(feat) /batches - track user_api_key_alias, user_api_key_team_alias etc for /batch requests (#7401)

* run azure testing on ci/cd

* update docs on azure batches endpoints

* add input azure.jsonl

* refactor - use separate file for batches endpoints

* fixes for passing custom llm provider to /batch endpoints

* pass custom llm provider to files endpoints

* update azure batches doc

* add info for azure batches api

* update batches endpoints

* use simple helper for raising proxy exception

* update config.yml

* fix imports

* add type hints to get_litellm_params

* update get_litellm_params

* update get_litellm_params

* update get slp

* QOL - stop double logging a create batch operations on custom loggers

* re use slp from og event

* _create_standard_logging_object_for_completed_batch

* fix linting errors

* reduce num changes in PR

* update BATCH_STATUS_POLL_MAX_ATTEMPTS

litellm/batches/batch_utils.py

@@ -277,17 +277,8 @@ def _create_standard_logging_object_for_completed_batch(
     """
     Create a standard logging object for a completed batch
     """
-    from litellm.litellm_core_utils.litellm_logging import (
-        get_standard_logging_object_payload,
-    )
-
-    standard_logging_object = get_standard_logging_object_payload(
-        kwargs=kwargs,
-        init_response_obj=None,
-        start_time=start_time,
-        end_time=end_time,
-        logging_obj=logging_obj,
-        status="success",
+    standard_logging_object = logging_obj.model_call_details.get(
+        "standard_logging_object", None
     )
 
     if standard_logging_object is None:

litellm/batches/main.py

@@ -19,13 +19,14 @@ 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.azure 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, CreateBatchRequest, RetrieveBatchRequest
 from litellm.types.router import GenericLiteLLMParams
-from litellm.utils import client, supports_httpx_timeout
+from litellm.utils import client, get_litellm_params, supports_httpx_timeout
 
 from .batch_utils import batches_async_logging
 
@@ -114,9 +115,22 @@ def create_batch(
     try:
         optional_params = GenericLiteLLMParams(**kwargs)
         _is_async = kwargs.pop("acreate_batch", False) is True
+        litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj", None)
         ### TIMEOUT LOGIC ###
         timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
-        # set timeout for 10 minutes by default
+        litellm_params = get_litellm_params(
+            custom_llm_provider=custom_llm_provider,
+            litellm_call_id=kwargs.get("litellm_call_id", None),
+            litellm_trace_id=kwargs.get("litellm_trace_id"),
+            litellm_metadata=kwargs.get("litellm_metadata"),
+        )
+        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

litellm/constants.py

@@ -93,5 +93,5 @@ BEDROCK_AGENT_RUNTIME_PASS_THROUGH_ROUTES = [
     "optimize-prompt/",
 ]
 
-BATCH_STATUS_POLL_INTERVAL_SECONDS = 10
-BATCH_STATUS_POLL_MAX_ATTEMPTS = 10
+BATCH_STATUS_POLL_INTERVAL_SECONDS = 3600  # 1 hour
+BATCH_STATUS_POLL_MAX_ATTEMPTS = 24  # for 24 hours

litellm/litellm_core_utils/litellm_logging.py

@@ -388,9 +388,15 @@ class Logging(LiteLLMLoggingBaseClass):
         return standard_callback_dynamic_params
 
     def update_environment_variables(
-        self, model, user, optional_params, litellm_params, **additional_params
+        self,
+        litellm_params: Dict,
+        optional_params: Dict,
+        model: Optional[str] = None,
+        user: Optional[str] = None,
+        **additional_params,
     ):
         self.optional_params = optional_params
+        if model is not None:
             self.model = model
         self.user = user
         self.litellm_params = scrub_sensitive_keys_in_metadata(litellm_params)
@@ -2885,9 +2891,11 @@ def get_standard_logging_object_payload(
         litellm_params = kwargs.get("litellm_params", {})
         proxy_server_request = litellm_params.get("proxy_server_request") or {}
         end_user_id = proxy_server_request.get("body", {}).get("user", None)
-        metadata = (
-            litellm_params.get("metadata", {}) or {}
-        )  # if litellm_params['metadata'] == None
+        metadata: dict = (
+            litellm_params.get("litellm_metadata")
+            or litellm_params.get("metadata", None)
+            or {}
+        )
         completion_start_time = kwargs.get("completion_start_time", end_time)
         call_type = kwargs.get("call_type")
         cache_hit = kwargs.get("cache_hit", False)

litellm/main.py

@@ -1140,6 +1140,7 @@ def completion(  # type: ignore # noqa: PLR0915
             litellm_trace_id=kwargs.get("litellm_trace_id"),
             hf_model_name=hf_model_name,
             custom_prompt_dict=custom_prompt_dict,
+            litellm_metadata=kwargs.get("litellm_metadata"),
         )
         logging.update_environment_variables(
             model=model,

litellm/rerank_api/main.py

@@ -138,7 +138,7 @@ def rerank(  # noqa: PLR0915
         litellm_logging_obj.update_environment_variables(
             model=model,
             user=user,
-            optional_params=optional_rerank_params,
+            optional_params=dict(optional_rerank_params),
             litellm_params={
                 "litellm_call_id": litellm_call_id,
                 "proxy_server_request": proxy_server_request,

litellm/types/utils.py

@@ -1592,6 +1592,7 @@ class StandardCallbackDynamicParams(TypedDict, total=False):
 
 all_litellm_params = [
     "metadata",
+    "litellm_metadata",
     "litellm_trace_id",
     "tags",
     "acompletion",

litellm/utils.py

@@ -1203,11 +1203,18 @@ def client(original_function):  # noqa: PLR0915
         return wrapper
 
