mirror of
https://github.com/langgenius/dify.git
synced 2025-10-31 02:42:59 +00:00
256 lines
9.4 KiB
Python
256 lines
9.4 KiB
Python
from typing import Optional
|
|
|
|
from flask import Flask
|
|
from pydantic import BaseModel
|
|
|
|
from configs import dify_config
|
|
from core.entities.provider_entities import QuotaUnit, RestrictModel
|
|
from core.model_runtime.entities.model_entities import ModelType
|
|
from models.provider import ProviderQuotaType
|
|
|
|
|
|
class HostingQuota(BaseModel):
|
|
quota_type: ProviderQuotaType
|
|
restrict_models: list[RestrictModel] = []
|
|
|
|
|
|
class TrialHostingQuota(HostingQuota):
|
|
quota_type: ProviderQuotaType = ProviderQuotaType.TRIAL
|
|
quota_limit: int = 0
|
|
"""Quota limit for the hosting provider models. -1 means unlimited."""
|
|
|
|
|
|
class PaidHostingQuota(HostingQuota):
|
|
quota_type: ProviderQuotaType = ProviderQuotaType.PAID
|
|
|
|
|
|
class FreeHostingQuota(HostingQuota):
|
|
quota_type: ProviderQuotaType = ProviderQuotaType.FREE
|
|
|
|
|
|
class HostingProvider(BaseModel):
|
|
enabled: bool = False
|
|
credentials: Optional[dict] = None
|
|
quota_unit: Optional[QuotaUnit] = None
|
|
quotas: list[HostingQuota] = []
|
|
|
|
|
|
class HostedModerationConfig(BaseModel):
|
|
enabled: bool = False
|
|
providers: list[str] = []
|
|
|
|
|
|
class HostingConfiguration:
|
|
provider_map: dict[str, HostingProvider] = {}
|
|
moderation_config: HostedModerationConfig = None
|
|
|
|
def init_app(self, app: Flask) -> None:
|
|
if dify_config.EDITION != "CLOUD":
|
|
return
|
|
|
|
self.provider_map["azure_openai"] = self.init_azure_openai()
|
|
self.provider_map["openai"] = self.init_openai()
|
|
self.provider_map["anthropic"] = self.init_anthropic()
|
|
self.provider_map["minimax"] = self.init_minimax()
|
|
self.provider_map["spark"] = self.init_spark()
|
|
self.provider_map["zhipuai"] = self.init_zhipuai()
|
|
|
|
self.moderation_config = self.init_moderation_config()
|
|
|
|
@staticmethod
|
|
def init_azure_openai() -> HostingProvider:
|
|
quota_unit = QuotaUnit.TIMES
|
|
if dify_config.HOSTED_AZURE_OPENAI_ENABLED:
|
|
credentials = {
|
|
"openai_api_key": dify_config.HOSTED_AZURE_OPENAI_API_KEY,
|
|
"openai_api_base": dify_config.HOSTED_AZURE_OPENAI_API_BASE,
|
|
"base_model_name": "gpt-35-turbo",
|
|
}
|
|
|
|
quotas = []
|
|
hosted_quota_limit = dify_config.HOSTED_AZURE_OPENAI_QUOTA_LIMIT
|
|
trial_quota = TrialHostingQuota(
|
|
quota_limit=hosted_quota_limit,
|
|
restrict_models=[
|
|
RestrictModel(model="gpt-4", base_model_name="gpt-4", model_type=ModelType.LLM),
|
|
RestrictModel(model="gpt-4o", base_model_name="gpt-4o", model_type=ModelType.LLM),
|
|
RestrictModel(model="gpt-4o-mini", base_model_name="gpt-4o-mini", model_type=ModelType.LLM),
|
|
RestrictModel(model="gpt-4-32k", base_model_name="gpt-4-32k", model_type=ModelType.LLM),
