chore: initial snapshot for gitea/github upload
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from .handler import AzureAIEmbedding
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"""
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Transformation logic from OpenAI /v1/embeddings format to Azure AI Cohere's /v1/embed.
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Why separate file? Make it easy to see how transformation works
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Convers
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- Cohere request format
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Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html
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"""
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from typing import List, Optional, Tuple
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from litellm.types.llms.azure_ai import ImageEmbeddingInput, ImageEmbeddingRequest
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from litellm.types.llms.openai import EmbeddingCreateParams
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from litellm.types.utils import EmbeddingResponse, Usage
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from litellm.utils import is_base64_encoded
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class AzureAICohereConfig:
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def __init__(self) -> None:
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pass
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def _map_azure_model_group(self, model: str) -> str:
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if model == "offer-cohere-embed-multili-paygo":
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return "Cohere-embed-v3-multilingual"
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elif model == "offer-cohere-embed-english-paygo":
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return "Cohere-embed-v3-english"
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return model
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def _transform_request_image_embeddings(
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self, input: List[str], optional_params: dict
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) -> ImageEmbeddingRequest:
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"""
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Assume all str in list is base64 encoded string
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"""
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image_input: List[ImageEmbeddingInput] = []
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for i in input:
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embedding_input = ImageEmbeddingInput(image=i)
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image_input.append(embedding_input)
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return ImageEmbeddingRequest(input=image_input, **optional_params)
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def _transform_request(
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self, input: List[str], optional_params: dict, model: str
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) -> Tuple[ImageEmbeddingRequest, EmbeddingCreateParams, List[int]]:
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"""
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Return the list of input to `/image/embeddings`, `/v1/embeddings`, list of image_embedding_idx for recombination
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"""
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image_embeddings: List[str] = []
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image_embedding_idx: List[int] = []
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for idx, i in enumerate(input):
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"""
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- is base64 -> route to image embeddings
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- is ImageEmbeddingInput -> route to image embeddings
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- else -> route to `/v1/embeddings`
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"""
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if is_base64_encoded(i):
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image_embeddings.append(i)
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image_embedding_idx.append(idx)
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## REMOVE IMAGE EMBEDDINGS FROM input list
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filtered_input = [
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item for idx, item in enumerate(input) if idx not in image_embedding_idx
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]
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v1_embeddings_request = EmbeddingCreateParams(
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input=filtered_input, model=model, **optional_params
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)
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image_embeddings_request = self._transform_request_image_embeddings(
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input=image_embeddings, optional_params=optional_params
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)
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return image_embeddings_request, v1_embeddings_request, image_embedding_idx
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def _transform_response(self, response: EmbeddingResponse) -> EmbeddingResponse:
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additional_headers: Optional[dict] = response._hidden_params.get(
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"additional_headers"
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)
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if additional_headers:
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# CALCULATE USAGE
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input_tokens: Optional[str] = additional_headers.get(
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"llm_provider-num_tokens"
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)
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if input_tokens:
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if response.usage:
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response.usage.prompt_tokens = int(input_tokens)
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else:
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response.usage = Usage(prompt_tokens=int(input_tokens))
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# SET MODEL
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base_model: Optional[str] = additional_headers.get(
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"llm_provider-azureml-model-group"
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)
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if base_model:
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response.model = self._map_azure_model_group(base_model)
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return response
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from typing import List, Optional, Union
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from openai import OpenAI
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import litellm
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from litellm.llms.custom_httpx.http_handler import (
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AsyncHTTPHandler,
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HTTPHandler,
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get_async_httpx_client,
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)
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from litellm.llms.openai.openai import OpenAIChatCompletion
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from litellm.types.llms.azure_ai import ImageEmbeddingRequest
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from litellm.types.utils import EmbeddingResponse
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from litellm.utils import convert_to_model_response_object
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from .cohere_transformation import AzureAICohereConfig
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class AzureAIEmbedding(OpenAIChatCompletion):
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def _process_response(
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self,
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image_embedding_responses: Optional[List],
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text_embedding_responses: Optional[List],
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image_embeddings_idx: List[int],
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model_response: EmbeddingResponse,
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input: List,
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):
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combined_responses = []
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if (
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image_embedding_responses is not None
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and text_embedding_responses is not None
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):
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# Combine and order the results
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text_idx = 0
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image_idx = 0
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for idx in range(len(input)):
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if idx in image_embeddings_idx:
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combined_responses.append(image_embedding_responses[image_idx])
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image_idx += 1
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else:
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combined_responses.append(text_embedding_responses[text_idx])
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text_idx += 1
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model_response.data = combined_responses
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elif image_embedding_responses is not None:
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model_response.data = image_embedding_responses
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elif text_embedding_responses is not None:
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model_response.data = text_embedding_responses
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response = AzureAICohereConfig()._transform_response(response=model_response) # type: ignore
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return response
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async def async_image_embedding(
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self,
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model: str,
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data: ImageEmbeddingRequest,
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timeout: float,
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logging_obj,
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model_response: EmbeddingResponse,
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optional_params: dict,
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api_key: Optional[str],
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api_base: Optional[str],
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client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
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) -> EmbeddingResponse:
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if client is None or not isinstance(client, AsyncHTTPHandler):
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client = get_async_httpx_client(
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llm_provider=litellm.LlmProviders.AZURE_AI,
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params={"timeout": timeout},
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)
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url = "{}/images/embeddings".format(api_base)
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response = await client.post(
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url=url,
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json=data, # type: ignore
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headers={"Authorization": "Bearer {}".format(api_key)},
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)
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embedding_response = response.json()
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embedding_headers = dict(response.headers)
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returned_response: EmbeddingResponse = convert_to_model_response_object( # type: ignore
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response_object=embedding_response,
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model_response_object=model_response,
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response_type="embedding",
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stream=False,
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_response_headers=embedding_headers,
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)
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return returned_response
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def image_embedding(
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self,
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model: str,
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data: ImageEmbeddingRequest,
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timeout: float,
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logging_obj,
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model_response: EmbeddingResponse,
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optional_params: dict,
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api_key: Optional[str],
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api_base: Optional[str],
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client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
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):
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if api_base is None:
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raise ValueError(
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"api_base is None. Please set AZURE_AI_API_BASE or dynamically via `api_base` param, to make the request."
