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aymanz12

MCP-Powered Video RAG

by aymanz12
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Python
Local

🎬 MCP-Powered Video RAG

Ask natural language questions about your videos — powered by Whisper + ChromaDB + Groq + FastMCP. Fully free and runs locally.

Architecture & How It Works

System Architecture

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

Related MCP server: Video Tools MCP

Stack (All Free)

Component

Tool

Transcription

OpenAI Whisper (local)

Embeddings

sentence-transformers (local)

Vector DB

ChromaDB (local)

MCP Framework

FastMCP

LLM for Q&A

Groq (free tier)

⚡ Quick Start

1. Install dependencies

uv sync

2. Get your FREE Groq API key

Go to https://console.groq.com/ → create account → copy API key.

3. Set up environment

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

4. Add the MCP server to Antigravity IDE

Copy mcp_config.json contents into your Antigravity IDE MCP settings. Update GROQ_API_KEY with your actual key.

5. Place videos in the videos/ folder

Supports: .mp4, .mkv, .avi, .mov, .webm, .mp3, .wav

🛠️ Available MCP Tools

Tool

Description

ingest_video(video_path)

Transcribe & index a video file

search_video(query)

Semantic search over transcripts

ask_video(question)

Full RAG Q&A with timestamps

list_videos()

Show all indexed videos

delete_video(video_path)

Remove a video from the index

📖 Example Usage (in Antigravity IDE)

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")

⚙️ Configuration (.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

💡 Tips

  • Use WHISPER_MODEL=tiny for fastest transcription (less accurate)

  • Use WHISPER_MODEL=medium for high accuracy (slower)

  • The first run downloads Whisper and embedding models (~200MB each) — subsequent runs are instant

  • ChromaDB data persists across restarts in ./chroma_db/

Tool Schema Changelog

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