mirror of
https://github.com/langgenius/dify-plugin-sdks.git
synced 2026-08-27 03:21:17 -04:00
takatost
303 lines
8.5 KiB
Python
303 lines
8.5 KiB
Python
from decimal import Decimal
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from enum import Enum
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from typing import Any, Optional
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from pydantic import BaseModel, ConfigDict, Field, model_validator
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from ...entities import I18nObject
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class DefaultParameterName(Enum):
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"""
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Enum class for parameter template variable.
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"""
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TEMPERATURE = "temperature"
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TOP_P = "top_p"
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PRESENCE_PENALTY = "presence_penalty"
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FREQUENCY_PENALTY = "frequency_penalty"
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MAX_TOKENS = "max_tokens"
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RESPONSE_FORMAT = "response_format"
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@classmethod
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def value_of(cls, value: Any) -> 'DefaultParameterName':
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"""
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Get parameter name from value.
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:param value: parameter value
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:return: parameter name
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"""
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for name in cls:
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if name.value == value:
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return name
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raise ValueError(f'invalid parameter name {value}')
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PARAMETER_RULE_TEMPLATE: dict[DefaultParameterName, dict] = {
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DefaultParameterName.TEMPERATURE: {
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'label': {
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'en_US': 'Temperature',
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'zh_Hans': '温度',
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},
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'type': 'float',
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'help': {
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'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.',
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'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。',
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},
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'required': False,
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'default': 0.0,
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'min': 0.0,
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'max': 1.0,
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'precision': 2,
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},
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DefaultParameterName.TOP_P: {
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'label': {
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'en_US': 'Top P',
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'zh_Hans': 'Top P',
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},
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'type': 'float',
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'help': {
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'en_US': 'Controls diversity via nucleus sampling: 0.5 means half of all likelihood-weighted options are considered.',
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'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。',
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},
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'required': False,
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'default': 1.0,
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'min': 0.0,
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'max': 1.0,
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'precision': 2,
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},
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DefaultParameterName.PRESENCE_PENALTY: {
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'label': {
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'en_US': 'Presence Penalty',
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'zh_Hans': '存在惩罚',
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},
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'type': 'float',
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'help': {
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'en_US': 'Applies a penalty to the log-probability of tokens already in the text.',
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'zh_Hans': '对文本中已有的标记的对数概率施加惩罚。',
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},
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'required': False,
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'default': 0.0,
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'min': 0.0,
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'max': 1.0,
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'precision': 2,
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},
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DefaultParameterName.FREQUENCY_PENALTY: {
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'label': {
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'en_US': 'Frequency Penalty',
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'zh_Hans': '频率惩罚',
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},
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'type': 'float',
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'help': {
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'en_US': 'Applies a penalty to the log-probability of tokens that appear in the text.',
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'zh_Hans': '对文本中出现的标记的对数概率施加惩罚。',
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},
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'required': False,
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'default': 0.0,
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'min': 0.0,
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'max': 1.0,
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'precision': 2,
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},
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DefaultParameterName.MAX_TOKENS: {
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'label': {
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'en_US': 'Max Tokens',
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'zh_Hans': '最大标记',
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},
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'type': 'int',
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'help': {
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'en_US': 'Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.',
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'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。',
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},
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'required': False,
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'default': 64,
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'min': 1,
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'max': 2048,
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'precision': 0,
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},
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DefaultParameterName.RESPONSE_FORMAT: {
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'label': {
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'en_US': 'Response Format',
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'zh_Hans': '回复格式',
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},
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'type': 'string',
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'help': {
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'en_US': 'Set a response format, ensure the output from llm is a valid code block as possible, such as JSON, XML, etc.',
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'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',
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},
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'required': False,
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'options': ['JSON', 'XML'],
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}
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}
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class ModelType(Enum):
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"""
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Enum class for model type.
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"""
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LLM = "llm"
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TEXT_EMBEDDING = "text-embedding"
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RERANK = "rerank"
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SPEECH2TEXT = "speech2text"
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MODERATION = "moderation"
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TTS = "tts"
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TEXT2IMG = "text2img"
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class FetchFrom(Enum):
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"""
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Enum class for fetch from.
