mirror of
https://github.com/ggerganov/llama.cpp.git
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3fd62a6b1c
* py : type-check all Python scripts with Pyright * server-tests : use trailing slash in openai base_url * server-tests : add more type annotations * server-tests : strip "chat" from base_url in oai_chat_completions * server-tests : model metadata is a dict * ci : disable pip cache in type-check workflow The cache is not shared between branches, and it's 250MB in size, so it would become quite a big part of the 10GB cache limit of the repo. * py : fix new type errors from master branch * tests : fix test-tokenizer-random.py Apparently, gcc applies optimisations even when pre-processing, which confuses pycparser. * ci : only show warnings and errors in python type-check The "information" level otherwise has entries from 'examples/pydantic_models_to_grammar.py', which could be confusing for someone trying to figure out what failed, considering that these messages can safely be ignored even though they look like errors.
1314 lines
55 KiB
Python
1314 lines
55 KiB
Python
from __future__ import annotations
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import inspect
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import json
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import re
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from copy import copy
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from enum import Enum
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from inspect import getdoc, isclass
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from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union, get_args, get_origin
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from docstring_parser import parse
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from pydantic import BaseModel, create_model
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if TYPE_CHECKING:
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from types import GenericAlias
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else:
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# python 3.8 compat
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from typing import _GenericAlias as GenericAlias
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# TODO: fix this
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# pyright: reportAttributeAccessIssue=information
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class PydanticDataType(Enum):
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"""
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Defines the data types supported by the grammar_generator.
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Attributes:
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STRING (str): Represents a string data type.
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BOOLEAN (str): Represents a boolean data type.
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INTEGER (str): Represents an integer data type.
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FLOAT (str): Represents a float data type.
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OBJECT (str): Represents an object data type.
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ARRAY (str): Represents an array data type.
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ENUM (str): Represents an enum data type.
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CUSTOM_CLASS (str): Represents a custom class data type.
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"""
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STRING = "string"
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TRIPLE_QUOTED_STRING = "triple_quoted_string"
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MARKDOWN_CODE_BLOCK = "markdown_code_block"
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BOOLEAN = "boolean"
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INTEGER = "integer"
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FLOAT = "float"
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OBJECT = "object"
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ARRAY = "array"
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ENUM = "enum"
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ANY = "any"
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NULL = "null"
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CUSTOM_CLASS = "custom-class"
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CUSTOM_DICT = "custom-dict"
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SET = "set"
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def map_pydantic_type_to_gbnf(pydantic_type: type[Any]) -> str:
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if isclass(pydantic_type) and issubclass(pydantic_type, str):
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return PydanticDataType.STRING.value
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elif isclass(pydantic_type) and issubclass(pydantic_type, bool):
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return PydanticDataType.BOOLEAN.value
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elif isclass(pydantic_type) and issubclass(pydantic_type, int):
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return PydanticDataType.INTEGER.value
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elif isclass(pydantic_type) and issubclass(pydantic_type, float):
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return PydanticDataType.FLOAT.value
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elif isclass(pydantic_type) and issubclass(pydantic_type, Enum):
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return PydanticDataType.ENUM.value
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elif isclass(pydantic_type) and issubclass(pydantic_type, BaseModel):
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return format_model_and_field_name(pydantic_type.__name__)
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elif get_origin(pydantic_type) is list:
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element_type = get_args(pydantic_type)[0]
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return f"{map_pydantic_type_to_gbnf(element_type)}-list"
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elif get_origin(pydantic_type) is set:
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element_type = get_args(pydantic_type)[0]
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return f"{map_pydantic_type_to_gbnf(element_type)}-set"
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elif get_origin(pydantic_type) is Union:
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union_types = get_args(pydantic_type)
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union_rules = [map_pydantic_type_to_gbnf(ut) for ut in union_types]
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return f"union-{'-or-'.join(union_rules)}"
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elif get_origin(pydantic_type) is Optional:
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element_type = get_args(pydantic_type)[0]
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return f"optional-{map_pydantic_type_to_gbnf(element_type)}"
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elif isclass(pydantic_type):
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return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(pydantic_type.__name__)}"
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elif get_origin(pydantic_type) is dict:
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key_type, value_type = get_args(pydantic_type)
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return f"custom-dict-key-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(key_type))}-value-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(value_type))}"
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else:
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return "unknown"
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def format_model_and_field_name(model_name: str) -> str:
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parts = re.findall("[A-Z][^A-Z]*", model_name)
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if not parts: # Check if the list is empty
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return model_name.lower().replace("_", "-")
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return "-".join(part.lower().replace("_", "-") for part in parts)
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def generate_list_rule(element_type):
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"""
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Generate a GBNF rule for a list of a given element type.
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:param element_type: The type of the elements in the list (e.g., 'string').
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:return: A string representing the GBNF rule for a list of the given type.
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"""
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rule_name = f"{map_pydantic_type_to_gbnf(element_type)}-list"
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element_rule = map_pydantic_type_to_gbnf(element_type)
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list_rule = rf'{rule_name} ::= "[" {element_rule} ("," {element_rule})* "]"'
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return list_rule
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def get_members_structure(cls, rule_name):
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if issubclass(cls, Enum):
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# Handle Enum types
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members = [f'"\\"{member.value}\\""' for name, member in cls.__members__.items()]
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return f"{cls.__name__.lower()} ::= " + " | ".join(members)
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if cls.__annotations__ and cls.__annotations__ != {}:
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result = f'{rule_name} ::= "{{"'
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# Modify this comprehension
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members = [
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f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param_type)}'
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for name, param_type in cls.__annotations__.items()
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if name != "self"
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]
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result += '"," '.join(members)
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result += ' "}"'
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return result
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if rule_name == "custom-class-any":
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result = f"{rule_name} ::= "
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result += "value"
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return result
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init_signature = inspect.signature(cls.__init__)
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parameters = init_signature.parameters
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result = f'{rule_name} ::= "{{"'
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# Modify this comprehension too
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members = [
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f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param.annotation)}'
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for name, param in parameters.items()
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if name != "self" and param.annotation != inspect.Parameter.empty
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]
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result += '", "'.join(members)
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result += ' "}"'
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return result
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def regex_to_gbnf(regex_pattern: str) -> str:
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"""
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Translate a basic regex pattern to a GBNF rule.
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Note: This function handles only a subset of simple regex patterns.
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"""
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gbnf_rule = regex_pattern
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# Translate common regex components to GBNF
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gbnf_rule = gbnf_rule.replace("\\d", "[0-9]")
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gbnf_rule = gbnf_rule.replace("\\s", "[ \t\n]")
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# Handle quantifiers and other regex syntax that is similar in GBNF
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# (e.g., '*', '+', '?', character classes)
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return gbnf_rule
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def generate_gbnf_integer_rules(max_digit=None, min_digit=None):
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"""
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Generate GBNF Integer Rules
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Generates GBNF (Generalized Backus-Naur Form) rules for integers based on the given maximum and minimum digits.
