mirror of
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examples : make pydantic scripts pass mypy and support py3.8 (#5099)
This commit is contained in:
Jared Van Bortel
GitHub
parent
256d1bb0dd
commit
d292f4f204
@ -1,14 +1,14 @@
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# Function calling example using pydantic models.
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import datetime
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import importlib
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import json
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from enum import Enum
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from typing import Union, Optional
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from typing import Optional, Union
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import requests
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from pydantic import BaseModel, Field
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import importlib
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from pydantic_models_to_grammar import generate_gbnf_grammar_and_documentation, convert_dictionary_to_pydantic_model, add_run_method_to_dynamic_model, create_dynamic_model_from_function
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from pydantic_models_to_grammar import (add_run_method_to_dynamic_model, convert_dictionary_to_pydantic_model,
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create_dynamic_model_from_function, generate_gbnf_grammar_and_documentation)
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# Function to get completion on the llama.cpp server with grammar.
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@ -35,7 +35,7 @@ class SendMessageToUser(BaseModel):
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print(self.message)
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# Enum for the calculator function.
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# Enum for the calculator tool.
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class MathOperation(Enum):
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ADD = "add"
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SUBTRACT = "subtract"
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@ -43,7 +43,7 @@ class MathOperation(Enum):
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DIVIDE = "divide"
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# Very simple calculator tool for the agent.
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# Simple pydantic calculator tool for the agent that can add, subtract, multiply, and divide. Docstring and description of fields will be used in system prompt.
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class Calculator(BaseModel):
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"""
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Perform a math operation on two numbers.
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@ -148,37 +148,6 @@ def get_current_datetime(output_format: Optional[str] = None):
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return datetime.datetime.now().strftime(output_format)
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# Enum for the calculator tool.
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class MathOperation(Enum):
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ADD = "add"
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SUBTRACT = "subtract"
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MULTIPLY = "multiply"
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DIVIDE = "divide"
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# Simple pydantic calculator tool for the agent that can add, subtract, multiply, and divide. Docstring and description of fields will be used in system prompt.
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class Calculator(BaseModel):
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"""
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Perform a math operation on two numbers.
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"""
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number_one: Union[int, float] = Field(..., description="First number.")
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operation: MathOperation = Field(..., description="Math operation to perform.")
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number_two: Union[int, float] = Field(..., description="Second number.")
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def run(self):
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if self.operation == MathOperation.ADD:
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return self.number_one + self.number_two
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elif self.operation == MathOperation.SUBTRACT:
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return self.number_one - self.number_two
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elif self.operation == MathOperation.MULTIPLY:
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return self.number_one * self.number_two
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elif self.operation == MathOperation.DIVIDE:
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return self.number_one / self.number_two
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else:
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raise ValueError("Unknown operation.")
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# Example function to get the weather
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def get_current_weather(location, unit):
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"""Get the current weather in a given location"""
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@ -1,15 +1,21 @@
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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 inspect import isclass, getdoc
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from types import NoneType
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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, create_model, Field
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from typing import Any, Type, List, get_args, get_origin, Tuple, Union, Optional, _GenericAlias
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from enum import Enum
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from typing import get_type_hints, Callable
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import re
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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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@ -43,7 +49,7 @@ class PydanticDataType(Enum):
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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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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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@ -57,22 +63,22 @@ def map_pydantic_type_to_gbnf(pydantic_type: Type[Any]) -> str:
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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) == list:
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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) == set:
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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) == Union:
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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) == Optional:
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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) == dict:
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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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@ -106,7 +112,6 @@ def get_members_structure(cls, rule_name):
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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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type_list_rules = []
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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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@ -116,27 +121,25 @@ def get_members_structure(cls, rule_name):
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result += '"," '.join(members)
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result += ' "}"'
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return result, type_list_rules
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elif rule_name == "custom-class-any":
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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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type_list_rules = []
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return result, type_list_rules
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else:
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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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type_list_rules = []
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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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return result
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result += '", "'.join(members)
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result += ' "}"'
