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Video RAG MCP Server

by FiloHany
GitHub
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  • Apple
  • Linux
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Video RAG (Retrieval-Augmented Generation)

A powerful video retrieval and analysis system that uses the Ragie API to process, index, and query video content with natural language. This project enables semantic search through video content, extracting relevant video chunks based on text queries.

šŸŽÆ MCP-powered video-RAG using Ragie

This project demonstrates how to build a video-based Retrieval Augmented Generation (RAG) system powered by the Model Context Protocol (MCP). It uses Ragie's video ingestion and retrieval capabilities to enable semantic search and Q&A over video content and integrate them as MCP tools via Cursor IDE.

Tech Stack

  • Ragie for video ingestion + retrieval (video-RAG)

  • Cursor as the MCP host

  • Model Context Protocol (MCP) for AI assistant integration

šŸŽÆ Features

  • Video Processing: Upload and process video files with audio-video analysis

  • Semantic Search: Query video content using natural language

  • Video Chunking: Extract specific video segments based on search results

  • MCP Integration: Model Context Protocol (MCP) server for AI assistant integration

  • Jupyter Notebook Support: Interactive development and experimentation

  • Automatic Indexing: Clear and rebuild video indexes as needed

šŸš€ Quick Start

Prerequisites

  • Python 3.12 or higher

  • Ragie API key

  • Video files to process

  • Cursor IDE (for MCP integration)

Setup and Installation

1. Install uv

First, let's install uv and set up our Python project and environment:

MacOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Clone and Setup Project

# Clone the repository git clone https://github.com/FiloHany/Video_RAG_MCP.git cd video_rag # Create virtual environment and activate it uv venv source .venv/bin/activate # MacOS/Linux # OR .venv\Scripts\activate # Windows # Install dependencies uv sync

3. Configure Environment Variables

Create a .env file in the project root:

RAGIE_API_KEY=your_ragie_api_key_here

4. Add Your Video Files

Place your video files in the video/ directory.

MCP Server Setup with Cursor IDE

1. Configure MCP Server in Cursor

  1. Go to Cursor settings

  2. Select MCP Tools

  3. Add new global MCP server

  4. In the JSON configuration, add:

{ "mcpServers": { "ragie": { "command": "uv", "args": [ "--directory", "/absolute/path/to/project_root", "run", "server.py" ], "env": { "RAGIE_API_KEY": "YOUR_RAGIE_API_KEY" } } } }

Note: Replace /absolute/path/to/project_root with the actual absolute path to your project directory.

2. Connect MCP Server

  1. In Cursor MCP settings, make sure to toggle the button to connect the server to the host

  2. You should now see the MCP server listed in the MCP settings

3. Available MCP Tools

Your custom MCP server provides 3 tools:

  • ingest_data_tool: Ingests the video data to the Ragie index

  • retrieve_data_tool: Retrieves relevant data from the video based on user query

  • show_video_tool: Creates a short video chunk from the specified segment from the original video

You can now ingest your videos, retrieve relevant data and query it all using the Cursor Agent. The agent can even create the desired chunks from your video just with a single query!

šŸ“– Usage

Basic Usage

Run the main script to process videos and perform queries:

python main.py

This will:

  1. Clear the existing index

  2. Ingest all videos from the video/ directory

  3. Perform a sample query

Interactive Development

Use the Jupyter notebook for interactive development:

jupyter notebook video_rag.ipynb

MCP Server

Start the MCP server for AI assistant integration:

python server.py

šŸ”§ API Reference

Core Functions

clear_index()

Removes all documents from the Ragie index.

ingest_data(directory: str)

Processes and uploads all video files from the specified directory to the Ragie index.

Parameters:

  • directory (str): Path to the directory containing video files

retrieve_data(query: str)

Performs semantic search on the indexed video content.

Parameters:

  • query (str): Natural language query to search for in video content

Returns:

  • List of dictionaries containing:

    • text: The retrieved text content

    • document_name: Name of the source video file

    • start_time: Start timestamp of the video segment

    • end_time: End timestamp of the video segment

chunk_video(document_name: str, start_time: float, end_time: float, directory: str = "videos")

Extracts a specific video segment and saves it as a new file.

Parameters:

  • document_name (str): Name of the source video file

  • start_time (float): Start time in seconds

  • end_time (float): End time in seconds

  • directory (str): Directory containing the source video (default: "videos")

Returns:

  • Path to the created video chunk file

MCP Tools

The project includes an MCP server with the following tools:

ingest_data_tool(directory: str)

MCP wrapper for the ingest_data function.

retrieve_data_tool(query: str)

MCP wrapper for the retrieve_data function.

show_video_tool(document_name: str, start_time: float, end_time: float)

MCP wrapper for the chunk_video function.

