---
title: Text Distance Search API
slug: text-distance-search
url: https://apyhub.com/apyhub/service/text-distance-search
provider: ApyHub
categories: [Developer Tools]
tags: [levenshtein, string-matching, fuzzy-search, text-similarity, developer-tools]
auth: api_key
---

# Text Distance Search API

Compare two strings with Levenshtein distance. Send source and target, get an integer edit distance for fuzzy matching and typo checks.

## Endpoints

| Method | Path | Description | Atoms |
| --- | --- | --- | --- |
| POST | `` | What it does Computes the Levenshtein edit distance between two input strings and returns the dista… | 25 |

## About

## What it does
Text Distance Search computes the Levenshtein edit distance between two strings. Send a `source` string and a `target` string, and get back a single integer in `data` showing how many insertions, deletions, or substitutions are needed to turn one into the other.

Use Text Distance Search when you need fuzzy matching or similarity scoring for user-entered text. It’s a practical fit for deduplicating names, catching typos in search queries, comparing product titles, or ranking candidate matches when exact string equality is too strict.

The request body is minimal: `source` and `target`. The response is equally direct: `data` contains the edit distance as an integer. If you are building search, validation, or cleanup workflows, this gives you a simple distance metric you can use to sort, filter, or threshold string comparisons.

Because the output is just the distance value, it is easy to plug into existing logic without mapping extra metadata or parsing a complex structure.

## Usage

Authenticate with an ApyHub API key in the `apy-token` header.
Full docs and a live playground: https://apyhub.com/apyhub/service/text-distance-search