OpenViSTAI · Agents · Blockchain · Crypto
Menu
AI Developer Tools & SDKs

Usearch.clj

Usearch.clj is a Clojure wrapper for the USearch vector search and clustering engine. It provides functions to create and populate vector indexes, query nearest vectors, retrieve stored vectors by key, and save or load indexes.

Independent OpenViST project profile, based on linked sources.

Usearch.clj — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a Clojure application needs to index existing vector representations and query nearest matches, with file-based index persistence.

Who it is for

  • Clojure developers adding vector indexing and nearest-neighbor search to applications.
  • Teams prototyping semantic document or image retrieval with vectors.

Documented uses

  • Create a keyed vector index from application data.
  • Search an index for the closest vector or a requested number of matches.
  • Retrieve a stored vector by key.
  • Save an index to disk and load it again.
Before you choose

Requirements & limits

  • The README lists native dependencies for Linux x86-64 and macOS x86-64 and ARM64.
  • The index example configures vector dimensions and f32 quantization, so input vectors must fit the chosen index configuration.
  • The library is not thread-safe and may crash the JVM if used concurrently from multiple threads.
  • The repository is licensed under Apache License 2.0.

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

Create, query, and persist a vector index

Use the documented Clojure API to populate an index, retrieve nearest matches, and save and reload it.

Before you start

  • A Clojure project with the usearch-clj dependency.
  • The matching native dependency for Linux x86-64, macOS x86-64, or macOS ARM64.
  • Vectors represented as float arrays with a consistent configured dimension.

Steps

  1. Initialize an index with its vector dimensions and quantization setting, optionally supplying keyed vectors.

  2. Add further vectors with keys; reserve additional capacity before adding when needed.

  3. Call search with a query vector to retrieve its closest match or request multiple matches.

  4. Save the index to a file, then initialize a matching index and load the saved file.

Expected outcome

A populated vector index that can return nearest-match results and be saved for later loading.

Guide limitations

  • The library is not thread-safe; concurrent use from multiple threads may crash the JVM.
  • The README lists native dependency artifacts for Linux x86-64 and macOS x86-64 and ARM64.
Evidence and transparency

Sources & review

Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
phronmophobic/usearch.clj · Official homepage title · retained in its source language