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Parameters:
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max_digit (int): The maximum number of digits for the integer. Default is None.
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min_digit (int): The minimum number of digits for the integer. Default is None.
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Returns:
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integer_rule (str): The identifier for the integer rule generated.
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additional_rules (list): A list of additional rules generated based on the given maximum and minimum digits.
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"""
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additional_rules = []
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# Define the rule identifier based on max_digit and min_digit
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integer_rule = "integer-part"
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if max_digit is not None:
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integer_rule += f"-max{max_digit}"
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if min_digit is not None:
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integer_rule += f"-min{min_digit}"
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# Handling Integer Rules
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if max_digit is not None or min_digit is not None:
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# Start with an empty rule part
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integer_rule_part = ""
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# Add mandatory digits as per min_digit
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if min_digit is not None:
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integer_rule_part += "[0-9] " * min_digit
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# Add optional digits up to max_digit
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if max_digit is not None:
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optional_digits = max_digit - (min_digit if min_digit is not None else 0)
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integer_rule_part += "".join(["[0-9]? " for _ in range(optional_digits)])
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# Trim the rule part and append it to additional rules
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integer_rule_part = integer_rule_part.strip()
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if integer_rule_part:
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additional_rules.append(f"{integer_rule} ::= {integer_rule_part}")
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return integer_rule, additional_rules
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def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None, min_precision=None):
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"""
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Generate GBNF float rules based on the given constraints.
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:param max_digit: Maximum number of digits in the integer part (default: None)
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:param min_digit: Minimum number of digits in the integer part (default: None)
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:param max_precision: Maximum number of digits in the fractional part (default: None)
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:param min_precision: Minimum number of digits in the fractional part (default: None)
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:return: A tuple containing the float rule and additional rules as a list
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Example Usage:
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max_digit = 3
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min_digit = 1
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max_precision = 2
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min_precision = 1
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generate_gbnf_float_rules(max_digit, min_digit, max_precision, min_precision)
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Output:
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('float-3-1-2-1', ['integer-part-max3-min1 ::= [0-9] [0-9] [0-9]?', 'fractional-part-max2-min1 ::= [0-9] [0-9]?', 'float-3-1-2-1 ::= integer-part-max3-min1 "." fractional-part-max2-min
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*1'])
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Note:
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GBNF stands for Generalized Backus-Naur Form, which is a notation technique to specify the syntax of programming languages or other formal grammars.
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"""
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additional_rules = []
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# Define the integer part rule
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integer_part_rule = (
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"integer-part"
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+ (f"-max{max_digit}" if max_digit is not None else "")
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+ (f"-min{min_digit}" if min_digit is not None else "")
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)
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# Define the fractional part rule based on precision constraints
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fractional_part_rule = "fractional-part"
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fractional_rule_part = ""
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if max_precision is not None or min_precision is not None:
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fractional_part_rule += (f"-max{max_precision}" if max_precision is not None else "") + (
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f"-min{min_precision}" if min_precision is not None else ""
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)
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# Minimum number of digits
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fractional_rule_part = "[0-9]" * (min_precision if min_precision is not None else 1)
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# Optional additional digits
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fractional_rule_part += "".join(
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[" [0-9]?"] * ((max_precision - (
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min_precision if min_precision is not None else 1)) if max_precision is not None else 0)
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)
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additional_rules.append(f"{fractional_part_rule} ::= {fractional_rule_part}")
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# Define the float rule
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float_rule = f"float-{max_digit if max_digit is not None else 'X'}-{min_digit if min_digit is not None else 'X'}-{max_precision if max_precision is not None else 'X'}-{min_precision if min_precision is not None else 'X'}"
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additional_rules.append(f'{float_rule} ::= {integer_part_rule} "." {fractional_part_rule}')
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# Generating the integer part rule definition, if necessary
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if max_digit is not None or min_digit is not None:
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integer_rule_part = "[0-9]"
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if min_digit is not None and min_digit > 1:
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integer_rule_part += " [0-9]" * (min_digit - 1)
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if max_digit is not None:
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integer_rule_part += "".join([" [0-9]?"] * (max_digit - (min_digit if min_digit is not None else 1)))
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additional_rules.append(f"{integer_part_rule} ::= {integer_rule_part.strip()}")
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return float_rule, additional_rules
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def generate_gbnf_rule_for_type(
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model_name, field_name, field_type, is_optional, processed_models, created_rules, field_info=None
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) -> tuple[str, list[str]]:
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"""
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Generate GBNF rule for a given field type.
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:param model_name: Name of the model.
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:param field_name: Name of the field.
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:param field_type: Type of the field.
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:param is_optional: Whether the field is optional.
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:param processed_models: List of processed models.
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:param created_rules: List of created rules.
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:param field_info: Additional information about the field (optional).
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:return: Tuple containing the GBNF type and a list of additional rules.
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:rtype: tuple[str, list]
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"""
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rules = []
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field_name = format_model_and_field_name(field_name)
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gbnf_type = map_pydantic_type_to_gbnf(field_type)
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if isclass(field_type) and issubclass(field_type, BaseModel):
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nested_model_name = format_model_and_field_name(field_type.__name__)
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nested_model_rules, _ = generate_gbnf_grammar(field_type, processed_models, created_rules)
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rules.extend(nested_model_rules)
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gbnf_type, rules = nested_model_name, rules
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elif isclass(field_type) and issubclass(field_type, Enum):
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enum_values = [f'"\\"{e.value}\\""' for e in field_type] # Adding escaped quotes
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enum_rule = f"{model_name}-{field_name} ::= {' | '.join(enum_values)}"
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rules.append(enum_rule)
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gbnf_type, rules = model_name + "-" + field_name, rules
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elif get_origin(field_type) == list: # Array
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element_type = get_args(field_type)[0]
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element_rule_name, additional_rules = generate_gbnf_rule_for_type(
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model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
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)
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rules.extend(additional_rules)
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array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """
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rules.append(array_rule)
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gbnf_type, rules = model_name + "-" + field_name, rules
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elif get_origin(field_type) == set or field_type == set: # Array
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element_type = get_args(field_type)[0]
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element_rule_name, additional_rules = generate_gbnf_rule_for_type(
