fuzzies 0.5.3

A fast, memory-mapped fuzzy search dictionary using FSTs and Levenshtein automata.
Documentation 
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# Fuzzies

**Fuzzies** is a fast, friendly integration layer that bridges the gap between low-level finite state transducers (`fst`) and Levenshtein automata, saving you from writing tedious boilerplate.

[![Crates.io](https://img.shields.io/crates/v/fuzzies.svg)](https://crates.io/crates/fuzzies)
[![Crates.io Total Downloads](https://img.shields.io/crates/d/fuzzies.svg)](https://crates.io/crates/fuzzies)
[![Docs.rs](https://docs.rs/fuzzies/badge.svg)](https://docs.rs/fuzzies)
[![Crates.io](https://img.shields.io/crates/l/fuzzies)](https://github.com/Isvane/fuzzies/blob/main/LICENSE)

More information about this crate can be found in the [crate documentation](https://docs.rs/fuzzies)

---

## Installation

```bash
cargo add fuzzies
```

---

## Example

```rust, no_run
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!");
    }

    // Perform a fuzzy search with a max typo distance of 2 and limit of 5 results
    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").lt("e")  // Optionally restrict search bounds (e.g., 'a' <= 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(())
}
```

### Embedding Data

If you don't want to manage external `.fst` files on disk, embed the dataset directly into your application:

```rust, ignore
static DICT_DATA: &[u8] = include_bytes!("../assets/words.fst");
let dict = Dictionary::from_embedded(DICT_DATA)?;
```

---

## Built with Fuzzies

Check out real-world projects utilizing `fuzzies`:

* **[Mamoru](https://github.com/Isvane/mamoru)**: A blazing-fast Git `commit-msg` hook that embeds a compiled dictionary of over 106,000 words to instantly catch and block typos before they make it into your version control history.

---

## 🎈 Performance

The following benchmarks were gathered using Criterion to evaluate lookup speeds for single and parallel batch searches.
You can re-run these benchmarks on your hardware using `cargo bench`.

### Single Search

```ignore
Dictionary Single Search/apple          6.8904 µs/iter (+/- 0.0174 µs)
Dictionary Single Search/baxana         8.1007 µs/iter (+/- 0.0321 µs)
Dictionary Single Search/missingword   12.1830 µs/iter (+/- 0.0285 µs)
```

### Batch Search

```ignore
Rayon Parallel Batch/100 queries      406.79 µs/iter (+/- 1.60 µs)
Rayon Parallel Batch/500 queries     1.9530 ms/iter (+/- 0.0051 ms)
Rayon Parallel Batch/1000 queries    3.9583 ms/iter (+/- 0.0141 ms)
```

> [!NOTE]
> Benchmarks were executed on an Intel Core i5-10300H (4 cores, 8 threads, Battery set to High Performance mode). Performance may scale significantly higher on more modern or high-end CPUs.

---

## License

This project is licensed under the [MIT license.](LICENSE)