Levenshtein#
These examples form a small learning path for exact edit distance and fuzzy ranking. They are deterministic, local and network-free.
two sequences
↓
unit-cost edit distance
↓
normalized similarity
↓
optional ranking / cutoff
↓
actual backend provenance
Start here#
Basics — the classic
kitten→sittingdistance and normalized metrics.Backends — inspect internal, RapidFuzz, explicit GPL and pure-Python capabilities without requiring any optional backend to be installed.
Ranking — stable ties,
limit,score_cutoffand objectkey.Sequences — bytes, token sequences and explicit Unicode normalization.
Corpus — use Levenshtein as a deterministic local Corpus retrieval scorer.
Reliability rules#
Every script:
uses only local literal data;
performs no network access or downloads;
has a pure-Python path;
asserts the result it demonstrates;
does not turn a wrong distance into a skip.
An unavailable optional accelerator can be reported as unavailable. Metric correctness cannot be skipped.
Backend note#
auto never chooses the external GPL Levenshtein backend. The examples
do not require it.
The gallery demonstrates the facade contract rather than benchmarking one machine’s accelerator installation.