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model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
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)
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rules.extend(additional_rules)
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array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """
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rules.append(array_rule)
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gbnf_type, rules = model_name + "-" + field_name, rules
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elif gbnf_type.startswith("custom-class-"):
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rules.append(get_members_structure(field_type, gbnf_type))
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elif gbnf_type.startswith("custom-dict-"):
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key_type, value_type = get_args(field_type)
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additional_key_type, additional_key_rules = generate_gbnf_rule_for_type(
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model_name, f"{field_name}-key-type", key_type, is_optional, processed_models, created_rules
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)
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additional_value_type, additional_value_rules = generate_gbnf_rule_for_type(
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model_name, f"{field_name}-value-type", value_type, is_optional, processed_models, created_rules
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)
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gbnf_type = rf'{gbnf_type} ::= "{{" ( {additional_key_type} ": " {additional_value_type} ("," "\n" ws {additional_key_type} ":" {additional_value_type})* )? "}}" '
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rules.extend(additional_key_rules)
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rules.extend(additional_value_rules)
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elif gbnf_type.startswith("union-"):
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union_types = get_args(field_type)
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union_rules = []
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for union_type in union_types:
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if isinstance(union_type, GenericAlias):
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union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(
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model_name, field_name, union_type, False, processed_models, created_rules
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)
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union_rules.append(union_gbnf_type)
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rules.extend(union_rules_list)
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elif not issubclass(union_type, type(None)):
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union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(
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model_name, field_name, union_type, False, processed_models, created_rules
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)
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union_rules.append(union_gbnf_type)
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rules.extend(union_rules_list)
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# Defining the union grammar rule separately
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if len(union_rules) == 1:
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union_grammar_rule = f"{model_name}-{field_name}-optional ::= {' | '.join(union_rules)} | null"
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else:
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union_grammar_rule = f"{model_name}-{field_name}-union ::= {' | '.join(union_rules)}"
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rules.append(union_grammar_rule)
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if len(union_rules) == 1:
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gbnf_type = f"{model_name}-{field_name}-optional"
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else:
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gbnf_type = f"{model_name}-{field_name}-union"
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elif isclass(field_type) and issubclass(field_type, str):
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if field_info and hasattr(field_info, "json_schema_extra") and field_info.json_schema_extra is not None:
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triple_quoted_string = field_info.json_schema_extra.get("triple_quoted_string", False)
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markdown_string = field_info.json_schema_extra.get("markdown_code_block", False)
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gbnf_type = PydanticDataType.TRIPLE_QUOTED_STRING.value if triple_quoted_string else PydanticDataType.STRING.value
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gbnf_type = PydanticDataType.MARKDOWN_CODE_BLOCK.value if markdown_string else gbnf_type
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elif field_info and hasattr(field_info, "pattern"):
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# Convert regex pattern to grammar rule
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regex_pattern = field_info.regex.pattern
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gbnf_type = f"pattern-{field_name} ::= {regex_to_gbnf(regex_pattern)}"
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else:
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gbnf_type = PydanticDataType.STRING.value
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elif (
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isclass(field_type)
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and issubclass(field_type, float)
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and field_info
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and hasattr(field_info, "json_schema_extra")
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and field_info.json_schema_extra is not None
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):
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# Retrieve precision attributes for floats
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max_precision = (
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field_info.json_schema_extra.get("max_precision") if field_info and hasattr(field_info,
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"json_schema_extra") else None
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)
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min_precision = (
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field_info.json_schema_extra.get("min_precision") if field_info and hasattr(field_info,
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"json_schema_extra") else None
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)
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max_digits = field_info.json_schema_extra.get("max_digit") if field_info and hasattr(field_info,
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"json_schema_extra") else None
|
|
min_digits = field_info.json_schema_extra.get("min_digit") if field_info and hasattr(field_info,
|
|
"json_schema_extra") else None
|
|
|
|
# Generate GBNF rule for float with given attributes
|
|
gbnf_type, rules = generate_gbnf_float_rules(
|
|
max_digit=max_digits, min_digit=min_digits, max_precision=max_precision, min_precision=min_precision
|
|
)
|
|
|
|
elif (
|
|
isclass(field_type)
|
|
and issubclass(field_type, int)
|
|
and field_info
|
|
and hasattr(field_info, "json_schema_extra")
|
|
and field_info.json_schema_extra is not None
|
|
):
|
|
# Retrieve digit attributes for integers
|
|
max_digits = field_info.json_schema_extra.get("max_digit") if field_info and hasattr(field_info,
|
|
"json_schema_extra") else None
|
|
min_digits = field_info.json_schema_extra.get("min_digit") if field_info and hasattr(field_info,
|
|
"json_schema_extra") else None
|
|
|
|
# Generate GBNF rule for integer with given attributes
|
|
gbnf_type, rules = generate_gbnf_integer_rules(max_digit=max_digits, min_digit=min_digits)
|
|
else:
|
|
gbnf_type, rules = gbnf_type, []
|
|
|
|
return gbnf_type, rules
|
|
|
|
|
|
def generate_gbnf_grammar(model: type[BaseModel], processed_models: set[type[BaseModel]], created_rules: dict[str, list[str]]) -> tuple[list[str], bool]:
|
|
"""
|
|
|
|
Generate GBnF Grammar
|
|
|
|
Generates a GBnF grammar for a given model.
|
|
|
|
:param model: A Pydantic model class to generate the grammar for. Must be a subclass of BaseModel.
|
|
:param processed_models: A set of already processed models to prevent infinite recursion.
|
|
:param created_rules: A dict containing already created rules to prevent duplicates.
|
|
:return: A list of GBnF grammar rules in string format. And two booleans indicating if an extra markdown or triple quoted string is in the grammar.
|
|
Example Usage:
|
|
```
|
|
model = MyModel
|
|
processed_models = set()
|
|
created_rules = dict()
|
|
|
|
gbnf_grammar = generate_gbnf_grammar(model, processed_models, created_rules)
|
|
```
|
|
"""
|
|
if model in processed_models:
|
|
return [], False
|
|
|
|
processed_models.add(model)
|
|
model_name = format_model_and_field_name(model.__name__)
|
|
|
|
if not issubclass(model, BaseModel):
|
|
# For non-Pydantic classes, generate model_fields from __annotations__ or __init__
|
|
if hasattr(model, "__annotations__") and model.__annotations__:
|
|
model_fields = {name: (typ, ...) for name, typ in model.__annotations__.items()} # pyright: ignore[reportGeneralTypeIssues]
|
|
else:
|
|
init_signature = inspect.signature(model.__init__)
|
|
parameters = init_signature.parameters
|
|
model_fields = {name: (param.annotation, param.default) for name, param in parameters.items() if
|
|
name != "self"}
|
|
else:
|
|
# For Pydantic models, use model_fields and check for ellipsis (required fields)
|
|
model_fields = model.__annotations__
|
|
|
|
model_rule_parts = []
|
|
nested_rules = []
|
|
has_markdown_code_block = False
|
|
has_triple_quoted_string = False
|
|
look_for_markdown_code_block = False
|
|
look_for_triple_quoted_string = False
|
|
for field_name, field_info in model_fields.items():
|
|
if not issubclass(model, BaseModel):
|
|
field_type, default_value = field_info
|
|
# Check if the field is optional (not required)
|
|
is_optional = (default_value is not inspect.Parameter.empty) and (default_value is not Ellipsis)
|
|
else:
|
|
field_type = field_info
|
|
field_info = model.model_fields[field_name]
|
|
is_optional = field_info.is_required is False and get_origin(field_type) is Optional
|
|
rule_name, additional_rules = generate_gbnf_rule_for_type(
|
|
model_name, format_model_and_field_name(field_name), field_type, is_optional, processed_models,
|
|
created_rules, field_info
|
|
)
|
|
look_for_markdown_code_block = True if rule_name == "markdown_code_block" else False
|
|
look_for_triple_quoted_string = True if rule_name == "triple_quoted_string" else False
|
|
if not look_for_markdown_code_block and not look_for_triple_quoted_string:
|
|
if rule_name not in created_rules:
|
|
created_rules[rule_name] = additional_rules
|
|
model_rule_parts.append(f' ws "\\"{field_name}\\"" ":" ws {rule_name}') # Adding escaped quotes
|
|
nested_rules.extend(additional_rules)
|
|
else:
|
|
has_triple_quoted_string = look_for_triple_quoted_string
|
|
has_markdown_code_block = look_for_markdown_code_block
|
|
|
|
fields_joined = r' "," "\n" '.join(model_rule_parts)
|
|
model_rule = rf'{model_name} ::= "{{" "\n" {fields_joined} "\n" ws "}}"'
|
|
|
|
has_special_string = False
|
|
if has_triple_quoted_string:
|
|
model_rule += '"\\n" ws "}"'
|
|
model_rule += '"\\n" triple-quoted-string'
|
|
has_special_string = True
|
|
if has_markdown_code_block:
|
|
model_rule += '"\\n" ws "}"'
|
|
model_rule += '"\\n" markdown-code-block'
|
|
has_special_string = True
|
|
all_rules = [model_rule] + nested_rules
|
|
|
|
return all_rules, has_special_string
|
|
|
|
|
|
def generate_gbnf_grammar_from_pydantic_models(
|
|
models: list[type[BaseModel]], outer_object_name: str | None = None, outer_object_content: str | None = None,
|
|
list_of_outputs: bool = False
|
|
) -> str:
|
|
"""
|
|
Generate GBNF Grammar from Pydantic Models.
