litellm/batches/main.py

"""
Main File for Batches API implementation

https://platform.openai.com/docs/api-reference/batch

- create_batch()
- retrieve_batch()
- cancel_batch()
- list_batch()

"""

import asyncio
import contextvars
import os
from functools import partial
from typing import Any, Coroutine, Dict, Literal, Optional, Union

import httpx

import litellm
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.azure.batches.handler import AzureBatchesAPI
from litellm.llms.openai.openai import OpenAIBatchesAPI
from litellm.llms.vertex_ai.batches.handler import VertexAIBatchPrediction
from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.openai import (
    Batch,
    CancelBatchRequest,
    CreateBatchRequest,
    RetrieveBatchRequest,
)
from litellm.types.router import GenericLiteLLMParams
from litellm.types.utils import LiteLLMBatch
from litellm.utils import client, get_litellm_params, supports_httpx_timeout

####### ENVIRONMENT VARIABLES ###################
openai_batches_instance = OpenAIBatchesAPI()
azure_batches_instance = AzureBatchesAPI()
vertex_ai_batches_instance = VertexAIBatchPrediction(gcs_bucket_name="")
#################################################


@client
async def acreate_batch(
    completion_window: Literal["24h"],
    endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"],
    input_file_id: str,
    custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
    metadata: Optional[Dict[str, str]] = None,
    extra_headers: Optional[Dict[str, str]] = None,
    extra_body: Optional[Dict[str, str]] = None,
    **kwargs,
) -> Batch:
    """
    Async: Creates and executes a batch from an uploaded file of request

    LiteLLM Equivalent of POST: https://api.openai.com/v1/batches
    """
    try:
        loop = asyncio.get_event_loop()
        kwargs["acreate_batch"] = True

        # Use a partial function to pass your keyword arguments
        func = partial(
            create_batch,
            completion_window,
            endpoint,
            input_file_id,
            custom_llm_provider,
            metadata,
            extra_headers,
            extra_body,
            **kwargs,
        )

        # Add the context to the function
        ctx = contextvars.copy_context()
        func_with_context = partial(ctx.run, func)
        init_response = await loop.run_in_executor(None, func_with_context)

        if asyncio.iscoroutine(init_response):
            response = await init_response
        else:
            response = init_response

        return response
    except Exception as e:
        raise e


@client
def create_batch(
    completion_window: Literal["24h"],
    endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"],
    input_file_id: str,
    custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
    metadata: Optional[Dict[str, str]] = None,
    extra_headers: Optional[Dict[str, str]] = None,
    extra_body: Optional[Dict[str, str]] = None,
    **kwargs,
) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
    """
    Creates and executes a batch from an uploaded file of request

    LiteLLM Equivalent of POST: https://api.openai.com/v1/batches
    """
    try:
        optional_params = GenericLiteLLMParams(**kwargs)
        litellm_call_id = kwargs.get("litellm_call_id", None)
        proxy_server_request = kwargs.get("proxy_server_request", None)
        model_info = kwargs.get("model_info", None)
        _is_async = kwargs.pop("acreate_batch", False) is True
        litellm_params = get_litellm_params(**kwargs)
        litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj", None)
        ### TIMEOUT LOGIC ###
        timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
        litellm_logging_obj.update_environment_variables(
            model=None,
            user=None,
            optional_params=optional_params.model_dump(),
            litellm_params={
                "litellm_call_id": litellm_call_id,
                "proxy_server_request": proxy_server_request,
                "model_info": model_info,
                "metadata": metadata,
                "preset_cache_key": None,
                "stream_response": {},
                **optional_params.model_dump(exclude_unset=True),
            },
            custom_llm_provider=custom_llm_provider,
        )

        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:
            timeout = 600.0

