litellm-mirror/litellm/llms/bedrock/embed/cohere_transformation.py
Krish Dholakia f9e6507cd1
LiteLLM Minor Fixes + Improvements (#5474)
* feat(proxy/_types.py): add lago billing to callbacks ui

Closes https://github.com/BerriAI/litellm/issues/5472

* fix(anthropic.py): return anthropic prompt caching information

Fixes https://github.com/BerriAI/litellm/issues/5364

* feat(bedrock/chat.py): support 'json_schema' for bedrock models

Closes https://github.com/BerriAI/litellm/issues/5434

* fix(bedrock/embed/embeddings.py): support async embeddings for amazon titan models

* fix: linting fixes

* fix: handle key errors

* fix(bedrock/chat.py): fix bedrock ai21 streaming object

* feat(bedrock/embed): support bedrock embedding optional params

* fix(databricks.py): fix usage chunk

* fix(internal_user_endpoints.py): apply internal user defaults, if user role updated

Fixes issue where user update wouldn't apply defaults

* feat(slack_alerting.py): provide multiple slack channels for a given alert type

multiple channels might be interested in receiving an alert for a given type

* docs(alerting.md): add multiple channel alerting to docs
2024-09-02 14:29:57 -07:00

40 lines
1.2 KiB
Python

"""
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
import litellm
from litellm.types.llms.bedrock import CohereEmbeddingRequest, CohereEmbeddingResponse
from litellm.types.utils import Embedding, EmbeddingResponse
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 _transform_request(
self, input: List[str], inference_params: dict
) -> CohereEmbeddingRequest:
transformed_request = CohereEmbeddingRequest(
texts=input,
input_type=litellm.COHERE_DEFAULT_EMBEDDING_INPUT_TYPE, # type: ignore
)
for k, v in inference_params.items():
transformed_request[k] = v # type: ignore
return transformed_request