from __future__ import annotations
import inspect
import json
import re
from copy import copy
from enum import Enum
from inspect import getdoc, isclass
from typing import TYPE_CHECKING, Any, Callable, Optional, Union, get_args, get_origin, get_type_hints
from docstring_parser import parse
from pydantic import BaseModel, create_model
if TYPE_CHECKING:
from types import GenericAlias
else:
# python 3.8 compat
from typing import _GenericAlias as GenericAlias
# TODO: fix this
# pyright: reportAttributeAccessIssue=information
class PydanticDataType(Enum):
"""
Defines the data types supported by the grammar_generator.
Attributes:
STRING (str): Represents a string data type.
BOOLEAN (str): Represents a boolean data type.
INTEGER (str): Represents an integer data type.
FLOAT (str): Represents a float data type.
OBJECT (str): Represents an object data type.
ARRAY (str): Represents an array data type.
ENUM (str): Represents an enum data type.
CUSTOM_CLASS (str): Represents a custom class data type.
"""
STRING = "string"
TRIPLE_QUOTED_STRING = "triple_quoted_string"
MARKDOWN_CODE_BLOCK = "markdown_code_block"
BOOLEAN = "boolean"
INTEGER = "integer"
FLOAT = "float"
OBJECT = "object"
ARRAY = "array"
ENUM = "enum"
ANY = "any"
NULL = "null"
CUSTOM_CLASS = "custom-class"
CUSTOM_DICT = "custom-dict"
SET = "set"
def map_pydantic_type_to_gbnf(pydantic_type: type[Any]) -> str:
origin_type = get_origin(pydantic_type)
origin_type = pydantic_type if origin_type is None else origin_type
if isclass(origin_type) and issubclass(origin_type, str):
return PydanticDataType.STRING.value
elif isclass(origin_type) and issubclass(origin_type, bool):
return PydanticDataType.BOOLEAN.value
elif isclass(origin_type) and issubclass(origin_type, int):
return PydanticDataType.INTEGER.value
elif isclass(origin_type) and issubclass(origin_type, float):
return PydanticDataType.FLOAT.value
elif isclass(origin_type) and issubclass(origin_type, Enum):
return PydanticDataType.ENUM.value
elif isclass(origin_type) and issubclass(origin_type, BaseModel):
return format_model_and_field_name(origin_type.__name__)
elif origin_type is list:
element_type = get_args(pydantic_type)[0]
return f"{map_pydantic_type_to_gbnf(element_type)}-list"
elif origin_type is set:
element_type = get_args(pydantic_type)[0]
return f"{map_pydantic_type_to_gbnf(element_type)}-set"
elif origin_type is Union:
union_types = get_args(pydantic_type)
union_rules = [map_pydantic_type_to_gbnf(ut) for ut in union_types]
return f"union-{'-or-'.join(union_rules)}"
elif origin_type is Optional:
element_type = get_args(pydantic_type)[0]
return f"optional-{map_pydantic_type_to_gbnf(element_type)}"
elif isclass(origin_type):
return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(origin_type.__name__)}"
elif origin_type is dict:
key_type, value_type = get_args(pydantic_type)
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))}"
else:
return "unknown"
def format_model_and_field_name(model_name: str) -> str:
parts = re.findall("[A-Z][^A-Z]*", model_name)
if not parts: # Check if the list is empty
return model_name.lower().replace("_", "-")
return "-".join(part.lower().replace("_", "-") for part in parts)
def generate_list_rule(element_type):
"""
Generate a GBNF rule for a list of a given element type.
:param element_type: The type of the elements in the list (e.g., 'string').
:return: A string representing the GBNF rule for a list of the given type.
"""
rule_name = f"{map_pydantic_type_to_gbnf(element_type)}-list"
element_rule = map_pydantic_type_to_gbnf(element_type)
list_rule = rf'{rule_name} ::= "[" {element_rule} ("," {element_rule})* "]"'
return list_rule
def get_members_structure(cls, rule_name):
if issubclass(cls, Enum):
# Handle Enum types
members = [f'"\\"{member.value}\\""' for name, member in cls.__members__.items()]
return f"{cls.__name__.lower()} ::= " + " | ".join(members)
if cls.__annotations__ and cls.__annotations__ != {}:
result = f'{rule_name} ::= "{{"'
# Modify this comprehension
members = [
f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param_type)}'
for name, param_type in get_type_hints(cls).items()
if name != "self"
]
result += '"," '.join(members)
result += ' "}"'
return result
if rule_name == "custom-class-any":
result = f"{rule_name} ::= "
result += "value"
return result
init_signature = inspect.signature(cls.__init__)
parameters = init_signature.parameters
result = f'{rule_name} ::= "{{"'
# Modify this comprehension too
members = [
f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param.annotation)}'
for name, param in parameters.items()
if name != "self" and param.annotation != inspect.Parameter.empty
]
result += '", "'.join(members)
result += ' "}"'
return result
def regex_to_gbnf(regex_pattern: str) -> str:
"""
Translate a basic regex pattern to a GBNF rule.
