Edit distance

As explained in Build Financial Software with Generative AI (From Scratch)

Levenshtein distance is a string metric counting the minimum single-character insertions, deletions, and substitutions needed to transform one string into another.

How Build Financial Software with Generative AI (From Scratch) covers it

How deep it goes

Defines edit-based similarity and demonstrates normalized PostgreSQL scoring, while stopping short of explaining the algorithm’s internal dynamic programming.

Examples

Uses normalized PPD and IAT names, sanctions-list aliases, SQL similarity scores, and an 80-percent filtering threshold.

What it assumes

Assumes SQL and ACH name data, while demonstrating extension setup, name normalization, comparison, and filtering.

What you can do after

Apply it via PostgreSQL fuzzystrmatch to rank potentially misspelled or altered transaction names for screening review.

How it connects

Connects fuzzy matching with phonetic matching, Soundex, Metaphone, Daitch–Mokotoff matching, SQL, and sanctions screening.

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Also explained in

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  liveBookmanning.com
  Natural Language Processing in Action, Second EditionA string distance equal to the minimum number of single-character insertions, deletions, and substitutions needed to transform one string into another.
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  liveBookmanning.com
  Advanced Algorithms and Data StructuresEdit distance is the minimum number of character insertions, deletions, or substitutions needed to transform one string into another.
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  liveBookmanning.com
  Clojure: The Essential ReferenceLevenshtein distance is a metric counting the minimum single-character insertions, deletions, or substitutions needed to transform one string into another.
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  liveBookmanning.com
  Elasticsearch in Action, Second EditionThe Levenshtein distance algorithm computes the minimum number of insertions, deletions, and substitutions needed to transform one string into another.
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  liveBookmanning.com
  AI-Powered SearchEdit distance is a measure of the minimum number of character-level insertions, deletions, substitutions, or related edits needed to transform one string into another.

Compare how 6 books explain edit distance →

Related concepts

• Damerau–Levenshtein distanceincludes · Damerau–Levenshtein distance is the minimum number of insertions, deletions, substitutions, and adjacent transpositions required to transform one string into another.
• ChatterBotused by · ChatterBot is an open-source Python chatbot project that generates replies from stored statement-response pairs.
• Elasticsearchused by · Elasticsearch is a distributed search and analytics engine that indexes data for searching, retrieval, and analysis.
• fuzzy queryused by · A fuzzy query is an Elasticsearch query that matches terms within a permitted edit distance of the supplied term.
• fuzzinessused by · Fuzziness is a search setting that controls the permitted edit distance between a query term and matching indexed terms.
• fuzzystrmatchused by · fuzzystrmatch is a PostgreSQL extension that provides string-similarity and phonetic-matching functions.

This explainer was generated by Manning's knowledge graph from the text of Build Financial Software with Generative AI (From Scratch). The section links point to the parts of the book it draws on.