|
|
|
|
This method takes a list of Pydantic models and uses them to generate a GBNF grammar string. The generated grammar string can be used for parsing and validating data using the generated
|
|
* grammar.
|
|
|
|
Args:
|
|
models (list[type[BaseModel]]): A list of Pydantic models to generate the grammar from.
|
|
outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling.
|
|
outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling.
|
|
list_of_outputs (str, optional): Allows a list of output objects
|
|
Returns:
|
|
str: The generated GBNF grammar string.
|
|
|
|
Examples:
|
|
models = [UserModel, PostModel]
|
|
grammar = generate_gbnf_grammar_from_pydantic(models)
|
|
print(grammar)
|
|
# Output:
|
|
# root ::= UserModel | PostModel
|
|
# ...
|
|
"""
|
|
processed_models: set[type[BaseModel]] = set()
|
|
all_rules = []
|
|
created_rules: dict[str, list[str]] = {}
|
|
if outer_object_name is None:
|
|
for model in models:
|
|
model_rules, _ = generate_gbnf_grammar(model, processed_models, created_rules)
|
|
all_rules.extend(model_rules)
|
|
|
|
if list_of_outputs:
|
|
root_rule = r'root ::= (" "| "\n") "[" ws grammar-models ("," ws grammar-models)* ws "]"' + "\n"
|
|
else:
|
|
root_rule = r'root ::= (" "| "\n") grammar-models' + "\n"
|
|
root_rule += "grammar-models ::= " + " | ".join(
|
|
[format_model_and_field_name(model.__name__) for model in models])
|
|
all_rules.insert(0, root_rule)
|
|
return "\n".join(all_rules)
|
|
elif outer_object_name is not None:
|
|
if list_of_outputs:
|
|
root_rule = (
|
|
rf'root ::= (" "| "\n") "[" ws {format_model_and_field_name(outer_object_name)} ("," ws {format_model_and_field_name(outer_object_name)})* ws "]"'
|
|
+ "\n"
|
|
)
|
|
else:
|
|
root_rule = f"root ::= {format_model_and_field_name(outer_object_name)}\n"
|
|
|
|
model_rule = (
|
|
rf'{format_model_and_field_name(outer_object_name)} ::= (" "| "\n") "{{" ws "\"{outer_object_name}\"" ":" ws grammar-models'
|
|
)
|
|
|
|
fields_joined = " | ".join(
|
|
[rf"{format_model_and_field_name(model.__name__)}-grammar-model" for model in models])
|
|
|
|
grammar_model_rules = f"\ngrammar-models ::= {fields_joined}"
|
|
mod_rules = []
|
|
for model in models:
|
|
mod_rule = rf"{format_model_and_field_name(model.__name__)}-grammar-model ::= "
|
|
mod_rule += (
|
|
rf'"\"{model.__name__}\"" "," ws "\"{outer_object_content}\"" ":" ws {format_model_and_field_name(model.__name__)}' + "\n"
|
|
)
|
|
mod_rules.append(mod_rule)
|
|
grammar_model_rules += "\n" + "\n".join(mod_rules)
|
|
|
|
for model in models:
|
|
model_rules, has_special_string = generate_gbnf_grammar(model, processed_models,
|
|
created_rules)
|
|
|
|
if not has_special_string:
|
|
model_rules[0] += r'"\n" ws "}"'
|
|
|
|
all_rules.extend(model_rules)
|
|
|
|
all_rules.insert(0, root_rule + model_rule + grammar_model_rules)
|
|
return "\n".join(all_rules)
|
|
|
|
|
|
def get_primitive_grammar(grammar):
|
|
"""
|
|
Returns the needed GBNF primitive grammar for a given GBNF grammar string.
|
|
|
|
Args:
|
|
grammar (str): The string containing the GBNF grammar.
|
|
|
|
Returns:
|
|
str: GBNF primitive grammar string.
|
|
"""
|
|
type_list: list[type[object]] = []
|
|
if "string-list" in grammar:
|
|
type_list.append(str)
|
|
if "boolean-list" in grammar:
|
|
type_list.append(bool)
|
|
if "integer-list" in grammar:
|
|
type_list.append(int)
|
|
if "float-list" in grammar:
|
|
type_list.append(float)
|
|
additional_grammar = [generate_list_rule(t) for t in type_list]
|
|
primitive_grammar = r"""
|
|
boolean ::= "true" | "false"
|
|
null ::= "null"
|
|
string ::= "\"" (
|
|
[^"\\] |
|
|
"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
|
|
)* "\"" ws
|
|
ws ::= ([ \t\n] ws)?
