vertexai: refactor: simplify content processing in anthropic formatter

libs/vertexai/langchain_google_vertexai/_anthropic_parsers.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Optional, Type
+from typing import Any, List, Optional, Type, Union
 
 from langchain_core.messages import AIMessage, ToolCall
 from langchain_core.messages.tool import tool_call
@@ -55,11 +55,18 @@ def _pydantic_parse(self, tool_call: dict) -> BaseModel:
         return cls_(**tool_call["args"])
 
 
-def _extract_tool_calls(content: List[dict]) -> List[ToolCall]:
-    tool_calls = []
-    for block in content:
-        if block["type"] == "tool_use":
+def _extract_tool_calls(content: Union[str, List[Union[str, dict]]]) -> List[ToolCall]:
+    """Extract tool calls from a list of content blocks."""
+    if isinstance(content, list):
+        tool_calls = []
+        for block in content:
+            if isinstance(block, str):
+                continue
+            if block["type"] != "tool_use":
+                continue
             tool_calls.append(
                 tool_call(name=block["name"], args=block["input"], id=block["id"])
             )
-    return tool_calls
+        return tool_calls
+    else:
+        return []

libs/vertexai/langchain_google_vertexai/model_garden.py

@@ -153,15 +153,20 @@ def validate_environment(self) -> Self:
             AsyncAnthropicVertex,
         )
 
+        if self.project is None:
+            raise ValueError("project is required for ChatAnthropicVertex")
+
+        project_id: str = self.project
+
         self.client = AnthropicVertex(
-            project_id=self.project,
+            project_id=project_id,
             region=self.location,
             max_retries=self.max_retries,
             access_token=self.access_token,
             credentials=self.credentials,
         )
         self.async_client = AsyncAnthropicVertex(
-            project_id=self.project,
+            project_id=project_id,
             region=self.location,
             max_retries=self.max_retries,
             access_token=self.access_token,
@@ -205,14 +210,18 @@ def _format_params(
 
     def _format_output(self, data: Any, **kwargs: Any) -> ChatResult:
         data_dict = data.model_dump()
-        content = [c for c in data_dict["content"] if c["type"] != "tool_use"]
-        content = content[0]["text"] if len(content) == 1 else content
+        content = data_dict["content"]
         llm_output = {
             k: v for k, v in data_dict.items() if k not in ("content", "role", "type")
         }
-        tool_calls = _extract_tool_calls(data_dict["content"])
-        if tool_calls:
-            msg = AIMessage(content=content, tool_calls=tool_calls)
+        if len(content) == 1 and content[0]["type"] == "text":
+            msg = AIMessage(content=content[0]["text"])
+        elif any(block["type"] == "tool_use" for block in content):
+            tool_calls = _extract_tool_calls(content)
+            msg = AIMessage(
+                content=content,
+                tool_calls=tool_calls,
+            )
         else:
             msg = AIMessage(content=content)
         # Collect token usage

libs/vertexai/pyproject.toml

@@ -42,6 +42,7 @@ numpy = [
 google-api-python-client = "^2.117.0"
 langchain = "^0.3.7"
 langchain-tests = "0.3.1"
+anthropic = { extras = ["vertexai"], version = ">=0.35.0,<1" }
 
 
 [tool.codespell]

libs/vertexai/tests/integration_tests/test_model_garden.py

@@ -168,7 +168,7 @@ async def test_anthropic_async() -> None:
 def _check_tool_calls(response: BaseMessage, expected_name: str) -> None:
     """Check tool calls are as expected."""
     assert isinstance(response, AIMessage)
-    assert isinstance(response.content, str)
+    assert isinstance(response.content, list)
     tool_calls = response.tool_calls
     assert len(tool_calls) == 1
     tool_call = tool_calls[0]

libs/vertexai/tests/unit_tests/test_chat_models.py

@@ -41,6 +41,7 @@
     _parse_examples,
     _parse_response_candidate,
 )
+from langchain_google_vertexai.model_garden import ChatAnthropicVertex
 
 
 def test_init() -> None:
@@ -1067,3 +1068,49 @@ def test_init_client_with_custom_api() -> None:
         transport = mock_prediction_service.call_args.kwargs["transport"]
         assert client_options.api_endpoint == "https://example.com"
         assert transport == "rest"
+
+
+def test_anthropic_format_output() -> None:
+    """Test format output handles different content structures correctly."""
+
+    @dataclass
+    class Usage:
+        input_tokens: int
+        output_tokens: int
+
+    @dataclass
+    class Message:
+        def model_dump(self):
+            return {
+                "content": [
+                    {
+                        "type": "tool_use",
+                        "id": "123",
+                        "name": "calculator",
+                        "input": {"number": 42},
+                    }
+                ],
+                "model": "baz",
+                "role": "assistant",
+                "usage": Usage(input_tokens=2, output_tokens=1),
+                "type": "message",
+            }
+
+        usage: Usage
+
+    test_msg = Message(usage=Usage(input_tokens=2, output_tokens=1))
+
+    model = ChatAnthropicVertex(project="test-project", location="test-location")
+    result = model._format_output(test_msg)
+
+    message = result.generations[0].message
+    assert isinstance(message, AIMessage)
+    assert message.content == test_msg.model_dump()["content"]
+    assert len(message.tool_calls) == 1
+    assert message.tool_calls[0]["name"] == "calculator"
+    assert message.tool_calls[0]["args"] == {"number": 42}
+    assert message.usage_metadata == {
+        "input_tokens": 2,
+        "output_tokens": 1,
+        "total_tokens": 3,
+    }