from core.model_runtime.entities.model_entities import DefaultParameterName

PARAMETER_RULE_TEMPLATE: dict[DefaultParameterName, dict] = {
    DefaultParameterName.TEMPERATURE: {
        "label": {
            "en_US": "Temperature",
            "zh_Hans": "温度",
        },
        "type": "float",
        "help": {
            "en_US": "Controls randomness. Lower temperature results in less random completions."
            " As the temperature approaches zero, the model will become deterministic and repetitive."
            " Higher temperature results in more random completions.",
            "zh_Hans": "温度控制随机性。较低的温度会导致较少的随机完成。随着温度接近零，模型将变得确定性和重复性。"
            "较高的温度会导致更多的随机完成。",
        },
        "required": False,
        "default": 0.0,
        "min": 0.0,
        "max": 1.0,
        "precision": 2,
    },
    DefaultParameterName.TOP_P: {
        "label": {
            "en_US": "Top P",
            "zh_Hans": "Top P",
        },
        "type": "float",
        "help": {
            "en_US": "Controls diversity via nucleus sampling: 0.5 means half of all likelihood-weighted options"
            " are considered.",
            "zh_Hans": "通过核心采样控制多样性：0.5表示考虑了一半的所有可能性加权选项。",
        },
        "required": False,
        "default": 1.0,
        "min": 0.0,
        "max": 1.0,
        "precision": 2,
    },
    DefaultParameterName.TOP_K: {
        "label": {
            "en_US": "Top K",
            "zh_Hans": "Top K",
        },
        "type": "int",
        "help": {
            "en_US": "Limits the number of tokens to consider for each step by keeping only the k most likely tokens.",
            "zh_Hans": "通过只保留每一步中最可能的 k 个标记来限制要考虑的标记数量。",
        },
        "required": False,
        "default": 50,
        "min": 1,
        "max": 100,
        "precision": 0,
    },
    DefaultParameterName.PRESENCE_PENALTY: {
        "label": {
            "en_US": "Presence Penalty",
            "zh_Hans": "存在惩罚",
        },
        "type": "float",
        "help": {
            "en_US": "Applies a penalty to the log-probability of tokens already in the text.",
            "zh_Hans": "对文本中已有的标记的对数概率施加惩罚。",
        },
        "required": False,
        "default": 0.0,
        "min": 0.0,
        "max": 1.0,
        "precision": 2,
    },
    DefaultParameterName.FREQUENCY_PENALTY: {
        "label": {
            "en_US": "Frequency Penalty",
            "zh_Hans": "频率惩罚",
        },
        "type": "float",
        "help": {
            "en_US": "Applies a penalty to the log-probability of tokens that appear in the text.",
            "zh_Hans": "对文本中出现的标记的对数概率施加惩罚。",
        },
        "required": False,
        "default": 0.0,
        "min": 0.0,
        "max": 1.0,
        "precision": 2,
    },
    DefaultParameterName.MAX_TOKENS: {
        "label": {
            "en_US": "Max Tokens",
            "zh_Hans": "最大标记",
        },
        "type": "int",
        "help": {
            "en_US": "Specifies the upper limit on the length of generated results."
            " If the generated results are truncated, you can increase this parameter.",
            "zh_Hans": "指定生成结果长度的上限。如果生成结果截断，可以调大该参数。",
        },
        "required": False,
        "default": 64,
        "min": 1,
        "max": 2048,
        "precision": 0,
    },
    DefaultParameterName.RESPONSE_FORMAT: {
        "label": {
            "en_US": "Response Format",
            "zh_Hans": "回复格式",
        },
        "type": "string",
        "help": {
            "en_US": "Set a response format, ensure the output from llm is a valid code block as possible,"
            " such as JSON, XML, etc.",
            "zh_Hans": "设置一个返回格式，确保llm的输出尽可能是有效的代码块，如JSON、XML等",
        },
        "required": False,
        "options": ["JSON", "XML"],
    },
    DefaultParameterName.JSON_SCHEMA: {
        "label": {
            "en_US": "JSON Schema",
        },
        "type": "text",
        "help": {
            "en_US": "Set a response json schema will ensure LLM to adhere it.",
            "zh_Hans": "设置返回的json schema，llm将按照它返回",
        },
        "required": False,
    },
}