phoenix/litellm-mirror
0
0
Fork 1
mirror of https://github.com/BerriAI/litellm.git synced 2025-04-25 18:54:30 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

fix linting errors

Browse source
This commit is contained in:
Ishaan Jaff 2024-08-23 15:44:31 -07:00
parent 49b25db516
commit c3987745fe
2 changed files with 37 additions and 2 deletions
Download patch file Download diff file Expand all files Collapse all files

2
litellm/llms/text_to_speech/vertex_ai.py
View file

@ -47,7 +47,6 @@ class VertexTextToSpeechAPI(VertexLLM):
def audio_speech( def audio_speech(
self, self,
logging_obj, logging_obj,
_is_async: bool,
vertex_project: Optional[str], vertex_project: Optional[str],
vertex_location: Optional[str], vertex_location: Optional[str],
vertex_credentials: Optional[str], vertex_credentials: Optional[str],
@ -56,6 +55,7 @@ class VertexTextToSpeechAPI(VertexLLM):
model: str, model: str,
input: str, input: str,
voice: str, voice: str,
_is_async: Optional[bool] = False,
optional_params: Optional[dict] = None, optional_params: Optional[dict] = None,
**kwargs, **kwargs,
): ):

37
litellm/main.py
View file

@ -121,6 +121,7 @@ from .llms.prompt_templates.factory import (
) )
from .llms.sagemaker import SagemakerLLM from .llms.sagemaker import SagemakerLLM
from .llms.text_completion_codestral import CodestralTextCompletion from .llms.text_completion_codestral import CodestralTextCompletion
from .llms.text_to_speech.vertex_ai import VertexTextToSpeechAPI
from .llms.triton import TritonChatCompletion from .llms.triton import TritonChatCompletion
from .llms.vertex_ai_partner import VertexAIPartnerModels from .llms.vertex_ai_partner import VertexAIPartnerModels
from .llms.vertex_httpx import VertexLLM from .llms.vertex_httpx import VertexLLM
@ -165,6 +166,7 @@ bedrock_chat_completion = BedrockLLM()
bedrock_converse_chat_completion = BedrockConverseLLM() bedrock_converse_chat_completion = BedrockConverseLLM()
vertex_chat_completion = VertexLLM() vertex_chat_completion = VertexLLM()
vertex_partner_models_chat_completion = VertexAIPartnerModels() vertex_partner_models_chat_completion = VertexAIPartnerModels()
vertex_text_to_speech = VertexTextToSpeechAPI()
watsonxai = IBMWatsonXAI() watsonxai = IBMWatsonXAI()
sagemaker_llm = SagemakerLLM() sagemaker_llm = SagemakerLLM()
####### COMPLETION ENDPOINTS ################ ####### COMPLETION ENDPOINTS ################
@ -945,7 +947,6 @@ def completion(
text_completion=kwargs.get("text_completion"), text_completion=kwargs.get("text_completion"),
azure_ad_token_provider=kwargs.get("azure_ad_token_provider"), azure_ad_token_provider=kwargs.get("azure_ad_token_provider"),
user_continue_message=kwargs.get("user_continue_message"), user_continue_message=kwargs.get("user_continue_message"),
) )
logging.update_environment_variables( logging.update_environment_variables(
model=model, model=model,
@ -4730,6 +4731,8 @@ def speech(
if max_retries is None: if max_retries is None:
max_retries = litellm.num_retries or openai.DEFAULT_MAX_RETRIES max_retries = litellm.num_retries or openai.DEFAULT_MAX_RETRIES
logging_obj = kwargs.get("litellm_logging_obj", None)
response: Optional[HttpxBinaryResponseContent] = None response: Optional[HttpxBinaryResponseContent] = None
if custom_llm_provider == "openai": if custom_llm_provider == "openai":
api_base = ( api_base = (
@ -4815,6 +4818,38 @@ def speech(
client=client, # pass AsyncOpenAI, OpenAI client client=client, # pass AsyncOpenAI, OpenAI client
aspeech=aspeech, aspeech=aspeech,
) )
elif custom_llm_provider == "vertex_ai" or custom_llm_provider == "vertex_ai_beta":
from litellm.types.router import GenericLiteLLMParams
generic_optional_params = GenericLiteLLMParams(**kwargs)
api_base = generic_optional_params.api_base or ""
vertex_ai_project = (
generic_optional_params.vertex_project
or litellm.vertex_project
or get_secret("VERTEXAI_PROJECT")
)
vertex_ai_location = (
generic_optional_params.vertex_location
or litellm.vertex_location
or get_secret("VERTEXAI_LOCATION")
)
vertex_credentials = generic_optional_params.vertex_credentials or get_secret(
"VERTEXAI_CREDENTIALS"
)
response = vertex_text_to_speech.audio_speech(
_is_async=aspeech,
vertex_credentials=vertex_credentials,
vertex_project=vertex_ai_project,
vertex_location=vertex_ai_location,
timeout=timeout,
api_base=api_base,
model=model,
input=input,
voice=voice,
optional_params=optional_params,
logging_obj=logging_obj,
)
if response is None: if response is None:
raise Exception( raise Exception(