String Similarity

The String Similarity tool measures how close two strings are, typically with the Levenshtein distance and a normalized similarity score from zero to one, where one means identical. A small distance means the strings differ by few edits, which makes the metric ideal for spotting near duplicates, for fuzzy matching, and for catching typos in a list of labels. Use it when you are de-duplicating data, building a suggestion feature, or checking whether two values that should match really do. The comparison runs locally in your browser, so the data you test is never sent anywhere. Measuring how close two strings are is the foundation of fuzzy matching, and the most common metric is edit distance, the minimum number of single character insertions, deletions, or substitutions needed to turn one string into the other. A raw distance is hard to interpret on its own, since a value of three means something different for short strings than for long ones, which is why normalising it into a similarity score between zero and one is more useful for ranking candidates. The metric shows up wherever near duplicates or typos matter: deduplicating a list of names that were entered by hand, matching a user search term against a set of known values, detecting when records from two systems refer to the same entity, and suggesting a correction when input does not match anything exactly. A practical approach is to set the threshold based on how many edits you would expect from a realistic mistake at that string length, then treat anything at or below it as a candidate for review rather than an automatic match. The comparison runs locally, so the data being tested stays private.

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