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@@ -1,115 +0,0 @@
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-from ast import literal_eval
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-from typing import Any, Literal, Optional, Type
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-
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-from pydantic import BaseModel, Field, create_model
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-
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-
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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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-
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- Args:
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- json_schema: The JSON schema to convert.
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-
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- Returns:
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- A Pydantic BaseModel class.
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- """
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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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-
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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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-
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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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-
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-
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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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-
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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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-
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- Returns:
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- A Pydantic field definition.
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- """
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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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-
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- # Get the field description.
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- description = json_schema.get("description")
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-
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- # Get the field examples.
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- examples = json_schema.get("examples")
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-
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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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-
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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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-
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- Args:
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- json_schema: The JSON schema to convert.
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-
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- Returns:
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- A Pydantic type.
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- """
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-
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- type_ = json_schema.get("type")
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-
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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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- enum = json_schema.get("enum")
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- if enum is None:
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- raise ValueError("Enum values must be provided for 'literal' type.")
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- return Literal[literal_eval(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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