Hey y'all, trying to add my function

"items": {
            "$ref": "#/definitions/Contact"
          }

from llama_index.llms.openai_utils import to_openai_tool

to_openai_tool(pydantic_class)

def to_openai_function(pydantic_class: Type[BaseModel]) -> Dict[str, Any]:
    """Convert pydantic class to OpenAI function."""
    schema = pydantic_class.schema()
    return {
        "name": schema["title"],
        "description": schema["description"],
        "parameters": pydantic_class.schema(),
    }

>>> from llama_index.llms.openai_utils import to_openai_tool
>>> from pydantic import BaseModel
>>> class Test(BaseModel):
...     """This is a test"""
...     name: str = "Logan"
... 
>>> to_openai_tool(Test)
{'type': 'function', 'function': {'name': 'Test', 'description': 'This is a test', 'parameters': {'title': 'Test', 'description': 'This is a test', 'type': 'object', 'properties': {'name': {'title': 'Name', 'default': 'Logan', 'type': 'string'}}}}}
>>> 

╰─ $ pip show pydantic
Name: pydantic
Version: 1.10.12

class ContactList(BaseModel):
    """
    Provides a list of contacts.
    Return empty list [] if not confident. It Is important to not hallicunate any of the data. email, phone, first name, last name.
    """

    contacts: Optional[List[Contact]] = Field(
        description="A list of contact information", default=""
    )

>>> to_openai_tool(Test2)
{'type': 'function', 'function': {'name': 'Test2', 'description': 'Nested thing', 'parameters': {'title': 'Test2', 'description': 'Nested thing', 'type': 'object', 'properties': {'prev': {'title': 'Prev', 'type': 'array', 'items': {'$ref': '#/definitions/Test'}}, 'name_2': {'title': 'Name 2', 'default': 'bmax', 'type': 'string'}}, 'definitions': {'Test': {'title': 'Test', 'description': 'This is a test', 'type': 'object', 'properties': {'name': {'title': 'Name', 'default': 'Logan', 'type': 'string'}}}}}}}
>>> from llama_index.llms import OpenAI
>>> llm = OpenAI()
>>> openai_fn_spec = to_openai_tool(Test2)
>>> from llama_index.llms import OpenAI, ChatMessage
>>> chat_response = llm.chat(messages=[ChatMessage(content="Hello!", role="user")], tools=[openai_fn_spec])
>>>