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DSL to express constraints

This library provides a Domain-Specific Language (DSL) to construct regular expressions in a more intuitive and modular way. It allows you to create complex regexes using simple building blocks that represent literal strings, patterns, and various quantifiers. Additionally, these custom regex types can be used directly as types in Pydantic schemas to enforce pattern constraints during text generation.

Why Use This DSL?

  1. Modularity & Readability: Instead of writing cryptic regular expression strings, you compose a regex as a tree of objects.
  2. Enhanced Debugging: Each expression can be visualized as an ASCII tree, making it easier to understand and debug complex regexes.
  3. Pydantic Integration: Use your DSL-defined regex as types in Pydantic models. The DSL seamlessly converts to JSON Schema with proper pattern constraints.
  4. Extensibility: Easily add or modify quantifiers and other regex components by extending the provided classes.

Building Blocks

Every regex component in this DSL is a Term. Here are two primary types:

  • String: Represents a literal string. It escapes the characters that have a special meaning in regular expressions.
  • Regex: Represents an existing regex pattern string.
from outlines.types import String, Regex

# A literal string "hello"
literal = String("hello")   # Internally represents "hello"

# A regex pattern to match one or more digits
digit = Regex(r"[0-9]+")     # Internally represents the pattern [0-9]+

# Converting to standard regex strings:
from outlines.types.dsl import to_regex

print(to_regex(literal))  # Output: hello
print(to_regex(digit))    # Output: [0-9]+

Early Introduction to Quantifiers & Combining Terms

The DSL supports common regex quantifiers as methods on every Term. These methods allow you to specify how many times a pattern should be matched. They include:

  • exactly(count): Matches the term exactly count times.
  • optional(): Matches the term zero or one time.
  • one_or_more(): Matches the term one or more times (Kleene Plus).
  • zero_or_more(): Matches the term zero or more times (Kleene Star).
  • between(min_count, max_count): Matches the term between min_count and max_count times (inclusive).
  • at_least(count): Matches the term at least count times.
  • at_most(count): Matches the term up to count times.

These quantifiers can also be used as functions that take the Term as an argument. If the term is a plain string, it will be automatically converted to a String object. Thus String("foo").optional() is equivalent to optional("foo").

Let's see these quantifiers side by side with examples.

Quantifiers in Action

exactly(count)

This method restricts the term to appear exactly count times.

# Example: exactly 5 digits
five_digits = Regex(r"\d").exactly(5)
print(to_regex(five_digits))  # Output: (\d){5}

You can also use the exactly function:

from outlines.types import exactly

# Example: exactly 5 digits
five_digits = exactly(Regex(r"\d"), 5)
print(to_regex(five_digits))  # Output: (\d){5}

optional()

This method makes a term optional, meaning it may occur zero or one time.

# Example: an optional "s" at the end of a word
maybe_s = String("s").optional()
print(to_regex(maybe_s))  # Output: (s)?

You can also use the optional function:

from outlines.types import optional

# Example: an optional "s" at the end of a word
maybe_s = optional("s")
print(to_regex(maybe_s))  # Output: (s)?

one_or_more()

This method indicates that the term must appear at least once.

# Example: one or more alphabetic characters
letters = Regex(r"[A-Za-z]").one_or_more()
print(to_regex(letters))  # Output: ([A-Za-z])+

You can also use the one_or_more function:

from outlines.types import one_or_more

# Example: one or more alphabetic characters
letters = one_or_more(Regex(r"[A-Za-z]"))
print(to_regex(letters))  # Output: ([A-Za-z])+

zero_or_more()

This method indicates that the term can occur zero or more times.

# Example: zero or more spaces
spaces = String(" ").zero_or_more()
print(to_regex(spaces))  # Output: ( )*

You can also use the zero_or_more function:

from outlines.types import zero_or_more

# Example: zero or more spaces
spaces = zero_or_more(" ")
print(to_regex(spaces))  # Output: ( )*

between(min_count, max_count)

This method indicates that the term can appear any number of times between min_count and max_count (inclusive).

# Example: Between 2 and 4 word characters
word_chars = Regex(r"\w").between(2, 4)
print(to_regex(word_chars))  # Output: (\w){2,4}

You can also use the between function:

from outlines.types import between

# Example: Between 2 and 4 word characters
word_chars = between(Regex(r"\w"), 2, 4)
print(to_regex(word_chars))  # Output: (\w){2,4}

at_least(count)

This method indicates that the term must appear at least count times.

