"""
    model_name = standardize_model_name(model_name, is_completion=is_completion)
    if model_name not in MODEL_COST_PER_1K_TOKENS:
        raise ValueError(
            f"Unknown model: {model_name}. Please provide a valid OpenAI model name."
            "Known models are: " + ", ".join(MODEL_COST_PER_1K_TOKENS.keys())
        )
    return MODEL_COST_PER_1K_TOKENS[model_name] * (num_tokens / 1000)
[docs]class OpenAICallbackHandler(BaseCallbackHandler):
    """Callback Handler that tracks OpenAI info."""
    total_tokens: int = 0
    prompt_tokens: int = 0
    completion_tokens: int = 0
    successful_requests: int = 0
    total_cost: float = 0.0
    def __repr__(self) -> str:
        return (
            f"Tokens Used: {self.total_tokens}\n"
            f"\tPrompt Tokens: {self.prompt_tokens}\n"
            f"\tCompletion Tokens: {self.completion_tokens}\n"
            f"Successful Requests: {self.successful_requests}\n"
            f"Total Cost (USD): ${self.total_cost}"
        )
    @property
    def always_verbose(self) -> bool:
        """Whether to call verbose callbacks even if verbose is False."""
        return True
[docs]    def on_llm_start(
        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
    ) -> None:
        """Print out the prompts."""
        pass
[docs]    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
        """Print out the token."""
        pass