mirror of
https://github.com/langgenius/dify-plugin-sdks.git
synced 2026-07-25 05:25:23 -04:00
Yeuoly
038edf957e
- Added CLI for generating documentation with `generate-docs` command. - Introduced `SchemaDocumentationGenerator` to create structured documentation from schemas. - Implemented `SchemaDoc` class for schema metadata and documentation management. - Updated `PluginConfiguration` to include descriptions for plugin components. - Added support for outside reference fields in the SchemaDocumentationGenerator to improve documentation clarity. - Updated SchemaDoc to include outside_reference_fields parameter. - Modified various entity classes to utilize outside_reference_fields for better schema representation. - Introduced new container type checks and helper methods for handling dynamic fields in documentation generation.
392 lines
12 KiB
Python
392 lines
12 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 dify_plugin.core.documentation.schema_doc import docs
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from dify_plugin.entities import I18nObject
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@docs(
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description="The default parameter name",
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)
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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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TOP_K = "top_k"
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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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JSON_SCHEMA = "json_schema"
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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.", # noqa: E501
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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。"
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"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: "
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"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.TOP_K: {
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"label": {
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"en_US": "Top K",
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"zh_Hans": "Top K",
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},
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"type": "int",
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"help": {
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"en_US": "Limits the number of tokens to consider for each step by keeping only the k most likely tokens.",
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"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。",
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},
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"required": False,
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"default": 50,
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"min": 1,
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"max": 100,
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"precision": 0,
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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. "
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"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, "
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"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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DefaultParameterName.JSON_SCHEMA: {
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"label": {
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"en_US": "JSON Schema",
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},
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"type": "text",
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"help": {
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"en_US": "Set a response json schema will ensure LLM to adhere it.",
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"zh_Hans": "设置返回的json schema，llm将按照它返回",
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},
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"required": False,
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},
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}
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@docs(
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description="The model type",
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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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@docs(
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description="The fetch from",
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)
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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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@docs(
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description="The model feature",
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)
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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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DOCUMENT = "document"
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VIDEO = "video"
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AUDIO = "audio"
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STRUCTURED_OUTPUT = "structured-output"
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@docs(
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description="The parameter type",
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)
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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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TEXT = "text"
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@docs(
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description="The model property key",
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)
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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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@docs(
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description="The provider model",
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)
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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 = Field(..., description="The model name")
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label: I18nObject = Field(..., description="The label of the model")
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model_type: ModelType = Field(..., description="The model type")
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features: Optional[list[ModelFeature]] = Field(default=None, description="The features of the model")
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fetch_from: FetchFrom = Field(default=FetchFrom.PREDEFINED_MODEL, description="The fetch from")
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model_properties: dict[ModelPropertyKey, Any] = Field(..., description="The model properties")
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deprecated: bool = Field(default=False, description="Whether the model is deprecated")
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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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@classmethod
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def validate_label(cls, data: dict) -> dict:
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if isinstance(data, dict) and 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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@docs(
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description="The parameter rule of the model",
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)
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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 = Field(..., description="The name of the parameter")
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use_template: Optional[str] = Field(default=None, description="The template of the parameter")
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label: I18nObject = Field(..., description="The label of the parameter")
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type: ParameterType = Field(..., description="The type of the parameter")
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help: Optional[I18nObject] = Field(default=None, description="The help of the parameter")
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required: bool = Field(default=False, description="Whether the parameter is required")
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default: Optional[Any] = Field(default=None, description="The default value of the parameter")
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min: Optional[float] = Field(default=None, description="The minimum value of the parameter")
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max: Optional[float] = Field(default=None, description="The maximum value of the parameter")
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precision: Optional[int] = Field(default=None, description="The precision of the parameter")
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options: list[str] = Field(default=[], description="The options of the parameter")
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@model_validator(mode="before")
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@classmethod
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def validate_label(cls, data: dict) -> dict:
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if isinstance(data, dict):
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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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if not data.get("label"):
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data["label"] = I18nObject(en_US=data["name"])
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return data
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@docs(
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description="The price config",
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)
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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 = Field(..., description="Input price")
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output: Optional[Decimal] = Field(default=None, description="Output price")
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unit: Decimal = Field(..., description="Unit, e.g. 0.0001 -> per 10000 tokens")
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currency: str = Field(..., description="Currency, e.g. USD")
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@docs(
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description="AI model entity",
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)
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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 = Field(..., description="The unit price, e.g. 0.000001")
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unit: Decimal = Field(..., description="The unit, e.g. 1000")
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total_amount: Decimal = Field(..., description="The total amount")
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currency: str = Field(..., description="The currency, e.g. USD")
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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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