mirror of
https://github.com/langgenius/dify-plugin-sdks.git
synced 2026-07-22 18:35:29 -04:00
Yeuoly
5f517df4f5
- Added `@final` decorator to the constructors of `AgentStrategy`, `Endpoint`, `AIModel`, and `Tool` classes to prevent overriding. - Included documentation notes in each constructor to indicate that they should not be overridden.
291 lines
10 KiB
Python
291 lines
10 KiB
Python
import decimal
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import socket
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from abc import ABC, abstractmethod
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from collections.abc import Mapping
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from typing import Optional, final
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import gevent.socket
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from pydantic import ConfigDict
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from dify_plugin.entities import I18nObject
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from dify_plugin.entities.model import (
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PARAMETER_RULE_TEMPLATE,
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AIModelEntity,
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DefaultParameterName,
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ModelType,
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PriceConfig,
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PriceInfo,
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PriceType,
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)
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from dify_plugin.errors.model import InvokeAuthorizationError, InvokeError
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if socket.socket is gevent.socket.socket:
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import gevent.threadpool
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threadpool = gevent.threadpool.ThreadPool(1)
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class AIModel(ABC):
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"""
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Base class for all models.
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"""
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model_type: ModelType
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model_schemas: list[AIModelEntity]
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started_at: float = 0
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# pydantic configs
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model_config = ConfigDict(protected_namespaces=())
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@final
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def __init__(self, model_schemas: list[AIModelEntity]) -> None:
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"""
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Initialize the model
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NOTE:
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- This method has been marked as final, DO NOT OVERRIDE IT.
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"""
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self.model_schemas = [
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model_schema for model_schema in model_schemas if model_schema.model_type == self.model_type
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]
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@abstractmethod
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def validate_credentials(self, model: str, credentials: Mapping) -> None:
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"""
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Validate model credentials
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:param model: model name
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:param credentials: model credentials
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:return:
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"""
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raise NotImplementedError
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@property
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@abstractmethod
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def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
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"""
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Map model invoke error to unified error
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The key is the error type thrown to the caller
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The value is the error type thrown by the model,
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which needs to be converted into a unified error type for the caller.
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:return: Invoke error mapping
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"""
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raise NotImplementedError
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def _transform_invoke_error(self, error: Exception) -> InvokeError:
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"""
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Transform invoke error to unified error
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:param error: model invoke error
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:return: unified error
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"""
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provider_name = self.__class__.__module__.split(".")[-3]
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for invoke_error, model_errors in self._invoke_error_mapping.items():
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if isinstance(error, tuple(model_errors)):
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if invoke_error == InvokeAuthorizationError:
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return invoke_error(
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description=f"[{provider_name}] Incorrect model credentials provided, "
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"please check and try again. "
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)
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return invoke_error(description=f"[{provider_name}] {invoke_error.description}, {error!s}")
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return InvokeError(description=f"[{provider_name}] Error: {error!s}")
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def get_price(self, model: str, credentials: dict, price_type: PriceType, tokens: int) -> PriceInfo:
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"""
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Get price for given model and tokens
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:param model: model name
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:param credentials: model credentials
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:param price_type: price type
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:param tokens: number of tokens
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:return: price info
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"""
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# get model schema
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model_schema = self.get_model_schema(model, credentials)
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# get price info from predefined model schema
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price_config: Optional[PriceConfig] = None
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if model_schema and model_schema.pricing:
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price_config = model_schema.pricing
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# get unit price
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unit_price = None
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if price_config:
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if price_type == PriceType.INPUT:
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unit_price = price_config.input
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elif price_type == PriceType.OUTPUT and price_config.output is not None:
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unit_price = price_config.output
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if unit_price is None:
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return PriceInfo(
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unit_price=decimal.Decimal("0.0"),
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unit=decimal.Decimal("0.0"),
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total_amount=decimal.Decimal("0.0"),
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currency="USD",
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)
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# calculate total amount
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if not price_config:
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raise ValueError(f"Price config not found for model {model}")
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total_amount = tokens * unit_price * price_config.unit
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total_amount = total_amount.quantize(decimal.Decimal("0.0000001"), rounding=decimal.ROUND_HALF_UP)
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return PriceInfo(
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unit_price=unit_price,
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unit=price_config.unit,
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total_amount=total_amount,
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currency=price_config.currency,
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)
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def predefined_models(self) -> list[AIModelEntity]:
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"""
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Get all predefined models for given provider.
