mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 10:44:24 +00:00
87f19d6f13
* add basic logging for create`batch` * add create_batch as a call type * add basic dd logging for batches * basic batch creation logging on DD
563 lines
20 KiB
Python
563 lines
20 KiB
Python
"""
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Main File for Batches API implementation
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https://platform.openai.com/docs/api-reference/batch
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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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import asyncio
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import contextvars
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import os
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from functools import partial
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from typing import Any, Coroutine, Dict, Literal, Optional, Union
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import httpx
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import litellm
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from litellm.llms.azure.azure import AzureBatchesAPI
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from litellm.llms.openai.openai import OpenAIBatchesAPI
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from litellm.llms.vertex_ai.batches.handler import VertexAIBatchPrediction
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import Batch, CreateBatchRequest, RetrieveBatchRequest
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from litellm.types.router import GenericLiteLLMParams
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from litellm.utils import client, 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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vertex_ai_batches_instance = VertexAIBatchPrediction(gcs_bucket_name="")
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#################################################
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@client
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async def acreate_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", "azure", "vertex_ai"] = "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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**kwargs,
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) -> Batch:
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"""
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Async: Creates and executes a batch from an uploaded file of request
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LiteLLM Equivalent of POST: https://api.openai.com/v1/batches
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"""
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try:
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loop = asyncio.get_event_loop()
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kwargs["acreate_batch"] = True
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# Use a partial function to pass your keyword arguments
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func = partial(
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create_batch,
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completion_window,
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endpoint,
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input_file_id,
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custom_llm_provider,
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metadata,
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extra_headers,
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extra_body,
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**kwargs,
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)
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# Add the context to the function
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ctx = contextvars.copy_context()
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func_with_context = partial(ctx.run, func)
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init_response = await loop.run_in_executor(None, func_with_context)
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if asyncio.iscoroutine(init_response):
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response = await init_response
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else:
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response = init_response # type: ignore
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return response
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except Exception as e:
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raise e
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@client
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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", "azure", "vertex_ai"] = "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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**kwargs,
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) -> Union[Batch, Coroutine[Any, Any, Batch]]:
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"""
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Creates and executes a batch from an uploaded file of request
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LiteLLM Equivalent of POST: https://api.openai.com/v1/batches
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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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api_base: Optional[str] = None
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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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optional_params.api_base
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or litellm.api_base
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or os.getenv("OPENAI_API_BASE")
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or "https://api.openai.com/v1"
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)
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organization = (
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optional_params.organization
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or litellm.organization
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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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response = openai_batches_instance.create_batch(
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api_base=api_base,
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api_key=api_key,
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organization=organization,
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create_batch_data=_create_batch_request,
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timeout=timeout,
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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 = (
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optional_params.api_base
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or litellm.api_base
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or get_secret_str("AZURE_API_BASE")
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)
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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_str("AZURE_API_VERSION")
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)
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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_str("AZURE_OPENAI_API_KEY")
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or get_secret_str("AZURE_API_KEY")
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)
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extra_body = optional_params.get("extra_body", {})
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if extra_body is not None:
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extra_body.pop("azure_ad_token", None)
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else:
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get_secret_str("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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elif custom_llm_provider == "vertex_ai":
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api_base = optional_params.api_base or ""
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vertex_ai_project = (
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optional_params.vertex_project
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or litellm.vertex_project
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or get_secret_str("VERTEXAI_PROJECT")
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)
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vertex_ai_location = (
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optional_params.vertex_location
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or litellm.vertex_location
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or get_secret_str("VERTEXAI_LOCATION")
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)
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vertex_credentials = optional_params.vertex_credentials or get_secret_str(
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"VERTEXAI_CREDENTIALS"
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)
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response = vertex_ai_batches_instance.create_batch(
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_is_async=_is_async,
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api_base=api_base,
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vertex_project=vertex_ai_project,
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vertex_location=vertex_ai_location,
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vertex_credentials=vertex_credentials,
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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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custom_llm_provider
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),
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model="n/a",
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llm_provider=custom_llm_provider,
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response=httpx.Response(
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status_code=400,
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content="Unsupported provider",
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request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
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),
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)
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return response
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except Exception as e:
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raise e
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async def aretrieve_batch(
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batch_id: str,
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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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**kwargs,
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) -> Batch:
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"""
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Async: Retrieves a batch.
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LiteLLM Equivalent of GET https://api.openai.com/v1/batches/{batch_id}
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"""
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try:
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loop = asyncio.get_event_loop()
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kwargs["aretrieve_batch"] = True
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# Use a partial function to pass your keyword arguments
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func = partial(
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retrieve_batch,
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batch_id,
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custom_llm_provider,
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metadata,
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extra_headers,
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extra_body,
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**kwargs,
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)
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# Add the context to the function
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ctx = contextvars.copy_context()
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func_with_context = partial(ctx.run, func)
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init_response = await loop.run_in_executor(None, func_with_context)
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if asyncio.iscoroutine(init_response):
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response = await init_response
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else:
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response = init_response # type: ignore
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return response
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except Exception as e:
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raise e
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def retrieve_batch(
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batch_id: str,
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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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**kwargs,
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) -> Union[Batch, Coroutine[Any, Any, Batch]]:
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"""
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Retrieves a batch.
