mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 11:14:04 +00:00
feat(batches): add azure openai batches endpoint support
Closes https://github.com/BerriAI/litellm/issues/5073
This commit is contained in:
Krrish Dholakia
parent
2f9f01e72c
commit
a9b5d5271f
7 changed files with 584 additions and 173 deletions
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@ -20,7 +20,8 @@ import httpx
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import litellm
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from litellm import client
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from litellm.llms.openai import OpenAIBatchesAPI, OpenAIFilesAPI
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from litellm.llms.azure import AzureBatchesAPI
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from litellm.llms.openai import OpenAIBatchesAPI
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from litellm.types.llms.openai import (
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Batch,
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CancelBatchRequest,
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@ -33,10 +34,11 @@ from litellm.types.llms.openai import (
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RetrieveBatchRequest,
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)
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from litellm.types.router import GenericLiteLLMParams
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from litellm.utils import supports_httpx_timeout
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from litellm.utils import get_secret, supports_httpx_timeout
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####### ENVIRONMENT VARIABLES ###################
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openai_batches_instance = OpenAIBatchesAPI()
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azure_batches_instance = AzureBatchesAPI()
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#################################################
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@ -90,7 +92,7 @@ def create_batch(
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completion_window: Literal["24h"],
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endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"],
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input_file_id: str,
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custom_llm_provider: Literal["openai"] = "openai",
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custom_llm_provider: Literal["openai", "azure"] = "openai",
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metadata: Optional[Dict[str, str]] = None,
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extra_headers: Optional[Dict[str, str]] = None,
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extra_body: Optional[Dict[str, str]] = None,
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@ -103,6 +105,32 @@ def create_batch(
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"""
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try:
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optional_params = GenericLiteLLMParams(**kwargs)
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_is_async = kwargs.pop("acreate_batch", False) is True
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### TIMEOUT LOGIC ###
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timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
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# set timeout for 10 minutes by default
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if (
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timeout is not None
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and isinstance(timeout, httpx.Timeout)
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and supports_httpx_timeout(custom_llm_provider) is False
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):
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read_timeout = timeout.read or 600
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timeout = read_timeout # default 10 min timeout
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elif timeout is not None and not isinstance(timeout, httpx.Timeout):
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timeout = float(timeout) # type: ignore
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elif timeout is None:
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timeout = 600.0
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_create_batch_request = CreateBatchRequest(
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completion_window=completion_window,
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endpoint=endpoint,
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input_file_id=input_file_id,
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metadata=metadata,
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extra_headers=extra_headers,
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extra_body=extra_body,
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)
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if custom_llm_provider == "openai":
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# for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
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@ -125,34 +153,6 @@ def create_batch(
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or litellm.openai_key
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or os.getenv("OPENAI_API_KEY")
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)
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### TIMEOUT LOGIC ###
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timeout = (
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optional_params.timeout or kwargs.get("request_timeout", 600) or 600
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)
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# set timeout for 10 minutes by default
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if (
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timeout is not None
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and isinstance(timeout, httpx.Timeout)
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and supports_httpx_timeout(custom_llm_provider) == False
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):
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read_timeout = timeout.read or 600
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timeout = read_timeout # default 10 min timeout
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elif timeout is not None and not isinstance(timeout, httpx.Timeout):
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timeout = float(timeout) # type: ignore
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elif timeout is None:
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timeout = 600.0
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_is_async = kwargs.pop("acreate_batch", False) is True
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_create_batch_request = CreateBatchRequest(
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completion_window=completion_window,
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endpoint=endpoint,
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input_file_id=input_file_id,
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metadata=metadata,
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extra_headers=extra_headers,
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extra_body=extra_body,
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)
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response = openai_batches_instance.create_batch(
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api_base=api_base,
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@ -163,6 +163,38 @@ def create_batch(
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max_retries=optional_params.max_retries,
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_is_async=_is_async,
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)
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elif custom_llm_provider == "azure":
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api_base = optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE") # type: ignore
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api_version = (
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optional_params.api_version
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or litellm.api_version
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or get_secret("AZURE_API_VERSION")
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) # type: ignore
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api_key = (
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optional_params.api_key
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or litellm.api_key
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or litellm.azure_key
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or get_secret("AZURE_OPENAI_API_KEY")
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or get_secret("AZURE_API_KEY")
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) # type: ignore
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extra_body = optional_params.get("extra_body", {})
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azure_ad_token: Optional[str] = None
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if extra_body is not None:
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azure_ad_token = extra_body.pop("azure_ad_token", None)
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else:
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azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore
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response = azure_batches_instance.create_batch(
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_is_async=_is_async,