        _create_batch_request = CreateBatchRequest(
            completion_window=completion_window,
            endpoint=endpoint,
            input_file_id=input_file_id,
            metadata=metadata,
            extra_headers=extra_headers,
            extra_body=extra_body,
        )
        api_base: Optional[str] = None
        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_BASE_URL")
                or os.getenv("OPENAI_API_BASE")
                or "https://api.openai.com/v1"
            )
            organization = (
                optional_params.organization
                or litellm.organization
                or os.getenv("OPENAI_ORGANIZATION", None)
                or None  # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
            )
            # set API KEY
            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")
            )

            response = openai_batches_instance.create_batch(
                api_base=api_base,
                api_key=api_key,
                organization=organization,
                create_batch_data=_create_batch_request,
                timeout=timeout,
                max_retries=optional_params.max_retries,
                _is_async=_is_async,
            )
        elif custom_llm_provider == "azure":
            api_base = (
                optional_params.api_base
                or litellm.api_base
                or get_secret_str("AZURE_API_BASE")
            )
            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.create_batch(
                _is_async=_is_async,
                api_base=api_base,
                api_key=api_key,
                api_version=api_version,
                timeout=timeout,
                max_retries=optional_params.max_retries,
                create_batch_data=_create_batch_request,
                litellm_params=litellm_params,
            )
        elif custom_llm_provider == "vertex_ai":
            api_base = optional_params.api_base or ""
            vertex_ai_project = (
                optional_params.vertex_project
                or litellm.vertex_project
                or get_secret_str("VERTEXAI_PROJECT")
            )
            vertex_ai_location = (
                optional_params.vertex_location
                or litellm.vertex_location
                or get_secret_str("VERTEXAI_LOCATION")
            )
            vertex_credentials = optional_params.vertex_credentials or get_secret_str(
                "VERTEXAI_CREDENTIALS"
            )

            response = vertex_ai_batches_instance.create_batch(
                _is_async=_is_async,
                api_base=api_base,
                vertex_project=vertex_ai_project,
                vertex_location=vertex_ai_location,
                vertex_credentials=vertex_credentials,
                timeout=timeout,
                max_retries=optional_params.max_retries,
                create_batch_data=_create_batch_request,
            )
        else:
            raise litellm.exceptions.BadRequestError(
                message="LiteLLM doesn't support custom_llm_provider={} for 'create_batch'".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_batch", url="https://github.com/BerriAI/litellm"),  # type: ignore
                ),
            )
        return response
    except Exception as e:
        raise e


@client
async def aretrieve_batch(
    batch_id: str,
    custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
    metadata: Optional[Dict[str, str]] = None,
    extra_headers: Optional[Dict[str, str]] = None,
    extra_body: Optional[Dict[str, str]] = None,
    **kwargs,
) -> LiteLLMBatch:
    """
    Async: Retrieves a batch.

    LiteLLM Equivalent of GET https://api.openai.com/v1/batches/{batch_id}
    """
    try:
        loop = asyncio.get_event_loop()
        kwargs["aretrieve_batch"] = True

        # Use a partial function to pass your keyword arguments
        func = partial(
            retrieve_batch,
            batch_id,
            custom_llm_provider,
            metadata,
            extra_headers,
            extra_body,
            **kwargs,
        )
        # Add the context to the function
        ctx = contextvars.copy_context()
        func_with_context = partial(ctx.run, func)
        init_response = await loop.run_in_executor(None, func_with_context)
        if asyncio.iscoroutine(init_response):
            response = await init_response
        else:
            response = init_response  # type: ignore

        return response
    except Exception as e:
        raise e


@client
def retrieve_batch(
    batch_id: str,
    custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
    metadata: Optional[Dict[str, str]] = None,
    extra_headers: Optional[Dict[str, str]] = None,
    extra_body: Optional[Dict[str, str]] = None,
    **kwargs,
) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
    """
    Retrieves a batch.