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LiteLLM Equivalent of GET https://api.openai.com/v1/batches/{batch_id}
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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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api_base: Optional[str] = None
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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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optional_params.api_base
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or litellm.api_base
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or os.getenv("OPENAI_API_BASE")
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or "https://api.openai.com/v1"
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)
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organization = (
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optional_params.organization
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or litellm.organization
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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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response = openai_batches_instance.retrieve_batch(
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_is_async=_is_async,
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retrieve_batch_data=_retrieve_batch_request,
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api_base=api_base,
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api_key=api_key,
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organization=organization,
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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 = (
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optional_params.api_base
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or litellm.api_base
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or get_secret_str("AZURE_API_BASE")
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)
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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_str("AZURE_API_VERSION")
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)
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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_str("AZURE_OPENAI_API_KEY")
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or get_secret_str("AZURE_API_KEY")
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)
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extra_body = optional_params.get("extra_body", {})
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if extra_body is not None:
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extra_body.pop("azure_ad_token", None)
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else:
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get_secret_str("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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custom_llm_provider
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),
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model="n/a",
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llm_provider=custom_llm_provider,
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response=httpx.Response(
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status_code=400,
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content="Unsupported provider",
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request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
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),
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)
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return response
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except Exception as e:
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raise e
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async def alist_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", "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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**kwargs,
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):
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"""
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Async: List your organization's batches.
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"""
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try:
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loop = asyncio.get_event_loop()
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kwargs["alist_batches"] = True
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# Use a partial function to pass your keyword arguments
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func = partial(
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list_batches,
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after,
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limit,
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custom_llm_provider,
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extra_headers,
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extra_body,
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**kwargs,
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)
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|
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# Add the context to the function
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ctx = contextvars.copy_context()
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func_with_context = partial(ctx.run, func)
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init_response = await loop.run_in_executor(None, func_with_context)
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|
if asyncio.iscoroutine(init_response):
|
|
response = await init_response
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|
else:
|
|
response = init_response # type: ignore
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return response
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except Exception as e:
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raise e
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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", "azure"] = "openai",
|
|
extra_headers: Optional[Dict[str, str]] = None,
|
|
extra_body: Optional[Dict[str, str]] = None,
|
|
**kwargs,
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):
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"""
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Lists batches
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|
List your organization's batches.
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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 = (
|
|
optional_params.api_key
|
|
or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
|
|
or litellm.openai_key
|
|
or os.getenv("OPENAI_API_KEY")
|
|
)
|
|
### TIMEOUT LOGIC ###
|
|
timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
|
|
# set timeout for 10 minutes by default
|
|
|
|
if (
|
|
timeout is not None
|
|
and isinstance(timeout, httpx.Timeout)
|
|
and supports_httpx_timeout(custom_llm_provider) is False
|
|
):
|
|
read_timeout = timeout.read or 600
|
|
timeout = read_timeout # default 10 min timeout
|
|
elif timeout is not None and not isinstance(timeout, httpx.Timeout):
|
|
timeout = float(timeout) # type: ignore
|
|
elif timeout is None:
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timeout = 600.0
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|
_is_async = kwargs.pop("alist_batches", False) is True
|
|
if custom_llm_provider == "openai":
|
|
# for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
|
|
api_base = (
|
|
optional_params.api_base
|
|
or litellm.api_base
|
|
or os.getenv("OPENAI_API_BASE")
|
|
or "https://api.openai.com/v1"
|
|
)
|
|
organization = (
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optional_params.organization
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|
or litellm.organization
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|
or os.getenv("OPENAI_ORGANIZATION", None)
|
|
or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
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)
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response = openai_batches_instance.list_batches(
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_is_async=_is_async,
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|
after=after,
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|
limit=limit,
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api_base=api_base,
|
|
api_key=api_key,
|
|
organization=organization,
|
|
timeout=timeout,
|
|
max_retries=optional_params.max_retries,
|
|
)
|
|
elif custom_llm_provider == "azure":
|
|
api_base = optional_params.api_base or litellm.api_base or get_secret_str("AZURE_API_BASE") # type: ignore
|
|
api_version = (
|
|
optional_params.api_version
|
|
or litellm.api_version
|
|
or get_secret_str("AZURE_API_VERSION")
|
|
)
|
|
|
|
api_key = (
|
|
optional_params.api_key
|
|
or litellm.api_key
|
|
or litellm.azure_key
|
|
or get_secret_str("AZURE_OPENAI_API_KEY")
|
|
or get_secret_str("AZURE_API_KEY")
|
|
)
|
|
|
|
extra_body = optional_params.get("extra_body", {})
|
|
if extra_body is not None:
|
|
extra_body.pop("azure_ad_token", None)
|
|
else:
|
|
get_secret_str("AZURE_AD_TOKEN") # type: ignore
|
|
|
|
response = azure_batches_instance.list_batches(
|
|
_is_async=_is_async,
|
|
api_base=api_base,
|
|
api_key=api_key,
|
|
api_version=api_version,
|
|
timeout=timeout,
|
|
max_retries=optional_params.max_retries,
|
|
)
|
|
else:
|
|
raise litellm.exceptions.BadRequestError(
|
|
message="LiteLLM doesn't support {} for 'list_batch'. Only 'openai' is supported.".format(
|
|
custom_llm_provider
|
|
),
|
|
model="n/a",
|
|
llm_provider=custom_llm_provider,
|
|
response=httpx.Response(
|
|
status_code=400,
|
|
content="Unsupported provider",
|
|
request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
|
|
),
|
|
)
|
|
return response
|
|
except Exception as e:
|
|
raise e
|
|
pass
|
|
|
|
|
|
def cancel_batch():
|
|
pass
|
|
|
|
|
|
async def acancel_batch():
|
|
pass
|