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return result, type_list_rules
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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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@ -269,7 +272,7 @@ def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None
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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]:
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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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@ -283,7 +286,7 @@ def generate_gbnf_rule_for_type(
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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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:rtype: tuple[str, list]
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"""
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rules = []
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@ -321,8 +324,7 @@ def generate_gbnf_rule_for_type(
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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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nested_model_rules, field_types = get_members_structure(field_type, gbnf_type)
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rules.append(nested_model_rules)
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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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@ -341,14 +343,14 @@ def generate_gbnf_rule_for_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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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, NoneType):
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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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@ -424,14 +426,10 @@ def generate_gbnf_rule_for_type(
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else:
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gbnf_type, rules = gbnf_type, []
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if gbnf_type not in created_rules:
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return gbnf_type, rules
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else:
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if gbnf_type in created_rules:
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return gbnf_type, rules
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return gbnf_type, rules
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def generate_gbnf_grammar(model: Type[BaseModel], processed_models: set, created_rules: dict) -> (list, bool, bool):
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def generate_gbnf_grammar(model: type[BaseModel], processed_models: set[type[BaseModel]], created_rules: dict[str, list[str]]) -> tuple[list[str], bool]:
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"""
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Generate GBnF Grammar
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@ -452,7 +450,7 @@ def generate_gbnf_grammar(model: Type[BaseModel], processed_models: set, created
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```
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"""
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if model in processed_models:
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return []
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return [], False
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processed_models.add(model)
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model_name = format_model_and_field_name(model.__name__)
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@ -518,7 +516,7 @@ def generate_gbnf_grammar(model: Type[BaseModel], processed_models: set, created
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def generate_gbnf_grammar_from_pydantic_models(
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models: List[Type[BaseModel]], outer_object_name: str = None, outer_object_content: str = None,
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models: list[type[BaseModel]], outer_object_name: str | None = None, outer_object_content: str | None = None,
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list_of_outputs: bool = False
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) -> str:
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"""
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@ -528,7 +526,7 @@ def generate_gbnf_grammar_from_pydantic_models(
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* grammar.
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Args:
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models (List[Type[BaseModel]]): A list of Pydantic models to generate the grammar from.
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models (list[type[BaseModel]]): A list of Pydantic models to generate the grammar from.
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outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling.
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outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling.
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list_of_outputs (str, optional): Allows a list of output objects
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@ -543,9 +541,9 @@ def generate_gbnf_grammar_from_pydantic_models(
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# root ::= UserModel | PostModel
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# ...
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"""
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processed_models = set()
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processed_models: set[type[BaseModel]] = set()
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all_rules = []
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created_rules = {}
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created_rules: dict[str, list[str]] = {}
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if outer_object_name is None:
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for model in models:
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model_rules, _ = generate_gbnf_grammar(model, processed_models, created_rules)
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@ -608,7 +606,7 @@ def get_primitive_grammar(grammar):
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Returns:
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str: GBNF primitive grammar string.
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"""
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type_list = []
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type_list: list[type[object]] = []
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if "string-list" in grammar:
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type_list.append(str)
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if "boolean-list" in grammar:
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@ -666,14 +664,14 @@ triple-quotes ::= "'''" """
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def generate_markdown_documentation(
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pydantic_models: List[Type[BaseModel]], model_prefix="Model", fields_prefix="Fields",
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pydantic_models: list[type[BaseModel]], model_prefix="Model", fields_prefix="Fields",
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documentation_with_field_description=True
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) -> str:
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"""
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Generate markdown documentation for a list of Pydantic models.
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Args:
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pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes.
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pydantic_models (list[type[BaseModel]]): list of Pydantic model classes.
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model_prefix (str): Prefix for the model section.
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fields_prefix (str): Prefix for the fields section.
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documentation_with_field_description (bool): Include field descriptions in the documentation.
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@ -731,7 +729,7 @@ def generate_markdown_documentation(
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def generate_field_markdown(
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field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1,
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field_name: str, field_type: type[Any], model: type[BaseModel], depth=1,
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documentation_with_field_description=True
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) -> str:
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"""
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@ -739,8 +737,8 @@ def generate_field_markdown(
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Args:
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field_name (str): Name of the field.