 
-def _is_async_request(kwargs: Optional[dict]) -> bool:
+def _is_async_request(
+    kwargs: Optional[dict],
+    is_pass_through: bool = False,
+) -> bool:
     """
     Returns True if the call type is an internal async request.
 
     eg. litellm.acompletion, litellm.aimage_generation, litellm.acreate_batch, litellm._arealtime
+
+    Args:
+        kwargs (dict): The kwargs passed to the litellm function
+        is_pass_through (bool): Whether the call is a pass-through call. By default all pass through calls are async.
     """
     if kwargs is None:
         return False
@@ -1221,6 +1228,7 @@ def _is_async_request(kwargs: Optional[dict]) -> bool:
         or kwargs.get("arerank", False) is True
         or kwargs.get("_arealtime", False) is True
         or kwargs.get("acreate_batch", False) is True
+        or is_pass_through is True
     ):
         return True
     return False
@@ -1927,7 +1935,7 @@ def register_model(model_cost: Union[str, dict]):  # noqa: PLR0915
 
 
 def get_litellm_params(
-    api_key=None,
+    api_key: Optional[str] = None,
     force_timeout=600,
     azure=False,
     logger_fn=None,
@@ -1935,12 +1943,12 @@ def get_litellm_params(
     hugging_face=False,
     replicate=False,
     together_ai=False,
-    custom_llm_provider=None,
-    api_base=None,
+    custom_llm_provider: Optional[str] = None,
+    api_base: Optional[str] = None,
     litellm_call_id=None,
     model_alias_map=None,
     completion_call_id=None,
-    metadata=None,
+    metadata: Optional[dict] = None,
     model_info=None,
     proxy_server_request=None,
     acompletion=None,
@@ -1954,10 +1962,11 @@ def get_litellm_params(
     text_completion=None,
     azure_ad_token_provider=None,
     user_continue_message=None,
-    base_model=None,
-    litellm_trace_id=None,
+    base_model: Optional[str] = None,
+    litellm_trace_id: Optional[str] = None,
     hf_model_name: Optional[str] = None,
     custom_prompt_dict: Optional[dict] = None,
+    litellm_metadata: Optional[dict] = None,
 ):
     litellm_params = {
         "acompletion": acompletion,
@@ -1989,8 +1998,8 @@ def get_litellm_params(
         "litellm_trace_id": litellm_trace_id,
         "hf_model_name": hf_model_name,
         "custom_prompt_dict": custom_prompt_dict,
+        "litellm_metadata": litellm_metadata,
     }
-
     return litellm_params
 
 

tests/batches_tests/test_openai_batches_and_files.py

@@ -12,6 +12,7 @@ load_dotenv()
 sys.path.insert(
     0, os.path.abspath("../..")
 )  # Adds the parent directory to the system-path
+
 import logging
 import time
 
@@ -191,12 +192,18 @@ async def test_async_create_batch(provider):
         batch_input_file_id is not None
     ), "Failed to create file, expected a non null file_id but got {batch_input_file_id}"
 
+    extra_metadata_field = {
+        "user_api_key_alias": "special_api_key_alias",
+        "user_api_key_team_alias": "special_team_alias",
+    }
     create_batch_response = await litellm.acreate_batch(
         completion_window="24h",
         endpoint="/v1/chat/completions",
         input_file_id=batch_input_file_id,
         custom_llm_provider=provider,
         metadata={"key1": "value1", "key2": "value2"},
+        # litellm specific param - used for logging metadata on logging callback
+        litellm_metadata=extra_metadata_field,
     )
 
     print("response from litellm.create_batch=", create_batch_response)
@@ -215,6 +222,18 @@ async def test_async_create_batch(provider):
     await asyncio.sleep(6)
     # Assert that the create batch event is logged on CustomLogger
     assert custom_logger.standard_logging_object is not None
+    print(
+        "standard_logging_object=",
+        json.dumps(custom_logger.standard_logging_object, indent=4, default=str),
+    )
+    assert (
+        custom_logger.standard_logging_object["metadata"]["user_api_key_alias"]
+        == extra_metadata_field["user_api_key_alias"]
+    )
+    assert (
+        custom_logger.standard_logging_object["metadata"]["user_api_key_team_alias"]
+        == extra_metadata_field["user_api_key_team_alias"]
+    )
 
     retrieved_batch = await litellm.aretrieve_batch(
         batch_id=create_batch_response.id, custom_llm_provider=provider