Merge branch 'main' into litellm_arize_dynamic_logging

litellm/integrations/arize/_utils.py

@@ -1,4 +1,3 @@
-import json
 from typing import TYPE_CHECKING, Any, Optional
 
 from litellm._logging import verbose_logger

litellm/litellm_core_utils/get_llm_provider_logic.py

@@ -129,17 +129,15 @@ def get_llm_provider(  # noqa: PLR0915
             model, custom_llm_provider
         )
 
-        if custom_llm_provider:
+        if custom_llm_provider and (
-            if (
+            model.split("/")[0] != custom_llm_provider
-                model.split("/")[0] == custom_llm_provider
+        ):  # handle scenario where model="azure/*" and custom_llm_provider="azure"
-            ):  # handle scenario where model="azure/*" and custom_llm_provider="azure"
+            model = custom_llm_provider + "/" + model
-                model = model.replace("{}/".format(custom_llm_provider), "")
-
-            return model, custom_llm_provider, dynamic_api_key, api_base
 
         if api_key and api_key.startswith("os.environ/"):
             dynamic_api_key = get_secret_str(api_key)
         # check if llm provider part of model name
+
         if (
             model.split("/", 1)[0] in litellm.provider_list
             and model.split("/", 1)[0] not in litellm.model_list_set
@@ -573,11 +571,11 @@ def _get_openai_compatible_provider_info(  # noqa: PLR0915
         dynamic_api_key = api_key or get_secret_str("GALADRIEL_API_KEY")
     elif custom_llm_provider == "snowflake":
         api_base = (
-                    api_base
+            api_base
-                    or get_secret("SNOWFLAKE_API_BASE")
+            or get_secret_str("SNOWFLAKE_API_BASE")
-                    or f"https://{get_secret('SNOWFLAKE_ACCOUNT_ID')}.snowflakecomputing.com/api/v2/cortex/inference:complete"
+            or f"https://{get_secret('SNOWFLAKE_ACCOUNT_ID')}.snowflakecomputing.com/api/v2/cortex/inference:complete"
-                )  # type: ignore
+        )  # type: ignore
-        dynamic_api_key = api_key or get_secret("SNOWFLAKE_JWT")
+        dynamic_api_key = api_key or get_secret_str("SNOWFLAKE_JWT")
 
     if api_base is not None and not isinstance(api_base, str):
         raise Exception("api base needs to be a string. api_base={}".format(api_base))

litellm/litellm_core_utils/litellm_logging.py

@@ -3257,6 +3257,7 @@ class StandardLoggingPayloadSetup:
             additional_headers=None,
             litellm_overhead_time_ms=None,
             batch_models=None,
+            litellm_model_name=None,
         )
         if hidden_params is not None:
             for key in StandardLoggingHiddenParams.__annotations__.keys():
@@ -3371,6 +3372,7 @@ def get_standard_logging_object_payload(
                         response_cost=None,
                         litellm_overhead_time_ms=None,
                         batch_models=None,
+                        litellm_model_name=None,
                     )
                 )
 
@@ -3656,6 +3658,7 @@ def create_dummy_standard_logging_payload() -> StandardLoggingPayload:
         additional_headers=None,
         litellm_overhead_time_ms=None,
         batch_models=None,
+        litellm_model_name=None,
     )
 
     # Convert numeric values to appropriate types

litellm/litellm_core_utils/llm_response_utils/response_metadata.py

@@ -44,6 +44,7 @@ class ResponseMetadata:
             "additional_headers": process_response_headers(
                 self._get_value_from_hidden_params("additional_headers") or {}
             ),
+            "litellm_model_name": model,
         }
         self._update_hidden_params(new_params)
 

