mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 11:14:04 +00:00
1a4910f6c0
* fix(health.md): add rerank model health check information * build(model_prices_and_context_window.json): add gemini 2.0 for google ai studio - pricing + commercial rate limits * build(model_prices_and_context_window.json): add gemini-2.0 supports audio output = true * docs(team_model_add.md): clarify allowing teams to add models is an enterprise feature * fix(o1_transformation.py): add support for 'n', 'response_format' and 'stop' params for o1 and 'stream_options' param for o1-mini * build(model_prices_and_context_window.json): add 'supports_system_message' to supporting openai models needed as o1-preview, and o1-mini models don't support 'system message * fix(o1_transformation.py): translate system message based on if o1 model supports it * fix(o1_transformation.py): return 'stream' param support if o1-mini/o1-preview o1 currently doesn't support streaming, but the other model versions do Fixes https://github.com/BerriAI/litellm/issues/7292 * fix(o1_transformation.py): return tool calling/response_format in supported params if model map says so Fixes https://github.com/BerriAI/litellm/issues/7292 * fix: fix linting errors * fix: update '_transform_messages' * fix(o1_transformation.py): fix provider passed for supported param checks * test(base_llm_unit_tests.py): skip test if api takes >5s to respond * fix(utils.py): return false in 'supports_factory' if can't find value * fix(o1_transformation.py): always return stream + stream_options as supported params + handle stream options being passed in for azure o1 * feat(openai.py): support stream faking natively in openai handler Allows o1 calls to be faked for just the "o1" model, allows native streaming for o1-mini, o1-preview Fixes https://github.com/BerriAI/litellm/issues/7292 * fix(openai.py): use inference param instead of original optional param
85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
from typing import List, Optional, Tuple
|
|
|
|
import litellm
|
|
from litellm._logging import verbose_logger
|
|
from litellm.litellm_core_utils.prompt_templates.common_utils import (
|
|
_audio_or_image_in_message_content,
|
|
convert_content_list_to_str,
|
|
)
|
|
from litellm.llms.openai.openai import OpenAIConfig
|
|
from litellm.secret_managers.main import get_secret_str
|
|
from litellm.types.llms.openai import AllMessageValues
|
|
from litellm.types.utils import ProviderField
|
|
|
|
|
|
class AzureAIStudioConfig(OpenAIConfig):
|
|
def get_required_params(self) -> List[ProviderField]:
|
|
"""For a given provider, return it's required fields with a description"""
|
|
return [
|
|
ProviderField(
|
|
field_name="api_key",
|
|
field_type="string",
|
|
field_description="Your Azure AI Studio API Key.",
|
|
field_value="zEJ...",
|
|
),
|
|
ProviderField(
|
|
field_name="api_base",
|
|
field_type="string",
|
|
field_description="Your Azure AI Studio API Base.",
|
|
field_value="https://Mistral-serverless.",
|
|
),
|
|
]
|
|
|
|
def _transform_messages(
|
|
self,
|
|
messages: List[AllMessageValues],
|
|
model: str,
|
|
) -> List:
|
|
"""
|
|
- Azure AI Studio doesn't support content as a list. This handles:
|
|
1. Transforms list content to a string.
|
|
2. If message contains an image or audio, send as is (user-intended)
|
|
"""
|
|
for message in messages:
|
|
|
|
# Do nothing if the message contains an image or audio
|
|
if _audio_or_image_in_message_content(message):
|
|
continue
|
|
|
|
texts = convert_content_list_to_str(message=message)
|
|
if texts:
|
|
message["content"] = texts
|
|
return messages
|
|
|
|
def _is_azure_openai_model(self, model: str) -> bool:
|
|
try:
|
|
if "/" in model:
|
|
model = model.split("/", 1)[1]
|
|
if (
|
|
model in litellm.open_ai_chat_completion_models
|
|
or model in litellm.open_ai_text_completion_models
|
|
or model in litellm.open_ai_embedding_models
|
|
):
|
|
return True
|
|
except Exception:
|
|
return False
|
|
return False
|
|
|
|
def _get_openai_compatible_provider_info(
|
|
self,
|
|
model: str,
|
|
api_base: Optional[str],
|
|
api_key: Optional[str],
|
|
custom_llm_provider: str,
|
|
) -> Tuple[Optional[str], Optional[str], str]:
|
|
api_base = api_base or get_secret_str("AZURE_AI_API_BASE")
|
|
dynamic_api_key = api_key or get_secret_str("AZURE_AI_API_KEY")
|
|
|
|
if self._is_azure_openai_model(model=model):
|
|
verbose_logger.debug(
|
|
"Model={} is Azure OpenAI model. Setting custom_llm_provider='azure'.".format(
|
|
model
|
|
)
|
|
)
|
|
custom_llm_provider = "azure"
|
|
return api_base, dynamic_api_key, custom_llm_provider
|