prompt: BasePromptTemplate to pass to the model.
        output_parser: BaseLLMOutputParser to use for parsing model outputs. By default
            will be inferred from the function types. If pydantic.BaseModels are passed
            in, then the OutputParser will try to parse outputs using those. Otherwise
            model outputs will simply be parsed as JSON.
    Returns:
        An LLMChain that will pass the given function to the model.
    Example:
        .. code-block:: python
                from langchain.chains.openai_functions import create_structured_output_chain
                from langchain.chat_models import ChatOpenAI
                from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
                from pydantic import BaseModel, Field
                class Dog(BaseModel):
                    \"\"\"Identifying information about a dog.\"\"\"
                    name: str = Field(..., description="The dog's name")
                    color: str = Field(..., description="The dog's color")
                    fav_food: Optional[str] = Field(None, description="The dog's favorite food")
                llm = ChatOpenAI(model="gpt-3.5-turbo-0613", temperature=0)
                prompt_msgs = [
                    SystemMessage(
                        content="You are a world class algorithm for extracting information in structured formats."
                    ),
                    HumanMessage(content="Use the given format to extract information from the following input:"),
                    HumanMessagePromptTemplate.from_template("{input}"),
                    HumanMessage(content="Tips: Make sure to answer in the correct format"),
                ]
                prompt = ChatPromptTemplate(messages=prompt_msgs)
                chain = create_structured_output_chain(Dog, llm, prompt)
                chain.run("Harry was a chubby brown beagle who loved chicken")