litellm/router_strategy/lowest_tpm_rpm_v2.py

@@ -65,10 +65,13 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
             # ------------
             # Setup values
             # ------------
+
             dt = get_utc_datetime()
             current_minute = dt.strftime("%H-%M")
             model_id = deployment.get("model_info", {}).get("id")
-            rpm_key = f"{model_id}:rpm:{current_minute}"
+            deployment_name = deployment.get("litellm_params", {}).get("model")
+            rpm_key = f"{model_id}:{deployment_name}:rpm:{current_minute}"
+
             local_result = self.router_cache.get_cache(
                 key=rpm_key, local_only=True
             )  # check local result first
@@ -151,7 +154,9 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
             dt = get_utc_datetime()
             current_minute = dt.strftime("%H-%M")
             model_id = deployment.get("model_info", {}).get("id")
-            rpm_key = f"{model_id}:rpm:{current_minute}"
+            deployment_name = deployment.get("litellm_params", {}).get("model")
+
+            rpm_key = f"{model_id}:{deployment_name}:rpm:{current_minute}"
             local_result = await self.router_cache.async_get_cache(
                 key=rpm_key, local_only=True
             )  # check local result first
@@ -228,8 +233,9 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
             if standard_logging_object is None:
                 raise ValueError("standard_logging_object not passed in.")
             model_group = standard_logging_object.get("model_group")
+            model = standard_logging_object["hidden_params"].get("litellm_model_name")
             id = standard_logging_object.get("model_id")
-            if model_group is None or id is None:
+            if model_group is None or id is None or model is None:
                 return
             elif isinstance(id, int):
                 id = str(id)
@@ -244,7 +250,7 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
                 "%H-%M"
             )  # use the same timezone regardless of system clock
 
-            tpm_key = f"{id}:tpm:{current_minute}"
+            tpm_key = f"{id}:{model}:tpm:{current_minute}"
             # ------------
             # Update usage
             # ------------
@@ -276,6 +282,7 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
             if standard_logging_object is None:
                 raise ValueError("standard_logging_object not passed in.")
             model_group = standard_logging_object.get("model_group")
+            model = standard_logging_object["hidden_params"]["litellm_model_name"]
             id = standard_logging_object.get("model_id")
             if model_group is None or id is None:
                 return
@@ -290,7 +297,7 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
                 "%H-%M"
             )  # use the same timezone regardless of system clock
 
-            tpm_key = f"{id}:tpm:{current_minute}"
+            tpm_key = f"{id}:{model}:tpm:{current_minute}"
             # ------------
             # Update usage
             # ------------
@@ -458,8 +465,9 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
                 id = m.get("model_info", {}).get(
                     "id"
                 )  # a deployment should always have an 'id'. this is set in router.py
-                tpm_key = "{}:tpm:{}".format(id, current_minute)
+                deployment_name = m.get("litellm_params", {}).get("model")
-                rpm_key = "{}:rpm:{}".format(id, current_minute)
+                tpm_key = "{}:{}:tpm:{}".format(id, deployment_name, current_minute)
+                rpm_key = "{}:{}:rpm:{}".format(id, deployment_name, current_minute)
 
                 tpm_keys.append(tpm_key)
                 rpm_keys.append(rpm_key)
@@ -576,8 +584,9 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
                 id = m.get("model_info", {}).get(
                     "id"
                 )  # a deployment should always have an 'id'. this is set in router.py
-                tpm_key = "{}:tpm:{}".format(id, current_minute)
+                deployment_name = m.get("litellm_params", {}).get("model")
-                rpm_key = "{}:rpm:{}".format(id, current_minute)
+                tpm_key = "{}:{}:tpm:{}".format(id, deployment_name, current_minute)
+                rpm_key = "{}:{}:rpm:{}".format(id, deployment_name, current_minute)
 
                 tpm_keys.append(tpm_key)
                 rpm_keys.append(rpm_key)

litellm/types/utils.py

@@ -1625,13 +1625,16 @@ class StandardLoggingAdditionalHeaders(TypedDict, total=False):
 
 
 class StandardLoggingHiddenParams(TypedDict):
-    model_id: Optional[str]
+    model_id: Optional[
+        str
+    ]  # id of the model in the router, separates multiple models with the same name but different credentials
     cache_key: Optional[str]
     api_base: Optional[str]
     response_cost: Optional[str]
     litellm_overhead_time_ms: Optional[float]
     additional_headers: Optional[StandardLoggingAdditionalHeaders]
     batch_models: Optional[List[str]]
+    litellm_model_name: Optional[str]  # the model name sent to the provider by litellm
 
 
 class StandardLoggingModelInformation(TypedDict):

tests/litellm/caching/test_redis_cache.py

@@ -20,7 +20,8 @@ from litellm.caching.redis_cache import RedisCache
 
 @pytest.mark.parametrize("namespace", [None, "test"])
 @pytest.mark.asyncio
-async def test_redis_cache_async_increment(namespace):
+async def test_redis_cache_async_increment(namespace, monkeypatch):
+    monkeypatch.setenv("REDIS_HOST", "https://my-test-host")
     redis_cache = RedisCache(namespace=namespace)
     # Create an AsyncMock for the Redis client
     mock_redis_instance = AsyncMock()
@@ -46,7 +47,8 @@ async def test_redis_cache_async_increment(namespace):
 
