| Name | matcher-py JSON |
| Version |
0.5.8
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| download |
| home_page | https://github.com/Lips7/Matcher |
| Summary | A high-performance matcher designed to solve LOGICAL and TEXT VARIATIONS problems in word matching, implemented in Rust. |
| upload_time | 2025-08-23 00:37:54 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
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# Matcher Rust Implementation with PyO3 Binding
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](../DESIGN.md).
## Features
- **Multiple Matching Methods**:
- Simple Word Matching
- Regex-Based Matching
- Similarity-Based Matching
- **Text Normalization**:
- **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`
- **Customizable Exemption Lists**: Exclude specific words from matching.
- **Efficient Handling of Large Word Lists**: Optimized for performance.
## Installation
### Use pip
```shell
pip install matcher_py
```
### Install pre-built binary
Visit the [release page](https://github.com/Lips7/Matcher/releases) to download the pre-built binary.
## Usage
All relevant types are defined in [extension_types.py](./python/matcher_py/extension_types.py).
### Explanation of the configuration
* `Matcher`'s configuration is defined by the `MatchTableMap = Dict[int, List[MatchTable]]` type, the key of `MatchTableMap` is called `match_id`, **for each `match_id`, the `table_id` inside is required to be unique**.
* `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**.
#### MatchTable
* `table_id`: The unique ID of the match table.
* `match_table_type`: The type of the match table.
* `word_list`: The word list of the match table.
* `exemption_process_type`: The type of the exemption simple match.
* `exemption_word_list`: The exemption word list of the match table.
For each match table, word matching is performed over the `word_list`, and exemption word matching is performed over the `exemption_word_list`. If the exemption word matching result is True, the word matching result will be False.
#### MatchTableType
* `Simple`: Supports simple multiple patterns matching with text normalization defined by `process_type`.
* It can handle combination patterns and repeated times sensitive matching, delimited by `&` and `~`, such as `hello&world&hello` will match `hellohelloworld` and `worldhellohello`, but not `helloworld` due to the repeated times of `hello`.
* `Regex`: Supports regex patterns matching.
* `SimilarChar`: Supports similar character matching using regex.
* `["hello,hallo,hollo,hi", "word,world,wrd,🌍", "!,?,~"]` will match `helloworld!`, `hollowrd?`, `hi🌍~` ··· any combinations of the words split by `,` in the list.
* `Acrostic`: Supports acrostic matching using regex **(currently only supports Chinese and simple English sentences)**.
* `["h,e,l,l,o", "你,好"]` will match `hope, endures, love, lasts, onward.` and `你的笑容温暖, 好心情常伴。`.
* `Regex`: Supports regex matching.
* `["h[aeiou]llo", "w[aeiou]rd"]` will match `hello`, `world`, `hillo`, `wurld` ··· any text that matches the regex in the list.
* `Similar`: Supports similar text matching based on distance and threshold.
* `Levenshtein`: Supports similar text matching based on Levenshtein distance.
#### ProcessType
* `None`: No transformation.
* `Fanjian`: Traditional Chinese to simplified Chinese transformation. Based on [FANJIAN](../matcher_rs/process_map/FANJIAN.txt).
* `妳好` -> `你好`
* `現⾝` -> `现身`
* `Delete`: Delete all punctuation, special characters and white spaces. Based on [TEXT_DELETE](../matcher_rs/process_map/TEXT-DELETE.txt) 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](../matcher_rs//process_map/NORM.txt) and [NUM_NORM](../matcher_rs//process_map/NUM-NORM.txt).
* `ℋЀ⒈㈠Õ` -> `he11o`
* `⒈Ƨ㊂` -> `123`
* `PinYin`: Convert all unicode Chinese characters to pinyin with boundaries. Based on [PINYIN](../matcher_rs/process_map/PINYIN.txt).
