test(test_gemini.py): add additional testing for additionalproperties case

Author: krrishdholakia

litellm/types/llms/vertex_ai.py

@@ -113,7 +113,6 @@ class Schema(TypedDict, total=False):
     pattern: str
     example: Any
     anyOf: List["Schema"]
-    additionalProperties: Any
 
 
 class FunctionDeclaration(TypedDict, total=False):

tests/llm_translation/test_gemini.py

@@ -269,7 +269,11 @@ def test_gemini_image_generation():
     assert len(response.choices[0].message.images) > 0
     assert response.choices[0].message.images[0]["image_url"] is not None
     assert response.choices[0].message.images[0]["image_url"]["url"] is not None
-    assert response.choices[0].message.images[0]["image_url"]["url"].startswith("data:image/png;base64,")
+    assert (
+        response.choices[0]
+        .message.images[0]["image_url"]["url"]
+        .startswith("data:image/png;base64,")
+    )
 
 
 def test_gemini_thinking():
@@ -661,7 +665,8 @@ def test_system_message_with_no_user_message():
     assert response is not None
 
     assert response.choices[0].message.content is not None
-  
+
+
 def get_current_weather(location, unit="fahrenheit"):
     """Get the current weather in a given location"""
     if "tokyo" in location.lower():
@@ -778,9 +783,9 @@ def test_gemini_reasoning_effort_minimal():
 
     # Test with different Gemini models to verify model-specific mapping
     test_cases = [
-        ("gemini/gemini-2.5-flash", 1),      # Flash: minimum 1 token
-        ("gemini/gemini-2.5-pro", 128),      # Pro: minimum 128 tokens  
-        ("gemini/gemini-2.5-flash-lite", 512), # Flash-Lite: minimum 512 tokens
+        ("gemini/gemini-2.5-flash", 1),  # Flash: minimum 1 token
+        ("gemini/gemini-2.5-pro", 128),  # Pro: minimum 128 tokens
+        ("gemini/gemini-2.5-flash-lite", 512),  # Flash-Lite: minimum 512 tokens
     ]
 
     for model, expected_min_budget in test_cases:
@@ -793,24 +798,32 @@ def test_gemini_reasoning_effort_minimal():
                 "reasoning_effort": "minimal",
             },
         )
-        
+
         # Verify that the thinking config is set correctly
         request_body = raw_request["raw_request_body"]
-        assert "generationConfig" in request_body, f"Model {model} should have generationConfig"
-        
+        assert (
+            "generationConfig" in request_body
+        ), f"Model {model} should have generationConfig"
+
         generation_config = request_body["generationConfig"]
-        assert "thinkingConfig" in generation_config, f"Model {model} should have thinkingConfig"
-        
+        assert (
+            "thinkingConfig" in generation_config
+        ), f"Model {model} should have thinkingConfig"
+
         thinking_config = generation_config["thinkingConfig"]
-        assert "thinkingBudget" in thinking_config, f"Model {model} should have thinkingBudget"
-        
+        assert (
+            "thinkingBudget" in thinking_config
+        ), f"Model {model} should have thinkingBudget"
+
         actual_budget = thinking_config["thinkingBudget"]
-        assert actual_budget == expected_min_budget, \
-            f"Model {model} should map 'minimal' to {expected_min_budget} tokens, got {actual_budget}"
-        
+        assert (
+            actual_budget == expected_min_budget
+        ), f"Model {model} should map 'minimal' to {expected_min_budget} tokens, got {actual_budget}"
+
         # Verify that includeThoughts is True for minimal reasoning effort
-        assert thinking_config.get("includeThoughts", True), \
-            f"Model {model} should have includeThoughts=True for minimal reasoning effort"
+        assert thinking_config.get(
+            "includeThoughts", True
+        ), f"Model {model} should have includeThoughts=True for minimal reasoning effort"
 
