feat: Update for anthropic models

Enable parallel tools for anthropic models, and add agent examples, and also added functional test for anthropic models.

PiperOrigin-RevId: 766703018

tests/unittests/models/test_anthropic_llm.py

@@ -0,0 +1,124 @@
+# 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.
+
+import os
+import sys
+from unittest import mock
+
+from anthropic import types as anthropic_types
+from google.adk import version as adk_version
+from google.adk.models import anthropic_llm
+from google.adk.models.anthropic_llm import Claude
+from google.adk.models.llm_request import LlmRequest
+from google.adk.models.llm_response import LlmResponse
+from google.genai import types
+from google.genai import version as genai_version
+from google.genai.types import Content
+from google.genai.types import Part
+import pytest
+
+
+@pytest.fixture
+def generate_content_response():
+  return anthropic_types.Message(
+      id="msg_vrtx_testid",
+      content=[
+          anthropic_types.TextBlock(
+              citations=None, text="Hi! How can I help you today?", type="text"
+          )
+      ],
+      model="claude-3-5-sonnet-v2-20241022",
+      role="assistant",
+      stop_reason="end_turn",
+      stop_sequence=None,
+      type="message",
+      usage=anthropic_types.Usage(
+          cache_creation_input_tokens=0,
+          cache_read_input_tokens=0,
+          input_tokens=13,
+          output_tokens=12,
+          server_tool_use=None,
+          service_tier=None,
+      ),
+  )
+
+
+@pytest.fixture
+def generate_llm_response():
+  return LlmResponse.create(
+      types.GenerateContentResponse(
+          candidates=[
+              types.Candidate(
+                  content=Content(
+                      role="model",
+                      parts=[Part.from_text(text="Hello, how can I help you?")],
+                  ),
+                  finish_reason=types.FinishReason.STOP,
+              )
+          ]
+      )
+  )
+
+
+@pytest.fixture
+def claude_llm():
+  return Claude(model="claude-3-5-sonnet-v2@20241022")
+
+
+@pytest.fixture
+def llm_request():
+  return LlmRequest(
+      model="claude-3-5-sonnet-v2@20241022",
+      contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
+      config=types.GenerateContentConfig(
+          temperature=0.1,
+          response_modalities=[types.Modality.TEXT],
+          system_instruction="You are a helpful assistant",
+      ),
+  )
+
+
+def test_supported_models():
+  models = Claude.supported_models()
+  assert len(models) == 2
+  assert models[0] == r"claude-3-.*"
+  assert models[1] == r"claude-.*-4.*"
+
+
+@pytest.mark.asyncio
+async def test_generate_content_async(
+    claude_llm, llm_request, generate_content_response, generate_llm_response
+):
+  with mock.patch.object(claude_llm, "_anthropic_client") as mock_client:
+    with mock.patch.object(
+        anthropic_llm,
+        "message_to_generate_content_response",
+        return_value=generate_llm_response,
+    ):
+      # Create a mock coroutine that returns the generate_content_response.
+      async def mock_coro():
+        return generate_content_response
+
+      # Assign the coroutine to the mocked method
+      mock_client.messages.create.return_value = mock_coro()
+
+      responses = [
+          resp
+          async for resp in claude_llm.generate_content_async(
+              llm_request, stream=False
+          )
+      ]
+      assert len(responses) == 1
+      assert isinstance(responses[0], LlmResponse)
+      assert responses[0].content.parts[0].text == "Hello, how can I help you?"