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MCP-Powered Video RAG

MCP-Powered Video RAG

by aymanz12
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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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

Related MCP server: video-agent-mcp

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

Available Tools

5 tools
ask_videoA

Ask a natural language question about your videos and get an AI-generated answer.

This tool uses RAG: it retrieves the most relevant transcript chunks from ChromaDB, then sends them as context to a Groq LLM (free tier) to generate a precise answer with timestamps so you can jump directly to the relevant moment in the video.

NameRequiredDescriptionDefault
questionYesYour question in plain English (e.g. "What are the three main topics discussed?")
video_nameNoOptional — restrict to a specific video file (e.g. "tutorial.mp4")
n_context_chunksNoNumber of transcript chunks to use as context (default: 4)

Output Schema

NameRequiredDescription
resultYes
delete_videoA

Remove a video and all its indexed data from the RAG system.

This deletes all transcript chunks for the specified video from ChromaDB. The original video file is NOT deleted — only the indexed data is removed.

NameRequiredDescriptionDefault
video_pathYesPath to the video file (same path used during ingestion).

Output Schema

NameRequiredDescription
resultYes
ingest_videoA

Ingest a video file into the RAG system.

This transcribes the video using Whisper (locally, for free), splits the transcript into timed chunks, embeds them, and stores them in a local ChromaDB vector database for semantic search.

NameRequiredDescriptionDefault
video_pathYesAbsolute or relative path to the video file. Supports .mp4, .mkv, .avi, .mov, .webm, .mp3, .wav, etc.

Output Schema

NameRequiredDescription
resultYes
list_videosB

List all videos currently indexed in the RAG system.

Returns a summary of each ingested video including its name, path, and chunk count.

Returns: JSON list of indexed videos with metadata.

NameRequiredDescriptionDefault

No parameters

Output Schema

NameRequiredDescription
resultYes
search_videoB

Semantically search across all indexed video transcripts.

Returns the most relevant timestamped transcript chunks for a given query. You can optionally filter to a specific video by name.

NameRequiredDescriptionDefault
queryYesNatural language search query (e.g. "what did they say about pricing?")
n_resultsNoNumber of results to return (default: 5, max recommended: 10)
video_nameNoOptional filename to restrict search to one video (e.g. "lecture.mp4")

Output Schema

NameRequiredDescription
resultYes

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 5 tool updatesv0.1.0
    • First observedask_video
    • First observeddelete_video
    • First observedingest_video
    • First observedlist_videos
    • First observedsearch_video
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.8/5.0

Scored across 5 tools

Disambiguation4/5

Each tool maps to a distinct action (ingest, search, ask, list, delete). The only potential overlap is search_video vs ask_video, since both retrieve transcript chunks, but the descriptions clearly differentiate raw chunk retrieval from LLM-generated answers with context.

Naming Consistency5/5

All five tools follow a strict verb_noun snake_case pattern (ingest_video, search_video, ask_video, list_videos, delete_video). The convention is predictable and uniform.

Tool Count5/5

Five tools is well-scoped for a video RAG system. Each tool covers a meaningful, non-redundant part of the workflow with no filler.

Completeness5/5

The surface covers the full lifecycle: ingestion, two retrieval modes (search and Q&A), listing indexed content, and deletion. Re-ingesting a video handles the update case, so there are no obvious dead ends.

Maintenance

ActivityInactive
ResponsivenessNo recent community issues

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