|
|
float ::= ("-"? ([0] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
|
|
|
|
integer ::= [0-9]+"""
|
|
|
|
any_block = ""
|
|
if "custom-class-any" in grammar:
|
|
any_block = """
|
|
value ::= object | array | string | number | boolean | null
|
|
|
|
object ::=
|
|
"{" ws (
|
|
string ":" ws value
|
|
("," ws string ":" ws value)*
|
|
)? "}" ws
|
|
|
|
array ::=
|
|
"[" ws (
|
|
value
|
|
("," ws value)*
|
|
)? "]" ws
|
|
|
|
number ::= integer | float"""
|
|
|
|
markdown_code_block_grammar = ""
|
|
if "markdown-code-block" in grammar:
|
|
markdown_code_block_grammar = r'''
|
|
markdown-code-block ::= opening-triple-ticks markdown-code-block-content closing-triple-ticks
|
|
markdown-code-block-content ::= ( [^`] | "`" [^`] | "`" "`" [^`] )*
|
|
opening-triple-ticks ::= "```" "python" "\n" | "```" "c" "\n" | "```" "cpp" "\n" | "```" "txt" "\n" | "```" "text" "\n" | "```" "json" "\n" | "```" "javascript" "\n" | "```" "css" "\n" | "```" "html" "\n" | "```" "markdown" "\n"
|
|
closing-triple-ticks ::= "```" "\n"'''
|
|
|
|
if "triple-quoted-string" in grammar:
|
|
markdown_code_block_grammar = r"""
|
|
triple-quoted-string ::= triple-quotes triple-quoted-string-content triple-quotes
|
|
triple-quoted-string-content ::= ( [^'] | "'" [^'] | "'" "'" [^'] )*
|
|
triple-quotes ::= "'''" """
|
|
return "\n" + "\n".join(additional_grammar) + any_block + primitive_grammar + markdown_code_block_grammar
|
|
|
|
|
|
def generate_markdown_documentation(
|
|
pydantic_models: list[type[BaseModel]], model_prefix="Model", fields_prefix="Fields",
|
|
documentation_with_field_description=True
|
|
) -> str:
|
|
"""
|
|
Generate markdown documentation for a list of Pydantic models.
|
|
|
|
Args:
|
|
pydantic_models (list[type[BaseModel]]): list of Pydantic model classes.
|
|
model_prefix (str): Prefix for the model section.
|
|
fields_prefix (str): Prefix for the fields section.
|
|
documentation_with_field_description (bool): Include field descriptions in the documentation.
|
|
|
|
Returns:
|
|
str: Generated text documentation.
|
|
"""
|
|
documentation = ""
|
|
pyd_models: list[tuple[type[BaseModel], bool]] = [(model, True) for model in pydantic_models]
|
|
for model, add_prefix in pyd_models:
|
|
if add_prefix:
|
|
documentation += f"{model_prefix}: {model.__name__}\n"
|
|
else:
|
|
documentation += f"Model: {model.__name__}\n"
|
|
|
|
# Handling multi-line model description with proper indentation
|
|
|
|
class_doc = getdoc(model)
|
|
base_class_doc = getdoc(BaseModel)
|
|
class_description = class_doc if class_doc and class_doc != base_class_doc else ""
|
|
if class_description != "":
|
|
documentation += " Description: "
|
|
documentation += format_multiline_description(class_description, 0) + "\n"
|
|
|
|
if add_prefix:
|
|
# Indenting the fields section
|
|
documentation += f" {fields_prefix}:\n"
|
|
else:
|
|
documentation += f" Fields:\n" # noqa: F541
|
|
if isclass(model) and issubclass(model, BaseModel):
|
|
for name, field_type in model.__annotations__.items():
|
|
# if name == "markdown_code_block":
|
|
# continue
|
|
if get_origin(field_type) == list:
|
|
element_type = get_args(field_type)[0]
|
|
if isclass(element_type) and issubclass(element_type, BaseModel):
|
|
pyd_models.append((element_type, False))
|
|
if get_origin(field_type) == Union:
|
|
element_types = get_args(field_type)
|
|
for element_type in element_types:
|
|
if isclass(element_type) and issubclass(element_type, BaseModel):
|
|
pyd_models.append((element_type, False))
|
|
documentation += generate_field_markdown(
|
|
name, field_type, model, documentation_with_field_description=documentation_with_field_description
|
|
)
|
|
documentation += "\n"
|
|
|
|
if hasattr(model, "Config") and hasattr(model.Config,
|
|
"json_schema_extra") and "example" in model.Config.json_schema_extra:
|
|
documentation += f" Expected Example Output for {format_model_and_field_name(model.__name__)}:\n"
|
|
json_example = json.dumps(model.Config.json_schema_extra["example"])
|
|
documentation += format_multiline_description(json_example, 2) + "\n"
|
|
|
|
return documentation
|
|
|
|
|
|
def generate_field_markdown(
|
|
field_name: str, field_type: type[Any], model: type[BaseModel], depth=1,
|
|
documentation_with_field_description=True
|
|
) -> str:
|
|
"""
|
|
Generate markdown documentation for a Pydantic model field.
|
|
|
|
Args:
|
|
field_name (str): Name of the field.
|
|
field_type (type[Any]): Type of the field.
|
|
model (type[BaseModel]): Pydantic model class.
|
|
depth (int): Indentation depth in the documentation.
|
|
documentation_with_field_description (bool): Include field descriptions in the documentation.
|
|
|
|
Returns:
|
|
str: Generated text documentation for the field.
|
|
"""
|
|
indent = " " * depth
|
|
|
|
field_info = model.model_fields.get(field_name)
|
|
field_description = field_info.description if field_info and field_info.description else ""
|
|
|
|
if get_origin(field_type) == list:
|
|
element_type = get_args(field_type)[0]
|
|
field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)} of {format_model_and_field_name(element_type.__name__)})"
|
|
if field_description != "":
|
|
field_text += ":\n"
|
|
else:
|
|
field_text += "\n"
|
|
elif get_origin(field_type) == Union:
|
|
element_types = get_args(field_type)
|
|
types = []
|
|
for element_type in element_types:
|
|
types.append(format_model_and_field_name(element_type.__name__))
|
|
field_text = f"{indent}{field_name} ({' or '.join(types)})"
|
|
if field_description != "":
|
|
field_text += ":\n"
|
|
else:
|
|
field_text += "\n"
|
|
else:
|
|
field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)})"
|
|
if field_description != "":
|
|
field_text += ":\n"
|
|
else:
|
|
field_text += "\n"
|
|
|
|
if not documentation_with_field_description:
|
|
return field_text
|
|
|
|
if field_description != "":
|
|
field_text += f" Description: {field_description}\n"
|
|
|
|
# Check for and include field-specific examples if available
|
|
if hasattr(model, "Config") and hasattr(model.Config,
|
|
"json_schema_extra") and "example" in model.Config.json_schema_extra:
|
|
field_example = model.Config.json_schema_extra["example"].get(field_name)
|
|
if field_example is not None:
|
|
example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example
|
|
field_text += f"{indent} Example: {example_text}\n"
|
|
|
|
if isclass(field_type) and issubclass(field_type, BaseModel):
|
|
field_text += f"{indent} Details:\n"
|
|
for name, type_ in field_type.__annotations__.items():
|
|
field_text += generate_field_markdown(name, type_, field_type, depth + 2)
|
|
|
|
return field_text
|
|
|
|
|
|
def format_json_example(example: dict[str, Any], depth: int) -> str:
|
|
"""
|
|
Format a JSON example into a readable string with indentation.