|
|
RestrictModel(
|
|
model="gpt-4-1106-preview", base_model_name="gpt-4-1106-preview", model_type=ModelType.LLM
|
|
),
|
|
RestrictModel(
|
|
model="gpt-4-vision-preview", base_model_name="gpt-4-vision-preview", model_type=ModelType.LLM
|
|
),
|
|
RestrictModel(model="gpt-35-turbo", base_model_name="gpt-35-turbo", model_type=ModelType.LLM),
|
|
RestrictModel(
|
|
model="gpt-35-turbo-1106", base_model_name="gpt-35-turbo-1106", model_type=ModelType.LLM
|
|
),
|
|
RestrictModel(
|
|
model="gpt-35-turbo-instruct", base_model_name="gpt-35-turbo-instruct", model_type=ModelType.LLM
|
|
),
|
|
RestrictModel(
|
|
model="gpt-35-turbo-16k", base_model_name="gpt-35-turbo-16k", model_type=ModelType.LLM
|
|
),
|
|
RestrictModel(
|
|
model="text-davinci-003", base_model_name="text-davinci-003", model_type=ModelType.LLM
|
|
),
|
|
RestrictModel(
|
|
model="text-embedding-ada-002",
|
|
base_model_name="text-embedding-ada-002",
|
|
model_type=ModelType.TEXT_EMBEDDING,
|
|
),
|
|
RestrictModel(
|
|
model="text-embedding-3-small",
|
|
base_model_name="text-embedding-3-small",
|
|
model_type=ModelType.TEXT_EMBEDDING,
|
|
),
|
|
RestrictModel(
|
|
model="text-embedding-3-large",
|
|
base_model_name="text-embedding-3-large",
|
|
model_type=ModelType.TEXT_EMBEDDING,
|
|
),
|
|
],
|
|
)
|
|
quotas.append(trial_quota)
|
|
|
|
return HostingProvider(enabled=True, credentials=credentials, quota_unit=quota_unit, quotas=quotas)
|
|
|
|
return HostingProvider(
|
|
enabled=False,
|
|
quota_unit=quota_unit,
|
|
)
|
|
|
|
def init_openai(self) -> HostingProvider:
|
|
quota_unit = QuotaUnit.CREDITS
|
|
quotas = []
|
|
|
|
if dify_config.HOSTED_OPENAI_TRIAL_ENABLED:
|
|
hosted_quota_limit = dify_config.HOSTED_OPENAI_QUOTA_LIMIT
|
|
trial_models = self.parse_restrict_models_from_env("HOSTED_OPENAI_TRIAL_MODELS")
|
|
trial_quota = TrialHostingQuota(quota_limit=hosted_quota_limit, restrict_models=trial_models)
|
|
quotas.append(trial_quota)
|
|
|
|
if dify_config.HOSTED_OPENAI_PAID_ENABLED:
|
|
paid_models = self.parse_restrict_models_from_env("HOSTED_OPENAI_PAID_MODELS")
|
|
paid_quota = PaidHostingQuota(restrict_models=paid_models)
|
|
quotas.append(paid_quota)
|
|
|
|
if len(quotas) > 0:
|
|
credentials = {
|
|
"openai_api_key": dify_config.HOSTED_OPENAI_API_KEY,
|
|
}
|
|
|
|
if dify_config.HOSTED_OPENAI_API_BASE:
|
|
credentials["openai_api_base"] = dify_config.HOSTED_OPENAI_API_BASE
|
|
|
|
if dify_config.HOSTED_OPENAI_API_ORGANIZATION:
|
|
credentials["openai_organization"] = dify_config.HOSTED_OPENAI_API_ORGANIZATION
|
|
|
|
return HostingProvider(enabled=True, credentials=credentials, quota_unit=quota_unit, quotas=quotas)
|
|
|
|
return HostingProvider(
|
|
enabled=False,
|
|
quota_unit=quota_unit,
|
|
)
|
|
|
|
@staticmethod
|
|
