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feat: claude api support (#572)

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This commit is contained in:
John Wang
2023-07-17 00:14:19 +08:00
committed by GitHub
parent 510389909c
commit 7599f79a17
52 changed files with 637 additions and 349 deletions
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3
api/core/llm/error.py
View File

@@ -40,6 +40,9 @@ class ProviderTokenNotInitError(Exception):
"""
description = "Provider Token Not Init"
def __init__(self, *args, **kwargs):
self.description = args[0] if args else self.description
class QuotaExceededError(Exception):
"""

70
api/core/llm/llm_builder.py
View File

@@ -8,9 +8,10 @@ from core.llm.provider.base import BaseProvider
from core.llm.provider.llm_provider_service import LLMProviderService
from core.llm.streamable_azure_chat_open_ai import StreamableAzureChatOpenAI
from core.llm.streamable_azure_open_ai import StreamableAzureOpenAI
from core.llm.streamable_chat_anthropic import StreamableChatAnthropic
from core.llm.streamable_chat_open_ai import StreamableChatOpenAI
from core.llm.streamable_open_ai import StreamableOpenAI
from models.provider import ProviderType
from models.provider import ProviderType, ProviderName
class LLMBuilder:
@@ -32,43 +33,43 @@ class LLMBuilder:
@classmethod
def to_llm(cls, tenant_id: str, model_name: str, **kwargs) -> Union[StreamableOpenAI, StreamableChatOpenAI]:
provider = cls.get_default_provider(tenant_id)
provider = cls.get_default_provider(tenant_id, model_name)
model_credentials = cls.get_model_credentials(tenant_id, provider, model_name)
llm_cls = None
mode = cls.get_mode_by_model(model_name)
if mode == 'chat':
if provider == 'openai':
if provider == ProviderName.OPENAI.value:
llm_cls = StreamableChatOpenAI
else:
elif provider == ProviderName.AZURE_OPENAI.value:
llm_cls = StreamableAzureChatOpenAI
elif provider == ProviderName.ANTHROPIC.value:
llm_cls = StreamableChatAnthropic
elif mode == 'completion':
if provider == 'openai':
if provider == ProviderName.OPENAI.value:
llm_cls = StreamableOpenAI
else:
elif provider == ProviderName.AZURE_OPENAI.value:
llm_cls = StreamableAzureOpenAI
else:
if not llm_cls:
raise ValueError(f"model name {model_name} is not supported.")
model_kwargs = {
'model_name': model_name,
'temperature': kwargs.get('temperature', 0),
'max_tokens': kwargs.get('max_tokens', 256),
'top_p': kwargs.get('top_p', 1),
'frequency_penalty': kwargs.get('frequency_penalty', 0),
'presence_penalty': kwargs.get('presence_penalty', 0),
'callbacks': kwargs.get('callbacks', None),
'streaming': kwargs.get('streaming', False),
}
model_extras_kwargs = model_kwargs if mode == 'completion' else {'model_kwargs': model_kwargs}
model_kwargs.update(model_credentials)
model_kwargs = llm_cls.get_kwargs_from_model_params(model_kwargs)
return llm_cls(
model_name=model_name,
temperature=kwargs.get('temperature', 0),
max_tokens=kwargs.get('max_tokens', 256),
**model_extras_kwargs,
callbacks=kwargs.get('callbacks', None),
streaming=kwargs.get('streaming', False),
# request_timeout=None
**model_credentials
)
return llm_cls(**model_kwargs)
@classmethod
def to_llm_from_model(cls, tenant_id: str, model: dict, streaming: bool = False,
@@ -118,14 +119,29 @@ class LLMBuilder:
return provider_service.get_credentials(model_name)
@classmethod
def get_default_provider(cls, tenant_id: str) -> str:
provider = BaseProvider.get_valid_provider(tenant_id)
if not provider:
raise ProviderTokenNotInitError()
def get_default_provider(cls, tenant_id: str, model_name: str) -> str:
provider_name = llm_constant.models[model_name]
if provider.provider_type == ProviderType.SYSTEM.value:
provider_name = 'openai'
else:
provider_name = provider.provider_name
if provider_name == 'openai':
# get the default provider (openai / azure_openai) for the tenant
openai_provider = BaseProvider.get_valid_provider(tenant_id, ProviderName.OPENAI.value)
azure_openai_provider = BaseProvider.get_valid_provider(tenant_id, ProviderName.AZURE_OPENAI.value)
provider = None
if openai_provider:
provider = openai_provider
elif azure_openai_provider:
provider = azure_openai_provider
if not provider:
raise ProviderTokenNotInitError(
f"No valid {provider_name} model provider credentials found. "
f"Please go to Settings -> Model Provider to complete your provider credentials."
)
if provider.provider_type == ProviderType.SYSTEM.value:
provider_name = 'openai'
else:
provider_name = provider.provider_name
return provider_name