     # Test with unknown model (should use generic fallback)
     try:
@@ -822,15 +835,41 @@ def test_gemini_reasoning_effort_minimal():
                 "reasoning_effort": "minimal",
             },
         )
-        
+
         request_body = raw_request["raw_request_body"]
         generation_config = request_body["generationConfig"]
         thinking_config = generation_config["thinkingConfig"]
         # Should use generic fallback (128 tokens)
-        assert thinking_config["thinkingBudget"] == 128, \
-            "Unknown model should use generic fallback of 128 tokens"
+        assert (
+            thinking_config["thinkingBudget"] == 128
+        ), "Unknown model should use generic fallback of 128 tokens"
     except Exception as e:
         # If return_raw_request doesn't work for unknown models, that's okay
         # The important part is that our known models work correctly
         print(f"Note: Unknown model test skipped due to: {e}")
         pass
+
+
+def test_gemini_additional_properties_bug():
+    # Simple tool with additionalProperties (simulating the TypedDict issue)
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "test_tool",
+                "description": "Test tool",
+                "parameters": {
+                    "type": "object",
+                    "properties": {"param1": {"type": "string"}},
+                    # This causes the error - any non-False value
+                    "additionalProperties": True,  # Could also be None, {}, etc.
+                },
+            },
+        }
+    ]
+
+    messages = [{"role": "user", "content": "Test message"}]
+
+    response = litellm.completion(
+        model="gemini/gemini-2.5-flash", messages=messages, tools=tools
+    )

tests/local_testing/test_amazing_vertex_completion.py

@@ -397,7 +397,7 @@ async def test_async_vertexai_response():
         | litellm.vertex_text_models
         | litellm.vertex_code_text_models
     )
-    
+
     test_models = random.sample(list(test_models), 1)
     test_models += list(litellm.vertex_language_models)  # always test gemini-pro
     for model in test_models:
@@ -504,7 +504,6 @@ async def test_async_vertexai_streaming_response():
             pytest.fail(f"An exception occurred: {e}")
 
 
-
 @pytest.mark.parametrize("load_pdf", [False])  # True,
 @pytest.mark.flaky(retries=3, delay=1)
 def test_completion_function_plus_pdf(load_pdf):
@@ -547,6 +546,7 @@ def test_completion_function_plus_pdf(load_pdf):
     except Exception as e:
         pytest.fail("Got={}".format(str(e)))
 
+
 def encode_image(image_path):
     import base64
 
@@ -910,7 +910,10 @@ async def test_partner_models_httpx(model, region, sync_mode):
     [
         ("vertex_ai/meta/llama-4-scout-17b-16e-instruct-maas", "us-east5"),
         ("vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas", "us-south1"),
-        ("vertex_ai/mistral-large-2411", "us-central1"), # critical - we had this issue: https://github.com/BerriAI/litellm/issues/13888
+        (
+            "vertex_ai/mistral-large-2411",
+            "us-central1",
+        ),  # critical - we had this issue: https://github.com/BerriAI/litellm/issues/13888
         ("vertex_ai/openai/gpt-oss-20b-maas", "us-central1"),
     ],
 )
@@ -3827,7 +3830,7 @@ def test_vertex_ai_gemini_audio_ogg():
 @pytest.mark.asyncio
 async def test_vertex_ai_deepseek():
     """Test that deepseek models use the correct v1 API endpoint instead of v1beta1."""
-    #load_vertex_ai_credentials()
+    # load_vertex_ai_credentials()
     litellm._turn_on_debug()
     from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
 
@@ -3840,21 +3843,17 @@ async def test_vertex_ai_deepseek():
             {
                 "message": {
                     "role": "assistant",
-                    "content": "Hello! How can I help you today?"
+                    "content": "Hello! How can I help you today?",
                 },
                 "index": 0,
-                "finish_reason": "stop"
+                "finish_reason": "stop",
             }
         ],
-        "usage": {
-            "prompt_tokens": 10,
-            "completion_tokens": 20,
-            "total_tokens": 30
-        },
-        "model": "deepseek-ai/deepseek-r1-0528-maas"
+        "usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30},
+        "model": "deepseek-ai/deepseek-r1-0528-maas",
     }
     mock_response.status_code = 200
-    
+
     with patch.object(client, "post", return_value=mock_response) as mock_post:
         response = await acompletion(
             model="vertex_ai/deepseek-ai/deepseek-r1-0528-maas",
@@ -3900,3 +3899,28 @@ def test_gemini_grounding_on_streaming():
             vertex_ai_grounding_metadata_shows_up = True
         print(chunk)
     assert vertex_ai_grounding_metadata_shows_up
+
+
+def test_gemini_additional_properties_bug():
+    # Simple tool with additionalProperties (simulating the TypedDict issue)
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "test_tool",
+                "description": "Test tool",
+                "parameters": {
+                    "type": "object",
+                    "properties": {"param1": {"type": "string"}},
+                    # This causes the error - any non-False value
+                    "additionalProperties": True,  # Could also be None, {}, etc.
+                },
+            },
+        }
+    ]
+
+    messages = [{"role": "user", "content": "Test message"}]
+
+    response = litellm.completion(
+        model="gemini/gemini-2.5-flash", messages=messages, tools=tools
+    )