|
|
|
|
Args:
|
|
example (dict): JSON example to be formatted.
|
|
depth (int): Indentation depth.
|
|
|
|
Returns:
|
|
str: Formatted JSON example string.
|
|
"""
|
|
indent = " " * depth
|
|
formatted_example = "{\n"
|
|
for key, value in example.items():
|
|
value_text = f"'{value}'" if isinstance(value, str) else value
|
|
formatted_example += f"{indent}{key}: {value_text},\n"
|
|
formatted_example = formatted_example.rstrip(",\n") + "\n" + indent + "}"
|
|
return formatted_example
|
|
|
|
|
|
def generate_text_documentation(
|
|
pydantic_models: list[type[BaseModel]], model_prefix="Model", fields_prefix="Fields",
|
|
documentation_with_field_description=True
|
|
) -> str:
|
|
"""
|
|
Generate text documentation for a list of Pydantic models.
|
|
|
|
Args:
|
|
pydantic_models (list[type[BaseModel]]): List of Pydantic model classes.
|
|
model_prefix (str): Prefix for the model section.
|
|
fields_prefix (str): Prefix for the fields section.
|
|
documentation_with_field_description (bool): Include field descriptions in the documentation.
|
|
|
|
Returns:
|
|
str: Generated text documentation.
|
|
"""
|
|
documentation = ""
|
|
pyd_models: list[tuple[type[BaseModel], bool]] = [(model, True) for model in pydantic_models]
|
|
for model, add_prefix in pyd_models:
|
|
if add_prefix:
|
|
documentation += f"{model_prefix}: {model.__name__}\n"
|
|
else:
|
|
documentation += f"Model: {model.__name__}\n"
|
|
|
|
# Handling multi-line model description with proper indentation
|
|
|
|
class_doc = getdoc(model)
|
|
base_class_doc = getdoc(BaseModel)
|
|
class_description = class_doc if class_doc and class_doc != base_class_doc else ""
|
|
if class_description != "":
|
|
documentation += " Description: "
|
|
documentation += "\n" + format_multiline_description(class_description, 2) + "\n"
|
|
|
|
if isclass(model) and issubclass(model, BaseModel):
|
|
documentation_fields = ""
|
|
for name, field_type in model.__annotations__.items():
|
|
# if name == "markdown_code_block":
|
|
# continue
|
|
if get_origin(field_type) == list:
|
|
element_type = get_args(field_type)[0]
|
|
if isclass(element_type) and issubclass(element_type, BaseModel):
|
|
pyd_models.append((element_type, False))
|
|
if get_origin(field_type) == Union:
|
|
element_types = get_args(field_type)
|
|
for element_type in element_types:
|
|
if isclass(element_type) and issubclass(element_type, BaseModel):
|
|
pyd_models.append((element_type, False))
|
|
documentation_fields += generate_field_text(
|
|
name, field_type, model, documentation_with_field_description=documentation_with_field_description
|
|
)
|
|
if documentation_fields != "":
|
|
if add_prefix:
|
|
documentation += f" {fields_prefix}:\n{documentation_fields}"
|
|
else:
|
|
documentation += f" Fields:\n{documentation_fields}"
|
|
documentation += "\n"
|
|
|
|
if hasattr(model, "Config") and hasattr(model.Config,
|
|
"json_schema_extra") and "example" in model.Config.json_schema_extra:
|
|
documentation += f" Expected Example Output for {format_model_and_field_name(model.__name__)}:\n"
|
|
json_example = json.dumps(model.Config.json_schema_extra["example"])
|
|
documentation += format_multiline_description(json_example, 2) + "\n"
|
|
|
|
return documentation
|
|
|
|
|
|
def generate_field_text(
|
|
field_name: str, field_type: type[Any], model: type[BaseModel], depth=1,
|
|
documentation_with_field_description=True
|
|
) -> str:
|
|
"""
|
|
Generate text documentation for a Pydantic model field.
|
|
|
|
Args:
|
|
field_name (str): Name of the field.
|
|
field_type (type[Any]): Type of the field.
|
|
model (type[BaseModel]): Pydantic model class.
|
|
depth (int): Indentation depth in the documentation.
|
|
documentation_with_field_description (bool): Include field descriptions in the documentation.
|
|
|
|
Returns:
|
|
str: Generated text documentation for the field.
|
|
"""
|
|
indent = " " * depth
|
|
|
|
field_info = model.model_fields.get(field_name)
|
|
field_description = field_info.description if field_info and field_info.description else ""
|
|
|
|
if get_origin(field_type) == list:
|
|
element_type = get_args(field_type)[0]
|
|
field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)} of {format_model_and_field_name(element_type.__name__)})"
|
|
if field_description != "":
|
|
field_text += ":\n"
|
|
else:
|
|
field_text += "\n"
|
|
elif get_origin(field_type) == Union:
|
|
element_types = get_args(field_type)
|
|
types = []
|
|
for element_type in element_types:
|
|
types.append(format_model_and_field_name(element_type.__name__))
|
|
field_text = f"{indent}{field_name} ({' or '.join(types)})"
|
|
if field_description != "":
|
|
field_text += ":\n"
|
|
else:
|
|
field_text += "\n"
|
|
else:
|
|
field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)})"
|
|
if field_description != "":
|
|
field_text += ":\n"
|
|
else:
|
|
field_text += "\n"
|
|
|
|
if not documentation_with_field_description:
|
|
return field_text
|
|
|
|
if field_description != "":
|
|
field_text += f"{indent} Description: " + field_description + "\n"
|
|
|
|
# Check for and include field-specific examples if available
|
|
if hasattr(model, "Config") and hasattr(model.Config,
|
|
"json_schema_extra") and "example" in model.Config.json_schema_extra:
|
|
field_example = model.Config.json_schema_extra["example"].get(field_name)
|
|
if field_example is not None:
|
|
example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example
|
|
field_text += f"{indent} Example: {example_text}\n"
|
|
|
|
if isclass(field_type) and issubclass(field_type, BaseModel):
|
|
field_text += f"{indent} Details:\n"
|
|
for name, type_ in field_type.__annotations__.items():
|
|
field_text += generate_field_text(name, type_, field_type, depth + 2)
|
|
|
|
return field_text
|
|
|
|
|
|
def format_multiline_description(description: str, indent_level: int) -> str:
|
|
"""
|
|
Format a multiline description with proper indentation.
|
|
|
|
Args:
|
|
description (str): Multiline description.
|
|
indent_level (int): Indentation level.
|
|
|
|
Returns:
|
|
str: Formatted multiline description.