141
api/core/llm/provider/anthropic_provider.py
View File

@@ -1,23 +1,138 @@
from typing import Optional
import json
import logging
from typing import Optional, Union
import anthropic
from langchain.chat_models import ChatAnthropic
from langchain.schema import HumanMessage
from core import hosted_llm_credentials
from core.llm.error import ProviderTokenNotInitError
from core.llm.provider.base import BaseProvider
from models.provider import ProviderName
from core.llm.provider.errors import ValidateFailedError
from models.provider import ProviderName, ProviderType
class AnthropicProvider(BaseProvider):
def get_models(self, model_id: Optional[str] = None) -> list[dict]:
credentials = self.get_credentials(model_id)
# todo
return []
return [
{
'id': 'claude-instant-1',
'name': 'claude-instant-1',
},
{
'id': 'claude-2',
'name': 'claude-2',
},
]
def get_credentials(self, model_id: Optional[str] = None) -> dict:
"""
Returns the API credentials for Azure OpenAI as a dictionary, for the given tenant_id.
The dictionary contains keys: azure_api_type, azure_api_version, azure_api_base, and azure_api_key.
"""
return {
'anthropic_api_key': self.get_provider_api_key(model_id=model_id)
}
return self.get_provider_api_key(model_id=model_id)
def get_provider_name(self):
return ProviderName.ANTHROPIC
return ProviderName.ANTHROPIC
def get_provider_configs(self, obfuscated: bool = False, only_custom: bool = False) -> Union[str | dict]:
"""
Returns the provider configs.
"""
try:
config = self.get_provider_api_key(only_custom=only_custom)
except:
config = {
'anthropic_api_key': ''
}
if obfuscated:
if not config.get('anthropic_api_key'):
config = {
'anthropic_api_key': ''
}
config['anthropic_api_key'] = self.obfuscated_token(config.get('anthropic_api_key'))
return config
return config
def get_encrypted_token(self, config: Union[dict | str]):
"""
Returns the encrypted token.
"""
return json.dumps({
'anthropic_api_key': self.encrypt_token(config['anthropic_api_key'])
})
def get_decrypted_token(self, token: str):
"""
Returns the decrypted token.
"""
config = json.loads(token)
config['anthropic_api_key'] = self.decrypt_token(config['anthropic_api_key'])
return config
def get_token_type(self):
return dict
def config_validate(self, config: Union[dict | str]):
"""
Validates the given config.
"""
# check OpenAI / Azure OpenAI credential is valid
openai_provider = BaseProvider.get_valid_provider(self.tenant_id, ProviderName.OPENAI.value)
azure_openai_provider = BaseProvider.get_valid_provider(self.tenant_id, ProviderName.AZURE_OPENAI.value)
provider = None
if openai_provider:
provider = openai_provider
elif azure_openai_provider:
provider = azure_openai_provider
if not provider:
raise ValidateFailedError(f"OpenAI or Azure OpenAI provider must be configured first.")
if provider.provider_type == ProviderType.SYSTEM.value:
quota_used = provider.quota_used if provider.quota_used is not None else 0
quota_limit = provider.quota_limit if provider.quota_limit is not None else 0
if quota_used >= quota_limit:
raise ValidateFailedError(f"Your quota for Dify Hosted OpenAI has been exhausted, "
f"please configure OpenAI or Azure OpenAI provider first.")
try:
if not isinstance(config, dict):
raise ValueError('Config must be a object.')
if 'anthropic_api_key' not in config:
raise ValueError('anthropic_api_key must be provided.')
chat_llm = ChatAnthropic(
model='claude-instant-1',
anthropic_api_key=config['anthropic_api_key'],
max_tokens_to_sample=10,
temperature=0,
default_request_timeout=60
)
messages = [
HumanMessage(
content="ping"
)
]
chat_llm(messages)
except anthropic.APIConnectionError as ex:
raise ValidateFailedError(f"Anthropic: Connection error, cause: {ex.__cause__}")
except (anthropic.APIStatusError, anthropic.RateLimitError) as ex:
raise ValidateFailedError(f"Anthropic: Error code: {ex.status_code} - "
f"{ex.body['error']['type']}: {ex.body['error']['message']}")
except Exception as ex:
logging.exception('Anthropic config validation failed')
raise ex
def get_hosted_credentials(self) -> Union[str | dict]:
if not hosted_llm_credentials.anthropic or not hosted_llm_credentials.anthropic.api_key:
raise ProviderTokenNotInitError(
f"No valid {self.get_provider_name().value} model provider credentials found. "
f"Please go to Settings -> Model Provider to complete your provider credentials."
)
return {'anthropic_api_key': hosted_llm_credentials.anthropic.api_key}

