matcher-py 0.12.2

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

Matcher Python Bindings (PyO3)

Python bindings for the Matcher library — a high-performance matcher designed to solve LOGICAL and TEXT VARIATIONS problems in word matching, implemented in Rust via PyO3.

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 space-separated Pinyin for fuzzy matching. Example: 西安 -> xi an, matches 洗按 -> xi an, but not 先 -> xian
  • PinYinChar: Convert Chinese characters to Pinyin without boundary spaces. 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
• Pickle Support: SimpleMatcher instances can be pickled and unpickled for serialization.

Installation

Use pip

pip install matcher_py

Build from source

Requires the Rust nightly toolchain.

git clone https://github.com/Lips7/Matcher.git
cd Matcher/matcher_py

# Option 1: Using uv (recommended for development)
pip install uv
uv sync

# Option 2: Using maturin directly
pip install maturin
maturin develop --release

Usage

All relevant types are defined in matcher_py.pyi.

Text Process Usage

Here’s an example of how to use the reduce_text_process and text_process functions:

from matcher_py import ProcessType, reduce_text_process, text_process

# Combine and reduce multiple transformations
print(reduce_text_process(ProcessType.DELETE_NORMALIZE, "hello, world!"))
# Perform a single transformation
print(text_process(ProcessType.DELETE, "hello, world!"))

Simple Matcher Basic Usage

Here’s an example of how to use the SimpleMatcher:

import json

from matcher_py import ProcessType, SimpleMatcher

simple_matcher = SimpleMatcher(
    json.dumps(
        {
            ProcessType.NONE: {
                1: "hello&world",
                2: "word&word~hello"
            },
            ProcessType.DELETE: {
                3: "hallo"
            }
        }
    ).encode()
)
# Check if a text matches
assert simple_matcher.is_match("hello^&!#*#&!^#*()world")
# Perform simple processing
result = simple_matcher.process("hello,world,word,word,hallo")
print(result)

Explanation of the configuration

• SimpleMatcher's configuration is defined by the SimpleTable = Dict[ProcessType, Dict[int, str]] type, the value Dict[int, str]'s key is called word_id, word_id is required to be globally unique.

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 Chinese characters to space-separated Pinyin. Based on PINYIN.
  • 你好 -> ni hao
  • 西安 -> xi an
• PINYIN_CHAR: Convert Chinese characters to Pinyin without boundary spaces. Based on PINYIN.
  • 你好 -> nihao
  • 西安 -> xian

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

Be careful combining PINYIN and PINYIN_CHAR: they preserve different word boundaries, so the same input can behave like xi + an in one pipeline and xian in the other.

Contributing

Contributions to matcher_py 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_py is licensed under the MIT OR Apache-2.0 license.

More Information

For more details, visit the GitHub repository.