"
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|||
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```
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看到 `✅ auto_dishu.py 保存成功` → 告诉宝宝！最后一步启动流水线了 🚀
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