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api_base=api_base,
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api_key=api_key,
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api_version=api_version,
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timeout=timeout,
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max_retries=optional_params.max_retries,
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create_batch_data=_create_batch_request,
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)
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else:
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raise litellm.exceptions.BadRequestError(
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message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
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@ -225,7 +257,7 @@ async def aretrieve_batch(
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def retrieve_batch(
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batch_id: str,
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custom_llm_provider: Literal["openai"] = "openai",
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custom_llm_provider: Literal["openai", "azure"] = "openai",
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metadata: Optional[Dict[str, str]] = None,
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extra_headers: Optional[Dict[str, str]] = None,
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extra_body: Optional[Dict[str, str]] = None,
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@ -238,6 +270,30 @@ def retrieve_batch(
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"""
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try:
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optional_params = GenericLiteLLMParams(**kwargs)
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### TIMEOUT LOGIC ###
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timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
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# set timeout for 10 minutes by default
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if (
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timeout is not None
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and isinstance(timeout, httpx.Timeout)
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and supports_httpx_timeout(custom_llm_provider) is False
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):
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read_timeout = timeout.read or 600
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timeout = read_timeout # default 10 min timeout
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elif timeout is not None and not isinstance(timeout, httpx.Timeout):
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timeout = float(timeout) # type: ignore
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elif timeout is None:
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timeout = 600.0
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_retrieve_batch_request = RetrieveBatchRequest(
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batch_id=batch_id,
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extra_headers=extra_headers,
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extra_body=extra_body,
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)
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_is_async = kwargs.pop("aretrieve_batch", False) is True
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if custom_llm_provider == "openai":
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# for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
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@ -260,31 +316,6 @@ def retrieve_batch(
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or litellm.openai_key
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or os.getenv("OPENAI_API_KEY")
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)
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### TIMEOUT LOGIC ###
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timeout = (
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optional_params.timeout or kwargs.get("request_timeout", 600) or 600
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)
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# set timeout for 10 minutes by default
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if (
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timeout is not None
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and isinstance(timeout, httpx.Timeout)
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and supports_httpx_timeout(custom_llm_provider) == False
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):
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read_timeout = timeout.read or 600
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timeout = read_timeout # default 10 min timeout
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elif timeout is not None and not isinstance(timeout, httpx.Timeout):
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timeout = float(timeout) # type: ignore
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elif timeout is None:
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timeout = 600.0
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_retrieve_batch_request = RetrieveBatchRequest(
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batch_id=batch_id,
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extra_headers=extra_headers,
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extra_body=extra_body,
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)
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_is_async = kwargs.pop("aretrieve_batch", False) is True
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response = openai_batches_instance.retrieve_batch(
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_is_async=_is_async,
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@ -295,6 +326,38 @@ def retrieve_batch(
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timeout=timeout,
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max_retries=optional_params.max_retries,
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)
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elif custom_llm_provider == "azure":
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api_base = optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE") # type: ignore
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api_version = (
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optional_params.api_version
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or litellm.api_version
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or get_secret("AZURE_API_VERSION")
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) # type: ignore
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api_key = (
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optional_params.api_key
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or litellm.api_key
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or litellm.azure_key
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or get_secret("AZURE_OPENAI_API_KEY")
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or get_secret("AZURE_API_KEY")
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) # type: ignore
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extra_body = optional_params.get("extra_body", {})
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azure_ad_token: Optional[str] = None
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if extra_body is not None:
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azure_ad_token = extra_body.pop("azure_ad_token", None)
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else:
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azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore
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response = azure_batches_instance.retrieve_batch(
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_is_async=_is_async,
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api_base=api_base,
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api_key=api_key,
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api_version=api_version,
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timeout=timeout,
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max_retries=optional_params.max_retries,
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retrieve_batch_data=_retrieve_batch_request,
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)
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else:
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raise litellm.exceptions.BadRequestError(
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message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
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@ -357,7 +420,7 @@ async def alist_batches(
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def list_batches(
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after: Optional[str] = None,
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limit: Optional[int] = None,
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custom_llm_provider: Literal["openai"] = "openai",
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custom_llm_provider: Literal["openai", "azure"] = "openai",
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extra_headers: Optional[Dict[str, str]] = None,
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extra_body: Optional[Dict[str, str]] = None,
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**kwargs,
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@ -368,7 +431,31 @@ def list_batches(
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List your organization's batches.
