234 lines
8.4 KiB
Python
234 lines
8.4 KiB
Python
"""
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Azure Batches API Handler
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"""
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from typing import Any, Coroutine, Optional, Union, cast
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import httpx
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from openai import AsyncOpenAI, OpenAI
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from litellm.llms.azure.azure import AsyncAzureOpenAI, AzureOpenAI
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from litellm.types.llms.openai import (
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CancelBatchRequest,
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CreateBatchRequest,
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RetrieveBatchRequest,
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)
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from litellm.types.utils import LiteLLMBatch
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from ..common_utils import BaseAzureLLM
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class AzureBatchesAPI(BaseAzureLLM):
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"""
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Azure methods to support for batches
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- create_batch()
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- retrieve_batch()
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- cancel_batch()
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- list_batch()
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"""
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def __init__(self) -> None:
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super().__init__()
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async def acreate_batch(
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self,
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create_batch_data: CreateBatchRequest,
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azure_client: Union[AsyncAzureOpenAI, AsyncOpenAI],
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) -> LiteLLMBatch:
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response = await azure_client.batches.create(**create_batch_data) # type: ignore[arg-type]
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return LiteLLMBatch(**response.model_dump())
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def create_batch(
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self,
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_is_async: bool,
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create_batch_data: CreateBatchRequest,
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api_key: Optional[str],
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api_base: Optional[str],
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api_version: Optional[str],
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timeout: Union[float, httpx.Timeout],
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max_retries: Optional[int],
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client: Optional[
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Union[AzureOpenAI, AsyncAzureOpenAI, OpenAI, AsyncOpenAI]
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] = None,
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litellm_params: Optional[dict] = None,
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) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
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azure_client: Optional[
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Union[AzureOpenAI, AsyncAzureOpenAI, OpenAI, AsyncOpenAI]
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] = self.get_azure_openai_client(
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api_key=api_key,
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api_base=api_base,
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api_version=api_version,
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client=client,
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_is_async=_is_async,
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litellm_params=litellm_params or {},
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)
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if azure_client is None:
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raise ValueError(
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"OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
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)
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if _is_async is True:
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if not isinstance(azure_client, (AsyncAzureOpenAI, AsyncOpenAI)):
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raise ValueError(
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"OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
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)
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return self.acreate_batch( # type: ignore
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create_batch_data=create_batch_data, azure_client=azure_client
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)
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response = cast(Union[AzureOpenAI, OpenAI], azure_client).batches.create(**create_batch_data) # type: ignore[arg-type]
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return LiteLLMBatch(**response.model_dump())
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async def aretrieve_batch(
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self,
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retrieve_batch_data: RetrieveBatchRequest,
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client: Union[AsyncAzureOpenAI, AsyncOpenAI],
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) -> LiteLLMBatch:
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response = await client.batches.retrieve(**retrieve_batch_data) # type: ignore[arg-type]
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return LiteLLMBatch(**response.model_dump())
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def retrieve_batch(
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self,
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_is_async: bool,
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retrieve_batch_data: RetrieveBatchRequest,
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api_key: Optional[str],
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api_base: Optional[str],
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api_version: Optional[str],
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timeout: Union[float, httpx.Timeout],
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max_retries: Optional[int],
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client: Optional[
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Union[AzureOpenAI, AsyncAzureOpenAI, OpenAI, AsyncOpenAI]
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] = None,
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litellm_params: Optional[dict] = None,
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):
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azure_client: Optional[
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Union[AzureOpenAI, AsyncAzureOpenAI, OpenAI, AsyncOpenAI]
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] = self.get_azure_openai_client(
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api_key=api_key,
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api_base=api_base,
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api_version=api_version,
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client=client,
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_is_async=_is_async,
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litellm_params=litellm_params or {},
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)
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if azure_client is None:
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raise ValueError(
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"OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
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)
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if _is_async is True:
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if not isinstance(azure_client, (AsyncAzureOpenAI, AsyncOpenAI)):
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raise ValueError(
