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String Distance | Документация для разработчиков

String Distance

Обновлено 4 марта 2024

Open In Colab

In information theory, linguistics, and computer science, the Levenshtein distance (Wikipedia) is a string metric for measuring the difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other. It is named after the Soviet mathematician Vladimir Levenshtein, who considered this distance in 1965.

One of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as Levenshtein or postfix distance. This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.

This can be accessed using the string_distance evaluator, which uses distance metrics from the rapidfuzz library.

Note: The returned scores are distances, meaning lower is typically "better".

For more information, check out the reference docs for the StringDistanceEvalChain for more info.

%pip install --upgrade --quiet  rapidfuzz
from langchain.evaluation import load_evaluator

evaluator = load_evaluator("string_distance")
evaluator.evaluate_strings(
prediction="The job is completely done.",
reference="The job is done",
)
    {'score': 0.11555555555555552}
# The results purely character-based, so it's less useful when negation is concerned
evaluator.evaluate_strings(
prediction="The job is done.",
reference="The job isn't done",
)
    {'score': 0.0724999999999999}

Configure the String Distance Metric

By default, the StringDistanceEvalChain uses levenshtein distance, but it also supports other string distance algorithms. Configure using the distance argument.

from langchain.evaluation import StringDistance

list(StringDistance)
    [<StringDistance.DAMERAU_LEVENSHTEIN: 'damerau_levenshtein'>,
<StringDistance.LEVENSHTEIN: 'levenshtein'>,
<StringDistance.JARO: 'jaro'>,
<StringDistance.JARO_WINKLER: 'jaro_winkler'>]
jaro_evaluator = load_evaluator("string_distance", distance=StringDistance.JARO)
jaro_evaluator.evaluate_strings(
prediction="The job is completely done.",
reference="The job is done",
)
    {'score': 0.19259259259259254}
jaro_evaluator.evaluate_strings(
prediction="The job is done.",
reference="The job isn't done",
)
    {'score': 0.12083333333333324}
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