 
 @pytest.mark.asyncio
-async def test_redis_client_init_with_socket_timeout():
+async def test_redis_client_init_with_socket_timeout(monkeypatch):
+    monkeypatch.setenv("REDIS_HOST", "my-fake-host")
     redis_cache = RedisCache(socket_timeout=1.0)
     assert redis_cache.redis_kwargs["socket_timeout"] == 1.0
     client = redis_cache.init_async_client()

tests/litellm/llms/azure_ai/chat/test_azure_ai_transformation.py

@@ -8,84 +8,24 @@ import pytest
 sys.path.insert(
     0, os.path.abspath("../../../../..")
 )  # Adds the parent directory to the system path
-import litellm
+from litellm.llms.azure_ai.chat.transformation import AzureAIStudioConfig
 
 
-@pytest.mark.parametrize("is_async", [True, False])
 @pytest.mark.asyncio
-async def test_azure_ai_request_format(is_async):
+async def test_get_openai_compatible_provider_info():
     """
     Test that Azure AI requests are formatted correctly with the proper endpoint and parameters
     for both synchronous and asynchronous calls
     """
-    litellm._turn_on_debug()
+    config = AzureAIStudioConfig()
 
-    # Set up the test parameters
+    api_base, dynamic_api_key, custom_llm_provider = (
-    api_key = "00xxx"
+        config._get_openai_compatible_provider_info(
-    api_base = "https://my-endpoint-europe-berri-992.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-08-01-preview"
+            model="azure_ai/gpt-4o-mini",
-    model = "azure_ai/gpt-4o-mini"
+            api_base="https://my-base",
-    messages = [
+            api_key="my-key",
-        {"role": "user", "content": "hi"},
+            custom_llm_provider="azure_ai",
-        {"role": "assistant", "content": "Hello! How can I assist you today?"},
+        )
-        {"role": "user", "content": "hi"},
+    )
-    ]
 
-    if is_async:
+    assert custom_llm_provider == "azure"
-        # Mock AsyncHTTPHandler.post method for async test
-        with patch(
-            "litellm.llms.custom_httpx.llm_http_handler.AsyncHTTPHandler.post"
-        ) as mock_post:
-            # Set up mock response
-            mock_post.return_value = AsyncMock()
-
-            # Call the acompletion function
-            try:
-                await litellm.acompletion(
-                    custom_llm_provider="azure_ai",
-                    api_key=api_key,
-                    api_base=api_base,
-                    model=model,
-                    messages=messages,
-                )
-            except Exception as e:
-                # We expect an exception since we're mocking the response
-                pass
-
-    else:
-        # Mock HTTPHandler.post method for sync test
-        with patch(
-            "litellm.llms.custom_httpx.llm_http_handler.HTTPHandler.post"
-        ) as mock_post:
-            # Set up mock response
-            mock_post.return_value = MagicMock()
-
-            # Call the completion function
-            try:
-                litellm.completion(
-                    custom_llm_provider="azure_ai",
-                    api_key=api_key,
-                    api_base=api_base,
-                    model=model,
-                    messages=messages,
-                )
-            except Exception as e:
-                # We expect an exception since we're mocking the response
-                pass
-
-    # Verify the request was made with the correct parameters
-    mock_post.assert_called_once()
-    call_args = mock_post.call_args
-    print("sync request call=", json.dumps(call_args.kwargs, indent=4, default=str))
-
-    # Check URL
-    assert call_args.kwargs["url"] == api_base
-
-    # Check headers
-    assert call_args.kwargs["headers"]["api-key"] == api_key
-
-    # Check request body
-    request_body = json.loads(call_args.kwargs["data"])
-    assert (
-        request_body["model"] == "gpt-4o-mini"
-    )  # Model name should be stripped of provider prefix
-    assert request_body["messages"] == messages

