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

SentrySearch enables sub-second semantic search over dashcam footage by leveraging Google Gemini's native video embedding capabilities to match text queries directly with video vectors.

Semantic search over dashcam footage. Type what you're looking for, get a trimmed clip back.

demo.mp4

SentrySearch splits your dashcam videos into overlapping chunks, embeds each chunk directly as video using Google's Gemini Embedding model, and stores the vectors in a local ChromaDB database. When you search, your text query is embedded into the same vector space and matched against the stored video embeddings. The top match is automatically trimmed from the original file and saved as a clip.

Clone and install:

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

Set up your API key:

sentrysearch init

This prompts for your Gemini API key, writes it to .env, and validates it with a test embedding.

Index your footage:

sentrysearch index /path/to/dashcam/footage

Search:

sentrysearch search "red truck running a stop sign"

ffmpeg is required for video chunking and trimming. If you don't have it system-wide, the bundled imageio-ffmpeg is used automatically.

Manual setup: If you prefer not to use sentrysearch init, you can copy .env.example to .env and add your key from aistudio.google.com/apikey manually.

$ sentrysearch init
Enter your Gemini API key (get one at https://aistudio.google.com/apikey): ****
Validating API key...
Setup complete. You're ready to go — run `sentrysearch index <directory>` to get started.

If a key is already configured, you'll be asked whether to overwrite it.

$ sentrysearch index /path/to/dashcam/footage
Indexing file 1/3: front_2024-01-15_14-30.mp4 [chunk 1/4]
Indexing file 1/3: front_2024-01-15_14-30.mp4 [chunk 2/4]
...
Indexed 12 new chunks from 3 files. Total: 12 chunks from 3 files.

Options:

--chunk-duration 30 — seconds per chunk --overlap 5 — overlap between chunks --no-preprocess — skip downscaling/frame rate reduction (send raw chunks) --target-resolution 480 — target height in pixels for preprocessing --target-fps 5 — target frame rate for preprocessing --no-skip-still — embed all chunks, even ones with no visual change

$ sentrysearch search "red truck running a stop sign"
#1 [0.87] front_2024-01-15_14-30.mp4 @ 02:15-02:45
#2 [0.74] left_2024-01-15_14-30.mp4 @ 02:10-02:40
#3 [0.61] front_2024-01-20_09-15.mp4 @ 00:30-01:00
Saved clip: ./match_front_2024-01-15_14-30_02m15s-02m45s.mp4

Options: --results N, --output-dir DIR, --no-trim to skip auto-trimming.

$ sentrysearch stats
Total chunks: 47
Source files: 12

Add --verbose to either command for debug info (embedding dimensions, API response times, similarity scores).

Gemini Embedding 2 can natively embed video — raw video pixels are projected into the same 768-dimensional vector space as text queries. There's no transcription, no frame captioning, no text middleman. A text query like "red truck at a stop sign" is directly comparable to a 30-second video clip at the vector level. This is what makes sub-second semantic search over hours of footage practical.

Indexing 1 hour of footage costs ~$2.50 with Gemini's embedding API (default settings: 30s chunks, 5s overlap). The API bills by video duration, so this cost is driven by the number of chunks, not file size.

Two built-in optimizations help reduce costs in different ways:

Preprocessing (on by default) — chunks are downscaled to 480p at 5fps before embedding. This reduces upload size and token count but does not reduce the number of API calls, so it primarily improves speed rather than cost. Still-frame skipping (on by default) — chunks with no meaningful visual change (e.g. a parked car) are skipped entirely. This saves real API calls and directly reduces cost. The savings depend on your footage — Sentry Mode recordings with hours of idle time benefit the most, while action-packed driving footage may have nothing to skip.

Search queries are negligible (text embedding only).

Tuning options:

--chunk-duration / --overlap — longer chunks with less overlap = fewer API calls = lower cost --no-skip-still — embed every chunk even if nothing is happening --target-resolution / --target-fps — adjust preprocessing quality --no-preprocess — send raw chunks to the API

Still-frame detection is heuristic — it uses JPEG file size comparison across sampled frames. It may occasionally skip chunks with subtle motion or embed chunks that are truly static. Disable with --no-skip-still if you need every chunk indexed. Search quality depends on chunk boundaries — if an event spans two chunks, the overlapping window helps but isn't perfect. Smarter chunking (e.g. scene detection) could improve this. Gemini Embedding 2 is in preview — API behavior and pricing may change.

This works with any footage in mp4 format, not just Tesla Sentry Mode. The directory scanner recursively finds all .mp4 files regardless of folder structure.