example_selector=example_selector,
                # Define how each example will be formatted.
                # In this case, each example will become 2 messages:
                # 1 human, and 1 AI
                example_prompt=(
                    HumanMessagePromptTemplate.from_template("{input}")
                    + AIMessagePromptTemplate.from_template("{output}")
                ),
            )
            # Define the overall prompt.
            final_prompt = (
                SystemMessagePromptTemplate.from_template(
                    "You are a helpful AI Assistant"
                )
                + few_shot_prompt
                + HumanMessagePromptTemplate.from_template("{input}")
            )
            # Show the prompt
            print(final_prompt.format_messages(input="What's 3+3?"))
            # Use within an LLM
            from langchain.chat_models import ChatAnthropic
            chain = final_prompt | ChatAnthropic()
            chain.invoke({"input": "What's 3+3?"})
    """
[docs]    @classmethod
    def is_lc_serializable(cls) -> bool:
        """Return whether or not the class is serializable."""
        return False
    input_variables: List[str] = Field(default_factory=list)
    """A list of the names of the variables the prompt template will use
    to pass to the example_selector, if provided."""
    example_prompt: Union[BaseMessagePromptTemplate, BaseChatPromptTemplate]
    """The class to format each example."""
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
[docs]    def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
        """Format kwargs into a list of messages.
        Args:
            **kwargs: keyword arguments to use for filling in templates in messages.
        Returns: