# 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.
[](https://crates.io/crates/fuzzies)
[](https://docs.rs/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 on an **Intel Core i5-10300H** (4 cores / 8 threads). You can re-run these on your hardware with `cargo bench`.
> [!NOTE]
> Running cargo bench on the published crate executes against a small, dynamically generated dataset. The 106,000-word benchmarks shown below were gathered independently using a local dictionary.
| contains (Hit) | 34.60 ns | 65.00 ns | ~1.8x |
| contains (Miss) | 11.08 ns | 100.06 ns | ~9.0x |
| Exact Search (dist = 0) | 2.18 µs | 4.48 µs | ~2.0x |
| Fuzzy Search (dist = 1) | 7.97 µs | 61.58 µs | ~7.7x |
| Prefix Search | 5.53 µs | 125.36 µs | Result-size bound |
| Range Search (`'b'..='c'`) | 4.35 µs | 626.37 µs | Result-size bound |
| Batch (1,000 queries) | 4.02 ms (4.0 µs/q) | 14.88 ms (14.8 µs/q) | ~3.7x |
---
## License
This project is licensed under the [MIT license.](LICENSE)