5
api/core/llm/provider/azure_provider.py
View File

@@ -52,12 +52,12 @@ class AzureProvider(BaseProvider):
def get_provider_name(self):
return ProviderName.AZURE_OPENAI
def get_provider_configs(self, obfuscated: bool = False) -> Union[str | dict]:
def get_provider_configs(self, obfuscated: bool = False, only_custom: bool = False) -> Union[str | dict]:
"""
Returns the provider configs.
"""
try:
config = self.get_provider_api_key()
config = self.get_provider_api_key(only_custom=only_custom)
except:
config = {
'openai_api_type': 'azure',
@@ -81,7 +81,6 @@ class AzureProvider(BaseProvider):
return config
def get_token_type(self):
# TODO: change to dict when implemented
return dict
def config_validate(self, config: Union[dict | str]):

54
api/core/llm/provider/base.py
View File

@@ -2,7 +2,7 @@ import base64
from abc import ABC, abstractmethod
from typing import Optional, Union
from core import hosted_llm_credentials
from core.constant import llm_constant
from core.llm.error import QuotaExceededError, ModelCurrentlyNotSupportError, ProviderTokenNotInitError
from extensions.ext_database import db
from libs import rsa
@@ -14,15 +14,18 @@ class BaseProvider(ABC):
def __init__(self, tenant_id: str):
self.tenant_id = tenant_id
def get_provider_api_key(self, model_id: Optional[str] = None, prefer_custom: bool = True) -> Union[str | dict]:
def get_provider_api_key(self, model_id: Optional[str] = None, only_custom: bool = False) -> Union[str | dict]:
"""
Returns the decrypted API key for the given tenant_id and provider_name.
If the provider is of type SYSTEM and the quota is exceeded, raises a QuotaExceededError.
If the provider is not found or not valid, raises a ProviderTokenNotInitError.
"""
provider = self.get_provider(prefer_custom)
provider = self.get_provider(only_custom)
if not provider:
raise ProviderTokenNotInitError()
raise ProviderTokenNotInitError(
f"No valid {llm_constant.models[model_id]} model provider credentials found. "
f"Please go to Settings -> Model Provider to complete your provider credentials."
)
if provider.provider_type == ProviderType.SYSTEM.value:
quota_used = provider.quota_used if provider.quota_used is not None else 0
@@ -38,18 +41,19 @@ class BaseProvider(ABC):
else:
return self.get_decrypted_token(provider.encrypted_config)
def get_provider(self, prefer_custom: bool) -> Optional[Provider]:
def get_provider(self, only_custom: bool = False) -> Optional[Provider]:
"""
Returns the Provider instance for the given tenant_id and provider_name.
If both CUSTOM and System providers exist, the preferred provider will be returned based on the prefer_custom flag.
"""
return BaseProvider.get_valid_provider(self.tenant_id, self.get_provider_name().value, prefer_custom)
return BaseProvider.get_valid_provider(self.tenant_id, self.get_provider_name().value, only_custom)
@classmethod
def get_valid_provider(cls, tenant_id: str, provider_name: str = None, prefer_custom: bool = False) -> Optional[Provider]:
