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https://github.com/BerriAI/litellm.git
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d37c8b5c6b
* LiteLLM Minor Fixes & Improvements (09/23/2024) (#5842) * feat(auth_utils.py): enable admin to allow client-side credentials to be passed Makes it easier for devs to experiment with finetuned fireworks ai models * feat(router.py): allow setting configurable_clientside_auth_params for a model Closes https://github.com/BerriAI/litellm/issues/5843 * build(model_prices_and_context_window.json): fix anthropic claude-3-5-sonnet max output token limit Fixes https://github.com/BerriAI/litellm/issues/5850 * fix(azure_ai/): support content list for azure ai Fixes https://github.com/BerriAI/litellm/issues/4237 * fix(litellm_logging.py): always set saved_cache_cost Set to 0 by default * fix(fireworks_ai/cost_calculator.py): add fireworks ai default pricing handles calling 405b+ size models * fix(slack_alerting.py): fix error alerting for failed spend tracking Fixes regression with slack alerting error monitoring * fix(vertex_and_google_ai_studio_gemini.py): handle gemini no candidates in streaming chunk error * docs(bedrock.md): add llama3-1 models * test: fix tests * fix(azure_ai/chat): fix transformation for azure ai calls
59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
from typing import Any, Callable, List, Optional, Union
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from httpx._config import Timeout
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from litellm.llms.bedrock.chat.invoke_handler import MockResponseIterator
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from litellm.llms.OpenAI.openai import OpenAIChatCompletion
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from litellm.types.utils import ModelResponse
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from litellm.utils import CustomStreamWrapper
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from .transformation import AzureAIStudioConfig
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class AzureAIChatCompletion(OpenAIChatCompletion):
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def completion(
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self,
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model_response: ModelResponse,
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timeout: Union[float, Timeout],
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optional_params: dict,
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logging_obj: Any,
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model: Optional[str] = None,
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messages: Optional[list] = None,
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print_verbose: Optional[Callable[..., Any]] = None,
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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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acompletion: bool = False,
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litellm_params=None,
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logger_fn=None,
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headers: Optional[dict] = None,
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custom_prompt_dict: dict = {},
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client=None,
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organization: Optional[str] = None,
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custom_llm_provider: Optional[str] = None,
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drop_params: Optional[bool] = None,
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):
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transformed_messages = AzureAIStudioConfig()._transform_messages(
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messages=messages # type: ignore
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)
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return super().completion(
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model_response,
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timeout,
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optional_params,
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logging_obj,
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model,
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transformed_messages,
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print_verbose,
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api_key,
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api_base,
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acompletion,
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litellm_params,
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logger_fn,
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headers,
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custom_prompt_dict,
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client,
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organization,
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custom_llm_provider,
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drop_params,
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)
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