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MCP-Powered Video RAG

by aymanz12
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🎬 MCP-Powered Video RAG

用自然语言向你的视频提问——由 Whisper + ChromaDB + Groq + FastMCP 驱动。完全免费，且本地运行。

架构与工作原理

系统架构

Your Video → Whisper (transcription) → ChromaDB (vector store)
                                              ↓
                              Antigravity IDE ← MCP Server ← Your Question
                                              ↓
                                         Groq LLM (free) → Answer + Timestamps

技术栈（全部免费）

组件

工具

转录

OpenAI Whisper（本地）

嵌入模型

sentence-transformers（本地）

向量数据库

ChromaDB（本地）

MCP 框架

FastMCP

用于问答的 LLM

Groq（免费层）

⚡ 快速开始

1. 安装依赖

uv sync

2. 获取你的免费 Groq API 密钥

前往 https://console.groq.com/ → 创建账户 → 复制 API 密钥。

3. 设置环境

copy .env.example .env
# Edit .env and paste your GROQ_API_KEY

4. 将 MCP 服务器添加到 Antigravity IDE

将 mcp_config.json 的内容复制到你的 Antigravity IDE MCP 设置中。 将 GROQ_API_KEY 更新为你的实际密钥。

5. 将视频放入 videos/ 文件夹

支持格式：.mp4, .mkv, .avi, .mov, .webm, .mp3, .wav

🛠️ 可用的 MCP 工具

工具

描述

ingest_video(video_path)

转录并索引视频文件

search_video(query)

对转录文本进行语义搜索

ask_video(question)

带时间戳的完整 RAG 问答

list_videos()

显示所有已索引的视频

delete_video(video_path)

从索引中移除视频

📖 示例用法（在 Antigravity IDE 中）

ingest_video("videos/my_lecture.mp4")

ask_video("What are the main topics discussed?")

search_video("neural networks explained", n_results=3)

ask_video("What did the speaker say about backpropagation?", video_name="my_lecture.mp4")

⚙️ 配置（.env）

GROQ_API_KEY=your_key_here        # Required — get free at console.groq.com
WHISPER_MODEL=base                # tiny | base | small | medium | large
EMBEDDING_MODEL=all-MiniLM-L6-v2  # local embedding model
GROQ_MODEL=llama-3.1-8b-instant  # Groq model for Q&A
CHROMA_DB_PATH=./chroma_db        # where to persist the vector DB

💡 提示

  • 使用 WHISPER_MODEL=tiny 可获得最快的转录速度（准确度较低）

  • 使用 WHISPER_MODEL=medium 可获得高准确度（速度较慢）

  • 首次运行会下载 Whisper 和嵌入模型（每个约 200MB）——后续运行即时完成

  • ChromaDB 数据在重启后仍会保留在 ./chroma_db/ 中

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