def get_valid_provider(cls, tenant_id: str, provider_name: str = None, only_custom: bool = False) -> Optional[
Provider]:
"""
Returns the Provider instance for the given tenant_id and provider_name.
If both CUSTOM and System providers exist, the preferred provider will be returned based on the prefer_custom flag.
If both CUSTOM and System providers exist.
"""
query = db.session.query(Provider).filter(
Provider.tenant_id == tenant_id
@@ -58,39 +62,31 @@ class BaseProvider(ABC):
if provider_name:
query = query.filter(Provider.provider_name == provider_name)
providers = query.order_by(Provider.provider_type.desc() if prefer_custom else Provider.provider_type).all()
if only_custom:
query = query.filter(Provider.provider_type == ProviderType.CUSTOM.value)
custom_provider = None
system_provider = None
providers = query.order_by(Provider.provider_type.asc()).all()
for provider in providers:
if provider.provider_type == ProviderType.CUSTOM.value and provider.is_valid and provider.encrypted_config:
custom_provider = provider
return provider
elif provider.provider_type == ProviderType.SYSTEM.value and provider.is_valid:
system_provider = provider
return provider
if custom_provider:
return custom_provider
elif system_provider:
return system_provider
else:
return None
return None
def get_hosted_credentials(self) -> str:
if self.get_provider_name() != ProviderName.OPENAI:
raise ProviderTokenNotInitError()
def get_hosted_credentials(self) -> Union[str | dict]:
raise ProviderTokenNotInitError(
f"No valid {self.get_provider_name().value} model provider credentials found. "
f"Please go to Settings -> Model Provider to complete your provider credentials."
)
if not hosted_llm_credentials.openai or not hosted_llm_credentials.openai.api_key:
raise ProviderTokenNotInitError()
return hosted_llm_credentials.openai.api_key
def get_provider_configs(self, obfuscated: bool = False) -> Union[str | dict]:
def get_provider_configs(self, obfuscated: bool = False, only_custom: bool = False) -> Union[str | dict]:
"""
Returns the provider configs.
"""
try:
config = self.get_provider_api_key()
config = self.get_provider_api_key(only_custom=only_custom)
except:
config = ''

8
api/core/llm/provider/llm_provider_service.py
View File

@@ -31,11 +31,11 @@ class LLMProviderService:
def get_credentials(self, model_id: Optional[str] = None) -> dict:
return self.provider.get_credentials(model_id)
def get_provider_configs(self, obfuscated: bool = False) -> Union[str | dict]:
return self.provider.get_provider_configs(obfuscated)
def get_provider_configs(self, obfuscated: bool = False, only_custom: bool = False) -> Union[str | dict]:
return self.provider.get_provider_configs(obfuscated=obfuscated, only_custom=only_custom)
def get_provider_db_record(self, prefer_custom: bool = False) -> Optional[Provider]:
return self.provider.get_provider(prefer_custom)
def get_provider_db_record(self) -> Optional[Provider]:
return self.provider.get_provider()
def config_validate(self, config: Union[dict | str]):
"""