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field_type (Type[Any]): Type of the field.
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model (Type[BaseModel]): Pydantic model class.
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field_type (type[Any]): Type of the field.
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model (type[BaseModel]): Pydantic model class.
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depth (int): Indentation depth in the documentation.
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documentation_with_field_description (bool): Include field descriptions in the documentation.
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@ -798,7 +796,7 @@ def generate_field_markdown(
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return field_text
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def format_json_example(example: dict, depth: int) -> str:
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def format_json_example(example: dict[str, Any], depth: int) -> str:
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"""
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Format a JSON example into a readable string with indentation.
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@ -819,14 +817,14 @@ def format_json_example(example: dict, depth: int) -> str:
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def generate_text_documentation(
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pydantic_models: List[Type[BaseModel]], model_prefix="Model", fields_prefix="Fields",
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pydantic_models: list[type[BaseModel]], model_prefix="Model", fields_prefix="Fields",
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documentation_with_field_description=True
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) -> str:
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"""
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Generate text documentation for a list of Pydantic models.
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Args:
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pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes.
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pydantic_models (list[type[BaseModel]]): List of Pydantic model classes.
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model_prefix (str): Prefix for the model section.
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fields_prefix (str): Prefix for the fields section.
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documentation_with_field_description (bool): Include field descriptions in the documentation.
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@ -885,7 +883,7 @@ def generate_text_documentation(
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def generate_field_text(
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field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1,
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field_name: str, field_type: type[Any], model: type[BaseModel], depth=1,
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documentation_with_field_description=True
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) -> str:
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"""
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@ -893,8 +891,8 @@ def generate_field_text(
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Args:
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field_name (str): Name of the field.
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field_type (Type[Any]): Type of the field.
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model (Type[BaseModel]): Pydantic model class.
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field_type (type[Any]): Type of the field.
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model (type[BaseModel]): Pydantic model class.
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depth (int): Indentation depth in the documentation.
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documentation_with_field_description (bool): Include field descriptions in the documentation.
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@ -1017,8 +1015,8 @@ def generate_and_save_gbnf_grammar_and_documentation(
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pydantic_model_list,
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grammar_file_path="./generated_grammar.gbnf",
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documentation_file_path="./generated_grammar_documentation.md",
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outer_object_name: str = None,
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outer_object_content: str = None,
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outer_object_name: str | None = None,
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outer_object_content: str | None = None,
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model_prefix: str = "Output Model",
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fields_prefix: str = "Output Fields",
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list_of_outputs: bool = False,
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@ -1053,8 +1051,8 @@ def generate_and_save_gbnf_grammar_and_documentation(
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def generate_gbnf_grammar_and_documentation(
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pydantic_model_list,
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outer_object_name: str = None,
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outer_object_content: str = None,
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outer_object_name: str | None = None,
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outer_object_content: str | None = None,
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model_prefix: str = "Output Model",
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fields_prefix: str = "Output Fields",
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list_of_outputs: bool = False,
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@ -1086,9 +1084,9 @@ def generate_gbnf_grammar_and_documentation(
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def generate_gbnf_grammar_and_documentation_from_dictionaries(
|
||||
dictionaries: List[dict],
|
||||
outer_object_name: str = None,
|
||||
outer_object_content: str = None,
|
||||
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,
|
||||
@ -1098,7 +1096,7 @@ def generate_gbnf_grammar_and_documentation_from_dictionaries(
|
||||
Generate GBNF grammar and documentation from a list of dictionaries.
|
||||
|
||||
Args:
|
||||
dictionaries (List[dict]): List of dictionaries representing Pydantic models.
|
||||
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.
|
||||
@ -1120,7 +1118,7 @@ def generate_gbnf_grammar_and_documentation_from_dictionaries(
|
||||
return grammar, documentation
|
||||
|
||||
|
||||
def create_dynamic_model_from_function(func: Callable):
|
||||
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.