tests/llm_translation/test_azure_ai.py

@@ -265,3 +265,32 @@ class TestAzureAIRerank(BaseLLMRerankTest):
             "api_base": os.getenv("AZURE_AI_COHERE_API_BASE"),
             "api_key": os.getenv("AZURE_AI_COHERE_API_KEY"),
         }
+
+
+@pytest.mark.asyncio
+async def test_azure_ai_request_format():
+    """
+    Test that Azure AI requests are formatted correctly with the proper endpoint and parameters
+    for both synchronous and asynchronous calls
+    """
+    from openai import AsyncAzureOpenAI, AzureOpenAI
+
+    litellm._turn_on_debug()
+
+    # Set up the test parameters
+    api_key = os.getenv("AZURE_API_KEY")
+    api_base = f"{os.getenv('AZURE_API_BASE')}/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview"
+    model = "azure_ai/gpt-4o"
+    messages = [
+        {"role": "user", "content": "hi"},
+        {"role": "assistant", "content": "Hello! How can I assist you today?"},
+        {"role": "user", "content": "hi"},
+    ]
+
+    await litellm.acompletion(
+        custom_llm_provider="azure_ai",
+        api_key=api_key,
+        api_base=api_base,
+        model=model,
+        messages=messages,
+    )

tests/local_testing/test_arize_ai.py

@@ -2,7 +2,9 @@ import asyncio
 import json
 import logging
 import os
-
+import time
+from unittest.mock import patch, Mock
+import opentelemetry.exporter.otlp.proto.grpc.trace_exporter
 from litellm import Choices
 import pytest
 from dotenv import load_dotenv
@@ -81,3 +83,52 @@ def test_get_arize_config_with_endpoints(mock_env_vars, monkeypatch):
     config = ArizeLogger.get_arize_config()
     assert config.endpoint == "grpc://test.endpoint"
     assert config.protocol == "otlp_grpc"
+
+
+@pytest.mark.skip(
+    reason="Works locally but not in CI/CD. We'll need a better way to test Arize on CI/CD"
+)
+def test_arize_callback():
+    litellm.callbacks = ["arize"]
+    os.environ["ARIZE_SPACE_KEY"] = "test_space_key"
+    os.environ["ARIZE_API_KEY"] = "test_api_key"
+    os.environ["ARIZE_ENDPOINT"] = "https://otlp.arize.com/v1"
+
+    # Set the batch span processor to quickly flush after a span has been added
+    # This is to ensure that the span is exported before the test ends
+    os.environ["OTEL_BSP_MAX_QUEUE_SIZE"] = "1"
+    os.environ["OTEL_BSP_MAX_EXPORT_BATCH_SIZE"] = "1"
+    os.environ["OTEL_BSP_SCHEDULE_DELAY_MILLIS"] = "1"
+    os.environ["OTEL_BSP_EXPORT_TIMEOUT_MILLIS"] = "5"
+
+    try:
+        with patch.object(
+            opentelemetry.exporter.otlp.proto.grpc.trace_exporter.OTLPSpanExporter,
+            "export",
+            new=Mock(),
+        ) as patched_export:
+            litellm.completion(
+                model="openai/test-model",
+                messages=[{"role": "user", "content": "arize test content"}],
+                stream=False,
+                mock_response="hello there!",
+            )
+
+            time.sleep(1)  # Wait for the batch span processor to flush
+            assert patched_export.called
+    finally:
+        # Clean up environment variables
+        for key in [
+            "ARIZE_SPACE_KEY",
+            "ARIZE_API_KEY",
+            "ARIZE_ENDPOINT",
+            "OTEL_BSP_MAX_QUEUE_SIZE",
+            "OTEL_BSP_MAX_EXPORT_BATCH_SIZE",
+            "OTEL_BSP_SCHEDULE_DELAY_MILLIS",
+            "OTEL_BSP_EXPORT_TIMEOUT_MILLIS",
+        ]:
+            if key in os.environ:
+                del os.environ[key]
+
+        # Reset callbacks
+        litellm.callbacks = []

tests/local_testing/test_get_llm_provider.py

@@ -216,3 +216,21 @@ def test_bedrock_invoke_anthropic():
     )
     assert custom_llm_provider == "bedrock"
     assert model == "invoke/anthropic.claude-3-5-sonnet-20240620-v1:0"
+
+
+@pytest.mark.parametrize("model", ["xai/grok-2-vision-latest", "grok-2-vision-latest"])
+def test_xai_api_base(model):
+    args = {
+        "model": model,
+        "custom_llm_provider": "xai",
+        "api_base": None,
+        "api_key": "xai-my-specialkey",
+        "litellm_params": None,
+    }
+    model, custom_llm_provider, dynamic_api_key, api_base = litellm.get_llm_provider(
+        **args
+    )
+    assert custom_llm_provider == "xai"
+    assert model == "grok-2-vision-latest"
+    assert api_base == "https://api.x.ai/v1"
+    assert dynamic_api_key == "xai-my-specialkey"