11
api/core/llm/provider/openai_provider.py
View File

@@ -4,6 +4,8 @@ from typing import Optional, Union
import openai
from openai.error import AuthenticationError, OpenAIError
from core import hosted_llm_credentials
from core.llm.error import ProviderTokenNotInitError
from core.llm.moderation import Moderation
from core.llm.provider.base import BaseProvider
from core.llm.provider.errors import ValidateFailedError
@@ -42,3 +44,12 @@ class OpenAIProvider(BaseProvider):
except Exception as ex:
logging.exception('OpenAI config validation failed')
raise ex
def get_hosted_credentials(self) -> Union[str | dict]:
if not hosted_llm_credentials.openai or not hosted_llm_credentials.openai.api_key:
raise ProviderTokenNotInitError(
f"No valid {self.get_provider_name().value} model provider credentials found. "
f"Please go to Settings -> Model Provider to complete your provider credentials."
)
return hosted_llm_credentials.openai.api_key

55
api/core/llm/streamable_azure_chat_open_ai.py
View File

@@ -1,11 +1,11 @@
from langchain.callbacks.manager import CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun, Callbacks
from langchain.schema import BaseMessage, ChatResult, LLMResult
from langchain.callbacks.manager import Callbacks
from langchain.schema import BaseMessage, LLMResult
from langchain.chat_models import AzureChatOpenAI
from typing import Optional, List, Dict, Any
from pydantic import root_validator
from core.llm.error_handle_wraps import handle_llm_exceptions, handle_llm_exceptions_async
from core.llm.wrappers.openai_wrapper import handle_openai_exceptions
class StreamableAzureChatOpenAI(AzureChatOpenAI):
@@ -46,30 +46,7 @@ class StreamableAzureChatOpenAI(AzureChatOpenAI):
"organization": self.openai_organization if self.openai_organization else None,
}
def get_messages_tokens(self, messages: List[BaseMessage]) -> int:
"""Get the number of tokens in a list of messages.
Args:
messages: The messages to count the tokens of.
Returns:
The number of tokens in the messages.
"""
tokens_per_message = 5
tokens_per_request = 3
message_tokens = tokens_per_request
message_strs = ''
for message in messages:
message_strs += message.content
message_tokens += tokens_per_message
# calc once
message_tokens += self.get_num_tokens(message_strs)
return message_tokens
@handle_llm_exceptions
@handle_openai_exceptions
def generate(
self,
messages: List[List[BaseMessage]],
@@ -79,12 +56,18 @@ class StreamableAzureChatOpenAI(AzureChatOpenAI):
) -> LLMResult:
return super().generate(messages, stop, callbacks, **kwargs)
@handle_llm_exceptions_async
async def agenerate(
self,
messages: List[List[BaseMessage]],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> LLMResult:
return await super().agenerate(messages, stop, callbacks, **kwargs)
@classmethod
def get_kwargs_from_model_params(cls, params: dict):
model_kwargs = {
'top_p': params.get('top_p', 1),
'frequency_penalty': params.get('frequency_penalty', 0),
'presence_penalty': params.get('presence_penalty', 0),
}
del params['top_p']
del params['frequency_penalty']
del params['presence_penalty']
params['model_kwargs'] = model_kwargs
return params

16
api/core/llm/streamable_azure_open_ai.py
View File

@@ -5,7 +5,7 @@ from typing import Optional, List, Dict, Mapping, Any
from pydantic import root_validator
from core.llm.error_handle_wraps import handle_llm_exceptions, handle_llm_exceptions_async
from core.llm.wrappers.openai_wrapper import handle_openai_exceptions
class StreamableAzureOpenAI(AzureOpenAI):
@@ -50,7 +50,7 @@ class StreamableAzureOpenAI(AzureOpenAI):
"organization": self.openai_organization if self.openai_organization else None,
}}
@handle_llm_exceptions
@handle_openai_exceptions
def generate(
self,
prompts: List[str],
@@ -60,12 +60,6 @@ class StreamableAzureOpenAI(AzureOpenAI):
) -> LLMResult:
return super().generate(prompts, stop, callbacks, **kwargs)
@handle_llm_exceptions_async
async def agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> LLMResult:
return await super().agenerate(prompts, stop, callbacks, **kwargs)
@classmethod
def get_kwargs_from_model_params(cls, params: dict):
return params

