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516c2a6a70
* fix(utils.py): initial commit to remove circular imports - moves llmproviders to utils.py * fix(router.py): fix 'litellm.EmbeddingResponse' import from router.py ' * refactor: fix litellm.ModelResponse import on pass through endpoints * refactor(litellm_logging.py): fix circular import for custom callbacks literal * fix(factory.py): fix circular imports inside prompt factory * fix(cost_calculator.py): fix circular import for 'litellm.Usage' * fix(proxy_server.py): fix potential circular import with `litellm.Router' * fix(proxy/utils.py): fix potential circular import in `litellm.Router` * fix: remove circular imports in 'auth_checks' and 'guardrails/' * fix(prompt_injection_detection.py): fix router impor t * fix(vertex_passthrough_logging_handler.py): fix potential circular imports in vertex pass through * fix(anthropic_pass_through_logging_handler.py): fix potential circular imports * fix(slack_alerting.py-+-ollama_chat.py): fix modelresponse import * fix(base.py): fix potential circular import * fix(handler.py): fix potential circular ref in codestral + cohere handler's * fix(azure.py): fix potential circular imports * fix(gpt_transformation.py): fix modelresponse import * fix(litellm_logging.py): add logging base class - simplify typing makes it easy for other files to type check the logging obj without introducing circular imports * fix(azure_ai/embed): fix potential circular import on handler.py * fix(databricks/): fix potential circular imports in databricks/ * fix(vertex_ai/): fix potential circular imports on vertex ai embeddings * fix(vertex_ai/image_gen): fix import * fix(watsonx-+-bedrock): cleanup imports * refactor(anthropic-pass-through-+-petals): cleanup imports * refactor(huggingface/): cleanup imports * fix(ollama-+-clarifai): cleanup circular imports * fix(openai_like/): fix impor t * fix(openai_like/): fix embedding handler cleanup imports * refactor(openai.py): cleanup imports * fix(sagemaker/transformation.py): fix import * ci(config.yml): add circular import test to ci/cd
300 lines
10 KiB
Python
300 lines
10 KiB
Python
import asyncio
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import copy
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import json
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import os
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from copy import deepcopy
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from typing import Any, Callable, List, Literal, Optional, Tuple, Union
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import httpx
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from openai import OpenAI
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import litellm
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from litellm.llms.cohere.embed.handler import embedding as cohere_embedding
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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_httpx_client,
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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 Embedding, EmbeddingResponse
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from litellm.utils import convert_to_model_response_object, is_base64_encoded
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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: litellm.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: litellm.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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) -> 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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)
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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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