šŸ“ Examples

Example 1: Basic Video Processing and Query

from main import clear_index, ingest_data, retrieve_data # Clear existing index clear_index() # Ingest videos from directory ingest_data("video") # Query the video content results = retrieve_data("What is the main topic of the video?") print(results)

Example 2: Extract Video Chunks

from main import retrieve_data, chunk_video # Get search results results = retrieve_data("Show me the goal scoring moments") # Extract video chunks for each result for result in results: if result['start_time'] and result['end_time']: chunk_path = chunk_video( result['document_name'], result['start_time'], result['end_time'] ) print(f"Created chunk: {chunk_path}")

Example 3: Jupyter Notebook Workflow

# Load environment and initialize Ragie import os from dotenv import load_dotenv from ragie import Ragie load_dotenv() ragie = Ragie(auth=os.getenv('RAGIE_API_KEY')) # Upload a video file_path = "video/messi-goals.mp4" result = ragie.documents.create(request={ "file": { "file_name": "messi-goals.mp4", "content": open(file_path, "rb"), }, "mode": { "video": "audio_video" } }) # Query the video response = ragie.retrievals.retrieve(request={ "query": "Give detailed description of the video with timestamp of the events" }) # Process results for chunk in response.scored_chunks: print(f"Time: {chunk.metadata.get('start_time')} - {chunk.metadata.get('end_time')}") print(f"Content: {chunk.text}") print("-" * 50)

šŸ—ļø Project Structure

video_rag/ ā”œā”€ā”€ main.py # Core functionality and main script ā”œā”€ā”€ server.py # MCP server implementation ā”œā”€ā”€ video_rag.ipynb # Jupyter notebook for development ā”œā”€ā”€ pyproject.toml # Project configuration and dependencies ā”œā”€ā”€ README.md # This file ā”œā”€ā”€ video/ # Directory for video files ā”‚ ā””ā”€ā”€ messi-goals.mp4 # Example video file ā””ā”€ā”€ video_chunks/ # Output directory for video chunks (created automatically)

šŸ”‘ Environment Variables

Variable

Description

Required

RAGIE_API_KEY

Your Ragie API authentication key

Yes

šŸ“¦ Dependencies

  • ragie: Video processing and retrieval API

  • moviepy: Video editing and manipulation

  • python-dotenv: Environment variable management

  • mcp: Model Context Protocol implementation

  • ipykernel: Jupyter notebook support

šŸ¤ Contributing

  1. Fork the repository

  2. Create a feature branch (git checkout -b feature/amazing-feature)

  3. Commit your changes (git commit -m 'Add some amazing feature')

  4. Push to the branch (git push origin feature/amazing-feature)

  5. Open a Pull Request

šŸ“„ License

This project is licensed under the MIT License - see the LICENSE file for details.

šŸ™ Acknowledgments

  • Ragie for providing the video processing API

  • MoviePy for video manipulation capabilities

  • MCP for AI assistant integration

šŸ“ž Support

If you encounter any issues or have questions:

  1. Check the Issues page

  2. Create a new issue with detailed information

  3. Include your Python version, error messages, and steps to reproduce

šŸ”„ Changelog

v0.1.0

  • Initial release

  • Basic video processing and retrieval functionality

  • MCP server integration

  • Jupyter notebook support

  • Video chunking capabilities

Deploy Server
A
security ā€“ no known vulnerabilities
F
license - not found
A
quality - confirmed to work

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Tools

Enables natural language search and interaction with video content through three tools: ingesting videos to a Ragie index, retrieving relevant video segments based on queries, and creating video chunks from specific timestamps.

  1. šŸŽÆ MCP-powered video-RAG using Ragie
    1. Tech Stack
  2. šŸŽÆ Features
    1. šŸš€ Quick Start
      1. Prerequisites
      2. Setup and Installation
      3. MCP Server Setup with Cursor IDE
    2. šŸ“– Usage
      1. Basic Usage
      2. Interactive Development
      3. MCP Server
    3. šŸ”§ API Reference
      1. Core Functions
      2. MCP Tools
    4. šŸ“ Examples
      1. Example 1: Basic Video Processing and Query
      2. Example 2: Extract Video Chunks
      3. Example 3: Jupyter Notebook Workflow
    5. šŸ—ļø Project Structure
      1. šŸ”‘ Environment Variables
        1. šŸ“¦ Dependencies
          1. šŸ¤ Contributing
            1. šŸ“„ License
              1. šŸ™ Acknowledgments
                1. šŸ“ž Support
                  1. šŸ”„ Changelog
                    1. v0.1.0

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