Note: This function handles only a subset of simple regex patterns.
"""
gbnf_rule = regex_pattern
# Translate common regex components to GBNF
gbnf_rule = gbnf_rule.replace("\\d", "[0-9]")
gbnf_rule = gbnf_rule.replace("\\s", "[ \t\n]")
# Handle quantifiers and other regex syntax that is similar in GBNF
# (e.g., '*', '+', '?', character classes)
return gbnf_rule
def generate_gbnf_integer_rules(max_digit=None, min_digit=None):
"""
Generate GBNF Integer Rules
Generates GBNF (Generalized Backus-Naur Form) rules for integers based on the given maximum and minimum digits.
Parameters:
max_digit (int): The maximum number of digits for the integer. Default is None.
min_digit (int): The minimum number of digits for the integer. Default is None.
Returns:
integer_rule (str): The identifier for the integer rule generated.
additional_rules (list): A list of additional rules generated based on the given maximum and minimum digits.
"""
additional_rules = []
# Define the rule identifier based on max_digit and min_digit
integer_rule = "integer-part"
if max_digit is not None:
integer_rule += f"-max{max_digit}"
if min_digit is not None:
integer_rule += f"-min{min_digit}"
# Handling Integer Rules
if max_digit is not None or min_digit is not None:
# Start with an empty rule part
integer_rule_part = ""
# Add mandatory digits as per min_digit
if min_digit is not None:
integer_rule_part += "[0-9] " * min_digit
# Add optional digits up to max_digit
if max_digit is not None:
optional_digits = max_digit - (min_digit if min_digit is not None else 0)
integer_rule_part += "".join(["[0-9]? " for _ in range(optional_digits)])
# Trim the rule part and append it to additional rules
integer_rule_part = integer_rule_part.strip()
if integer_rule_part:
additional_rules.append(f"{integer_rule} ::= {integer_rule_part}")
return integer_rule, additional_rules
def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None, min_precision=None):
"""
Generate GBNF float rules based on the given constraints.
:param max_digit: Maximum number of digits in the integer part (default: None)
:param min_digit: Minimum number of digits in the integer part (default: None)
:param max_precision: Maximum number of digits in the fractional part (default: None)
:param min_precision: Minimum number of digits in the fractional part (default: None)
:return: A tuple containing the float rule and additional rules as a list
Example Usage:
max_digit = 3
min_digit = 1
max_precision = 2
min_precision = 1
generate_gbnf_float_rules(max_digit, min_digit, max_precision, min_precision)
Output:
('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
*1'])
Note:
GBNF stands for Generalized Backus-Naur Form, which is a notation technique to specify the syntax of programming languages or other formal grammars.
"""
additional_rules = []
# Define the integer part rule
integer_part_rule = (
"integer-part"
+ (f"-max{max_digit}" if max_digit is not None else "")
+ (f"-min{min_digit}" if min_digit is not None else "")
)
# Define the fractional part rule based on precision constraints
fractional_part_rule = "fractional-part"
fractional_rule_part = ""
if max_precision is not None or min_precision is not None:
fractional_part_rule += (f"-max{max_precision}" if max_precision is not None else "") + (
f"-min{min_precision}" if min_precision is not None else ""
)
# Minimum number of digits
fractional_rule_part = "[0-9]" * (min_precision if min_precision is not None else 1)
# Optional additional digits
fractional_rule_part += "".join(
[" [0-9]?"] * ((max_precision - (
min_precision if min_precision is not None else 1)) if max_precision is not None else 0)
)
additional_rules.append(f"{fractional_part_rule} ::= {fractional_rule_part}")
# Define the float rule
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'}"
additional_rules.append(f'{float_rule} ::= {integer_part_rule} "." {fractional_part_rule}')
# Generating the integer part rule definition, if necessary
if max_digit is not None or min_digit is not None:
integer_rule_part = "[0-9]"
if min_digit is not None and min_digit > 1:
integer_rule_part += " [0-9]" * (min_digit - 1)
if max_digit is not None:
integer_rule_part += "".join([" [0-9]?"] * (max_digit - (min_digit if min_digit is not None else 1)))
additional_rules.append(f"{integer_part_rule} ::= {integer_rule_part.strip()}")
return float_rule, additional_rules
def generate_gbnf_rule_for_type(
model_name, field_name, field_type, is_optional, processed_models, created_rules, field_info=None
) -> tuple[str, list[str]]:
"""
Generate GBNF rule for a given field type.
:param model_name: Name of the model.
:param field_name: Name of the field.
:param field_type: Type of the field.
:param is_optional: Whether the field is optional.
:param processed_models: List of processed models.
:param created_rules: List of created rules.
:param field_info: Additional information about the field (optional).
:return: Tuple containing the GBNF type and a list of additional rules.
