Qiaomu 万物转 NotebookLM：将任意内容源转换为 Google NotebookLM

# 安装包
pip install qiaomu-notebooklm

# 基本用法：转换 URL
from qiaomu_notebooklm import ContentConverter

converter = ContentConverter()

# 转换 YouTube 视频
result = converter.convert(
    source_url="https://youtube.com/watch?v=example",
    output_format="notebooklm"
)
print(f"已将 {result.word_count} 个单词转换为 NotebookLM 格式")

# 高级转换，带预处理选项
result = converter.convert(
    source_url="https://example.com/article",
    output_format="notebooklm",
    preprocess_options={
        "remove_noise": True,
        "preserve_headings": True,
        "extract_quotes": True,
        "max_chunk_size": 4000,
        "language": "en"
    }
)

# 导出为 NotebookLM 兼容格式
converter.export(
    result,
    output_path="./notebooklm_sources/",
    formats=["markdown", "pdf", "text"]
)

# 列出已导出的文件
import os
for f in os.listdir("./notebooklm_sources/"):
    filepath = os.path.join("./notebooklm_sources/", f)
    size = os.path.getsize(filepath)
    print(f"{f}: {size / 1024:.1f} KB")

# 上传到 NotebookLM
notebooklm = converter.connect_notebooklm(
    google_account="your_email@gmail.com"
)

# 创建或选择笔记本
notebook = notebooklm.create_notebook(
    title="机器学习课程笔记",
    description="来自 ML 教程视频的笔记"
)

# 上传来源
notebook.upload_source("./notebooklm_sources/youtube_tutorial.md")
notebook.upload_source("./notebooklm_sources/paper_abstract.pdf")

# 通过 pip 安装
pip install qiaomu-notebooklm

# 验证安装
python -c "import qiaomu_notebooklm; print(qiaomu_notebooklm.__version__)"

# 安装所有可选依赖
pip install qiaomu-notebooklm[all]

# 克隆仓库
git clone https://github.com/joeseesun/qiaomu-anything-to-notebooklm.git
cd qiaomu-anything-to-notebooklm

# 以开发模式安装
pip install -e .

# 安装开发依赖
pip install -r requirements-dev.txt

# 在 Claude Code 配置目录中
mkdir -p ~/.claude/skills

# 复制 Qiaomu 技能
cp -r qiaomu-notebooklm/claude-code-skill/ ~/.claude/skills/

# 重启 Claude Code
claude --reload-skills

# 批处理 URL 列表
urls = [
    "https://youtube.com/watch?v=video1",
    "https://youtube.com/watch?v=video2",
    "https://arxiv.org/abs/2023.12345",
    "https://example.com/blog/post",
    "https://example.com/paper.pdf"
]

converter = ContentConverter()
results = converter.batch_convert(
    urls=urls,
    output_dir="./notebooklm_batch/",
    concurrent_workers=4
)

for url, result in results.items():
    status = "成功" if result.success else "失败"
    print(f"[{status}] {url}: {result.word_count} 个单词已转换")

import schedule
import time
from datetime import datetime

def daily_content_sync():
    """每日检查新内容并转换为 NotebookLM。"""
    converter = ContentConverter()
    
    # 监控 YouTube 频道
    youtube_results = converter.convert_youtube_channel(
        channel_id="UCexample",
        since_last_run=True  # 仅新视频
    )
    
    # 监控 RSS 订阅
    rss_results = converter.convert_rss_feed(
        feed_url="https://example.com/rss",
        since_last_run=True
    )
    
    # 上传到 NotebookLM
    notebooklm = converter.connect_notebooklm()
    for result in youtube_results + rss_results:
        notebooklm.upload_source(result.file_path)
        print(f"已上传: {result.file_path}")

# 每天 6 点定时
schedule.every().day.at("06:00").do(daily_content_sync)

while True:
    schedule.run_pending()
    time.sleep(60)

# 绕过付费墙提取文章内容
result = converter.convert(
    source_url="https://premium-article.example.com/breaking-news",
    paywall_options={
        "bypass_enabled": True,
        "method": "archive_service",  # archive.org、archive.is 等
        "fallback_to_text_only": True
    }
)

# 工具尝试多种策略：
# 1. 直接提取
# 2. 代理服务查找
# 3. 纯文本回退
# 4. 基于代理的访问（通过 WebShare）

from qiaomu_notebooklm.plugins import BaseExtractor

@BaseExtractor.register("my_custom_source")
class MyCustomExtractor(BaseExtractor):
    def extract(self, url: str) -> dict:
        """从自定义来源提取内容。"""
        # 你的自定义提取逻辑
        content = self.fetch_content(url)
        cleaned = self.clean_content(content)
        
        return {
            "text": cleaned,
            "title": "自定义来源标题",
            "source_url": url,
            "word_count": len(cleaned.split()),
            "metadata": {"type": "custom", "author": "unknown"}
        }

# 使用自定义提取器
converter = ContentConverter()
result = converter.convert(
    source_url="https://custom-source.example.com/article",
    extractor="my_custom_source"
)

notebooklm = converter.connect_notebooklm()

# 为每个项目创建笔记本
projects = {
    "machine_learning": [
        "https://youtube.com/watch?v=ml_tutorial_1",
        "https://arxiv.org/abs/2301.12345"
    ],
    "product_design": [
        "https://youtube.com/watch?v=design_talk",
        "https://example.com/blog/design_principles"
    ]
}

for notebook_name, urls in projects.items():
    notebook = notebooklm.create_notebook(
        title=f"{notebook_name.replace('_', ' ').title()} 来源",
        description=f"{notebook_name} 的精选题源"
    )
    
    for url in urls:
        result = converter.convert(url, output_format="notebooklm")
        notebook.upload_source(result.file_path)
        print(f"已添加到 {notebook_name}: {url}")

from qiaomu_notebooklm import KnowledgeExtractor

extractor = KnowledgeExtractor()

# 从转换后的内容中提取实体和关系
knowledge_graph = extractor.build_graph(
    sources=["./notebooklm_sources/"],
    output_format="neo4j"
)

# 保存知识图谱
knowledge_graph.save("./knowledge_graph.json")

# 查询图谱
entities = knowledge_graph.get_entities_by_type("Person")
print(f"找到 {len(entities)} 个实体: {[e.name for e in entities]}")

pip install qiaomu-notebooklm