BiliMind-MCP
An MCP server that generates structured notes from Bilibili videos by automatically downloading audio, transcribing with Whisper, and processing through LLM.
README
BiliMind-MCP
功能特点
- 从B站视频生成结构化笔记
- 支持音频自动下载和转录
- 使用 Whisper 进行语音识别
- 使用 LLM 生成结构化笔记
- 提供 MCP 服务接口
环境准备
系统要求
- Python >= 3.10
- FFmpeg（用于音频处理）
1. 安装系统依赖
# Ubuntu/Debian
sudo apt update && sudo apt install ffmpeg -y
# CentOS/RHEL
sudo yum install ffmpeg -y
2. 安装 uv
# 使用 pip 安装 uv
pip install uv
# 或使用 curl 安装
curl -LsSf https://astral.sh/uv/install.sh | sh
3. 创建并激活虚拟环境
# 创建虚拟环境
uv venv
# 激活虚拟环境
source .venv/bin/activate # Linux/macOS
# 或
.venv\Scripts\activate # Windows
4. 安装项目依赖
uv pip install -r requirements.txt
5. 配置环境变量
# 复制环境变量示例文件
cp .env.example .env
# 编辑 .env 文件，填入必要的配置
nano .env
使用方法
启动 MCP 服务器
python demo/bilimind_mcp.py
API 调用示例
from mcp.client import Client
# 连接到 MCP 服务器
client = Client("bili_note_generator")
# 生成笔记
video_url = "https://www.bilibili.com/video/BVxxxxxx"
notes = await client.generate_bilibili_notes(video_url)
环境变量说明
OPENAI_API_KEY: LLM API密钥API_BASE: API基础URLMCP_PORT: MCP服务器端口（默认8001）DEFAULT_OUTPUT_DIR: 下载文件保存目录DEFAULT_MODEL_DIR: 模型文件保存目录WHISPER_MODEL_SIZE: Whisper模型大小（默认tiny）
注意事项
- 首次运行会自动下载 Whisper 模型文件
- 音频文件会在处理完成后自动删除
- 需要确保有足够的磁盘空间存储临时文件和模型文件
- API调用需要有效的 API 密钥
目录结构
BiliMind-MCP/
├── demo/ # 示例代码
├── tests/ # 测试文件
├── .env.example # 环境变量示例
├── requirements.txt # 项目依赖
└── README.md # 项目文档
致谢
本项目受到 BiliNote 项目的启发。BiliNote 是一个优秀的开源 AI 视频笔记助手，支持多平台视频内容的自动笔记生成。
License
MIT License
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