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.
Compare two strings by sending them as JSON and get back their edit distance.
curl -X POST "https://api.eu.apyhub.com/apyhub/text-distance-search" \
-H "apy-token: $APY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"source":"kitten","target":"sitting"}'Returns a JSON object with a data integer field — the Levenshtein edit distance between source and target (for example, kitten → sitting returns 3).
{
"data": 3
}Loading your default key…Computes the Levenshtein edit distance between two input strings and returns the distance as an integer.
| Parameter | Type | Description |
|---|---|---|
| source | String | First string to compare. |
| target | String | Second string to compare. |
Returns a JSON object with a data integer field containing the Levenshtein edit distance between source and target.
| Parameter | Type | Description |
|---|---|---|
| data | Integer | Levenshtein edit distance between the two input strings. |
bad_requestRequired parameter missing or malformed body.
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