39
api/core/llm/streamable_chat_anthropic.py Normal file
View File

@@ -0,0 +1,39 @@
from typing import List, Optional, Any, Dict
from langchain.callbacks.manager import Callbacks
from langchain.chat_models import ChatAnthropic
from langchain.schema import BaseMessage, LLMResult
from core.llm.wrappers.anthropic_wrapper import handle_anthropic_exceptions
class StreamableChatAnthropic(ChatAnthropic):
"""
Wrapper around Anthropic's large language model.
"""
@handle_anthropic_exceptions
def generate(
self,
messages: List[List[BaseMessage]],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
*,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> LLMResult:
return super().generate(messages, stop, callbacks, tags=tags, metadata=metadata, **kwargs)
@classmethod
def get_kwargs_from_model_params(cls, params: dict):
params['model'] = params.get('model_name')
del params['model_name']
params['max_tokens_to_sample'] = params.get('max_tokens')
del params['max_tokens']
del params['frequency_penalty']
del params['presence_penalty']
return params

51
api/core/llm/streamable_chat_open_ai.py
View File

@@ -7,7 +7,7 @@ from typing import Optional, List, Dict, Any
from pydantic import root_validator
from core.llm.error_handle_wraps import handle_llm_exceptions, handle_llm_exceptions_async
from core.llm.wrappers.openai_wrapper import handle_openai_exceptions
class StreamableChatOpenAI(ChatOpenAI):
@@ -48,30 +48,7 @@ class StreamableChatOpenAI(ChatOpenAI):
"organization": self.openai_organization if self.openai_organization else None,
}
def get_messages_tokens(self, messages: List[BaseMessage]) -> int:
"""Get the number of tokens in a list of messages.
Args:
messages: The messages to count the tokens of.
Returns:
The number of tokens in the messages.
"""
tokens_per_message = 5
tokens_per_request = 3
message_tokens = tokens_per_request
message_strs = ''
for message in messages:
message_strs += message.content
message_tokens += tokens_per_message
# calc once
message_tokens += self.get_num_tokens(message_strs)
return message_tokens
@handle_llm_exceptions
@handle_openai_exceptions
def generate(
self,
messages: List[List[BaseMessage]],
@@ -81,12 +58,18 @@ class StreamableChatOpenAI(ChatOpenAI):
) -> LLMResult:
return super().generate(messages, stop, callbacks, **kwargs)
@handle_llm_exceptions_async
async def agenerate(
self,
messages: List[List[BaseMessage]],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> LLMResult:
return await super().agenerate(messages, stop, callbacks, **kwargs)
@classmethod
def get_kwargs_from_model_params(cls, params: dict):
model_kwargs = {
'top_p': params.get('top_p', 1),
'frequency_penalty': params.get('frequency_penalty', 0),
'presence_penalty': params.get('presence_penalty', 0),
}
del params['top_p']
del params['frequency_penalty']
del params['presence_penalty']
params['model_kwargs'] = model_kwargs
return params