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:return:
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"""
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return self.model_schemas
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def get_model_schema(self, model: str, credentials: Optional[Mapping] = None) -> Optional[AIModelEntity]:
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"""
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Get model schema by model name and credentials
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:param model: model name
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:param credentials: model credentials
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:return: model schema
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"""
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# get predefined models (predefined_models)
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models = self.predefined_models()
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model_map = {model.model: model for model in models}
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if model in model_map:
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return model_map[model]
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if credentials:
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model_schema = self.get_customizable_model_schema_from_credentials(model, credentials)
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if model_schema:
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return model_schema
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return None
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def get_customizable_model_schema_from_credentials(
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self, model: str, credentials: Mapping
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) -> Optional[AIModelEntity]:
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"""
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Get customizable model schema from credentials
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:param model: model name
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:param credentials: model credentials
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:return: model schema
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"""
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return self._get_customizable_model_schema(model, credentials)
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def _get_customizable_model_schema(self, model: str, credentials: Mapping) -> Optional[AIModelEntity]:
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"""
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Get customizable model schema and fill in the template
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"""
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schema = self.get_customizable_model_schema(model, credentials)
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if not schema:
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return None
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# fill in the template
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new_parameter_rules = []
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for parameter_rule in schema.parameter_rules:
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if parameter_rule.use_template:
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try:
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default_parameter_name = DefaultParameterName.value_of(parameter_rule.use_template)
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default_parameter_rule = self._get_default_parameter_rule_variable_map(default_parameter_name)
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if not parameter_rule.max and "max" in default_parameter_rule:
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parameter_rule.max = default_parameter_rule["max"]
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if not parameter_rule.min and "min" in default_parameter_rule:
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parameter_rule.min = default_parameter_rule["min"]
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if not parameter_rule.default and "default" in default_parameter_rule:
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parameter_rule.default = default_parameter_rule["default"]
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if not parameter_rule.precision and "precision" in default_parameter_rule:
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parameter_rule.precision = default_parameter_rule["precision"]
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if not parameter_rule.required and "required" in default_parameter_rule:
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parameter_rule.required = default_parameter_rule["required"]
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if not parameter_rule.help and "help" in default_parameter_rule:
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parameter_rule.help = I18nObject(
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en_US=default_parameter_rule["help"]["en_US"],
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)
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if (
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parameter_rule.help
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and not parameter_rule.help.en_US
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and ("help" in default_parameter_rule and "en_US" in default_parameter_rule["help"])
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):
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parameter_rule.help.en_US = default_parameter_rule["help"]["en_US"]
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if (
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parameter_rule.help
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and not parameter_rule.help.zh_Hans
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and ("help" in default_parameter_rule and "zh_Hans" in default_parameter_rule["help"])
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):
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parameter_rule.help.zh_Hans = default_parameter_rule["help"].get(
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"zh_Hans", default_parameter_rule["help"]["en_US"]
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)
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except ValueError:
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pass
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new_parameter_rules.append(parameter_rule)
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schema.parameter_rules = new_parameter_rules
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return schema
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def get_customizable_model_schema(self, model: str, credentials: Mapping) -> Optional[AIModelEntity]:
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"""
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Get customizable model schema
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:param model: model name
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:param credentials: model credentials
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:return: model schema
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"""
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return None
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def _get_default_parameter_rule_variable_map(self, name: DefaultParameterName) -> dict:
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"""
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Get default parameter rule for given name
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:param name: parameter name
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:return: parameter rule
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"""
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default_parameter_rule = PARAMETER_RULE_TEMPLATE.get(name)
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if not default_parameter_rule:
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raise Exception(f"Invalid model parameter rule name {name}")
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return default_parameter_rule
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def _get_num_tokens_by_gpt2(self, text: str) -> int:
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"""
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Get number of tokens for given prompt messages by gpt2
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Some provider models do not provide an interface for obtaining the number of tokens.
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Here, the gpt2 tokenizer is used to calculate the number of tokens.
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This method can be executed offline, and the gpt2 tokenizer has been cached in the project.
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:param text: plain text of prompt. You need to convert the original message to plain text
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:return: number of tokens
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"""
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# ENHANCEMENT:
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# to avoid performance issue, do not calculate the number of tokens for too long text
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# only to promise text length is less than 100000
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if len(text) >= 100000:
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return len(text)
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# check if gevent is patched to main thread
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import socket
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import tiktoken
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if socket.socket is gevent.socket.socket:
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# using gevent real thread to avoid blocking main thread
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result = threadpool.spawn(lambda: len(tiktoken.encoding_for_model("gpt2").encode(text)))
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return result.get(block=True) or 0
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return len(tiktoken.encoding_for_model("gpt2").encode(text))
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