* `你好` -> ` ni hao `
* `西安` -> ` xi an `
* `PinYinChar`: Convert all unicode Chinese characters to pinyin without boundaries. Based on [PINYIN](../matcher_rs/process_map/PINYIN.txt).
* `你好` -> `nihao`
* `西安` -> `xian`
You can combine these transformations as needed. Pre-defined combinations like `DeleteNormalize` and `FanjianDeleteNormalize` are provided for convenience.
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`.
### Text Process Usage
Here’s an example of how to use the `reduce_text_process` and `text_process` functions:
```python
from matcher_py import reduce_text_process, text_process
from matcher_py.extension_types import ProcessType
print(reduce_text_process(ProcessType.MatchDeleteNormalize, "hello, world!"))
print(text_process(ProcessType.MatchDelete, "hello, world!"))
```
### Matcher Basic Usage
Here’s an example of how to use the `Matcher`:
```python
import json
from matcher_py import Matcher
from matcher_py.extension_types import MatchTable, MatchTableType, ProcessType, RegexMatchType, SimMatchType
matcher = Matcher(
json.dumps({
1: [
MatchTable(
table_id=1,
match_table_type=MatchTableType.Simple(process_type = ProcessType.MatchFanjianDeleteNormalize),
word_list=["hello", "world"],
exemption_process_type=ProcessType.MatchNone,
exemption_word_list=["word"],
),
MatchTable(
table_id=2,
match_table_type=MatchTableType.Regex(
process_type = ProcessType.MatchFanjianDeleteNormalize,
regex_match_type=RegexMatchType.Regex
),
word_list=["h[aeiou]llo"],
exemption_process_type=ProcessType.MatchNone,
exemption_word_list=[],
)
],
2: [
MatchTable(
table_id=3,
match_table_type=MatchTableType.Similar(
process_type = ProcessType.MatchFanjianDeleteNormalize,
sim_match_type=SimMatchType.MatchLevenshtein,
threshold=0.5
),
word_list=["halxo"],
exemption_process_type=ProcessType.MatchNone,
exemption_word_list=[],
)
]
}).encode()
)
# Check if a text matches
assert matcher.is_match("hello")
assert not matcher.is_match("word")
# Perform process as a list
result = matcher.process("hello")
assert result == [{'match_id': 1,
'table_id': 2,
'word_id': 0,
'word': 'h[aeiou]llo',
'similarity': 1.0},
{'match_id': 1,
'table_id': 1,
'word_id': 0,
'word': 'hello',
'similarity': 1.0},
{'match_id': 2,
'table_id': 3,
'word_id': 0,
'word': 'halxo',
'similarity': 0.6}]
# Perform word matching as a dict
assert matcher.word_match(r"hello, world")[1] == [{'match_id': 1,
'table_id': 2,
'word_id': 0,
'word': 'h[aeiou]llo',
'similarity': 1.0},
{'match_id': 1,
'table_id': 1,
'word_id': 0,
'word': 'hello',
'similarity': 1.0},
{'match_id': 1,
'table_id': 1,
'word_id': 1,
'word': 'world',
'similarity': 1.0}]
# Perform word matching as a string
result = matcher.word_match_as_string("hello")
assert result == """{"2":[{"match_id":2,"table_id":3,"word_id":0,"word":"halxo","similarity":0.6}],"1":[{"match_id":1,"table_id":2,"word_id":0,"word":"h[aeiou]llo","similarity":1.0},{"match_id":1,"table_id":1,"word_id":0,"word":"hello","similarity":1.0}]}"""
```
### Simple Matcher Basic Usage
Here’s an example of how to use the `SimpleMatcher`:
```python
import json
from matcher_py import SimpleMatcher
from matcher_py.extension_types import ProcessType
simple_matcher = SimpleMatcher(
json.dumps(
{
ProcessType.MatchNone: {
1: "hello&world",
2: "word&word~hello"
},
ProcessType.MatchDelete: {
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")
assert result == [{'word_id': 1, 'word': 'hello&world'}, {'word_id': 3, 'word': 'hallo'}]
```
## 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](https://github.com/Lips7/Matcher). 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](https://github.com/Lips7/Matcher).
