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https://github.com/genomoncology/FuzzTypes

Pydantic extension for annotating autocorrecting fields.
https://github.com/genomoncology/FuzzTypes

data-cleaning fuzzy-string-matching named-entity-linking pydantic

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Pydantic extension for annotating autocorrecting fields.

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README 

        
# FuzzTypes



FuzzTypes is a set of "autocorrecting" annotation types that expands

upon [Pydantic](https://github.com/pydantic/pydantic)'s included [data

conversions.](https://docs.pydantic.dev/latest/concepts/conversion_table/)

Designed for simplicity, it provides powerful normalization capabilities

(e.g. named entity linking) to ensure structured data is composed of

"smart things" not "dumb strings".



## Getting Started



Pydantic supports basic conversion of data between types. For instance:



```python

from pydantic import BaseModel



class Normal(BaseModel):

    boolean: bool

    float: float

    integer: int

    

obj = Normal(

    boolean='yes',

    float='2',

    integer='3',

)

assert obj.boolean is True

assert obj.float == 2.0

assert obj.integer == 3

```



FuzzTypes expands on the standard data conversions handled by Pydantic and

provides a variety of autocorrecting annotation types. 



```python

from datetime import datetime

from typing import Annotated



from pydantic import BaseModel



from fuzztypes import (

    ASCII,

    Datetime,

    Email,

    Fuzzmoji,

    InMemoryValidator,

    Integer,

    Person,

    RegexValidator,

    ZipCode,

    flags,

)



# define a source, see EntitySource for using TSV, CSV, JSONL

inventors = ["Ada Lovelace", "Alan Turing", "Claude Shannon"]



# define a in memory validator with fuzz search enabled.

Inventor = Annotated[

    str, InMemoryValidator(inventors, search_flag=flags.FuzzSearch)

]



# custom Regex type for finding twitter handles.

Handle = Annotated[

    str, RegexValidator(r"@\w{1,15}", examples=["@genomoncology"])

]



# define a Pydantic class with 9 fuzzy type attributes

class Fuzzy(BaseModel):

    ascii: ASCII

    email: Email

    emoji: Fuzzmoji

    handle: Handle

    integer: Integer

    inventor: Inventor

    person: Person

    time: Datetime

    zipcode: ZipCode



# create an instance of class Fuzzy

obj = Fuzzy(

    ascii="άνθρωπος",

    email="John Doe ",

    emoji='thought bubble',

    handle='Ian Maurer (@imaurer)',

    integer='fifty-five',

    inventor='ada luvlace',

    person='mr. arthur herbert fonzarelli (fonzie)',

    time='5am on Jan 1, 2025',

    zipcode="(Zipcode: 12345-6789)",

)



# test the autocorrecting performed



# greek for man: https://en.wiktionary.org/wiki/άνθρωπος

assert obj.ascii == "anthropos"



# extract email via regular expression

assert obj.email == "[email protected]"



# fuzzy match "thought bubble" to "thought balloon" emoji

assert obj.emoji == "💭"



# simple, inline regex example (see above Handle type)

assert obj.handle == "@imaurer"



# convert integer word phrase to integer value

assert obj.integer == 55



# case-insensitive fuzzy match on lowercase, misspelled name

assert obj.inventor == "Ada Lovelace"



# human name parser (title, first, middle, last, suffix, nickname)

assert str(obj.person) == "Mr. Arthur H. Fonzarelli (fonzie)"

assert obj.person.short_name == "Arthur Fonzarelli"

assert obj.person.nickname == "fonzie"

assert obj.person.last == "Fonzarelli"



# convert time phrase to datetime object

assert obj.time.isoformat() == "2025-01-01T05:00:00"



# extract zip5 or zip9 formats using regular expressions

assert obj.zipcode == "12345-6789"



# print JSON on success

assert obj.model_dump() == {

    "ascii": "anthropos",

    "email": "[email protected]",

    "emoji": "💭",

    "handle": "@imaurer",

    "integer": 55,

    "inventor": "Ada Lovelace",

    "person": {

        "first": "Arthur",

        "init_format": "{first} {middle} {last}",

        "last": "Fonzarelli",

        "middle": "H.",

        "name_format": "{title} {first} {middle} {last} {suffix} "

        "({nickname})",

        "nickname": "fonzie",

        "suffix": "",

        "title": "Mr.",

    },

    "time": datetime(2025, 1, 1, 5),

    "zipcode": "12345-6789",

}