tests/local_testing/test_tpm_rpm_routing_v2.py

@@ -20,7 +20,7 @@ sys.path.insert(
 from unittest.mock import AsyncMock, MagicMock, patch
 from litellm.types.utils import StandardLoggingPayload
 import pytest
-
+from litellm.types.router import DeploymentTypedDict
 import litellm
 from litellm import Router
 from litellm.caching.caching import DualCache
@@ -47,12 +47,14 @@ def test_tpm_rpm_updated():
     deployment_id = "1234"
     deployment = "azure/chatgpt-v-2"
     total_tokens = 50
-    standard_logging_payload = create_standard_logging_payload()
+    standard_logging_payload: StandardLoggingPayload = create_standard_logging_payload()
     standard_logging_payload["model_group"] = model_group
     standard_logging_payload["model_id"] = deployment_id
     standard_logging_payload["total_tokens"] = total_tokens
+    standard_logging_payload["hidden_params"]["litellm_model_name"] = deployment
     kwargs = {
         "litellm_params": {
+            "model": deployment,
             "metadata": {
                 "model_group": model_group,
                 "deployment": deployment,
@@ -62,10 +64,16 @@ def test_tpm_rpm_updated():
         "standard_logging_object": standard_logging_payload,
     }
 
+    litellm_deployment_dict: DeploymentTypedDict = {
+        "model_name": model_group,
+        "litellm_params": {"model": deployment},
+        "model_info": {"id": deployment_id},
+    }
+
     start_time = time.time()
     response_obj = {"usage": {"total_tokens": total_tokens}}
     end_time = time.time()
-    lowest_tpm_logger.pre_call_check(deployment=kwargs["litellm_params"])
+    lowest_tpm_logger.pre_call_check(deployment=litellm_deployment_dict)
     lowest_tpm_logger.log_success_event(
         response_obj=response_obj,
         kwargs=kwargs,
@@ -74,8 +82,8 @@ def test_tpm_rpm_updated():
     )
     dt = get_utc_datetime()
     current_minute = dt.strftime("%H-%M")
-    tpm_count_api_key = f"{deployment_id}:tpm:{current_minute}"
+    tpm_count_api_key = f"{deployment_id}:{deployment}:tpm:{current_minute}"
-    rpm_count_api_key = f"{deployment_id}:rpm:{current_minute}"
+    rpm_count_api_key = f"{deployment_id}:{deployment}:rpm:{current_minute}"
 
     print(f"tpm_count_api_key={tpm_count_api_key}")
     assert response_obj["usage"]["total_tokens"] == test_cache.get_cache(
@@ -113,6 +121,7 @@ def test_get_available_deployments():
     standard_logging_payload["model_group"] = model_group
     standard_logging_payload["model_id"] = deployment_id
     standard_logging_payload["total_tokens"] = total_tokens
+    standard_logging_payload["hidden_params"]["litellm_model_name"] = deployment
     kwargs = {
         "litellm_params": {
             "metadata": {
@@ -135,10 +144,11 @@ def test_get_available_deployments():
     ## DEPLOYMENT 2 ##
     total_tokens = 20
     deployment_id = "5678"
-    standard_logging_payload = create_standard_logging_payload()
+    standard_logging_payload: StandardLoggingPayload = create_standard_logging_payload()
     standard_logging_payload["model_group"] = model_group
     standard_logging_payload["model_id"] = deployment_id
     standard_logging_payload["total_tokens"] = total_tokens
+    standard_logging_payload["hidden_params"]["litellm_model_name"] = deployment
     kwargs = {
         "litellm_params": {
             "metadata": {
@@ -209,11 +219,12 @@ def test_router_get_available_deployments():
     print(f"router id's: {router.get_model_ids()}")
     ## DEPLOYMENT 1 ##
     deployment_id = 1
-    standard_logging_payload = create_standard_logging_payload()
+    standard_logging_payload: StandardLoggingPayload = create_standard_logging_payload()
     standard_logging_payload["model_group"] = "azure-model"
     standard_logging_payload["model_id"] = str(deployment_id)
     total_tokens = 50
     standard_logging_payload["total_tokens"] = total_tokens
+    standard_logging_payload["hidden_params"]["litellm_model_name"] = "azure/gpt-turbo"
     kwargs = {
         "litellm_params": {
             "metadata": {
@@ -237,6 +248,9 @@ def test_router_get_available_deployments():
     standard_logging_payload = create_standard_logging_payload()
     standard_logging_payload["model_group"] = "azure-model"
     standard_logging_payload["model_id"] = str(deployment_id)
+    standard_logging_payload["hidden_params"][
+        "litellm_model_name"
+    ] = "azure/gpt-35-turbo"
     kwargs = {
         "litellm_params": {
             "metadata": {
@@ -293,10 +307,11 @@ def test_router_skip_rate_limited_deployments():
     ## DEPLOYMENT 1 ##
     deployment_id = 1
     total_tokens = 1439
-    standard_logging_payload = create_standard_logging_payload()
+    standard_logging_payload: StandardLoggingPayload = create_standard_logging_payload()
     standard_logging_payload["model_group"] = "azure-model"
     standard_logging_payload["model_id"] = str(deployment_id)
     standard_logging_payload["total_tokens"] = total_tokens
+    standard_logging_payload["hidden_params"]["litellm_model_name"] = "azure/gpt-turbo"
     kwargs = {
         "litellm_params": {
             "metadata": {
@@ -699,3 +714,54 @@ def test_return_potential_deployments():
     )
 