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"description": "# Matcher Rust Implementation with PyO3 Binding\n\nA high-performance matcher designed to solve **LOGICAL** and **TEXT VARIATIONS** problems in word matching, implemented in Rust.\n\nFor detailed implementation, see the [Design Document](../DESIGN.md).\n\n## Features\n\n- **Multiple Matching Methods**:\n - Simple Word Matching\n - Regex-Based Matching\n - Similarity-Based Matching\n- **Text Normalization**:\n - **Fanjian**: Simplify traditional Chinese characters to simplified ones.\n Example: `\u87f2\u8278` -> `\u866b\u8279`\n - **Delete**: Remove specific characters.\n Example: `*Fu&*iii&^%%*&kkkk` -> `Fuiiikkkk`\n - **Normalize**: Normalize special characters to identifiable characters.\n Example: `\ud835\udf22\ud835\udd70\ud835\udd43\ud835\ude7b\ud835\udf67 \ud835\ude52\u24de\u1d63\u2112\ud835\udc9f!` -> `hello world!`\n - **PinYin**: Convert Chinese characters to Pinyin for fuzzy matching.\n Example: `\u897f\u5b89` -> ` xi an `, matches `\u6d17\u6309` -> ` xi an `, but not `\u5148` -> ` xian `\n - **PinYinChar**: Convert Chinese characters to Pinyin.\n Example: `\u897f\u5b89` -> `xian`, matches `\u6d17\u6309` and `\u5148` -> `xian`\n- **AND OR NOT Word Matching**:\n - Takes into account the number of repetitions of words.\n - Example: `hello&world` matches `hello world` and `world,hello`\n - Example: `\u65e0&\u6cd5&\u65e0&\u5929` matches `\u65e0\u65e0\u6cd5\u5929` (because `\u65e0` is repeated twice), but not `\u65e0\u6cd5\u5929`\n - Example: `hello~helloo~hhello` matches `hello` but not `helloo` and `hhello`\n- **Customizable Exemption Lists**: Exclude specific words from matching.\n- **Efficient Handling of Large Word Lists**: Optimized for performance.\n\n## Installation\n\n### Use pip\n\n```shell\npip install matcher_py\n```\n\n### Install pre-built binary\n\nVisit the [release page](https://github.com/Lips7/Matcher/releases) to download the pre-built binary.\n\n## Usage\n\nAll relevant types are defined in [extension_types.py](./python/matcher_py/extension_types.py).\n\n### Explanation of the configuration\n\n* `Matcher`'s configuration is defined by the `MatchTableMap = Dict[int, List[MatchTable]]` type, the key of `MatchTableMap` is called `match_id`, **for each `match_id`, the `table_id` inside is required to be unique**.\n* `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**.\n\n#### MatchTable\n\n* `table_id`: The unique ID of the match table.\n* `match_table_type`: The type of the match table.\n* `word_list`: The word list of the match table.\n* `exemption_process_type`: The type of the exemption simple match.\n* `exemption_word_list`: The exemption word list of the match table.\n\nFor each match table, word matching is performed over the `word_list`, and exemption word matching is performed over the `exemption_word_list`. If the exemption word matching result is True, the word matching result will be False.\n\n#### MatchTableType\n\n* `Simple`: Supports simple multiple patterns matching with text normalization defined by `process_type`.\n * It can handle combination patterns and repeated times sensitive matching, delimited by `&` and `~`, such as `hello&world&hello` will match `hellohelloworld` and `worldhellohello`, but not `helloworld` due to the repeated times of `hello`.