```



## Installation



Available on [PyPI](https://pypi.org/project/FuzzTypes/):



```bash

pip install fuzztypes

```



To install all dependencies (see below), you can copy and paste this:



```bash

pip install anyascii dateparser emoji lancedb nameparser number-parser rapidfuzz sentence-transformers tantivy

```



## Google Colab Notebook



There is a read-only notebook that you can copy and edit to try out FuzzTypes:



[https://colab.research.google.com/drive/1GNngxcTUXpWDqK_qNsJoP2NhSN9vKCzZ?usp=sharing](https://colab.research.google.com/drive/1GNngxcTUXpWDqK_qNsJoP2NhSN9vKCzZ?usp=sharing)



## Base Validators



Base validators are the building blocks of FuzzTypes that can be used for creating custom "usable types".



| Type                | Description                                                                                 |

|---------------------|---------------------------------------------------------------------------------------------|

| `DateType`          | Base date type, pass in arguments such as `date_order`, `strict` and `relative_base`.       |

| `FuzzValidator`     | Validator class that calls a provided function and handles core and json schema config.     |

| `InMemoryValidator` | Enables matching entities in memory using exact, alias, fuzzy, or semantic search.          |

| `OnDiskValidator`   | Performs matching entities stored on disk using exact, alias, fuzzy, or semantic search.    |

| `RegexValidator`    | Regular expression pattern matching base validator.                                         |

| `DatetimeType`      | Base datetime type, pass in arguments such as `date_order`, `timezone` and `relative_base`. |



These base types offer flexibility and extensibility, enabling you to create custom annotation types that suit your

specific data validation and normalization requirements.



## Usable Types



Usable types are pre-built annotation types in FuzzTypes that can be directly used in Pydantic models. They provide

convenient and ready-to-use functionality for common data types and scenarios.



| Type           | Description                                                                               |

|----------------|-------------------------------------------------------------------------------------------|

| `ASCII`        | Converts Unicode strings to ASCII equivalents using either `anyascii` or `unidecode`.     |

| `Date`         | Converts date strings to `date` objects using `dateparser`.                               |

| `Email`        | Extracts email addresses from strings using a regular expression.                         |

| `Emoji`        | Matches emojis based on Unicode Consortium aliases using the `emoji` library.             |

| `Fuzzmoji`     | Matches emojis using fuzzy string matching against aliases.                               |

| `Integer`      | Converts numeric strings or words to integers using `number-parser`.                      |

| `LanguageCode` | Resolves language to ISO language codes (e.g., "en").                                     |

| `LanguageName` | Resolves language to ISO language names (e.g., "English").                                |

| `Language`     | Resolves language to ISO language object (name, alpha_2, alpha_3, scope, type, etc.).     |

| `Person`       | Parses person names into subfields (e.g., first, last, suffix) using `python-nameparser`. |

| `SSN`          | Extracts U.S. Social Security Numbers from strings using a regular expression.            |

| `Time`         | Converts datetime strings to `datetime` objects using `dateparser`.                       |

| `Vibemoji`     | Matches emojis using semantic similarity against aliases.                                 |

| `Zipcode`      | Extracts U.S. ZIP codes (5 or 9 digits) from strings using a regular expression.          |



These usable types provide a wide range of commonly needed data validations and transformations, making it

easier to work with various data formats and perform tasks like parsing, extraction, and matching.



