Crate fuzzies 

Fuzzies

Fuzzies is a memory-mapped FST spellchecker and fuzzy dictionary built on fst and levenshtein-automata.

More information about this crate can be found in the crate documentation.

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Installation

cargo add fuzzies

See the Node.js README for Node-specific APIs and its usage details.

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Example

use fuzzies::{Dictionary, DictionaryError};

fn main() -> Result<(), DictionaryError> {
    // Prepare your raw text file (must be sorted lexicographically)
    // Fuzzies provides a handy in-place sorter for convenience:
    Dictionary::sort("words.txt")?;

    // Build the immutable binary FST from the sorted text file
    Dictionary::build("words.txt", "words.fst")?;

    // Load the dictionary (memory-mapped from disk)
    let dict = Dictionary::open("words.fst")?;

    // Check for exact matches instantly
    if dict.contains("banana") {
        println!("Exact match found!");
    }

    // Quick single-suggestion lookup
    if let Some(top_match) = dict.suggest("banaan")? {
        println!("Best suggestion: {}", top_match.key);
    }

    // Perform a fuzzy search with custom options
    let results = dict.search("banaan")
        .distance(2)
        .transposition(true) // Handles adjacent swaps (e.g., "teh" -> "the")
        .prefix(false)       // Set to true for prefix fuzzy lookups
        .ge("a")             // Optionally restrict search bounds (key >= "a")
        .lt("e")             // (key < "e")
        .limit(5)
        .execute()?;
    
    for result in results {
        println!("Found: {}", result);
    }

    // Batch search (multithreaded, defaults to a distance of 1)
    let queries = vec!["aple", "baxana", "cherri"];
    let batch_results = dict.batch_search(&queries).execute();

    for (query, result) in queries.iter().zip(batch_results) {
        match result {
            Ok(matches) => println!("Query '{}' found {} matches", query, matches.len()),
            Err(e) => eprintln!("Error searching for '{}': {}", query, e),
        }
    }

    Ok(())
}

If you don’t want to manage external .fst files on disk, you can construct a Dictionary directly:

// From an embedded FST binary slice:
static DICT_DATA: &[u8] = include_bytes!("../assets/words.fst");
let dict = Dictionary::from_embedded(DICT_DATA)?;

// From an iterator of strings (automatically sorted and deduplicated):
let words = vec!["apple", "banana", "cherry"];
let dict = Dictionary::from_iterator(words)?;

See examples for more.

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🎈 Performance

• Instant Exact Matches: Finding a word takes about 130ns. If a word isn’t in the dictionary, it figures that out and rejects it in under 10ns.
• Fast Typo Correction: Searching for a word with a 1-character typo through 50,000 words takes about 75µs.
• Multithreading: Throwing 1,000 fuzzy searches at it in parallel (via Rayon) finishes in just ~5ms.
• Zero Loading Screen: Opening a saved dictionary is instant, no matter how huge the file is, because it just maps the file directly to memory.

Want the hard numbers? Check out the Benchmarks.

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Safety

This crate uses unsafe in a single location:

• Memory-Mapped I/O (Dictionary::open): Calls memmap2::Mmap::map(&file) to map FST data directly from disk into memory.

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License

This project is licensed under the MIT license.

Structs

Dictionary

Memory-mapped FST dictionary for fuzzy string lookups.

SearchBuilder

Query builder for configuring single and batch fuzzy searches.

SearchResult

A matched item from a fuzzy search.

Enums

DictionaryError

Type Aliases

BatchSearchBuilder

Query builder type alias for batch searches.