Show HN: Gemini can now natively embed video, so I built sub-second video search

TL;DR Highlight

Google's Gemini Embedding model can now embed video directly into vectors without text transcription, enabling natural language search over dashcam footage — describe 'red truck running a stop sign' and get the clip back.

Who Should Read

Backend developers working with video analysis, security cameras, or dashcam footage, and developers looking to build multimodal search systems using the Gemini API.

Core Mechanics

Gemini's new video embedding capability generates vectors directly from video frames, capturing visual semantics without requiring a transcription or OCR step.
The embeddings support cross-modal retrieval: you can query with text and retrieve semantically matching video segments, or query with a video clip and find similar scenes.
The dashcam search demo indexes hours of footage and returns the specific timestamp and clip matching a natural language description — working even for events that occur without audio cues.
Embedding quality degrades for fast-motion scenes and low-light footage, which is a known limitation of frame-based visual embedding approaches.
The API returns embeddings per video segment (configurable window size), making it suitable for long-form video indexing at scale.

Evidence

The demo showing 'red truck running a stop sign' retrieval from hours of dashcam footage generated excitement in the CV/ML community — the latency and accuracy shown were impressive.
Commenters noted this makes real-time video search practical for applications that were previously too expensive (requiring full transcription or frame-by-frame human review).
Some questioned the privacy implications: if you can search video this easily, surveillance footage becomes much more powerful — a concern for civil liberties advocates.
Developers who tried the API in beta reported it works well for well-lit, reasonably paced footage but struggles with fast action, night footage, or very subtle events.

How to Apply

For security camera applications, pre-index all footage using Gemini video embeddings and store vectors in a vector database. Enable natural language search without real-time processing.
Set the embedding window size based on your event duration — for traffic incidents (2-5 seconds), use shorter windows; for activity patterns (minutes), use longer windows.
Combine video embeddings with text embeddings of metadata (timestamps, location, camera ID) for richer search — a hybrid search approach works better than pure video similarity.
Test your use case at small scale first: embed a representative sample of your footage and verify retrieval quality before committing to full indexing.

Code Example

snippet

# Installation and basic usage
git clone https://github.com/ssrajadh/sentrysearch.git
cd sentrysearch
python -m venv venv && source venv/bin/activate
pip install -e .

# Set Gemini API key (obtain from aistudio.google.com/apikey)
sentrysearch init

# Index video directory (default: 30-second chunks, 5-second overlap)
sentrysearch index /path/to/dashcam/footage

# Custom chunk settings example
sentrysearch index /path/to/footage --chunk-duration 30 --overlap 5

# Search using natural language
sentrysearch search "red truck running a stop sign"
# → The matching segment is saved as a clip file extracted from the original

Terminology

Video EmbeddingA numerical vector representation of video content that captures semantic meaning, enabling similarity search and retrieval.

Cross-modal RetrievalSearching across different modalities — e.g., querying with text to retrieve video, or querying with an image to retrieve text.

Vector DatabaseA database optimized for storing and searching vector embeddings — examples include Pinecone, Weaviate, and pgvector.

Embedding WindowThe duration of video segment that gets embedded as a single vector, determining the granularity of search results.