:rtype: tuple[str, list]
"""
rules = []
field_name = format_model_and_field_name(field_name)
gbnf_type = map_pydantic_type_to_gbnf(field_type)
origin_type = get_origin(field_type)
origin_type = field_type if origin_type is None else origin_type
if isclass(origin_type) and issubclass(origin_type, BaseModel):
nested_model_name = format_model_and_field_name(field_type.__name__)
nested_model_rules, _ = generate_gbnf_grammar(field_type, processed_models, created_rules)
rules.extend(nested_model_rules)
gbnf_type, rules = nested_model_name, rules
elif isclass(origin_type) and issubclass(origin_type, Enum):
enum_values = [f'"\\"{e.value}\\""' for e in field_type] # Adding escaped quotes
enum_rule = f"{model_name}-{field_name} ::= {' | '.join(enum_values)}"
rules.append(enum_rule)
gbnf_type, rules = model_name + "-" + field_name, rules
elif origin_type is list: # Array
element_type = get_args(field_type)[0]
element_rule_name, additional_rules = generate_gbnf_rule_for_type(
model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
)
rules.extend(additional_rules)
array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """
rules.append(array_rule)
gbnf_type, rules = model_name + "-" + field_name, rules
elif origin_type is set: # Array
element_type = get_args(field_type)[0]
element_rule_name, additional_rules = generate_gbnf_rule_for_type(
model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
)
rules.extend(additional_rules)
array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """
rules.append(array_rule)
gbnf_type, rules = model_name + "-" + field_name, rules
elif gbnf_type.startswith("custom-class-"):
rules.append(get_members_structure(field_type, gbnf_type))
elif gbnf_type.startswith("custom-dict-"):
key_type, value_type = get_args(field_type)
additional_key_type, additional_key_rules = generate_gbnf_rule_for_type(
model_name, f"{field_name}-key-type", key_type, is_optional, processed_models, created_rules
)
additional_value_type, additional_value_rules = generate_gbnf_rule_for_type(
model_name, f"{field_name}-value-type", value_type, is_optional, processed_models, created_rules
)
gbnf_type = rf'{gbnf_type} ::= "{{" ( {additional_key_type} ": " {additional_value_type} ("," "\n" ws {additional_key_type} ":" {additional_value_type})* )? "}}" '
rules.extend(additional_key_rules)
rules.extend(additional_value_rules)
elif gbnf_type.startswith("union-"):
union_types = get_args(field_type)
union_rules = []
for union_type in union_types:
if isinstance(union_type, GenericAlias):
union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(
model_name, field_name, union_type, False, processed_models, created_rules
)
union_rules.append(union_gbnf_type)
rules.extend(union_rules_list)
elif not issubclass(union_type, type(None)):
union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(
model_name, field_name, union_type, False, processed_models, created_rules
)
union_rules.append(union_gbnf_type)
rules.extend(union_rules_list)
# Defining the union grammar rule separately
if len(union_rules) == 1:
union_grammar_rule = f"{model_name}-{field_name}-optional ::= {' | '.join(union_rules)} | null"
else:
union_grammar_rule = f"{model_name}-{field_name}-union ::= {' | '.join(union_rules)}"
rules.append(union_grammar_rule)
if len(union_rules) == 1:
gbnf_type = f"{model_name}-{field_name}-optional"
else:
gbnf_type = f"{model_name}-{field_name}-union"
elif isclass(origin_type) and issubclass(origin_type, str):
if field_info and hasattr(field_info, "json_schema_extra") and field_info.json_schema_extra is not None:
triple_quoted_string = field_info.json_schema_extra.get("triple_quoted_string", False)
markdown_string = field_info.json_schema_extra.get("markdown_code_block", False)
gbnf_type = PydanticDataType.TRIPLE_QUOTED_STRING.value if triple_quoted_string else PydanticDataType.STRING.value
gbnf_type = PydanticDataType.MARKDOWN_CODE_BLOCK.value if markdown_string else gbnf_type
elif field_info and hasattr(field_info, "pattern"):
# Convert regex pattern to grammar rule
regex_pattern = field_info.regex.pattern
gbnf_type = f"pattern-{field_name} ::= {regex_to_gbnf(regex_pattern)}"
else:
gbnf_type = PydanticDataType.STRING.value
elif (
isclass(origin_type)
and issubclass(origin_type, float)
and field_info
and hasattr(field_info, "json_schema_extra")
and field_info.json_schema_extra is not None
):
# Retrieve precision attributes for floats
max_precision = (
field_info.json_schema_extra.get("max_precision") if field_info and hasattr(field_info,
"json_schema_extra") else None
)
min_precision = (
field_info.json_schema_extra.get("min_precision") if field_info and hasattr(field_info,
"json_schema_extra") else None
)
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 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(origin_type)
and issubclass(origin_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 get_type_hints(model).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 = get_type_hints(model)
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 get_type_hints(model).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 ""
origin_type = get_origin(field_type)
origin_type = field_type if origin_type is None else origin_type
if origin_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 origin_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(origin_type) and issubclass(origin_type, BaseModel):
field_text += f"{indent} Details:\n"
for name, type_ in get_type_hints(field_type).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 get_type_hints(model).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 get_type_hints(field_type).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 '{getattr(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 '{getattr(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"{getattr(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], ...) # ty: ignore[invalid-type-form]
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