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| """ | |
| Transformation logic from OpenAI /v1/embeddings format to Bedrock Amazon Titan multimodal /invoke format. | |
| Why separate file? Make it easy to see how transformation works | |
| Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-mm.html | |
| """ | |
| from typing import List | |
| from litellm.types.llms.bedrock import ( | |
| AmazonTitanMultimodalEmbeddingConfig, | |
| AmazonTitanMultimodalEmbeddingRequest, | |
| AmazonTitanMultimodalEmbeddingResponse, | |
| ) | |
| from litellm.types.utils import Embedding, EmbeddingResponse, Usage | |
| from litellm.utils import get_base64_str, is_base64_encoded | |
| class AmazonTitanMultimodalEmbeddingG1Config: | |
| """ | |
| Reference - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-mm.html | |
| """ | |
| def __init__(self) -> None: | |
| pass | |
| def get_supported_openai_params(self) -> List[str]: | |
| return ["dimensions"] | |
| def map_openai_params( | |
| self, non_default_params: dict, optional_params: dict | |
| ) -> dict: | |
| for k, v in non_default_params.items(): | |
| if k == "dimensions": | |
| optional_params[ | |
| "embeddingConfig" | |
| ] = AmazonTitanMultimodalEmbeddingConfig(outputEmbeddingLength=v) | |
| return optional_params | |
| def _transform_request( | |
| self, input: str, inference_params: dict | |
| ) -> AmazonTitanMultimodalEmbeddingRequest: | |
| ## check if b64 encoded str or not ## | |
| is_encoded = is_base64_encoded(input) | |
| if is_encoded: # check if string is b64 encoded image or not | |
| b64_str = get_base64_str(input) | |
| transformed_request = AmazonTitanMultimodalEmbeddingRequest( | |
| inputImage=b64_str | |
| ) | |
| else: | |
| transformed_request = AmazonTitanMultimodalEmbeddingRequest(inputText=input) | |
| for k, v in inference_params.items(): | |
| transformed_request[k] = v # type: ignore | |
| return transformed_request | |
| def _transform_response( | |
| self, response_list: List[dict], model: str | |
| ) -> EmbeddingResponse: | |
| total_prompt_tokens = 0 | |
| transformed_responses: List[Embedding] = [] | |
| for index, response in enumerate(response_list): | |
| _parsed_response = AmazonTitanMultimodalEmbeddingResponse(**response) # type: ignore | |
| transformed_responses.append( | |
| Embedding( | |
| embedding=_parsed_response["embedding"], | |
| index=index, | |
| object="embedding", | |
| ) | |
| ) | |
| total_prompt_tokens += _parsed_response["inputTextTokenCount"] | |
| usage = Usage( | |
| prompt_tokens=total_prompt_tokens, | |
| completion_tokens=0, | |
| total_tokens=total_prompt_tokens, | |
| ) | |
| return EmbeddingResponse(model=model, usage=usage, data=transformed_responses) | |