forked from phoenix/litellm-mirror
2b9db05e08
* feat(proxy_cli.py): add new 'log_config' cli param Allows passing logging.conf to uvicorn on startup * docs(cli.md): add logging conf to uvicorn cli docs * fix(get_llm_provider_logic.py): fix default api base for litellm_proxy Fixes https://github.com/BerriAI/litellm/issues/6332 * feat(openai_like/embedding): Add support for jina ai embeddings Closes https://github.com/BerriAI/litellm/issues/6337 * docs(deploy.md): update entrypoint.sh filepath post-refactor Fixes outdated docs * feat(prometheus.py): emit time_to_first_token metric on prometheus Closes https://github.com/BerriAI/litellm/issues/6334 * fix(prometheus.py): only emit time to first token metric if stream is True enables more accurate ttft usage * test: handle vertex api instability * fix(get_llm_provider_logic.py): fix import * fix(openai.py): fix deepinfra default api base * fix(anthropic/transformation.py): remove anthropic beta header (#6361)
68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
from typing import List, Optional, Tuple
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import litellm
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from litellm._logging import verbose_logger
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from litellm.llms.OpenAI.openai import OpenAIConfig
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from litellm.llms.prompt_templates.common_utils import convert_content_list_to_str
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import AllMessageValues
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from litellm.types.utils import ProviderField
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class AzureAIStudioConfig(OpenAIConfig):
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def get_required_params(self) -> List[ProviderField]:
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"""For a given provider, return it's required fields with a description"""
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return [
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ProviderField(
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field_name="api_key",
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field_type="string",
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field_description="Your Azure AI Studio API Key.",
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field_value="zEJ...",
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),
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ProviderField(
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field_name="api_base",
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field_type="string",
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field_description="Your Azure AI Studio API Base.",
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field_value="https://Mistral-serverless.",
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),
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]
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def _transform_messages(self, messages: List[AllMessageValues]) -> List:
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for message in messages:
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texts = convert_content_list_to_str(message=message)
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if texts:
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message["content"] = texts
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return messages
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def _is_azure_openai_model(self, model: str) -> bool:
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try:
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if "/" in model:
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model = model.split("/", 1)[1]
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if (
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model in litellm.open_ai_chat_completion_models
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or model in litellm.open_ai_text_completion_models
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or model in litellm.open_ai_embedding_models
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):
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return True
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except Exception:
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return False
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return False
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def _get_openai_compatible_provider_info(
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self,
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model: str,
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api_base: Optional[str],
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api_key: Optional[str],
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custom_llm_provider: str,
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) -> Tuple[Optional[str], Optional[str], str]:
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api_base = api_base or get_secret_str("AZURE_AI_API_BASE")
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dynamic_api_key = api_key or get_secret_str("AZURE_AI_API_KEY")
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if self._is_azure_openai_model(model=model):
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verbose_logger.debug(
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"Model={} is Azure OpenAI model. Setting custom_llm_provider='azure'.".format(
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model
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)
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)
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custom_llm_provider = "azure"
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return api_base, dynamic_api_key, custom_llm_provider
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