## InMemoryValidator and OnDiskValidator Configuration



The InMemory and OnDisk Validator objects work with lists of Entities.



The following table describes the available configuration options:



| Argument          | Type                                    | Default               | Description                                                                                                                                                                                                                                                                                                                             |

|-------------------|-----------------------------------------|-----------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|

| `case_sensitive`  | `bool`                                  | `False`               | If `True`, matches are case-sensitive. If `False`, matches are case-insensitive.                                                                                                                                                                                                                                                        |

| `device`          | `Literal["cpu", "cuda", "mps"]`         | `"cpu"`               | The device to use for generating semantic embeddings and LanceDB indexing. Available options are "cpu", "cuda" (for NVIDIA GPUs), and "mps" (for Apple's Metal Performance Shaders).                                                                                                                                                    |

| `encoder`         | `Union[Callable, str, Any]`             | `None`                | The encoder to use for generating semantic embeddings. It can be a callable function, a string specifying the name or path of a pre-trained model, or any other object that implements the encoding functionality.                                                                                                                      |

| `examples`        | `List[Any]`                             | `None`                | A list of example values to be used in schema generation. These examples are included in the generated JSON schema to provide guidance on the expected format of the input values.                                                                                                                                                      |

| `fuzz_scorer`     | `Literal["token_sort_ratio", ...]`      | `"token_sort_ratio"`  | The scoring algorithm to use for fuzzy string matching. Available options include "token_sort_ratio", "ratio", "partial_ratio", "token_set_ratio", "partial_token_set_ratio", "token_ratio", "partial_token_ratio", "WRatio", and "QRatio". Each algorithm has its own characteristics and trade-offs between accuracy and performance. |

| `limit`           | `int`                                   | `10`                  | The maximum number of matches to return when performing fuzzy or semantic searches.                                                                                                                                                                                                                                                     |

| `min_similarity`  | `float`                                 | `80.0`                | The minimum similarity score required for a match to be considered valid. Matches with a similarity score below this threshold will be discarded.                                                                                                                                                                                       |

| `notfound_mode`   | `Literal["raise", "none", "allow"]`     | `"raise"`             | The action to take when a matching entity is not found. Available options are "raise" (raises an exception), "none" (returns `None`), and "allow" (returns the input key as the value).                                                                                                                                                 |

| `search_flag`     | `flags.SearchFlag`                      | `flags.DefaultSearch` | The search strategy to use for finding matches. It is a combination of flags that determine which fields of the `NamedEntity` are considered for matching and whether fuzzy or semantic search is enabled. Available options are defined in the `flags` module.                                                                         |

| `tiebreaker_mode` | `Literal["raise", "lesser", "greater"]` | `"raise"`             | The strategy to use for resolving ties when multiple matches have the same similarity score. Available options are "raise" (raises an exception), "lesser" (returns the match with the lower value), and "greater" (returns the match with the greater value).                                                                          |



## Lazy Dependencies



FuzzTypes leverages several powerful libraries to extend its functionality.



These dependencies are not installed by default with FuzzTypes to keep the

installation lightweight. Instead, they are optional and can be installed

as needed depending on which types you use.



Below is a list of these dependencies, including their licenses, purpose, and what

specific Types require them.



Right now, you must pip install the modules directly, in the future you will 

be able to install them automatically as part of the main install using pip extras.



