Matcher

A high-performance matcher designed to solve LOGICAL and TEXT VARIATIONS problems in word matching, implemented in Rust.

For detailed implementation, see the Design Document.

Features

• Text Transformation:
  • Fanjian: Simplify traditional Chinese characters to simplified ones. Example: 蟲艸 -> 虫艹
  • Delete: Remove specific characters. Example: *Fu&*iii&^%%*&kkkk -> Fuiiikkkk
  • Normalize: Normalize special characters to identifiable characters. Example: 𝜢𝕰𝕃𝙻𝝧 𝙒ⓞᵣℒ𝒟! -> hello world!
  • PinYin: Convert Chinese characters to Pinyin for fuzzy matching. Example: 西安 -> xi an, matches 洗按 -> xi an, but not 先 -> xian
  • PinYinChar: Convert Chinese characters to Pinyin. Example: 西安 -> xian, matches 洗按 and 先 -> xian
• AND OR NOT Word Matching:
  • Takes into account the number of repetitions of words.
  • Example: hello&world matches hello world and world,hello
  • Example: 无&法&无&天 matches 无无法天 (because 无 is repeated twice), but not 无法天
  • Example: hello~helloo~hhello matches hello but not helloo and hhello
• Efficient Handling of Large Word Lists: Optimized for performance.

Usage

Adding to Your Project

To use matcher_rs in your Rust project, run the following command:

cargo add matcher_rs

Explanation of the configuration

ProcessType

• None: No transformation.
• Fanjian: Traditional Chinese to simplified Chinese transformation. Based on FANJIAN.
  • 妳好 -> 你好
  • 現⾝ -> 现身
• Delete: Delete all punctuation, special characters and white spaces. Based on TEXT_DELETE and WHITE_SPACE.
  • hello, world! -> helloworld
  • 《你∷好》 -> 你好
• Normalize: Normalize all English character variations and number variations to basic characters. Based on NORM and NUM_NORM.
  • ℋЀ⒈㈠Õ -> he11o
  • ⒈Ƨ㊂ -> 123
• PinYin: Convert all unicode Chinese characters to pinyin with boundaries. Based on PINYIN.
  • 你好 -> ni hao
  • 西安 -> xi an
• PinYinChar: Convert all unicode Chinese characters to pinyin without boundaries. Based on PINYIN.
  • 你好 -> nihao
  • 西安 -> xian

You can combine these transformations as needed. Pre-defined combinations like DeleteNormalize and FanjianDeleteNormalize are provided for convenience.

Including None in a composite ProcessType keeps the raw-text path alongside transformed variants. For example, ProcessType::None | ProcessType::PinYin allows one part of a rule to match the original text while another part matches the Pinyin-transformed text.

Avoid combining PinYin and PinYinChar due to that PinYin is a more limited version of PinYinChar, in some cases like xian, can be treat as two words xi and an, or only one word xian.

Basic Example

Here’s a basic example of how to use the SimpleMatcher for text matching:

use matcher_rs::{text_process, reduce_text_process, ProcessType};

let result = text_process(ProcessType::Delete, "你好，世界！");
let results = reduce_text_process(ProcessType::FanjianDeleteNormalize, "你好，世界！");

text_process returns only the final transformed text. reduce_text_process returns each distinct intermediate variant along the pipeline. For shared-prefix multi-variant traversal, SimpleMatcher uses the internal DAG helpers instead of recomputing each path independently.

use matcher_rs::{ProcessType, SimpleMatcherBuilder};

let matcher = SimpleMatcherBuilder::new()
    .add_word(ProcessType::Fanjian, 1, "你好")
    .add_word(ProcessType::Fanjian, 2, "世界")
    .build();

let text = "你好，世界！";
let results = matcher.process(text);

For more detailed usage examples, please refer to the test_simple_matcher.rs file.

Feature Flags

• runtime_build: Build transformation tables from the source text maps at startup instead of embedding precompiled binaries.
• dfa: Use DFA-backed Aho-Corasick automata where applicable. This is enabled by default and improves speed at the cost of higher memory consumption.
• simd_runtime_dispatch: Enabled by default. Selects the best available transform kernel at runtime (AVX2 on x86-64, NEON on ARM64, portable fallback elsewhere).

Feature Comparison & Recommendation

Benchmarks

Benchmarked on MacBook Air M4 (24GB RAM). Test data: CN_WORD_LIST_100000 against CN_HAYSTACK and EN_WORD_LIST_100000 against EN_HAYSTACK.

Full records are stored in bench_records/. Latest: latest.txt.

To compare two benchmark records:

python matcher_rs/scripts/compare_benchmarks.py \
  "matcher_rs/bench_records/2026-03-10 12:22:24.txt" \
  "matcher_rs/bench_records/2026-03-11 23:16:38.txt"

The script treats the first file as the baseline and prints two sections: Regression and Improvement, using median latency by default.

Contributing

Contributions to matcher_rs are welcome! If you find a bug or have a feature request, please open an issue on the GitHub repository. If you would like to contribute code, please fork the repository and submit a pull request.

License

matcher_rs is licensed under the MIT OR Apache-2.0 license.

More Information

For more details, visit the GitHub repository.