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)
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if api_key is None:
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raise ValueError(
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"api_key is None. Please set AZURE_AI_API_KEY or dynamically via `api_key` param, to make the request."
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)
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if client is None or not isinstance(client, HTTPHandler):
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client = HTTPHandler(timeout=timeout, concurrent_limit=1)
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url = "{}/images/embeddings".format(api_base)
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response = client.post(
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url=url,
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json=data, # type: ignore
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headers={"Authorization": "Bearer {}".format(api_key)},
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)
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embedding_response = response.json()
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embedding_headers = dict(response.headers)
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returned_response: EmbeddingResponse = convert_to_model_response_object( # type: ignore
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response_object=embedding_response,
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model_response_object=model_response,
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response_type="embedding",
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stream=False,
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_response_headers=embedding_headers,
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)
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return returned_response
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async def async_embedding(
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self,
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model: str,
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input: List,
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timeout: float,
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logging_obj,
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model_response: EmbeddingResponse,
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optional_params: dict,
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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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client=None,
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) -> EmbeddingResponse:
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(
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image_embeddings_request,
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v1_embeddings_request,
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image_embeddings_idx,
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) = AzureAICohereConfig()._transform_request(
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input=input, optional_params=optional_params, model=model
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)
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image_embedding_responses: Optional[List] = None
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text_embedding_responses: Optional[List] = None
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if image_embeddings_request["input"]:
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image_response = await self.async_image_embedding(
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model=model,
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data=image_embeddings_request,
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timeout=timeout,
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logging_obj=logging_obj,
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model_response=model_response,
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optional_params=optional_params,
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api_key=api_key,
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api_base=api_base,
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client=client,
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)
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image_embedding_responses = image_response.data
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if image_embedding_responses is None:
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raise Exception("/image/embeddings route returned None Embeddings.")
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if v1_embeddings_request["input"]:
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response: EmbeddingResponse = await super().embedding( # type: ignore
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model=model,
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input=input,
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timeout=timeout,
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logging_obj=logging_obj,
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model_response=model_response,
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optional_params=optional_params,
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api_key=api_key,
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api_base=api_base,
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client=client,
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aembedding=True,
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)
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text_embedding_responses = response.data
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if text_embedding_responses is None:
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raise Exception("/v1/embeddings route returned None Embeddings.")
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return self._process_response(
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image_embedding_responses=image_embedding_responses,
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text_embedding_responses=text_embedding_responses,
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image_embeddings_idx=image_embeddings_idx,
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model_response=model_response,
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input=input,
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)
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def embedding(
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self,
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model: str,
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input: List,
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timeout: float,
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logging_obj,
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model_response: EmbeddingResponse,
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optional_params: dict,
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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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client=None,
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aembedding=None,
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max_retries: Optional[int] = None,
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shared_session=None,
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) -> EmbeddingResponse:
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"""
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- Separate image url from text
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-> route image url call to `/image/embeddings`
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-> route text call to `/v1/embeddings` (OpenAI route)
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assemble result in-order, and return
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"""
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if aembedding is True:
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return self.async_embedding( # type: ignore
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model,
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input,
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timeout,
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logging_obj,
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model_response,
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optional_params,
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api_key,
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api_base,
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client,
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)
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(
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image_embeddings_request,
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v1_embeddings_request,
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image_embeddings_idx,
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) = AzureAICohereConfig()._transform_request(
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input=input, optional_params=optional_params, model=model
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)
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image_embedding_responses: Optional[List] = None
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text_embedding_responses: Optional[List] = None
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if image_embeddings_request["input"]:
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image_response = self.image_embedding(
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model=model,
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data=image_embeddings_request,
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timeout=timeout,
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logging_obj=logging_obj,
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model_response=model_response,
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optional_params=optional_params,
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api_key=api_key,
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api_base=api_base,
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client=client,
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)
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image_embedding_responses = image_response.data
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if image_embedding_responses is None:
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raise Exception("/image/embeddings route returned None Embeddings.")
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if v1_embeddings_request["input"]:
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response: EmbeddingResponse = super().embedding( # type: ignore
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model,
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input,
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timeout,
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logging_obj,
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model_response,
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optional_params,
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api_key,
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api_base,
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client=(
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client
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if client is not None and isinstance(client, OpenAI)
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else None
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),
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aembedding=aembedding,
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shared_session=shared_session,
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)
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text_embedding_responses = response.data
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if text_embedding_responses is None:
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raise Exception("/v1/embeddings route returned None Embeddings.")
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return self._process_response(
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image_embedding_responses=image_embedding_responses,
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text_embedding_responses=text_embedding_responses,
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image_embeddings_idx=image_embeddings_idx,
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model_response=model_response,
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input=input,
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)
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