feat: Add integration tests for litellm with and without turn on add_function_to_prompt

Add experiments for https://github.com/google/adk-python/issues/1273

PiperOrigin-RevId: 771145715

tests/integration/models/test_litellm_no_function.py

@@ -0,0 +1,65 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from google.adk.models import LlmRequest
+from google.adk.models import LlmResponse
+from google.adk.models.lite_llm import LiteLlm
+from google.genai import types
+from google.genai.types import Content
+from google.genai.types import Part
+import pytest
+
+_TEST_MODEL_NAME = "vertex_ai/meta/llama-4-maverick-17b-128e-instruct-maas"
+
+
+_SYSTEM_PROMPT = """You are a helpful assistant."""
+
+
+@pytest.fixture
+def oss_llm():
+  return LiteLlm(model=_TEST_MODEL_NAME)
+
+
+@pytest.fixture
+def llm_request():
+  return LlmRequest(
+      model=_TEST_MODEL_NAME,
+      contents=[Content(role="user", parts=[Part.from_text(text="hello")])],
+      config=types.GenerateContentConfig(
+          temperature=0.1,
+          response_modalities=[types.Modality.TEXT],
+          system_instruction=_SYSTEM_PROMPT,
+      ),
+  )
+
+
+@pytest.mark.asyncio
+async def test_generate_content_async(oss_llm, llm_request):
+  async for response in oss_llm.generate_content_async(llm_request):
+    assert isinstance(response, LlmResponse)
+    assert response.content.parts[0].text
+
+
+# Note that, this test disabled streaming because streaming is not supported
+# properly in the current test model for now.
+@pytest.mark.asyncio
+async def test_generate_content_async_stream(oss_llm, llm_request):
+  responses = [
+      resp
+      async for resp in oss_llm.generate_content_async(
+          llm_request, stream=False
+      )
+  ]
+  part = responses[0].content.parts[0]
+  assert len(part.text) > 0

tests/integration/models/test_litellm_with_function.py

@@ -0,0 +1,104 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from google.adk.models import LlmRequest
+from google.adk.models import LlmResponse
+from google.adk.models.lite_llm import LiteLlm
+from google.genai import types
+from google.genai.types import Content
+from google.genai.types import Part
+import litellm
+import pytest
+
+litellm.add_function_to_prompt = True
+
+_TEST_MODEL_NAME = "vertex_ai/meta/llama-4-maverick-17b-128e-instruct-maas"
+
+
+_SYSTEM_PROMPT = """
+You are a helpful assistant, and call tools optionally.
+If call tools, the tool format should be in json, and the tool arguments should be parsed from users inputs.
+"""
+
+
+_FUNCTIONS = [{
+    "name": "get_weather",
+    "description": "Get the weather in a given location",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "city": {
+                "type": "string",
+                "description": "The city, e.g. San Francisco",
+            },
+        },
+        "required": ["city"],
+    },
+}]
+
+
+def get_weather(city: str) -> str:
+  """Simulates a web search. Use it get information on weather.
+
+  Args:
+      city: A string containing the location to get weather information for.
+
+  Returns:
+      A string with the simulated weather information for the queried city.
+  """
+  if "sf" in city.lower() or "san francisco" in city.lower():
+    return "It's 70 degrees and foggy."
+  return "It's 80 degrees and sunny."
+
+
+@pytest.fixture
+def oss_llm_with_function():
+  return LiteLlm(model=_TEST_MODEL_NAME, functions=_FUNCTIONS)
+
+
+@pytest.fixture
+def llm_request():
+  return LlmRequest(
+      model=_TEST_MODEL_NAME,
+      contents=[
+          Content(
+              role="user",
+              parts=[
+                  Part.from_text(text="What is the weather in San Francisco?")
+              ],
+          )
+      ],
+      config=types.GenerateContentConfig(
+          temperature=0.1,
+          response_modalities=[types.Modality.TEXT],
+          system_instruction=_SYSTEM_PROMPT,
+      ),
+  )
+
+
+# Note that, this test disabled streaming because streaming is not supported
+# properly in the current test model for now.
+@pytest.mark.asyncio
+async def test_generate_content_asyn_with_function(
+    oss_llm_with_function, llm_request
+):
+  responses = [
+      resp
+      async for resp in oss_llm_with_function.generate_content_async(
+          llm_request, stream=False
+      )
+  ]
+  function_call = responses[0].content.parts[0].function_call
+  assert function_call.name == "get_weather"
+  assert function_call.args["city"] == "San Francisco"