mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-29 08:41:22 +00:00
1311 lines
54 KiB
Python
1311 lines
54 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, get_type_hints
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from docstring_parser import parse
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from pydantic import BaseModel, Field, 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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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" + (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
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min_digits = field_info.json_schema_extra.get("min_digit") if field_info and hasattr(field_info,
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"json_schema_extra") else None
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# Generate GBNF rule for float with given attributes
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gbnf_type, rules = generate_gbnf_float_rules(
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max_digit=max_digits, min_digit=min_digits, max_precision=max_precision, min_precision=min_precision
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)
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elif (
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isclass(field_type)
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and issubclass(field_type, int)
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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 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()}
|
|
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 ::= ("-"? ([0-9] | [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 = [(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"
|
|
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 = [(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) # type: ignore[call-overload]
|
|
|
|
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
|
|
|
|
|
|
from enum import Enum
|
|
|
|
|
|
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], ...) # type: ignore[valid-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:
|
|
fields[key] = (Optional[fields[key][0]], ...)
|
|
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:
|
|
fields[key] = (Optional[fields[key][0]], ...)
|
|
custom_model = create_model(model_name, **fields)
|
|
return custom_model
|