chore: initial public snapshot for github upload
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"""
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Translate from OpenAI's `/v1/chat/completions` to Amazon Nova's `/v1/chat/completions`
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"""
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from typing import Any, List, Optional, Tuple
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import httpx
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import litellm
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import (
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AllMessageValues,
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)
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from litellm.types.utils import ModelResponse
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from ...openai_like.chat.transformation import OpenAILikeChatConfig
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class AmazonNovaChatConfig(OpenAILikeChatConfig):
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max_completion_tokens: Optional[int] = None
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max_tokens: Optional[int] = None
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metadata: Optional[int] = None
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temperature: Optional[int] = None
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top_p: Optional[int] = None
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tools: Optional[list] = None
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reasoning_effort: Optional[list] = None
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def __init__(
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self,
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max_completion_tokens: Optional[int] = None,
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max_tokens: Optional[int] = None,
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temperature: Optional[int] = None,
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top_p: Optional[int] = None,
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tools: Optional[list] = None,
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reasoning_effort: Optional[list] = None,
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) -> None:
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locals_ = locals().copy()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@property
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def custom_llm_provider(self) -> Optional[str]:
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return "amazon_nova"
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@classmethod
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def get_config(cls):
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return super().get_config()
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def _get_openai_compatible_provider_info(
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self, api_base: Optional[str], api_key: Optional[str]
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) -> Tuple[Optional[str], Optional[str]]:
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# Amazon Nova is openai compatible, we just need to set this to custom_openai and have the api_base be Nova's endpoint
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api_base = (
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api_base
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or get_secret_str("AMAZON_NOVA_API_BASE")
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or "https://api.nova.amazon.com/v1"
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) # type: ignore
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# Get API key from multiple sources
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key = (
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api_key
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or litellm.amazon_nova_api_key
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or get_secret_str("AMAZON_NOVA_API_KEY")
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or litellm.api_key
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)
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return api_base, key
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def get_supported_openai_params(self, model: str) -> List:
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return [
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"top_p",
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"temperature",
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"max_tokens",
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"max_completion_tokens",
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"metadata",
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"stop",
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"stream",
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"stream_options",
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"tools",
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"tool_choice",
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"reasoning_effort",
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]
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def transform_response(
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self,
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model: str,
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raw_response: httpx.Response,
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model_response: ModelResponse,
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logging_obj: LiteLLMLoggingObj,
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request_data: dict,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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encoding: Any,
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api_key: Optional[str] = None,
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json_mode: Optional[bool] = None,
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) -> ModelResponse:
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model_response = super().transform_response(
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model=model,
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model_response=model_response,
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raw_response=raw_response,
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messages=messages,
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logging_obj=logging_obj,
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request_data=request_data,
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encoding=encoding,
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optional_params=optional_params,
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json_mode=json_mode,
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litellm_params=litellm_params,
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api_key=api_key,
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)
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# Storing amazon_nova in the model response for easier cost calculation later
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setattr(model_response, "model", "amazon-nova/" + model)
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return model_response
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