|
||||
|
||||
@ -1135,6 +1133,7 @@ def create_dynamic_model_from_function(func: Callable):
|
||||
sig = inspect.signature(func)
|
||||
|
||||
# Parse the docstring
|
||||
assert func.__doc__ is not None
|
||||
docstring = parse(func.__doc__)
|
||||
|
||||
dynamic_fields = {}
|
||||
@ -1157,7 +1156,6 @@ def create_dynamic_model_from_function(func: Callable):
|
||||
f"Parameter '{param.name}' in function '{func.__name__}' lacks a description in the docstring")
|
||||
|
||||
# Add parameter details to the schema
|
||||
param_doc = next((d for d in docstring.params if d.arg_name == param.name), None)
|
||||
param_docs.append((param.name, param_doc))
|
||||
if param.default == inspect.Parameter.empty:
|
||||
default_value = ...
|
||||
@ -1166,10 +1164,10 @@ def create_dynamic_model_from_function(func: Callable):
|
||||
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)
|
||||
dynamic_model = create_model(f"{func.__name__}", **dynamic_fields) # type: ignore[call-overload]
|
||||
|
||||
for param_doc in param_docs:
|
||||
dynamic_model.model_fields[param_doc[0]].description = param_doc[1].description
|
||||
for name, param_doc in param_docs:
|
||||
dynamic_model.model_fields[name].description = param_doc.description
|
||||
|
||||
dynamic_model.__doc__ = docstring.short_description
|
||||
|
||||
@ -1182,16 +1180,16 @@ def create_dynamic_model_from_function(func: Callable):
|
||||
return dynamic_model
|
||||
|
||||
|
||||
def add_run_method_to_dynamic_model(model: Type[BaseModel], func: Callable):
|
||||
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.
|
||||
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.
|
||||
type[BaseModel]: Pydantic model class with the added 'run' method.
|
||||
"""
|
||||
|
||||
def run_method_wrapper(self):
|
||||
@ -1204,15 +1202,15 @@ def add_run_method_to_dynamic_model(model: Type[BaseModel], func: Callable):
|
||||
return model
|
||||
|
||||
|
||||
def create_dynamic_models_from_dictionaries(dictionaries: List[dict]):
|
||||
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.
|
||||
dictionaries (list[dict]): List of dictionaries representing model structures.
|
||||
|
||||
Returns:
|
||||
List[Type[BaseModel]]: List of generated dynamic Pydantic model classes.
|
||||
list[type[BaseModel]]: List of generated dynamic Pydantic model classes.
|
||||
"""
|
||||
dynamic_models = []
|
||||
for func in dictionaries:
|
||||
@ -1249,7 +1247,7 @@ 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, model_name: str = "CustomModel") -> Type[BaseModel]:
|
||||
def convert_dictionary_to_pydantic_model(dictionary: dict[str, Any], model_name: str = "CustomModel") -> type[Any]:
|
||||
"""
|
||||
Convert a dictionary to a Pydantic model class.
|
||||
|
||||
@ -1258,9 +1256,9 @@ def convert_dictionary_to_pydantic_model(dictionary: dict, model_name: str = "Cu
|
||||
model_name (str): Name of the generated Pydantic model.
|
||||
|
||||
Returns:
|
||||
Type[BaseModel]: Generated Pydantic model class.
|
||||
type[BaseModel]: Generated Pydantic model class.
|
||||
"""
|
||||
fields = {}
|
||||
fields: dict[str, Any] = {}
|
||||
|
||||
if "properties" in dictionary:
|
||||
for field_name, field_data in dictionary.get("properties", {}).items():
|
||||
@ -1277,7 +1275,7 @@ def convert_dictionary_to_pydantic_model(dictionary: dict, model_name: str = "Cu
|
||||
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], ...)
|
||||
fields[field_name] = (List[array_type], ...) # type: ignore[valid-type]
|
||||
else:
|
||||
fields[field_name] = (list, ...)
|
||||
elif field_type == "object":
|
||||
|
||||
Loading…
Reference in New Issue
Block a user