\n* `Regex`: Supports regex patterns matching.\n * `SimilarChar`: Supports similar character matching using regex.\n * `[\"hello,hallo,hollo,hi\", \"word,world,wrd,\ud83c\udf0d\", \"!,?,~\"]` will match `helloworld!`, `hollowrd?`, `hi\ud83c\udf0d~` \u00b7\u00b7\u00b7 any combinations of the words split by `,` in the list.\n * `Acrostic`: Supports acrostic matching using regex **(currently only supports Chinese and simple English sentences)**.\n * `[\"h,e,l,l,o\", \"\u4f60,\u597d\"]` will match `hope, endures, love, lasts, onward.` and `\u4f60\u7684\u7b11\u5bb9\u6e29\u6696, \u597d\u5fc3\u60c5\u5e38\u4f34\u3002`.\n * `Regex`: Supports regex matching.\n * `[\"h[aeiou]llo\", \"w[aeiou]rd\"]` will match `hello`, `world`, `hillo`, `wurld` \u00b7\u00b7\u00b7 any text that matches the regex in the list.\n* `Similar`: Supports similar text matching based on distance and threshold.\n * `Levenshtein`: Supports similar text matching based on Levenshtein distance.\n\n#### ProcessType\n\n* `None`: No transformation.\n* `Fanjian`: Traditional Chinese to simplified Chinese transformation. Based on [FANJIAN](../matcher_rs/process_map/FANJIAN.txt).\n * `\u59b3\u597d` -> `\u4f60\u597d`\n * `\u73fe\u2f9d` -> `\u73b0\u8eab`\n* `Delete`: Delete all punctuation, special characters and white spaces. Based on [TEXT_DELETE](../matcher_rs/process_map/TEXT-DELETE.txt) and `WHITE_SPACE`.\n * `hello, world!` -> `helloworld`\n * `\u300a\u4f60\u2237\u597d\u300b` -> `\u4f60\u597d`\n* `Normalize`: Normalize all English character variations and number variations to basic characters. Based on [NORM](../matcher_rs//process_map/NORM.txt) and [NUM_NORM](../matcher_rs//process_map/NUM-NORM.txt).\n * `\u210b\u0400\u2488\u3220\u00d5` -> `he11o`\n * `\u2488\u01a7\u3282` -> `123`\n* `PinYin`: Convert all unicode Chinese characters to pinyin with boundaries. Based on [PINYIN](../matcher_rs/process_map/PINYIN.txt).\n * `\u4f60\u597d` -> ` ni hao `\n * `\u897f\u5b89` -> ` xi an `\n* `PinYinChar`: Convert all unicode Chinese characters to pinyin without boundaries. Based on [PINYIN](../matcher_rs/process_map/PINYIN.txt).\n * `\u4f60\u597d` -> `nihao`\n * `\u897f\u5b89` -> `xian`\n\nYou can combine these transformations as needed. Pre-defined combinations like `DeleteNormalize` and `FanjianDeleteNormalize` are provided for convenience.\n\nAvoid 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`.\n\n### Text Process Usage\n\nHere\u2019s an example of how to use the `reduce_text_process` and `text_process` functions:\n\n```python\nfrom matcher_py import reduce_text_process, text_process\nfrom matcher_py.extension_types import ProcessType\n\nprint(reduce_text_process(ProcessType.MatchDeleteNormalize, \"hello, world!\"))\nprint(text_process(ProcessType.MatchDelete, \"hello, world!