chore: initial public snapshot for github upload

llm-gateway-competitors/litellm-wheel-src/litellm/llms/amazon_nova/chat/transformation.py

@@ -0,0 +1,115 @@
+"""
+Translate from OpenAI's `/v1/chat/completions` to Amazon Nova's `/v1/chat/completions`
+"""
+from typing import Any, List, Optional, Tuple
+
+import httpx
+
+import litellm
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.openai import (
+    AllMessageValues,
+)
+from litellm.types.utils import ModelResponse
+
+from ...openai_like.chat.transformation import OpenAILikeChatConfig
+
+
+class AmazonNovaChatConfig(OpenAILikeChatConfig):
+    max_completion_tokens: Optional[int] = None
+    max_tokens: Optional[int] = None
+    metadata: Optional[int] = None
+    temperature: Optional[int] = None
+    top_p: Optional[int] = None
+    tools: Optional[list] = None
+    reasoning_effort: Optional[list] = None
+
+    def __init__(
+        self,
+        max_completion_tokens: Optional[int] = None,
+        max_tokens: Optional[int] = None,
+        temperature: Optional[int] = None,
+        top_p: Optional[int] = None,
+        tools: Optional[list] = None,
+        reasoning_effort: Optional[list] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @property
+    def custom_llm_provider(self) -> Optional[str]:
+        return "amazon_nova"
+
+    @classmethod
+    def get_config(cls):
+        return super().get_config()
+
+    def _get_openai_compatible_provider_info(
+        self, api_base: Optional[str], api_key: Optional[str]
+    ) -> Tuple[Optional[str], Optional[str]]:
+        # Amazon Nova is openai compatible, we just need to set this to custom_openai and have the api_base be Nova's endpoint
+        api_base = (
+            api_base
+            or get_secret_str("AMAZON_NOVA_API_BASE")
+            or "https://api.nova.amazon.com/v1"
+        )  # type: ignore
+
+        # Get API key from multiple sources
+        key = (
+            api_key
+            or litellm.amazon_nova_api_key
+            or get_secret_str("AMAZON_NOVA_API_KEY")
+            or litellm.api_key
+        )
+        return api_base, key
+
+    def get_supported_openai_params(self, model: str) -> List:
+        return [
+            "top_p",
+            "temperature",
+            "max_tokens",
+            "max_completion_tokens",
+            "metadata",
+            "stop",
+            "stream",
+            "stream_options",
+            "tools",
+            "tool_choice",
+            "reasoning_effort",
+        ]
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        request_data: dict,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ModelResponse:
+        model_response = super().transform_response(
+            model=model,
+            model_response=model_response,
+            raw_response=raw_response,
+            messages=messages,
+            logging_obj=logging_obj,
+            request_data=request_data,
+            encoding=encoding,
+            optional_params=optional_params,
+            json_mode=json_mode,
+            litellm_params=litellm_params,
+            api_key=api_key,
+        )
+
+        # Storing amazon_nova in the model response for easier cost calculation later
+        setattr(model_response, "model", "amazon-nova/" + model)
+
+        return model_response

llm-gateway-competitors/litellm-wheel-src/litellm/llms/amazon_nova/cost_calculation.py

@@ -0,0 +1,21 @@
+"""
+Helper util for handling amazon nova cost calculation
+- e.g.: prompt caching
+"""
+
+from typing import TYPE_CHECKING, Tuple
+
+from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token
+
+if TYPE_CHECKING:
+    from litellm.types.utils import Usage
+
+
+def cost_per_token(model: str, usage: "Usage") -> Tuple[float, float]:
+    """
+    Calculates the cost per token for a given model, prompt tokens, and completion tokens.
+    Follows the same logic as Anthropic's cost per token calculation.
+    """
+    return generic_cost_per_token(
+        model=model, usage=usage, custom_llm_provider="amazon_nova"
+    )