What “AI photo search” means
Traditional photo search works best when a picture has useful metadata: a date, a location, an album name, or recognized text. Semantic search adds another route. It represents the visual content of a photo as numbers so a phrase such as “a red bicycle beside a brick wall” can be compared with the library.
Queryable uses Apple’s MobileCLIP family of models to place images and text in the same 512-dimensional embedding space. A closer vector match is treated as a more relevant visual match. It is similarity ranking, not a claim that the app has identified every object with certainty.
- Natural-language scene descriptions
- Reference-image search
- Optional time-range filtering
- Local result ranking
What works offline—and what may not
Once an original photo has been indexed and its vector is stored locally, Queryable can encode a text query and compare it with the local index without an internet connection. The current app also keeps incremental index data so it does not need to rebuild everything after every change.
The boundary is iCloud Photos. Queryable’s current index path keeps PhotoKit network access disabled. If Optimize iPhone Storage has left no usable local image data for an item, make it available through Apple Photos first, then index it locally. That Apple download is different from sending a photo to Queryable for cloud AI processing.
Practical note: For the most predictable first index, use Apple Photos to make required cloud items available locally before starting Queryable. Power, Wi‑Fi and adequate storage help Apple Photos complete that preparation.
Questions to ask any private photo-search app
“On-device” should describe the actual search path, not just the user interface. Check whether photo content leaves the phone, whether an account is required, where the index lives, and whether search still works in airplane mode after setup.
Queryable is open source under the MIT license, so its Core ML encoders, embedding store and ranking path can be inspected. The website makes the narrower, verifiable promise that Queryable does not operate a server that receives your library for semantic indexing or search.
- No account required for core search
- No Queryable photo-upload server
- Local semantic index
- Public source code
Where semantic search has limits
Semantic similarity is strong for common scenes, objects and broad visual ideas, but it is not perfect. Very small details, exact identities, obscure proper nouns, or text inside an image can require a different tool. Results also depend on the language and clarity of the query.
A practical workflow is to start with the most distinctive visual fact, inspect the first results, then add a time range. If you already have a similar picture, image-to-image search can be more effective than trying to phrase the scene.
Common questions
Can an iPhone search photos offline with AI?
Yes. An app can run an image-text model on-device and compare a query against a local photo index. Cloud-only items need usable local image data before Queryable’s network-disabled index request can represent them.
Does offline AI search recognize faces?
Semantic scene search and face identification are different capabilities. Queryable focuses on visual and language similarity; use Apple Photos for its people and pets features.
Does Queryable need an account?
No account is required for Queryable’s core local photo search.