# Example: At least 3 digits
at_least_three = Regex(r"\d").at_least(3)
print(to_regex(at_least_three))  # Output: (\d){3,}

You can also use the at_least function:

from outlines.types import at_least

# Example: At least 3 digits
at_least_three = at_least(Regex(r"\d"), 3)
print(to_regex(at_least_three))  # Output: (\d){3,}

at_most(count)

This method indicates that the term can appear at most count times.

# Example: At most 3 digits
up_to_three = Regex(r"\d").at_most(3)
print(to_regex(up_to_three))  # Output: (\d){0,3}

You can also use the at_most function:

from outlines.types import at_most

# Example: At most 3 digits
up_to_three = at_most(Regex(r"\d"), 3)
print(to_regex(up_to_three))  # Output: (\d){0,3}

Combining Terms

The DSL allows you to combine basic terms into more complex patterns using concatenation and alternation.

Concatenation (+)

The + operator (and its reflected variant) concatenates terms, meaning that the terms are matched in sequence.

# Example: Match "hello world"
pattern = String("hello") + " " + Regex(r"\w+")
print(to_regex(pattern))  # Output: hello\ (\w+)

Alternation (either())

The either() function creates alternatives, allowing a match for one of several patterns. You can provide as many terms as you want.

# Example: Match either "cat" or "dog" or "mouse"
animal = either(String("cat"), "dog", "mouse")
print(to_regex(animal))  # Output: (cat|dog|mouse)

Note: When using either() with plain strings (such as "dog"), the DSL automatically wraps them in a String object that escapes the characters that have a special meaning in regular expressions, just like with quantifier functions.

Custom types

The DSL comes "batteries included" with types that represent common text constructs:

  • integer represents an integer number as recognized by int
  • boolean represents a boolean, "True" or "False" as recognized by bool
  • number represents a floating-point number recognize by Python's float
  • date represents a date as understood by datetime.date
  • time represents a time as undestood by datetime.time
  • datetime represents a time as understoof by datetime.datetime
  • digit represents a single digit
  • char represents a single character
  • newline represents a new line character
  • whitespace represents a white space
  • sentence represents a sentence
  • paragraph reprensents a pagraph (one or more sentences separated by one or more ilne breaks)

For instance you can describe the answers in the GSM8K dataset using the following pattern:

from outlines.types import sentence, digit

answer = "A: " + sentence.between(2,4) + " So the answer is: " + digit.between(1,4)

Practical Examples

Example 1: Matching a Custom ID Format

Suppose you want to create a regex that matches an ID format like "ID-12345", where: - The literal "ID-" must be at the start. - Followed by exactly 5 digits.

id_pattern = "ID-" + Regex(r"\d").exactly(5)
print(to_regex(id_pattern))  # Output: ID-(\d){5}

Example 2: Email Validation with Pydantic

You can define a regex for email validation and use it as a type in a Pydantic model.

from pydantic import BaseModel, ValidationError

# Define an email regex term (this is a simplified version)
email_regex = Regex(r"[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+")

class User(BaseModel):
    name: str
    email: email_regex  # Use our DSL regex as a field type

# Valid input
user = User(name="Alice", email="alice@example.com")
print(user)

# Invalid input (raises a ValidationError)
try:
    User(name="Bob", email="not-an-email")
except ValidationError as e:
    print(e)

When used in a Pydantic model, the email field is automatically validated against the regex pattern and its JSON Schema includes the pattern constraint.

Example 3: Building a Complex Pattern

Consider a pattern to match a simple date format: YYYY-MM-DD.

year = Regex(r"\d").exactly(4)         # Four digits for the year
month = Regex(r"\d").exactly(2)        # Two digits for the month
day = Regex(r"\d").exactly(2)          # Two digits for the day

# Combine with literal hyphens
date_pattern = year + "-" + month + "-" + day
print(to_regex(date_pattern))
# Output: (\d){4}\-(\d){2}\-(\d){2}

Visualizing Your Pattern

One of the unique features of this DSL is that each term can print its underlying structure as an ASCII tree. This visualization can be particularly helpful when dealing with complex expressions.

# A composite pattern using concatenation and quantifiers
pattern = "a" + String("b").one_or_more() + "c"
print(pattern)

Expected Output:

└── Sequence
    ├── String('a')
    ├── KleenePlus(+)
    │   └── String('b')
    └── String('c')

This tree representation makes it easy to see the hierarchy and order of operations in your regular expression.

Final Words

This DSL is designed to simplify the creation and management of regular expressions—whether you're validating inputs in a web API, constraining the output of an LLM, or just experimenting with regex patterns. With intuitive methods for common quantifiers and operators, clear visual feedback, and built-in integration with Pydantic, you can build robust and maintainable regex-based validations with ease.

Feel free to explore the library further and adapt the examples to your use cases. Happy regexing!