113 lines
3.2 KiB
Python
113 lines
3.2 KiB
Python
from ast import literal_eval
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from typing import Any, Literal, Optional, Type
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from pydantic import BaseModel, Field, create_model
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def json_schema_to_model(tool_dict: dict[str, Any]) -> Type[BaseModel]:
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"""
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Converts a JSON schema to a Pydantic BaseModel class.
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Args:
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json_schema: The JSON schema to convert.
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Returns:
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A Pydantic BaseModel class.
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"""
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# Extract the model name from the schema title.
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model_name = tool_dict["name"]
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schema = tool_dict["parameters"]
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# Extract the field definitions from the schema properties.
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field_definitions = {
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name: json_schema_to_pydantic_field(name, prop, schema.get("required", []))
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for name, prop in schema.get("properties", {}).items()
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}
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# Create the BaseModel class using create_model().
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return create_model(model_name, **field_definitions)
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def json_schema_to_pydantic_field(
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name: str, json_schema: dict[str, Any], required: list[str]
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) -> Any:
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"""
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Converts a JSON schema property to a Pydantic field definition.
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Args:
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name: The field name.
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json_schema: The JSON schema property.
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Returns:
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A Pydantic field definition.
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"""
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# Get the field type.
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type_ = json_schema_to_pydantic_type(json_schema)
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# Get the field description.
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description = json_schema.get("description")
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# Get the field examples.
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examples = json_schema.get("examples")
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# Create a Field object with the type, description, and examples.
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# The 'required' flag will be set later when creating the model.
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return (
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type_,
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Field(
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description=description,
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examples=examples,
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default=... if name in required else None,
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),
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)
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def json_schema_to_pydantic_type(json_schema: dict[str, Any]) -> Any:
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"""
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Converts a JSON schema type to a Pydantic type.
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Args:
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json_schema: The JSON schema to convert.
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Returns:
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A Pydantic type.
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"""
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type_ = json_schema.get("type")
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if type_ == "string" or type_ == "str":
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return str
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elif type_ == "integer" or type_ == "int":
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return int
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elif type_ == "number" or type_ == "float":
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return float
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elif type_ == "boolean" or type_ == "bool":
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return bool
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elif type_ == "array" or type_ == "list":
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items_schema = json_schema.get("items")
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if items_schema:
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item_type = json_schema_to_pydantic_type(items_schema)
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return list[item_type]
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else:
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return list
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elif type_ == "object":
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# Handle nested models.
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properties = json_schema.get("properties")
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if properties:
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nested_model = json_schema_to_model(json_schema)
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return nested_model
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else:
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return dict
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elif type_ == "null":
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return Optional[Any] # Use Optional[Any] for nullable fields
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elif type_ == "literal":
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return Literal[literal_eval(json_schema.get("enum"))]
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elif type_ == "optional":
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inner_schema = json_schema.get("items", {"type": "string"})
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inner_type = json_schema_to_pydantic_type(inner_schema)
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return Optional[inner_type]
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else:
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raise ValueError(f"Unsupported JSON schema type: {type_}")
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