|
|
"""
|
|
indent = " " * indent_level
|
|
return indent + description.replace("\n", "\n" + indent)
|
|
|
|
|
|
def save_gbnf_grammar_and_documentation(
|
|
grammar, documentation, grammar_file_path="./grammar.gbnf", documentation_file_path="./grammar_documentation.md"
|
|
):
|
|
"""
|
|
Save GBNF grammar and documentation to specified files.
|
|
|
|
Args:
|
|
grammar (str): GBNF grammar string.
|
|
documentation (str): Documentation string.
|
|
grammar_file_path (str): File path to save the GBNF grammar.
|
|
documentation_file_path (str): File path to save the documentation.
|
|
|
|
Returns:
|
|
None
|
|
"""
|
|
try:
|
|
with open(grammar_file_path, "w") as file:
|
|
file.write(grammar + get_primitive_grammar(grammar))
|
|
print(f"Grammar successfully saved to {grammar_file_path}")
|
|
except IOError as e:
|
|
print(f"An error occurred while saving the grammar file: {e}")
|
|
|
|
try:
|
|
with open(documentation_file_path, "w") as file:
|
|
file.write(documentation)
|
|
print(f"Documentation successfully saved to {documentation_file_path}")
|
|
except IOError as e:
|
|
print(f"An error occurred while saving the documentation file: {e}")
|
|
|
|
|
|
def remove_empty_lines(string):
|
|
"""
|
|
Remove empty lines from a string.
|
|
|
|
Args:
|
|
string (str): Input string.
|
|
|
|
Returns:
|
|
str: String with empty lines removed.
|
|
"""
|
|
lines = string.splitlines()
|
|
non_empty_lines = [line for line in lines if line.strip() != ""]
|
|
string_no_empty_lines = "\n".join(non_empty_lines)
|
|
return string_no_empty_lines
|
|
|
|
|
|
def generate_and_save_gbnf_grammar_and_documentation(
|
|
pydantic_model_list,
|
|
grammar_file_path="./generated_grammar.gbnf",
|
|
documentation_file_path="./generated_grammar_documentation.md",
|
|
outer_object_name: str | None = None,
|
|
outer_object_content: str | None = None,
|
|
model_prefix: str = "Output Model",
|
|
fields_prefix: str = "Output Fields",
|
|
list_of_outputs: bool = False,
|
|
documentation_with_field_description=True,
|
|
):
|
|
"""
|
|
Generate GBNF grammar and documentation, and save them to specified files.
|
|
|
|
Args:
|
|
pydantic_model_list: List of Pydantic model classes.
|
|
grammar_file_path (str): File path to save the generated GBNF grammar.
|
|
documentation_file_path (str): File path to save the generated documentation.
|
|
outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling.
|
|
outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling.
|
|
model_prefix (str): Prefix for the model section in the documentation.
|
|
fields_prefix (str): Prefix for the fields section in the documentation.
|
|
list_of_outputs (bool): Whether the output is a list of items.
|
|
documentation_with_field_description (bool): Include field descriptions in the documentation.
|
|
|
|
Returns:
|
|
None
|
|
"""
|
|
documentation = generate_markdown_documentation(
|
|
pydantic_model_list, model_prefix, fields_prefix,
|
|
documentation_with_field_description=documentation_with_field_description
|
|
)
|
|
grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content,
|
|
list_of_outputs)
|
|
grammar = remove_empty_lines(grammar)
|
|
save_gbnf_grammar_and_documentation(grammar, documentation, grammar_file_path, documentation_file_path)
|
|
|
|
|
|
def generate_gbnf_grammar_and_documentation(
|
|
pydantic_model_list,
|
|
outer_object_name: str | None = None,
|
|
outer_object_content: str | None = None,
|
|
model_prefix: str = "Output Model",
|
|
fields_prefix: str = "Output Fields",
|
|
list_of_outputs: bool = False,
|
|
documentation_with_field_description=True,
|
|
):
|
|
"""
|
|
Generate GBNF grammar and documentation for a list of Pydantic models.
|
|
|
|
Args:
|
|
pydantic_model_list: List of Pydantic model classes.
|
|
outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling.
|
|
outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling.
|
|
model_prefix (str): Prefix for the model section in the documentation.
|
|
fields_prefix (str): Prefix for the fields section in the documentation.
|
|
list_of_outputs (bool): Whether the output is a list of items.
|
|
documentation_with_field_description (bool): Include field descriptions in the documentation.
|
|
|
|
Returns:
|
|
tuple: GBNF grammar string, documentation string.
|
|
"""
|
|
documentation = generate_markdown_documentation(
|
|
copy(pydantic_model_list), model_prefix, fields_prefix,
|
|
documentation_with_field_description=documentation_with_field_description
|
|
)
|
|
grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content,
|
|
list_of_outputs)
|
|
grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar))
|
|
return grammar, documentation
|
|
|
|
|
|
def generate_gbnf_grammar_and_documentation_from_dictionaries(
|
|
dictionaries: list[dict[str, Any]],
|
|
outer_object_name: str | None = None,
|
|
outer_object_content: str | None = None,
|
|
model_prefix: str = "Output Model",
|
|
fields_prefix: str = "Output Fields",
|
|
list_of_outputs: bool = False,
|
|
documentation_with_field_description=True,
|
|
):
|
|
"""
|
|
Generate GBNF grammar and documentation from a list of dictionaries.
|
|
|
|
Args:
|
|
dictionaries (list[dict]): List of dictionaries representing Pydantic models.
|
|
outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling.
|
|
outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling.
|
|
model_prefix (str): Prefix for the model section in the documentation.
|
|
fields_prefix (str): Prefix for the fields section in the documentation.
|
|
list_of_outputs (bool): Whether the output is a list of items.
|
|
documentation_with_field_description (bool): Include field descriptions in the documentation.
|
|
|
|
Returns:
|
|
tuple: GBNF grammar string, documentation string.
|
|
"""
|
|
pydantic_model_list = create_dynamic_models_from_dictionaries(dictionaries)
|
|
documentation = generate_markdown_documentation(
|
|
copy(pydantic_model_list), model_prefix, fields_prefix,
|
|
documentation_with_field_description=documentation_with_field_description
|
|
)
|
|
grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content,
|
|
list_of_outputs)
|
|
grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar))
|
|
return grammar, documentation
|
|
|
|
|
|
def create_dynamic_model_from_function(func: Callable[..., Any]):
|
|
"""
|
|
Creates a dynamic Pydantic model from a given function's type hints and adds the function as a 'run' method.
|
|
|
|
Args:
|
|
func (Callable): A function with type hints from which to create the model.
|
|
|
|
Returns:
|
|
A dynamic Pydantic model class with the provided function as a 'run' method.