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"""
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PREDEFINED_MODEL = "predefined-model"
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CUSTOMIZABLE_MODEL = "customizable-model"
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class ModelFeature(Enum):
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"""
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Enum class for llm feature.
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"""
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TOOL_CALL = "tool-call"
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MULTI_TOOL_CALL = "multi-tool-call"
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AGENT_THOUGHT = "agent-thought"
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VISION = "vision"
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STREAM_TOOL_CALL = "stream-tool-call"
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class ParameterType(Enum):
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"""
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Enum class for parameter type.
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"""
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FLOAT = "float"
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INT = "int"
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STRING = "string"
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BOOLEAN = "boolean"
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class ModelPropertyKey(Enum):
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"""
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Enum class for model property key.
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"""
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MODE = "mode"
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CONTEXT_SIZE = "context_size"
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MAX_CHUNKS = "max_chunks"
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FILE_UPLOAD_LIMIT = "file_upload_limit"
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SUPPORTED_FILE_EXTENSIONS = "supported_file_extensions"
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MAX_CHARACTERS_PER_CHUNK = "max_characters_per_chunk"
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DEFAULT_VOICE = "default_voice"
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VOICES = "voices"
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WORD_LIMIT = "word_limit"
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AUDIO_TYPE = "audio_type"
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MAX_WORKERS = "max_workers"
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class ProviderModel(BaseModel):
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"""
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Model class for provider model.
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"""
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model: str
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label: I18nObject
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model_type: ModelType
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features: Optional[list[ModelFeature]] = None
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fetch_from: FetchFrom = Field(default=FetchFrom.PREDEFINED_MODEL)
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model_properties: dict[ModelPropertyKey, Any]
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deprecated: bool = False
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model_config = ConfigDict(protected_namespaces=())
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"""
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use model as label
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"""
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@model_validator(mode='before')
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def validate_label(cls, data: dict) -> dict:
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if isinstance(data, dict):
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if not data.get("label"):
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data["label"] = I18nObject(en_US=data["model"])
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return data
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class ParameterRule(BaseModel):
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"""
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Model class for parameter rule.
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"""
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name: str
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use_template: Optional[str] = None
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label: I18nObject
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type: ParameterType
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help: Optional[I18nObject] = None
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required: bool = False
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default: Optional[Any] = None
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min: Optional[float] = None
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max: Optional[float] = None
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precision: Optional[int] = None
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options: list[str] = []
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@model_validator(mode='before')
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def validate_label(cls, data: dict) -> dict:
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if isinstance(data, dict):
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if not data.get("label"):
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data["label"] = I18nObject(en_US=data["name"])
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# check if there is a template
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if 'use_template' in data:
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try:
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default_parameter_name = DefaultParameterName.value_of(data['use_template'])
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default_parameter_rule = PARAMETER_RULE_TEMPLATE.get(default_parameter_name)
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if not default_parameter_rule:
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raise Exception(f"Invalid model parameter rule name {default_parameter_name}")
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copy_default_parameter_rule = default_parameter_rule.copy()
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copy_default_parameter_rule.update(data)
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data = copy_default_parameter_rule
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except ValueError:
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pass
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return data
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class PriceConfig(BaseModel):
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"""
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Model class for pricing info.
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"""
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input: Decimal
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output: Optional[Decimal] = None
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unit: Decimal
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currency: str
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class AIModelEntity(ProviderModel):
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"""
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Model class for AI model.
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"""
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parameter_rules: list[ParameterRule] = []
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pricing: Optional[PriceConfig] = None
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class ModelUsage(BaseModel):
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pass
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class PriceType(Enum):
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"""
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Enum class for price type.
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"""
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INPUT = "input"
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OUTPUT = "output"
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class PriceInfo(BaseModel):
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"""
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Model class for price info.
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"""
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unit_price: Decimal
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unit: Decimal
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total_amount: Decimal
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currency: str
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class BaseModelConfig(BaseModel):
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provider: str
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model: str
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model_type: ModelType
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model_config = ConfigDict(protected_namespaces=())
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class EmbeddingInputType(Enum):
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"""
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Enum for embedding input type.
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"""
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DOCUMENT = "document"
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QUERY = "query"
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