test: refactor testing to handle routing correctly

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_key = "00xxx"
-    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"
-    messages = [
-        {"role": "user", "content": "hi"},
-        {"role": "assistant", "content": "Hello! How can I assist you today?"},
-        {"role": "user", "content": "hi"},
-    ]
+    api_base, dynamic_api_key, custom_llm_provider = (
+        config._get_openai_compatible_provider_info(
+            model="azure_ai/gpt-4o-mini",
+            api_base="https://my-base",
+            api_key="my-key",
+            custom_llm_provider="azure_ai",
+        )
+    )
 
-    if is_async:
-        # 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
+    assert custom_llm_provider == "azure"

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,
+    )