two version of R2R are hereHEADmaster

1 files changed, 794 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/batches/main.py b/.venv/lib/python3.12/site-packages/litellm/batches/main.py
new file mode 100644
index 00000000..1ddcafce
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/batches/main.py
@@ -0,0 +1,794 @@
+"""
+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_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_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_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_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