chore: import upstream snapshot with attribution

tests/integration/models/test_litellm_no_function.py

@@ -0,0 +1,165 @@
+# Copyright 2026 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.lite_llm import LiteLlm
+from google.adk.models.llm_request import LlmRequest
+from google.adk.models.llm_response import LlmResponse
+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-3.1-405b-instruct-maas"
+
+_SYSTEM_PROMPT = """You are a helpful assistant."""
+
+
+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():
+  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.fixture
+def llm_request_with_tools():
+  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,
+          tools=[
+              types.Tool(
+                  function_declarations=[
+                      types.FunctionDeclaration(
+                          name="get_weather",
+                          description="Get the weather in a given location",
+                          parameters=types.Schema(
+                              type=types.Type.OBJECT,
+                              properties={
+                                  "city": types.Schema(
+                                      type=types.Type.STRING,
+                                      description=(
+                                          "The city to get the weather for."
+                                      ),
+                                  ),
+                              },
+                              required=["city"],
+                          ),
+                      )
+                  ]
+              )
+          ],
+      ),
+  )
+
+
+@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
+
+
+@pytest.mark.asyncio
+async def test_generate_content_async(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
+
+
+@pytest.mark.asyncio
+async def test_generate_content_async_with_tools(
+    oss_llm, llm_request_with_tools
+):
+  responses = [
+      resp
+      async for resp in oss_llm.generate_content_async(
+          llm_request_with_tools, stream=False
+      )
+  ]
+  function_call = responses[0].content.parts[0].function_call
+  assert function_call.name == "get_weather"
+  assert function_call.args["city"] == "San Francisco"
+
+
+@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=True)
+  ]
+  text = ""
+  for i in range(len(responses) - 1):
+    assert responses[i].partial is True
+    assert responses[i].content.parts[0].text
+    text += responses[i].content.parts[0].text
+
+  # Last message should be accumulated text
+  assert responses[-1].content.parts[0].text == text
+  assert not responses[-1].partial
+
+
+@pytest.mark.asyncio
+async def test_generate_content_async_stream_with_tools(
+    oss_llm, llm_request_with_tools
+):
+  responses = [
+      resp
+      async for resp in oss_llm.generate_content_async(
+          llm_request_with_tools, stream=True
+      )
+  ]
+  function_call = responses[-1].content.parts[0].function_call
+  assert function_call.name == "get_weather"
+  assert function_call.args["city"] == "San Francisco"