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