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chore(api/core): apply ruff reformatting (#7624)

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Bowen Liang
2024-09-10 17:00:20 +08:00
committed by GitHub
parent 178730266d
commit 2cf1187b32
724 changed files with 21180 additions and 21123 deletions
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176
api/core/model_runtime/entities/defaults.py
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@@ -2,123 +2,123 @@ 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',
"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。',
"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,
"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',
"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。',
"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,
"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',
"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。',
"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,
"required": False,
"default": 50,
"min": 1,
"max": 100,
"precision": 0,
},
DefaultParameterName.PRESENCE_PENALTY: {
'label': {
'en_US': 'Presence Penalty',
'zh_Hans': '存在惩罚',
"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。',
"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,
"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',
"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。',
"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,
"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',
"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。',
"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,
"required": False,
"default": 64,
"min": 1,
"max": 2048,
"precision": 0,
},
DefaultParameterName.RESPONSE_FORMAT: {
'label': {
'en_US': 'Response Format',
'zh_Hans': '回复格式',
"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',
"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'],
"required": False,
"options": ["JSON", "XML"],
},
DefaultParameterName.JSON_SCHEMA: {
'label': {
'en_US': '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',
"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,
"required": False,
},
}