🎬 Multimodal Video Moment Finder

Find any moment in a video using images or text. Drop a screenshot to find where it appears, or describe a scene in words. Pure visual matching, zero transcription.

Powered by Gemini Embedding 2 for native cross-modal search.

How It Works

1. Upload a video — frames are extracted at 1fps using ffmpeg
2. Each frame is embedded natively with gemini-embedding-2-preview
3. Search by image — embed your photo, cosine similarity against all frames
4. Search by text — embed your description, cross-modal match against frames
5. Jump to the moment — click any result to play the video at that timestamp

No transcription. No captions. No OCR. The embedding model understands visual content directly.

Stack

• Backend: FastAPI + Gemini Embedding 2 + ChromaDB
• Frontend: Next.js (dark theme, split panel)
• Frame extraction: ffmpeg (1fps)
• Frame descriptions: Gemini 3 Flash
• Models: gemini-embedding-2-preview (embeddings), gemini-3-flash-preview (descriptions)

Project Structure

advanced_llm_apps/multimodal_video_moment_finder/
├── backend/
│   ├── server.py           # FastAPI server with upload, search & video management endpoints
│   ├── video_store.py      # Video processing, frame extraction, embedding & ChromaDB storage
│   └── requirements.txt    # Python dependencies
├── frontend/
│   ├── app/
│   │   ├── page.tsx        # Main UI — video upload, image/text search, result playback
│   │   ├── layout.tsx      # Root layout
│   │   └── globals.css     # Global styles
│   ├── package.json
│   ├── next.config.ts
│   └── tsconfig.json
└── README.md

Setup

Prerequisites

• Python 3.10+
• Node.js 18+
• ffmpeg installed (brew install ffmpeg or apt install ffmpeg)
• Google AI API key

Backend

cd advanced_llm_apps/multimodal_video_moment_finder/backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

export GOOGLE_API_KEY="your-api-key"
python server.py

Backend runs on http://localhost:8890.

Frontend

cd advanced_llm_apps/multimodal_video_moment_finder/frontend
npm install
echo 'NEXT_PUBLIC_API_URL=http://localhost:8890' > .env.local
npm run dev

Frontend runs on http://localhost:3000.

Usage

1. Open http://localhost:3000
2. Upload a video (any format ffmpeg supports)
3. Wait for frame extraction and embedding (1 frame/second)
4. Search:
   • Image: drop a screenshot or photo to find where it appears
   • Text: describe a scene ("person on stage", "aerial view of city")
5. Click any result to jump the video to that moment

API Endpoints

Architecture

Key Insight

Gemini Embedding 2 embeds images and text into the same vector space natively. This means you can search for a visual moment using either another image or a text description, without any intermediate captioning or transcription step. The model understands what's in the frame directly.