To install all dependencies, you can copy and paste this:



```bash

pip install anyascii dateparser emoji lancedb nameparser number-parser rapidfuzz sentence-transformers tantivy

```



| Fuzz Type         | Library                                                                  | License    | Purpose                                                    |

|-------------------|--------------------------------------------------------------------------|------------|------------------------------------------------------------|

| ASCII             | [anyascii](https://github.com/anyascii/anyascii)                         | ISC        | Converting Unicode into ASCII equivalents (not GPL)        |

| ASCII             | [unidecode](https://github.com/avian2/unidecode)                         | GPL        | Converting Unicode into ASCII equivalents (better quality) |

| Date              | [dateparser](https://github.com/scrapinghub/dateparser)                  | BSD-3      | Parsing dates from strings                                 |

| Emoji             | [emoji](https://github.com/carpedm20/emoji/)                             | BSD        | Handling and manipulating emoji characters                 |

| Fuzz              | [rapidfuzz](https://github.com/rapidfuzz/RapidFuzz)                      | MIT        | Performing fuzzy string matching                           |

| InMemoryValidator | [numpy](https://numpy.org/)                                              | BSD        | Numerical computing in Python                              |

| InMemoryValidator | [scikit-learn](https://scikit-learn.org/)                                | BSD        | Machine learning in Python                                 |

| InMemoryValidator | [sentence-transformers](https://github.com/UKPLab/sentence-transformers) | Apache-2.0 | Encoding sentences into high-dimensional vectors           |

| Integer           | [number-parser](https://github.com/scrapinghub/number-parser)            | BSD-3      | Parsing numbers from strings                               |

| OnDiskValidator   | [lancedb](https://github.com/lancedb/lancedb)                            | Apache-2.0 | High-performance, on-disk vector database                  |

| OnDiskValidator   | [pyarrow](https://github.com/apache/arrow)                               | Apache-2.0 | In-memory columnar data format and processing library      |

| OnDiskValidator   | [sentence-transformers](https://github.com/UKPLab/sentence-transformers) | Apache-2.0 | Encoding sentences into high-dimensional vectors           |

| OnDiskValidator   | [tantivy](https://github.com/quickwit-oss/tantivy-py)                    | MIT        | Full-text search (FTS) for LanceDB.                        |

| Person            | [nameparser](https://github.com/derek73/python-nameparser)               | LGPL       | Parsing person names                                       |



## Maintainer



FuzzTypes was created by [Ian Maurer](https://x.com/imaurer), the CTO of [GenomOncology](https://genomoncology.com).



This MIT-based open-source project was extracted from our product which includes the ability to normalize biomedical

data for use in precision oncology clinical decision support systems. Contact me to learn more about our product

offerings.



| Type           | Description                                                                               |

|----------------|-------------------------------------------------------------------------------------------|

| `AirportCode`  | Represents airport codes (e.g., "ORD").                                                   |

| `Airport`      | Represents airport names (e.g., "O'Hare International Airport").                          |

| `CountryCode`  | Represents ISO country codes (e.g., "US").                                                |

| `Country`      | Represents country names (e.g., "United States").                                         |

| `Currency`     | Represents currency codes (e.g., "USD").                                                  |

| `Quantity`     | Converts strings to `Quantity` objects with value and unit using `pint`.                  |

| `URL`          | Represents normalized URLs with tracking parameters removed using `url-normalize`.        |

| `USStateCode`  | Represents U.S. state codes (e.g., "CA").                                                 |

| `USState`      | Represents U.S. state names (e.g., "California").                                         |



## Structured Data Generation via LLM Function Calling and Custom GPT Actions



Several libraries (e.g. [Instructor](https://github.com/jxnl/instructor),

[Outlines](https://github.com/outlines-dev/outlines),

[Marvin](https://github.com/prefecthq/marvin)) use Pydantic to define models for structured data generation

using Large Language Models (LLMs) via function calling or a grammar/regex

based sampling approach based on the [JSON schema generated by Pydantic](https://docs.pydantic.dev/latest/concepts/json_schema/).



This approach allows for the enumeration of allowed values using

Python's `Literal`, `Enum` or JSON Schema's `examples` field directly

in your Pydantic class declaration which is used by the LLM to

generate valid values. This approach works exceptionally well for

low-cardinality (not many unique allowed values) such as the world's

continents (7 in total).