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"OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
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)
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return self.aretrieve_batch( # type: ignore
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retrieve_batch_data=retrieve_batch_data, client=azure_client
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)
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response = cast(Union[AzureOpenAI, OpenAI], azure_client).batches.retrieve(
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**retrieve_batch_data
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)
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return LiteLLMBatch(**response.model_dump())
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async def acancel_batch(
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self,
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cancel_batch_data: CancelBatchRequest,
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client: Union[AsyncAzureOpenAI, AsyncOpenAI],
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) -> LiteLLMBatch:
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response = await client.batches.cancel(**cancel_batch_data)
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return LiteLLMBatch(**response.model_dump())
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def cancel_batch(
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self,
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_is_async: bool,
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cancel_batch_data: CancelBatchRequest,
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api_key: Optional[str],
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api_base: Optional[str],
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api_version: Optional[str],
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timeout: Union[float, httpx.Timeout],
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max_retries: Optional[int],
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client: Optional[
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Union[AzureOpenAI, AsyncAzureOpenAI, OpenAI, AsyncOpenAI]
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] = None,
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litellm_params: Optional[dict] = None,
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):
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azure_client: Optional[
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Union[AzureOpenAI, AsyncAzureOpenAI, OpenAI, AsyncOpenAI]
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] = self.get_azure_openai_client(
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api_key=api_key,
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api_base=api_base,
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api_version=api_version,
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client=client,
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_is_async=_is_async,
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litellm_params=litellm_params or {},
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)
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if azure_client is None:
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raise ValueError(
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"OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
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)
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if _is_async is True:
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if not isinstance(azure_client, (AsyncAzureOpenAI, AsyncOpenAI)):
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raise ValueError(
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"Azure client is not an instance of AsyncAzureOpenAI or AsyncOpenAI. Make sure you passed an async client."
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)
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return self.acancel_batch( # type: ignore
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cancel_batch_data=cancel_batch_data, client=azure_client
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)
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# At this point, azure_client is guaranteed to be a sync client
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if not isinstance(azure_client, (AzureOpenAI, OpenAI)):
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raise ValueError(
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"Azure client is not an instance of AzureOpenAI or OpenAI. Make sure you passed a sync client."
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)
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response = azure_client.batches.cancel(**cancel_batch_data)
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return LiteLLMBatch(**response.model_dump())
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async def alist_batches(
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self,
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client: Union[AsyncAzureOpenAI, AsyncOpenAI],
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after: Optional[str] = None,
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limit: Optional[int] = None,
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):
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response = await client.batches.list(after=after, limit=limit) # type: ignore
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return response
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def list_batches(
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self,
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_is_async: bool,
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api_key: Optional[str],
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api_base: Optional[str],
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api_version: Optional[str],
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timeout: Union[float, httpx.Timeout],
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max_retries: Optional[int],
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after: Optional[str] = None,
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limit: Optional[int] = None,
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client: Optional[
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Union[AzureOpenAI, AsyncAzureOpenAI, OpenAI, AsyncOpenAI]
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] = None,
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litellm_params: Optional[dict] = None,
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):
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azure_client: Optional[
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Union[AzureOpenAI, AsyncAzureOpenAI, OpenAI, AsyncOpenAI]
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] = self.get_azure_openai_client(
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api_key=api_key,
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api_base=api_base,
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api_version=api_version,
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client=client,
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_is_async=_is_async,
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litellm_params=litellm_params or {},
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)
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if azure_client is None:
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raise ValueError(
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"OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
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)
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if _is_async is True:
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if not isinstance(azure_client, (AsyncAzureOpenAI, AsyncOpenAI)):
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raise ValueError(
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"OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
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)
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return self.alist_batches( # type: ignore
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client=azure_client, after=after, limit=limit
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)
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response = azure_client.batches.list(after=after, limit=limit) # type: ignore
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return response
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