use fuzzy

Functions

fn best

Find the best matching items from a list, sorted by score descending. Items with score 0 are excluded.

Signature: (query:str items:ptr max_results:i64 -- results:ptr count:i64)

fn contains_ci

Check if a string contains a substring (case-insensitive).

Signature: (str str -- i64)

fn distance

Compute Levenshtein edit distance between two strings. The edit distance is the minimum number of single-character edits (insertions, deletions, substitutions) required to transform one string into another.

Signature: (a:str b:str -- dist:i64)

Example:

"kitten" "sitting" fuzzy::distance  // 3
"hello" "hello" fuzzy::distance  // 0
"" "abc" fuzzy::distance  // 3

fn matches

Check if query approximately matches target within a similarity threshold.

Signature: (str str f64 -- i64)

Example:

"helo" "hello" 0.7 fuzzy::matches  // 1
"xyz" "hello" 0.7 fuzzy::matches  // 0

fn score

Compute a fuzzy match score combining multiple matching strategies. Higher scores indicate better matches. Returns 0 if no match. Scoring: - Exact match: 1000 - Prefix match: 500 + (query_len / target_len * 100) - Substring match: 300 + (query_len / target_len * 100) - Word boundary match (after _ or at start): 200 - Similarity match (>= 0.6): similarity * 100

Signature: (query:str target:str -- score:i64)

Example:

"sin" "sin" fuzzy::score  // 1000
"sin" "asin" fuzzy::score  // ~350 (substring)
"rf" "read_file" fuzzy::score  // 200 (word boundary)

fn similarity

Compute similarity between two strings as a value from 0.0 to 1.0. 1.0 means identical, 0.0 means completely different. Based on normalized Levenshtein distance: 1 - (distance / max_length).

Signature: (a:str b:str -- sim:f64)

Example:

"hello" "hello" fuzzy::similarity  // 1.0
"hello" "hallo" fuzzy::similarity  // 0.8
"abc" "xyz" fuzzy::similarity  // 0.0

fn starts_with_ci

Check if a string starts with a given prefix (case-insensitive).

Signature: (str str -- i64)