    LiteLLM Equivalent of GET https://api.openai.com/v1/batches/{batch_id}
    """
    try:
        optional_params = GenericLiteLLMParams(**kwargs)
        litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj", None)
        ### TIMEOUT LOGIC ###
        timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
        litellm_params = get_litellm_params(
            custom_llm_provider=custom_llm_provider,
            **kwargs,
        )
        litellm_logging_obj.update_environment_variables(
            model=None,
            user=None,
            optional_params=optional_params.model_dump(),
            litellm_params=litellm_params,
            custom_llm_provider=custom_llm_provider,
        )

        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:
            timeout = 600.0

        _retrieve_batch_request = RetrieveBatchRequest(
            batch_id=batch_id,
            extra_headers=extra_headers,
            extra_body=extra_body,
        )

        _is_async = kwargs.pop("aretrieve_batch", False) is True
        api_base: Optional[str] = None
        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_BASE_URL")
                or os.getenv("OPENAI_API_BASE")
                or "https://api.openai.com/v1"
            )
            organization = (
                optional_params.organization
                or litellm.organization
                or os.getenv("OPENAI_ORGANIZATION", None)
                or None  # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
            )
            # set API KEY
            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")
            )

            response = openai_batches_instance.retrieve_batch(
                _is_async=_is_async,
                retrieve_batch_data=_retrieve_batch_request,
                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")
            )
            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.retrieve_batch(
                _is_async=_is_async,
                api_base=api_base,
                api_key=api_key,
                api_version=api_version,
                timeout=timeout,
                max_retries=optional_params.max_retries,
                retrieve_batch_data=_retrieve_batch_request,
                litellm_params=litellm_params,
            )
        elif custom_llm_provider == "vertex_ai":
            api_base = optional_params.api_base or ""
            vertex_ai_project = (
                optional_params.vertex_project
                or litellm.vertex_project
                or get_secret_str("VERTEXAI_PROJECT")
            )
            vertex_ai_location = (
                optional_params.vertex_location
                or litellm.vertex_location
                or get_secret_str("VERTEXAI_LOCATION")
            )
            vertex_credentials = optional_params.vertex_credentials or get_secret_str(
                "VERTEXAI_CREDENTIALS"
            )

            response = vertex_ai_batches_instance.retrieve_batch(
                _is_async=_is_async,
                batch_id=batch_id,
                api_base=api_base,
                vertex_project=vertex_ai_project,
                vertex_location=vertex_ai_location,
                vertex_credentials=vertex_credentials,
                timeout=timeout,
                max_retries=optional_params.max_retries,
            )
        else:
            raise litellm.exceptions.BadRequestError(
                message="LiteLLM doesn't support {} for 'create_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


async def alist_batches(
    after: Optional[str] = None,
    limit: Optional[int] = None,
    custom_llm_provider: Literal["openai", "azure"] = "openai",
    metadata: Optional[Dict[str, str]] = None,
    extra_headers: Optional[Dict[str, str]] = None,
    extra_body: Optional[Dict[str, str]] = None,
    **kwargs,
):
    """
    Async: List your organization's batches.
    """
    try:
        loop = asyncio.get_event_loop()
        kwargs["alist_batches"] = True

        # Use a partial function to pass your keyword arguments
        func = partial(
            list_batches,
            after,
            limit,
            custom_llm_provider,
            extra_headers,
            extra_body,
            **kwargs,
        )

        # Add the context to the function
        ctx = contextvars.copy_context()
        func_with_context = partial(ctx.run, func)
        init_response = await loop.run_in_executor(None, func_with_context)
        if asyncio.iscoroutine(init_response):
            response = await init_response
        else:
            response = init_response  # type: ignore

        return response
    except Exception as e:
        raise e


def list_batches(
    after: Optional[str] = None,
    limit: Optional[int] = None,
    custom_llm_provider: Literal["openai", "azure"] = "openai",
    extra_headers: Optional[Dict[str, str]] = None,
    extra_body: Optional[Dict[str, str]] = None,
    **kwargs,
):
    """
    Lists batches

    List your organization's batches.
    """
    try:
        # set API KEY
        optional_params = GenericLiteLLMParams(**kwargs)
        litellm_params = get_litellm_params(
            custom_llm_provider=custom_llm_provider,
            **kwargs,
        )
        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:
            timeout = 600.0

