feat add support for alist_batches

litellm/batches/main.py

@@ -20,10 +20,8 @@ import httpx
 
 import litellm
 from litellm import client
-from litellm.utils import supports_httpx_timeout
+from litellm.llms.openai import OpenAIBatchesAPI, OpenAIFilesAPI
-
+from litellm.types.llms.openai import (
-from ..llms.openai import OpenAIBatchesAPI, OpenAIFilesAPI
-from ..types.llms.openai import (
     Batch,
     CancelBatchRequest,
     CreateBatchRequest,
@@ -34,7 +32,8 @@ from ..types.llms.openai import (
     HttpxBinaryResponseContent,
     RetrieveBatchRequest,
 )
-from ..types.router import *
+from litellm.types.router import GenericLiteLLMParams
+from litellm.utils import supports_httpx_timeout
 
 ####### ENVIRONMENT VARIABLES ###################
 openai_batches_instance = OpenAIBatchesAPI()
@@ -314,17 +313,139 @@ def retrieve_batch(
         raise e
 
 
-def cancel_batch():
+async def alist_batches(
-    pass
+    after: Optional[str] = None,
+    limit: Optional[int] = None,
+    custom_llm_provider: Literal["openai"] = "openai",
+    metadata: Optional[Dict[str, str]] = None,
+    extra_headers: Optional[Dict[str, str]] = None,
+    extra_body: Optional[Dict[str, str]] = None,
+    **kwargs,
+) -> Batch:
+    """
+    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_batch():
+def list_batches(
-    pass
+    after: Optional[str] = None,
+    limit: Optional[int] = None,
+    custom_llm_provider: Literal["openai"] = "openai",
+    extra_headers: Optional[Dict[str, str]] = None,
+    extra_body: Optional[Dict[str, str]] = None,
+    **kwargs,
+):
+    """
+    Lists batches
 
+    List your organization's batches.
+    """
+    try:
+        optional_params = GenericLiteLLMParams(**kwargs)
+        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")
+            )
+            ### TIMEOUT LOGIC ###
+            timeout = (
+                optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+            )
+            # set timeout for 10 minutes by default
 
-async def acancel_batch():
+            if (
+                timeout is not None
+                and isinstance(timeout, httpx.Timeout)
+                and supports_httpx_timeout(custom_llm_provider) == 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
+
+            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,
+            )
+        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
     pass
 
 
 async def alist_batch():
     pass
+
+
+def cancel_batch():
+    pass
+
+
+async def acancel_batch():
+    pass

litellm/llms/openai.py

@@ -2602,26 +2602,52 @@ class OpenAIBatchesAPI(BaseLLM):
         response = openai_client.batches.cancel(**cancel_batch_data)
         return response
 
-    # def list_batch(
+    async def alist_batches(
-    #     self,
+        self,
-    #     list_batch_data: ListBatchRequest,
+        openai_client: AsyncOpenAI,
-    #     api_key: Optional[str],
+        after: Optional[str] = None,
-    #     api_base: Optional[str],
+        limit: Optional[int] = None,
-    #     timeout: Union[float, httpx.Timeout],
+    ):
-    #     max_retries: Optional[int],
+        verbose_logger.debug("listing batches, after= %s, limit= %s", after, limit)
-    #     organization: Optional[str],
+        response = await openai_client.batches.list(after=after, limit=limit)
-    #     client: Optional[OpenAI] = None,
+        return response
-    # ):
+
-    #     openai_client: OpenAI = self.get_openai_client(
+    def list_batches(
-    #         api_key=api_key,
+        self,
-    #         api_base=api_base,
+        _is_async: bool,
-    #         timeout=timeout,
+        api_key: Optional[str],
-    #         max_retries=max_retries,
+        api_base: Optional[str],
-    #         organization=organization,
+        timeout: Union[float, httpx.Timeout],
-    #         client=client,
+        max_retries: Optional[int],
-    #     )
+        organization: Optional[str],
-    #     response = openai_client.batches.list(**list_batch_data)
+        after: Optional[str] = None,
-    #     return response
+        limit: Optional[int] = None,
+        client: Optional[OpenAI] = None,
+    ):
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.alist_batches(  # type: ignore
+                openai_client=openai_client, after=after, limit=limit
+            )
+        response = openai_client.batches.list(after=after, limit=limit)
+        return response
 
 
 class OpenAIAssistantsAPI(BaseLLM):