16
api/core/llm/streamable_open_ai.py
View File

@@ -6,7 +6,7 @@ from typing import Optional, List, Dict, Any, Mapping
from langchain import OpenAI
from pydantic import root_validator
from core.llm.error_handle_wraps import handle_llm_exceptions, handle_llm_exceptions_async
from core.llm.wrappers.openai_wrapper import handle_openai_exceptions
class StreamableOpenAI(OpenAI):
@@ -49,7 +49,7 @@ class StreamableOpenAI(OpenAI):
"organization": self.openai_organization if self.openai_organization else None,
}}
@handle_llm_exceptions
@handle_openai_exceptions
def generate(
self,
prompts: List[str],
@@ -59,12 +59,6 @@ class StreamableOpenAI(OpenAI):
) -> LLMResult:
return super().generate(prompts, stop, callbacks, **kwargs)
@handle_llm_exceptions_async
async def agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> LLMResult:
return await super().agenerate(prompts, stop, callbacks, **kwargs)
@classmethod
def get_kwargs_from_model_params(cls, params: dict):
return params

5
api/core/llm/whisper.py
View File

@@ -1,6 +1,7 @@
import openai
from core.llm.wrappers.openai_wrapper import handle_openai_exceptions
from models.provider import ProviderName
from core.llm.error_handle_wraps import handle_llm_exceptions
from core.llm.provider.base import BaseProvider
@@ -13,7 +14,7 @@ class Whisper:
self.client = openai.Audio
self.credentials = provider.get_credentials()
@handle_llm_exceptions
@handle_openai_exceptions
def transcribe(self, file):
return self.client.transcribe(
model='whisper-1',

27
api/core/llm/wrappers/anthropic_wrapper.py Normal file
View File

@@ -0,0 +1,27 @@
import logging
from functools import wraps
import anthropic
from core.llm.error import LLMAPIConnectionError, LLMAPIUnavailableError, LLMRateLimitError, LLMAuthorizationError, \
LLMBadRequestError
def handle_anthropic_exceptions(func):
@wraps(func)
def wrapper(*args, **kwargs):
try:
return func(*args, **kwargs)
except anthropic.APIConnectionError as e:
logging.exception("Failed to connect to Anthropic API.")
raise LLMAPIConnectionError(f"Anthropic: The server could not be reached, cause: {e.__cause__}")
except anthropic.RateLimitError:
raise LLMRateLimitError("Anthropic: A 429 status code was received; we should back off a bit.")
except anthropic.AuthenticationError as e:
raise LLMAuthorizationError(f"Anthropic: {e.message}")
except anthropic.BadRequestError as e:
raise LLMBadRequestError(f"Anthropic: {e.message}")
except anthropic.APIStatusError as e:
raise LLMAPIUnavailableError(f"Anthropic: code: {e.status_code}, cause: {e.message}")
return wrapper

26
api/core/llm/error_handle_wraps.py → api/core/llm/wrappers/openai_wrapper.py
View File

@@ -7,7 +7,7 @@ from core.llm.error import LLMAPIConnectionError, LLMAPIUnavailableError, LLMRat
LLMBadRequestError
def handle_llm_exceptions(func):
def handle_openai_exceptions(func):
@wraps(func)
def wrapper(*args, **kwargs):
try:
@@ -29,27 +29,3 @@ def handle_llm_exceptions(func):
raise LLMBadRequestError(e.__class__.__name__ + ":" + str(e))
return wrapper
def handle_llm_exceptions_async(func):
@wraps(func)
async def wrapper(*args, **kwargs):
try:
return await func(*args, **kwargs)
except openai.error.InvalidRequestError as e:
logging.exception("Invalid request to OpenAI API.")
raise LLMBadRequestError(str(e))
except openai.error.APIConnectionError as e:
logging.exception("Failed to connect to OpenAI API.")
raise LLMAPIConnectionError(e.__class__.__name__ + ":" + str(e))
except (openai.error.APIError, openai.error.ServiceUnavailableError, openai.error.Timeout) as e:
logging.exception("OpenAI service unavailable.")
raise LLMAPIUnavailableError(e.__class__.__name__ + ":" + str(e))
except openai.error.RateLimitError as e:
raise LLMRateLimitError(str(e))
except openai.error.AuthenticationError as e:
raise LLMAuthorizationError(str(e))
except openai.error.OpenAIError as e:
raise LLMBadRequestError(e.__class__.__name__ + ":" + str(e))
return wrapper