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Swiftsemanticsearch

Swiftsemanticsearch is an open-source Swift demo app for Apple devices that uses UForm to embed multilingual text and images and USearch to retrieve semantically similar images. It supports text-to-image and image-to-image search, and can embed and search camera frames as they arrive.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Swiftsemanticsearch — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the repository when you want a concrete Swift reference for on-device image embeddings and retrieval on Apple devices, or a bundled sample dataset for trying the documented project workflow.

Who it is for

  • Developers exploring on-device semantic image search in Swift.
  • Teams prototyping text-based or visual-similarity search over images.
  • Developers investigating live camera-frame search or geo-spatial search over image coordinates.

Documented uses

  • Find images using a natural-language description.
  • Retrieve visually related images from an image query.
  • Search incoming camera frames against text or image queries.
  • Explore geo-spatial search over image coordinates alongside semantic search.
Before you choose

Requirements & limits

  • The project is a Swift demo app, not a hosted search service.
  • The documented sample workflow uses a bundled dataset containing images, names, embeddings and a search index.
  • The README directs users to Xcode and an Apple device to run the project.
  • The implementation uses UForm for multimodal embeddings and USearch for semantic retrieval; it also describes geo-spatial search over image coordinates.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the sample image dataset

Prepare the repository’s bundled sample data before opening the Xcode project.

Before you start

  • Git
  • A local environment with unzip
  • Xcode and an Apple device for the documented app-running stage

Steps

  1. Clone https://github.com/ashvardanian/SwiftSemanticSearch.git.

  2. Change into the SwiftSemanticSearch directory.

  3. Extract the bundled dataset with unzip dataset.zip.

  4. Open the Xcode project; the README then directs readers to run the app on an Apple device.

Expected outcome

The sample images, image-name list, precomputed embeddings and precomputed search index are extracted and ready for the project workflow.

Guide limitations

  • The README does not provide further Xcode configuration or troubleshooting steps.
  • This is setup guidance from the project README, not a verified execution test.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
ashvardanian/SwiftSemanticSearch · Official homepage title · retained in its source language
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