This approach, however, doesn't scale well for high-cardinality (many unique

allowed values) such as the number of known human genomic variants (~325M).

Where exactly the cutoff is between "low" and "high" cardinality is an exercise

left to the reader and their use case.



That's where FuzzTypes come in. The allowed values are managed by the FuzzTypes

annotations and the values are resolved during the Pydantic validation process.

This can include fuzzy and semantic searching that throws an exception if the

provided value doesn't meet a minimum similarity threshold defined by the

developer.



Errors discovered via Pydantic can be caught and resubmitted to the LLM for

correction. The error will contain examples, expected patterns, and closest

matches to help steer the LLM to provide a better informed guess.



## Creating Custom Types



FuzzTypes provides a set of base types that you can use to create

your own custom annotation types. These base types offer different

capabilities and can be extended to suit your specific data validation

and normalization needs.



### EntitySource



FuzzTypes provides the `EntitySource` class to manage and load

entity data from various sources. It supports JSON Lines (`.jsonl`),

CSV (`.csv`), TSV (`.tsv`), and Text (`.txt`) formats, as well as

loading entities from a callable function.



Example:

```python

from pathlib import Path

from fuzztypes import EntitySource, NamedEntity



# Load entities from a CSV file

fruit_source = EntitySource(Path("path/to/fruits.csv"))



# Load entities from a callable function

def load_animals():

    return [

        NamedEntity(value="Dog", aliases=["Canine"]),

        NamedEntity(value="Cat", aliases=["Feline"]),

    ]



animal_source = EntitySource(load_animals)

```



### InMemoryValidator Base Type



The `InMemoryValidator` base type enables matching entities in memory using

exact, alias, fuzzy, or semantic search. It is suitable for small

to medium-sized datasets that can fit in memory and provides fast

matching capabilities.



Example:

```python

from typing import Annotated

from pydantic import BaseModel

from fuzztypes import InMemoryValidator, flags



# Create a custom annotation type for matching fruits

fruits = ["Apple", "Banana", "Orange"]

Fruit = Annotated[

    str, InMemoryValidator(fruits, search_flag=flags.FuzzSearch)

]



class MyModel(BaseModel):

    fruit: Fruit



model = MyModel(fruit="appel")

assert model.fruit == "Apple"

```



### OnDiskValidator Base Type



The `OnDiskValidator` base type performs matching entities stored on disk

using exact, alias, fuzzy, or semantic search. It leverages the

LanceDB library for efficient storage and retrieval of entities.

`OnDiskValidator` is recommended for large datasets that cannot fit in memory.



Example:

```python

from typing import Annotated

from pydantic import BaseModel

from fuzztypes import OnDiskValidator



# Create a custom annotation type for matching countries stored on disk

countries = [

    ("United States", "US"),

    ("United Kingdom", "UK"),

    ("Canada", "CA"),

]

Country = Annotated[str, OnDiskValidator("Country", countries)]



class MyModel(BaseModel):

    country: Country



assert MyModel(country="Canada").country == "Canada"

assert MyModel(country="US").country == "United States"

```



### DateType and TimeType



The `DateValidator` and `DatetimeValidator` base types provide fuzzy parsing

capabilities for date and datetime objects, respectively. They allow

you to define flexible date and time formats and perform parsing

based on specified settings such as date order, timezone, and

relative base.



Example:



```python

from datetime import date, datetime

from pydantic import BaseModel

from typing import Annotated

from fuzztypes import DateValidator, DatetimeValidator



MyDate = Annotated[date, DateValidator(date_order="MDY")]

MyTime = Annotated[datetime, DatetimeValidator(timezone="UTC")]



class MyModel(BaseModel):

    date: MyDate

    time: MyTime



model = MyModel(date="1/1/2023", time="1/1/23 at 10:30 PM")

assert model.date.isoformat() == "2023-01-01"

assert model.time.isoformat() == "2023-01-01T22:30:00+00:00"

```



### FuzzValidator



The `FuzzValidator` is the base of the fuzztypes typing system.

