Using Semantic Search

Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating embeddings — numerical vector representations — for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results.

Frigate has support for Jina AI's CLIP model to create embeddings, which runs locally. Embeddings are then saved to Frigate's database.

Semantic Search is accessed via the Explore view in the Frigate UI.

Configuration

Semantic search is disabled by default, and must be enabled in your config file before it can be used. Semantic Search is a global configuration setting.

semantic_search:
  enabled: True
  reindex: False

tip

The embeddings database can be re-indexed from the existing tracked objects in your database by adding reindex: True to your semantic_search configuration. Depending on the number of tracked objects you have, it can take a long while to complete and may max out your CPU while indexing. Make sure to set the config back to False before restarting Frigate again.

If you are enabling the Search feature for the first time, be advised that Frigate does not automatically index older tracked objects. You will need to enable the reindex feature in order to do that.

Jina AI CLIP

The vision model is able to embed both images and text into the same vector space, which allows image -> image and text -> image similarity searches. Frigate uses this model on tracked objects to encode the thumbnail image and store it in the database. When searching for tracked objects via text in the search box, Frigate will perform a text -> image similarity search against this embedding. When clicking "Find Similar" in the tracked object detail pane, Frigate will perform an image -> image similarity search to retrieve the closest matching thumbnails.

The text model is used to embed tracked object descriptions and perform searches against them. Descriptions can be created, viewed, and modified on the Search page when clicking on the gray tracked object chip at the top left of each review item. See the Generative AI docs for more information on how to automatically generate tracked object descriptions.

Differently weighted CLIP models are available and can be selected by setting the model_size config option:

tip

The CLIP models are downloaded in ONNX format, which means they will be accelerated using GPU hardware when available. This depends on the Docker build that is used. See the object detector docs for more information.

semantic_search:
  enabled: True
  model_size: small

• Configuring the large model employs the full Jina model and will automatically run on the GPU if applicable.
• Configuring the small model employs a quantized version of the model that uses much less RAM and runs faster on CPU with a very negligible difference in embedding quality.

Usage

1. Semantic search is used in conjunction with the other filters available on the Search page. Use a combination of traditional filtering and semantic search for the best results.
2. The comparison between text and image embedding distances generally means that results matching description will appear first, even if a thumbnail embedding may be a better match. Play with the "Search Type" filter to help find what you are looking for.
3. Make your search language and tone closely match your descriptions. If you are using thumbnail search, phrase your query as an image caption.
4. Semantic search on thumbnails tends to return better results when matching large subjects that take up most of the frame. Small things like "cat" tend to not work well.
5. Experiment! Find a tracked object you want to test and start typing keywords to see what works for you.