Spaces:
Paused
Paused
| """ | |
| Transformation logic from OpenAI /v1/embeddings format to Bedrock Cohere /invoke format. | |
| Why separate file? Make it easy to see how transformation works | |
| """ | |
| from typing import List | |
| from litellm.llms.cohere.embed.transformation import CohereEmbeddingConfig | |
| from litellm.types.llms.bedrock import CohereEmbeddingRequest | |
| class BedrockCohereEmbeddingConfig: | |
| def __init__(self) -> None: | |
| pass | |
| def get_supported_openai_params(self) -> List[str]: | |
| return ["encoding_format"] | |
| def map_openai_params( | |
| self, non_default_params: dict, optional_params: dict | |
| ) -> dict: | |
| for k, v in non_default_params.items(): | |
| if k == "encoding_format": | |
| optional_params["embedding_types"] = v | |
| return optional_params | |
| def _is_v3_model(self, model: str) -> bool: | |
| return "3" in model | |
| def _transform_request( | |
| self, model: str, input: List[str], inference_params: dict | |
| ) -> CohereEmbeddingRequest: | |
| transformed_request = CohereEmbeddingConfig()._transform_request( | |
| model, input, inference_params | |
| ) | |
| new_transformed_request = CohereEmbeddingRequest( | |
| input_type=transformed_request["input_type"], | |
| ) | |
| for k in CohereEmbeddingRequest.__annotations__.keys(): | |
| if k in transformed_request: | |
| new_transformed_request[k] = transformed_request[k] # type: ignore | |
| return new_transformed_request | |