A high-performance matcher designed to solve LOGICAL and TEXT VARIATIONS problems in word matching, implemented in Rust.
Project description
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:
- VariantNorm: 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:
ABⅣ①℉->ab41°f - Romanize: Convert CJK characters to space-separated romanized form (Pinyin, Romaji, RR) for fuzzy matching.
Example:
西安->xi an, matches洗按->xi an, but not先->xian - RomanizeChar: Convert CJK characters to romanized form without boundary spaces.
Example:
西安->xian, matches洗按and先->xian - EmojiNorm: Convert emoji to English words (CLDR short names) and strip modifiers.
Example:
👍🏽->thumbs_up,🔥->fire
- VariantNorm: Simplify traditional Chinese characters to simplified ones.
Example:
- AND OR NOT Word Matching:
- Takes into account the number of repetitions of words.
&(AND):hello&worldmatcheshello worldandworld,hello|(OR):color|colourmatchescolorandcolour~(NOT):hello~helloo~hhellomatcheshellobut nothellooandhhello\b(word boundary):\bcat\bmatches "the cat" but not "concatenate"- Repeated segments:
无&法&无&天matches无无法天(because无is repeated twice), but not无法天 - Combined:
color|colour&bright~darkmatches "bright color" but not "dark colour"
- Pickle Support:
SimpleMatcherinstances 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)
OR, NOT, and Word Boundary
import json
from matcher_py import ProcessType, SimpleMatcher
matcher = SimpleMatcher(
json.dumps(
{
ProcessType.NONE: {
1: "color|colour", # OR: matches either spelling
2: "banana~peel", # NOT: "banana" without "peel"
3: r"\bcat\b", # word boundary: whole word only
4: r"bright&color|colour~\bdark\b", # combined
}
}
).encode()
)
assert matcher.is_match("nice colour") # OR
assert matcher.is_match("banana split") # NOT (no veto)
assert not matcher.is_match("banana peel") # NOT (vetoed)
assert matcher.is_match("the cat sat") # word boundary
assert not matcher.is_match("concatenate") # "cat" is substring
assert matcher.is_match("bright colour") # combined: AND + OR
assert not matcher.is_match("bright dark color") # combined: vetoed by \bdark\b
assert matcher.is_match("bright darken color") # "darken" ≠ \bdark\b
Explanation of the configuration
SimpleMatcher's configuration is defined by theSimpleTable = Dict[ProcessType, Dict[int, str]]type, the valueDict[int, str]'s key is calledword_id,word_idis required to be globally unique.
ProcessType
NONE: No transformation.VARIANT_NORM: Traditional Chinese to simplified Chinese transformation. Based on VARIANT_NORM.測試->测试現⾝->现身
DELETE: Delete all punctuation, special characters, separator characters, and configured control/format codepoints. Based on TEXT_DELETE.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.ABⅣ①℉->ab41°fⅠⅡⅢ->123
ROMANIZE: Convert CJK characters to space-separated romanization (Pinyin, Romaji, RR). Based on ROMANIZE.你好->ni hao西安->xi an
ROMANIZE_CHAR: Convert CJK characters to romanized form without boundary spaces. Based on ROMANIZE.你好->nihao西安->xian
EMOJI_NORM: Convert emoji to English words (CLDR short names) and strip modifiers. Based on EMOJI_NORM.👍🏽->thumbs_up🔥->fire
You can combine these transformations as needed. Pre-defined combinations like DELETE_NORMALIZE and VARIANT_NORM_DELETE_NORMALIZE are provided for convenience.
Be careful combining ROMANIZE and ROMANIZE_CHAR: they preserve different word boundaries, so the same input can behave like xi + an in one pipeline and xian in the other.
Error Handling
- Construction (
SimpleMatcher(bytes)): raisesValueErrorif the JSON is malformed or contains invalidProcessTypevalues. This is the only operation that can fail. - Matching (
is_match,process,batch_*): infallible once the matcher is built. These methods never raise exceptions.
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.
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