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"""
Transformation logic from OpenAI /v1/embeddings format to Bedrock Amazon Titan V2 /invoke format.
Why separate file? Make it easy to see how transformation works
Convers
- v2 request format
Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html
"""
import types
from typing import List, Optional
from litellm.types.llms.bedrock import (
AmazonTitanV2EmbeddingRequest,
AmazonTitanV2EmbeddingResponse,
)
from litellm.types.utils import Embedding, EmbeddingResponse, Usage
class AmazonTitanV2Config:
"""
Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html
normalize: boolean - flag indicating whether or not to normalize the output embeddings. Defaults to true
dimensions: int - The number of dimensions the output embeddings should have. The following values are accepted: 1024 (default), 512, 256.
"""
normalize: Optional[bool] = None
dimensions: Optional[int] = None
def __init__(
self, normalize: Optional[bool] = None, dimensions: Optional[int] = None
) -> None:
locals_ = locals()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@classmethod
def get_config(cls):
return {
k: v
for k, v in cls.__dict__.items()
if not k.startswith("__")
and not isinstance(
v,
(
types.FunctionType,
types.BuiltinFunctionType,
classmethod,
staticmethod,
),
)
and v is not None
}
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["dimensions"] = v
return optional_params
def _transform_request(
self, input: str, inference_params: dict
) -> AmazonTitanV2EmbeddingRequest:
return AmazonTitanV2EmbeddingRequest(inputText=input, **inference_params) # type: ignore
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 = AmazonTitanV2EmbeddingResponse(**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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