        _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_BASE_URL")
                or os.getenv("OPENAI_API_BASE")
                or "https://api.openai.com/v1"
            )
            organization = (
                optional_params.organization
                or litellm.organization
                or os.getenv("OPENAI_ORGANIZATION", None)
                or None  # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
            )

            response = openai_batches_instance.list_batches(
                _is_async=_is_async,
                after=after,
                limit=limit,
                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,
                litellm_params=litellm_params,
            )
        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


async def acancel_batch(
    batch_id: str,
    custom_llm_provider: Literal["openai", "azure"] = "openai",
    metadata: Optional[Dict[str, str]] = None,
    extra_headers: Optional[Dict[str, str]] = None,
    extra_body: Optional[Dict[str, str]] = None,
    **kwargs,
) -> Batch:
    """
    Async: Cancels a batch.

    LiteLLM Equivalent of POST https://api.openai.com/v1/batches/{batch_id}/cancel
    """
    try:
        loop = asyncio.get_event_loop()
        kwargs["acancel_batch"] = True

        # Use a partial function to pass your keyword arguments
        func = partial(
            cancel_batch,
            batch_id,
            custom_llm_provider,
            metadata,
            extra_headers,
            extra_body,
            **kwargs,
        )
        # Add the context to the function
        ctx = contextvars.copy_context()
        func_with_context = partial(ctx.run, func)
        init_response = await loop.run_in_executor(None, func_with_context)
        if asyncio.iscoroutine(init_response):
            response = await init_response
        else:
            response = init_response

        return response
    except Exception as e:
        raise e


def cancel_batch(
    batch_id: str,
    custom_llm_provider: Literal["openai", "azure"] = "openai",
    metadata: Optional[Dict[str, str]] = None,
    extra_headers: Optional[Dict[str, str]] = None,
    extra_body: Optional[Dict[str, str]] = None,
    **kwargs,
) -> Union[Batch, Coroutine[Any, Any, Batch]]:
    """
    Cancels a batch.

    LiteLLM Equivalent of POST https://api.openai.com/v1/batches/{batch_id}/cancel
    """
    try:
        optional_params = GenericLiteLLMParams(**kwargs)
        litellm_params = get_litellm_params(
            custom_llm_provider=custom_llm_provider,
            **kwargs,
        )
        ### 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:
            timeout = 600.0

        _cancel_batch_request = CancelBatchRequest(
            batch_id=batch_id,
            extra_headers=extra_headers,
            extra_body=extra_body,
        )

        _is_async = kwargs.pop("acancel_batch", False) is True
        api_base: Optional[str] = None
        if custom_llm_provider == "openai":
            api_base = (
                optional_params.api_base
                or litellm.api_base
                or os.getenv("OPENAI_BASE_URL")
                or os.getenv("OPENAI_API_BASE")
                or "https://api.openai.com/v1"
            )
            organization = (
                optional_params.organization
                or litellm.organization
                or os.getenv("OPENAI_ORGANIZATION", None)
                or None
            )
            api_key = (
                optional_params.api_key
                or litellm.api_key
                or litellm.openai_key
                or os.getenv("OPENAI_API_KEY")
            )

            response = openai_batches_instance.cancel_batch(
                _is_async=_is_async,
                cancel_batch_data=_cancel_batch_request,
                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")
            )
            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.cancel_batch(
                _is_async=_is_async,
                api_base=api_base,
                api_key=api_key,
                api_version=api_version,
                timeout=timeout,
                max_retries=optional_params.max_retries,
                cancel_batch_data=_cancel_batch_request,
                litellm_params=litellm_params,
            )
        else:
            raise litellm.exceptions.BadRequestError(
                message="LiteLLM doesn't support {} for 'cancel_batch'. Only 'openai' and 'azure' are 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="cancel_batch", url="https://github.com/BerriAI/litellm"),  # type: ignore
                ),
            )
        return response
    except Exception as e:
        raise e