model / 模型和最大代币数：
这个参数就是 OpenAI 的模型，不过中间数值是该模型的最大 Token 。
temperature / 创造性：
What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
top-p / 准确性：
An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.
max-tokens-completion / 单次回复可以消耗的最大代币数：
The maximum number of tokens to generate in the chat completion.
The total length of input tokens and generated tokens is limited by the model's context length. Example Python code for counting tokens.
presence-penalty / 话题分散性：
Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
frequency-penalty / 强调观点：
Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