It can be used directly to wrap any python function.



Example:

```python

from typing import Annotated

from pydantic import BaseModel

from fuzztypes import FuzzValidator



# Create a custom annotation type that converts a value to uppercase

UpperCase = Annotated[str, FuzzValidator(str.upper)]



class MyModel(BaseModel):

    name: UpperCase



model = MyModel(name="john")

assert model.name == "JOHN"

```



### Regex



The `Regex` base type allows matching values using a regular

expression pattern. It is useful for creating annotation types that

validate and extract specific patterns from input values.



Example:

```python

from typing import Annotated

from pydantic import BaseModel

from fuzztypes import RegexValidator



# Create a custom annotation type for matching email addresses

IPAddress = Annotated[

    str, RegexValidator(r"(?:[0-9]{1,3}\.){3}[0-9]{1,3}$")

]



class MyModel(BaseModel):

    ip_address: IPAddress



model = MyModel(ip_address="My internet IP address is 192.168.127.12")

assert model.ip_address == "192.168.127.12"

```



### Languages



Languages are loaded from the [Debian iso-codes](https://salsa.debian.org/iso-codes-team/iso-codes/) project.



Languages are resolved using their preferred, common, inverted, bibliographic name, or 2 or 3 letter alpha code.



Languages can be included as a string name (LanguageName), string code (LanguageCode) or full language object.



The preferred code is the 2 letter version and will be used if available. Otherwise, the 3 letter alpha code is used.



Example:



```python

from pydantic import BaseModel

from fuzztypes import (

    Language,

    LanguageName,

    LanguageCode,

    LanguageScope,

    LanguageType,

    LanguageNamedEntity,

    validate_python,

)

class Model(BaseModel):

    language_code: LanguageCode

    language_name: LanguageName

    language: Language



# Test that Language resolves to the complete language object

data = dict(language_code="en", language="English", language_name="ENG")

obj = validate_python(Model, data)

assert obj.language_code == "en"

assert obj.language_name == "English"

assert obj.language.scope == LanguageScope.INDIVIDUAL

assert obj.language.type == LanguageType.LIVING

assert isinstance(obj.language, LanguageNamedEntity)

assert obj.model_dump(exclude_defaults=True, mode="json") == {

    "language": {

        "aliases": ["en", "eng"],

        "alpha_2": "en",

        "alpha_3": "eng",

        "scope": "I",

        "type": "L",

        "value": "English",

    },

    "language_code": "en",

    "language_name": "English",

}

```



### Validate Python and JSON functions



Functional approach to validating python and json are available.

Below are examples for the `validate_python` and `validate_json` functions:



```python

from pydantic import BaseModel

from fuzztypes import validate_python, validate_json, Integer, Date



# validate python

assert validate_python(Integer, "two hundred") == 200



# validate json

class MyModel(BaseModel):

    date: Date



json = '{"date": "July 4th 2021"}'

obj = validate_json(MyModel, json)

assert obj.date.isoformat() == "2021-07-04"

```



### Resolve Entities from FuzzValidator or Annotation



Entities can be resolved from the `FuzzValidator` validators such as InMemoryValidator

or OnDiskValidator or the defined `Annotation` type using the `resolve_entity` function:



```python

from typing import Annotated

from fuzztypes import resolve_entity, InMemoryValidator



elements = ["earth", "fire", "water", "air"]

ElementValidator = InMemoryValidator(elements)

Element = Annotated[str, ElementValidator]



assert resolve_entity(ElementValidator, "EARTH").model_dump() == {

    "aliases": [],

    "label": None,

    "meta": None,

    "priority": None,

    "value": "earth",

}



assert resolve_entity(Element, "Air").model_dump(

    exclude_defaults=True

) == {"value": "air"}

```