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"""
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try:
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# set API KEY
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optional_params = GenericLiteLLMParams(**kwargs)
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api_key = (
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optional_params.api_key
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or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
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or litellm.openai_key
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or os.getenv("OPENAI_API_KEY")
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)
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### TIMEOUT LOGIC ###
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timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
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# set timeout for 10 minutes by default
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if (
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timeout is not None
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and isinstance(timeout, httpx.Timeout)
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and supports_httpx_timeout(custom_llm_provider) is False
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):
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read_timeout = timeout.read or 600
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timeout = read_timeout # default 10 min timeout
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elif timeout is not None and not isinstance(timeout, httpx.Timeout):
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timeout = float(timeout) # type: ignore
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elif timeout is None:
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timeout = 600.0
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_is_async = kwargs.pop("alist_batches", False) is True
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if custom_llm_provider == "openai":
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# for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
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api_base = (
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@ -383,32 +470,6 @@ def list_batches(
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or os.getenv("OPENAI_ORGANIZATION", None)
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or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
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)
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# set API KEY
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api_key = (
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optional_params.api_key
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or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
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or litellm.openai_key
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or os.getenv("OPENAI_API_KEY")
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)
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### TIMEOUT LOGIC ###
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timeout = (
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optional_params.timeout or kwargs.get("request_timeout", 600) or 600
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)
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# set timeout for 10 minutes by default
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if (
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timeout is not None
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and isinstance(timeout, httpx.Timeout)
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and supports_httpx_timeout(custom_llm_provider) == False
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):
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read_timeout = timeout.read or 600
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timeout = read_timeout # default 10 min timeout
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elif timeout is not None and not isinstance(timeout, httpx.Timeout):
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timeout = float(timeout) # type: ignore
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elif timeout is None:
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timeout = 600.0
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_is_async = kwargs.pop("alist_batches", False) is True
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response = openai_batches_instance.list_batches(
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_is_async=_is_async,
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@ -420,9 +481,40 @@ def list_batches(
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timeout=timeout,
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max_retries=optional_params.max_retries,
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)
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elif custom_llm_provider == "azure":
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api_base = optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE") # type: ignore
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api_version = (
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optional_params.api_version
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or litellm.api_version
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or get_secret("AZURE_API_VERSION")
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) # type: ignore
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api_key = (
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optional_params.api_key
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or litellm.api_key
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or litellm.azure_key
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or get_secret("AZURE_OPENAI_API_KEY")
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or get_secret("AZURE_API_KEY")
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) # type: ignore
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extra_body = optional_params.get("extra_body", {})
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azure_ad_token: Optional[str] = None
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if extra_body is not None:
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azure_ad_token = extra_body.pop("azure_ad_token", None)
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else:
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azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore
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response = azure_batches_instance.list_batches(
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_is_async=_is_async,
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api_base=api_base,
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api_key=api_key,
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api_version=api_version,
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timeout=timeout,
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max_retries=optional_params.max_retries,
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)
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else:
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raise litellm.exceptions.BadRequestError(
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message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
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message="LiteLLM doesn't support {} for 'list_batch'. Only 'openai' is supported.".format(
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custom_llm_provider
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),
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model="n/a",
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