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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)
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