\"))\n```\n\n### Matcher Basic Usage\n\nHere\u2019s an example of how to use the `Matcher`:\n\n```python\nimport json\n\nfrom matcher_py import Matcher\nfrom matcher_py.extension_types import MatchTable, MatchTableType, ProcessType, RegexMatchType, SimMatchType\n\nmatcher = Matcher(\n json.dumps({\n 1: [\n MatchTable(\n table_id=1,\n match_table_type=MatchTableType.Simple(process_type = ProcessType.MatchFanjianDeleteNormalize),\n word_list=[\"hello\", \"world\"],\n exemption_process_type=ProcessType.MatchNone,\n exemption_word_list=[\"word\"],\n ),\n MatchTable(\n table_id=2,\n match_table_type=MatchTableType.Regex(\n process_type = ProcessType.MatchFanjianDeleteNormalize,\n regex_match_type=RegexMatchType.Regex\n ),\n word_list=[\"h[aeiou]llo\"],\n exemption_process_type=ProcessType.MatchNone,\n exemption_word_list=[],\n )\n ],\n 2: [\n MatchTable(\n table_id=3,\n match_table_type=MatchTableType.Similar(\n process_type = ProcessType.MatchFanjianDeleteNormalize,\n sim_match_type=SimMatchType.MatchLevenshtein,\n threshold=0.5\n ),\n word_list=[\"halxo\"],\n exemption_process_type=ProcessType.MatchNone,\n exemption_word_list=[],\n )\n ]\n }).encode()\n)\n# Check if a text matches\nassert matcher.is_match(\"hello\")\nassert not matcher.is_match(\"word\")\n# Perform process as a list\nresult = matcher.process(\"hello\")\nassert result == [{'match_id': 1,\n 'table_id': 2,\n 'word_id': 0,\n 'word': 'h[aeiou]llo',\n 'similarity': 1.0},\n {'match_id': 1,\n 'table_id': 1,\n 'word_id': 0,\n 'word': 'hello',\n 'similarity': 1.0},\n {'match_id': 2,\n 'table_id': 3,\n 'word_id': 0,\n 'word': 'halxo',\n 'similarity': 0.6}]\n# Perform word matching as a dict\nassert matcher.word_match(r\"hello, world\")[1] == [{'match_id': 1,\n 'table_id': 2,\n 'word_id': 0,\n 'word': 'h[aeiou]llo',\n 'similarity': 1.0},\n {'match_id': 1,\n 'table_id': 1,\n 'word_id': 0,\n 'word': 'hello',\n 'similarity': 1.0},\n {'match_id': 1,\n 'table_id': 1,\n 'word_id': 1,\n 'word': 'world',\n 'similarity': 1.0}]\n# Perform word matching as a string\nresult = matcher.word_match_as_string(\"hello\")\nassert result == \"\"\"{\"2\":[{\"match_id\":2,\"table_id\":3,\"word_id\":0,\"word\":\"halxo\",\"similarity\":0.6}],\"1\":[{\"match_id\":1,\"table_id\":2,\"word_id\":0,\"word\":\"h[aeiou]llo\",\"similarity\":1.0},{\"match_id\":1,\"table_id\":1,\"word_id\":0,\"word\":\"hello\",\"similarity\":1.0}]}\"\"\"\n```\n\n### Simple Matcher Basic Usage\n\nHere\u2019s an example of how to use the `SimpleMatcher`:\n\n```python\nimport json\n\nfrom matcher_py import SimpleMatcher\nfrom matcher_py.extension_types import ProcessType\n\nsimple_matcher = SimpleMatcher(\n json.dumps(\n {\n ProcessType.MatchNone: {\n 1: \"hello&world\",\n 2: \"word&word~hello\"\n },\n ProcessType.MatchDelete: {\n 3: \"hallo\"\n }\n }\n ).encode()\n)\n# Check if a text matches\nassert simple_matcher.is_match(\"hello^&!#*#&!^#*()world\")\n# Perform simple processing\nresult = simple_matcher.process(\"hello,world,word,word,hallo\")\nassert result == [{'word_id': 1, 'word': 'hello&world'}, {'word_id': 3, 'word': 'hallo'}]\n```\n\n## Contributing\n\nContributions to `matcher_py` are welcome! If you find a bug or have a feature request, please open an issue on the [GitHub repository](https://github.com/Lips7/Matcher). If you would like to contribute code, please fork the repository and submit a pull request.\n\n## License\n\n`matcher_py` is licensed under the MIT OR Apache-2.0 license.\n\n## More Information\n\nFor more details, visit the [GitHub repository](https://github.com/Lips7/Matcher).\n",
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