def init_anthropic() -> HostingProvider:
|
|
quota_unit = QuotaUnit.TOKENS
|
|
quotas = []
|
|
|
|
if dify_config.HOSTED_ANTHROPIC_TRIAL_ENABLED:
|
|
hosted_quota_limit = dify_config.HOSTED_ANTHROPIC_QUOTA_LIMIT
|
|
trial_quota = TrialHostingQuota(quota_limit=hosted_quota_limit)
|
|
quotas.append(trial_quota)
|
|
|
|
if dify_config.HOSTED_ANTHROPIC_PAID_ENABLED:
|
|
paid_quota = PaidHostingQuota()
|
|
quotas.append(paid_quota)
|
|
|
|
if len(quotas) > 0:
|
|
credentials = {
|
|
"anthropic_api_key": dify_config.HOSTED_ANTHROPIC_API_KEY,
|
|
}
|
|
|
|
if dify_config.HOSTED_ANTHROPIC_API_BASE:
|
|
credentials["anthropic_api_url"] = dify_config.HOSTED_ANTHROPIC_API_BASE
|
|
|
|
return HostingProvider(enabled=True, credentials=credentials, quota_unit=quota_unit, quotas=quotas)
|
|
|
|
return HostingProvider(
|
|
enabled=False,
|
|
quota_unit=quota_unit,
|
|
)
|
|
|
|
@staticmethod
|
|
def init_minimax() -> HostingProvider:
|
|
quota_unit = QuotaUnit.TOKENS
|
|
if dify_config.HOSTED_MINIMAX_ENABLED:
|
|
quotas = [FreeHostingQuota()]
|
|
|
|
return HostingProvider(
|
|
enabled=True,
|
|
credentials=None, # use credentials from the provider
|
|
quota_unit=quota_unit,
|
|
quotas=quotas,
|
|
)
|
|
|
|
return HostingProvider(
|
|
enabled=False,
|
|
quota_unit=quota_unit,
|
|
)
|
|
|
|
@staticmethod
|
|
def init_spark() -> HostingProvider:
|
|
quota_unit = QuotaUnit.TOKENS
|
|
if dify_config.HOSTED_SPARK_ENABLED:
|
|
quotas = [FreeHostingQuota()]
|
|
|
|
return HostingProvider(
|
|
enabled=True,
|
|
credentials=None, # use credentials from the provider
|
|
quota_unit=quota_unit,
|
|
quotas=quotas,
|
|
)
|
|
|
|
return HostingProvider(
|
|
enabled=False,
|
|
quota_unit=quota_unit,
|
|
)
|
|
|
|
@staticmethod
|
|
def init_zhipuai() -> HostingProvider:
|
|
quota_unit = QuotaUnit.TOKENS
|
|
if dify_config.HOSTED_ZHIPUAI_ENABLED:
|
|
quotas = [FreeHostingQuota()]
|
|
|
|
return HostingProvider(
|
|
enabled=True,
|
|
credentials=None, # use credentials from the provider
|
|
quota_unit=quota_unit,
|
|
quotas=quotas,
|
|
)
|
|
|
|
return HostingProvider(
|
|
enabled=False,
|
|
quota_unit=quota_unit,
|
|
)
|
|
|
|
@staticmethod
|
|
def init_moderation_config() -> HostedModerationConfig:
|
|
if dify_config.HOSTED_MODERATION_ENABLED and dify_config.HOSTED_MODERATION_PROVIDERS:
|
|
return HostedModerationConfig(enabled=True, providers=dify_config.HOSTED_MODERATION_PROVIDERS.split(","))
|
|
|
|
return HostedModerationConfig(enabled=False)
|
|
|
|
@staticmethod
|
|
def parse_restrict_models_from_env(env_var: str) -> list[RestrictModel]:
|
|
models_str = dify_config.model_dump().get(env_var)
|
|
models_list = models_str.split(",") if models_str else []
|
|
return [
|
|
RestrictModel(model=model_name.strip(), model_type=ModelType.LLM)
|
|
for model_name in models_list
|
|
if model_name.strip()
|
|
]
|