     assert len(potential_deployments) == 1
+
+
+@pytest.mark.asyncio
+async def test_tpm_rpm_routing_model_name_checks():
+    deployment = {
+        "model_name": "gpt-3.5-turbo",
+        "litellm_params": {
+            "model": "azure/chatgpt-v-2",
+            "api_key": os.getenv("AZURE_API_KEY"),
+            "api_base": os.getenv("AZURE_API_BASE"),
+            "mock_response": "Hey, how's it going?",
+        },
+    }
+    router = Router(model_list=[deployment], routing_strategy="usage-based-routing-v2")
+
+    async def side_effect_pre_call_check(*args, **kwargs):
+        return args[0]
+
+    with patch.object(
+        router.lowesttpm_logger_v2,
+        "async_pre_call_check",
+        side_effect=side_effect_pre_call_check,
+    ) as mock_object, patch.object(
+        router.lowesttpm_logger_v2, "async_log_success_event"
+    ) as mock_logging_event:
+        response = await router.acompletion(
+            model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey!"}]
+        )
+
+        mock_object.assert_called()
+        print(f"mock_object.call_args: {mock_object.call_args[0][0]}")
+        assert (
+            mock_object.call_args[0][0]["litellm_params"]["model"]
+            == deployment["litellm_params"]["model"]
+        )
+
+        await asyncio.sleep(1)
+
+        mock_logging_event.assert_called()
+
+        print(f"mock_logging_event: {mock_logging_event.call_args.kwargs}")
+        standard_logging_payload: StandardLoggingPayload = (
+            mock_logging_event.call_args.kwargs.get("kwargs", {}).get(
+                "standard_logging_object"
+            )
+        )
+
+        assert (
+            standard_logging_payload["hidden_params"]["litellm_model_name"]
+            == "azure/chatgpt-v-2"
+        )

tests/logging_callback_tests/test_arize_logging.py

@@ -15,35 +15,6 @@ import litellm
 from litellm.types.utils import Choices
 
 
-def test_arize_callback():
-    litellm.callbacks = ["arize"]
-    os.environ["ARIZE_SPACE_KEY"] = "test_space_key"
-    os.environ["ARIZE_API_KEY"] = "test_api_key"
-    os.environ["ARIZE_ENDPOINT"] = "https://otlp.arize.com/v1"
-
-    # Set the batch span processor to quickly flush after a span has been added
-    # This is to ensure that the span is exported before the test ends
-    os.environ["OTEL_BSP_MAX_QUEUE_SIZE"] = "1"
-    os.environ["OTEL_BSP_MAX_EXPORT_BATCH_SIZE"] = "1"
-    os.environ["OTEL_BSP_SCHEDULE_DELAY_MILLIS"] = "1"
-    os.environ["OTEL_BSP_EXPORT_TIMEOUT_MILLIS"] = "5"
-
-    with patch.object(
-        opentelemetry.exporter.otlp.proto.grpc.trace_exporter.OTLPSpanExporter,
-        "export",
-        new=Mock(),
-    ) as patched_export:
-        completion(
-            model="openai/test-model",
-            messages=[{"role": "user", "content": "arize test content"}],
-            stream=False,
-            mock_response="hello there!",
-        )
-
-        time.sleep(1)  # Wait for the batch span processor to flush
-        assert patched_export.called
-
-
 def test_arize_set_attributes():
     """
     Test setting attributes for Arize