|
|
"""
|
|
|
|
# Get the signature of the function
|
|
sig = inspect.signature(func)
|
|
|
|
# Parse the docstring
|
|
assert func.__doc__ is not None
|
|
docstring = parse(func.__doc__)
|
|
|
|
dynamic_fields = {}
|
|
param_docs = []
|
|
for param in sig.parameters.values():
|
|
# Exclude 'self' parameter
|
|
if param.name == "self":
|
|
continue
|
|
|
|
# Assert that the parameter has a type annotation
|
|
if param.annotation == inspect.Parameter.empty:
|
|
raise TypeError(f"Parameter '{param.name}' in function '{func.__name__}' lacks a type annotation")
|
|
|
|
# Find the parameter's description in the docstring
|
|
param_doc = next((d for d in docstring.params if d.arg_name == param.name), None)
|
|
|
|
# Assert that the parameter has a description
|
|
if not param_doc or not param_doc.description:
|
|
raise ValueError(
|
|
f"Parameter '{param.name}' in function '{func.__name__}' lacks a description in the docstring")
|
|
|
|
# Add parameter details to the schema
|
|
param_docs.append((param.name, param_doc))
|
|
if param.default == inspect.Parameter.empty:
|
|
default_value = ...
|
|
else:
|
|
default_value = param.default
|
|
dynamic_fields[param.name] = (
|
|
param.annotation if param.annotation != inspect.Parameter.empty else str, default_value)
|
|
# Creating the dynamic model
|
|
dynamic_model = create_model(f"{func.__name__}", **dynamic_fields)
|
|
|
|
for name, param_doc in param_docs:
|
|
dynamic_model.model_fields[name].description = param_doc.description
|
|
|
|
dynamic_model.__doc__ = docstring.short_description
|
|
|
|
def run_method_wrapper(self):
|
|
func_args = {name: getattr(self, name) for name, _ in dynamic_fields.items()}
|
|
return func(**func_args)
|
|
|
|
# Adding the wrapped function as a 'run' method
|
|
setattr(dynamic_model, "run", run_method_wrapper)
|
|
return dynamic_model
|
|
|
|
|
|
def add_run_method_to_dynamic_model(model: type[BaseModel], func: Callable[..., Any]):
|
|
"""
|
|
Add a 'run' method to a dynamic Pydantic model, using the provided function.
|
|
|
|
Args:
|
|
model (type[BaseModel]): Dynamic Pydantic model class.
|
|
func (Callable): Function to be added as a 'run' method to the model.
|
|
|
|
Returns:
|
|
type[BaseModel]: Pydantic model class with the added 'run' method.
|
|
"""
|
|
|
|
def run_method_wrapper(self):
|
|
func_args = {name: getattr(self, name) for name in model.model_fields}
|
|
return func(**func_args)
|
|
|
|
# Adding the wrapped function as a 'run' method
|
|
setattr(model, "run", run_method_wrapper)
|
|
|
|
return model
|
|
|
|
|
|
def create_dynamic_models_from_dictionaries(dictionaries: list[dict[str, Any]]):
|
|
"""
|
|
Create a list of dynamic Pydantic model classes from a list of dictionaries.
|
|
|
|
Args:
|
|
dictionaries (list[dict]): List of dictionaries representing model structures.
|
|
|
|
Returns:
|
|
list[type[BaseModel]]: List of generated dynamic Pydantic model classes.
|
|
"""
|
|
dynamic_models = []
|
|
for func in dictionaries:
|
|
model_name = format_model_and_field_name(func.get("name", ""))
|
|
dyn_model = convert_dictionary_to_pydantic_model(func, model_name)
|
|
dynamic_models.append(dyn_model)
|
|
return dynamic_models
|
|
|
|
|
|
def map_grammar_names_to_pydantic_model_class(pydantic_model_list):
|
|
output = {}
|
|
for model in pydantic_model_list:
|
|
output[format_model_and_field_name(model.__name__)] = model
|
|
|
|
return output
|
|
|
|
|
|
def json_schema_to_python_types(schema):
|
|
type_map = {
|
|
"any": Any,
|
|
"string": str,
|
|
"number": float,
|
|
"integer": int,
|
|
"boolean": bool,
|
|
"array": list,
|
|
}
|
|
return type_map[schema]
|
|
|
|
|
|
def list_to_enum(enum_name, values):
|
|
return Enum(enum_name, {value: value for value in values})
|
|
|
|
|
|
def convert_dictionary_to_pydantic_model(dictionary: dict[str, Any], model_name: str = "CustomModel") -> type[Any]:
|
|
"""
|
|
Convert a dictionary to a Pydantic model class.
|
|
|
|
Args:
|
|
dictionary (dict): Dictionary representing the model structure.
|
|
model_name (str): Name of the generated Pydantic model.
|
|
|
|
Returns:
|
|
type[BaseModel]: Generated Pydantic model class.
|
|
"""
|
|
fields: dict[str, Any] = {}
|
|
|
|
if "properties" in dictionary:
|
|
for field_name, field_data in dictionary.get("properties", {}).items():
|
|
if field_data == "object":
|
|
submodel = convert_dictionary_to_pydantic_model(dictionary, f"{model_name}_{field_name}")
|
|
fields[field_name] = (submodel, ...)
|
|
else:
|
|
field_type = field_data.get("type", "str")
|
|
|
|
if field_data.get("enum", []):
|
|
fields[field_name] = (list_to_enum(field_name, field_data.get("enum", [])), ...)
|
|
elif field_type == "array":
|
|
items = field_data.get("items", {})
|
|
if items != {}:
|
|
array = {"properties": items}
|
|
array_type = convert_dictionary_to_pydantic_model(array, f"{model_name}_{field_name}_items")
|
|
fields[field_name] = (List[array_type], ...)
|
|
else:
|
|
fields[field_name] = (list, ...)
|
|
elif field_type == "object":
|
|
submodel = convert_dictionary_to_pydantic_model(field_data, f"{model_name}_{field_name}")
|
|
fields[field_name] = (submodel, ...)
|
|
elif field_type == "required":
|
|
required = field_data.get("enum", [])
|
|
for key, field in fields.items():
|
|
if key not in required:
|
|
optional_type = fields[key][0]
|
|
fields[key] = (Optional[optional_type], ...)
|
|
else:
|
|
field_type = json_schema_to_python_types(field_type)
|
|
fields[field_name] = (field_type, ...)
|
|
if "function" in dictionary:
|
|
for field_name, field_data in dictionary.get("function", {}).items():
|
|
if field_name == "name":
|
|
model_name = field_data
|
|
elif field_name == "description":
|
|
fields["__doc__"] = field_data
|
|
elif field_name == "parameters":
|
|
return convert_dictionary_to_pydantic_model(field_data, f"{model_name}")
|
|
|
|
if "parameters" in dictionary:
|
|
field_data = {"function": dictionary}
|
|
return convert_dictionary_to_pydantic_model(field_data, f"{model_name}")
|
|
if "required" in dictionary:
|
|
required = dictionary.get("required", [])
|
|
for key, field in fields.items():
|
|
if key not in required:
|
|
optional_type = fields[key][0]
|
|
fields[key] = (Optional[optional_type], ...)
|
|
custom_model = create_model(model_name, **fields)
|
|
return custom_model
|