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"""
Transformation logic from OpenAI /v1/embeddings format to Cohere's /v1/embed format.
Why separate file? Make it easy to see how transformation works
Convers
- v3 embedding models
- v2 embedding models
Docs - https://docs.cohere.com/v2/reference/embed
"""
from typing import Any, List, Optional, Union, cast
import httpx
import litellm
from litellm import COHERE_DEFAULT_EMBEDDING_INPUT_TYPE
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.base_llm import BaseEmbeddingConfig
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.types.llms.bedrock import (
CohereEmbeddingRequest,
CohereEmbeddingRequestWithModel,
)
from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
from litellm.types.utils import EmbeddingResponse, PromptTokensDetailsWrapper, Usage
from litellm.utils import is_base64_encoded
from ..common_utils import CohereError
class CohereEmbeddingConfig(BaseEmbeddingConfig):
"""
Reference: https://docs.cohere.com/v2/reference/embed
"""
def __init__(self) -> None:
pass
def get_supported_openai_params(self, model: str) -> List[str]:
return ["encoding_format", "dimensions"]
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool = False,
) -> dict:
for k, v in non_default_params.items():
if k == "encoding_format":
if isinstance(v, list):
optional_params["embedding_types"] = v
else:
optional_params["embedding_types"] = [v]
elif k == "dimensions":
optional_params["output_dimension"] = v
return optional_params
def validate_environment(
self,
headers: dict,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
) -> dict:
default_headers = {
"Content-Type": "application/json",
}
if api_key:
default_headers["Authorization"] = f"Bearer {api_key}"
headers = {**default_headers, **headers}
return headers
def _is_v3_model(self, model: str) -> bool:
return "3" in model
def get_complete_url(
self,
api_base: Optional[str],
api_key: Optional[str],
model: str,
optional_params: dict,
litellm_params: dict,
stream: Optional[bool] = None,
) -> str:
return api_base or "https://api.cohere.ai/v2/embed"
def _transform_request(
self, model: str, input: List[str], inference_params: dict
) -> CohereEmbeddingRequestWithModel:
is_encoded = False
for input_str in input:
is_encoded = is_base64_encoded(input_str)
if is_encoded: # check if string is b64 encoded image or not
transformed_request = CohereEmbeddingRequestWithModel(
model=model,
images=input,
input_type="image",
)
else:
transformed_request = CohereEmbeddingRequestWithModel(
model=model,
texts=input,
input_type=COHERE_DEFAULT_EMBEDDING_INPUT_TYPE,
)
for k, v in inference_params.items():
transformed_request[k] = v # type: ignore
return transformed_request
def transform_embedding_request(
self,
model: str,
input: AllEmbeddingInputValues,
optional_params: dict,
headers: dict,
) -> dict:
if isinstance(input, list) and (
isinstance(input[0], list) or isinstance(input[0], int)
):
raise ValueError("Input must be a list of strings")
return cast(
dict,
self._transform_request(
model=model,
input=cast(List[str], input) if isinstance(input, List) else [input],
inference_params=optional_params,
),
)
def _calculate_usage(self, input: List[str], encoding: Any, meta: dict) -> Usage:
input_tokens = 0
text_tokens: Optional[int] = meta.get("billed_units", {}).get("input_tokens")
image_tokens: Optional[int] = meta.get("billed_units", {}).get("images")
prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None
if image_tokens is None and text_tokens is None:
for text in input:
input_tokens += len(encoding.encode(text))
else:
prompt_tokens_details = PromptTokensDetailsWrapper(
image_tokens=image_tokens,
text_tokens=text_tokens,
)
if image_tokens:
input_tokens += image_tokens
if text_tokens:
input_tokens += text_tokens
return Usage(
prompt_tokens=input_tokens,
completion_tokens=0,
total_tokens=input_tokens,
prompt_tokens_details=prompt_tokens_details,
)
def _transform_response(
self,
response: httpx.Response,
api_key: Optional[str],
logging_obj: LiteLLMLoggingObj,
data: Union[dict, CohereEmbeddingRequest],
model_response: EmbeddingResponse,
model: str,
encoding: Any,
input: list,
) -> EmbeddingResponse:
response_json = response.json()
## LOGGING
logging_obj.post_call(
input=input,
api_key=api_key,
additional_args={"complete_input_dict": data},
original_response=response_json,
)
"""
response
{
'object': "list",
'data': [
]
'model',
'usage'
}
"""
embeddings = response_json["embeddings"]
output_data = []
for k, embedding_list in embeddings.items():
for idx, embedding in enumerate(embedding_list):
output_data.append(
{"object": "embedding", "index": idx, "embedding": embedding}
)
model_response.object = "list"
model_response.data = output_data
model_response.model = model
input_tokens = 0
for text in input:
input_tokens += len(encoding.encode(text))
setattr(
model_response,
"usage",
self._calculate_usage(input, encoding, response_json.get("meta", {})),
)
return model_response
def transform_embedding_response(
self,
model: str,
raw_response: httpx.Response,
model_response: EmbeddingResponse,
logging_obj: LiteLLMLoggingObj,
api_key: Optional[str],
request_data: dict,
optional_params: dict,
litellm_params: dict,
) -> EmbeddingResponse:
return self._transform_response(
response=raw_response,
api_key=api_key,
logging_obj=logging_obj,
data=request_data,
model_response=model_response,
model=model,
encoding=litellm.encoding,
input=logging_obj.model_call_details["input"],
)
def get_error_class(
self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
) -> BaseLLMException:
return CohereError(
status_code=status_code,
message=error_message,
)
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