standard-tests: split tool calling test (#20803)

just making it a bit easier to grok

libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py

@@ -117,13 +117,16 @@ class ChatModelIntegrationTests(ABC):
             assert isinstance(result.content, str)
             assert len(result.content) > 0
 
-    def test_tool_message_histories(
+    def test_tool_message_histories_string_content(
         self,
         chat_model_class: Type[BaseChatModel],
         chat_model_params: dict,
         chat_model_has_tool_calling: bool,
     ) -> None:
-        """Test that message histories are compatible across providers."""
+        """
+        Test that message histories are compatible with string tool contents
+        (e.g. OpenAI).
+        """
         if not chat_model_has_tool_calling:
             pytest.skip("Test requires tool calling.")
         model = chat_model_class(**chat_model_params)
@@ -131,55 +134,71 @@ class ChatModelIntegrationTests(ABC):
         function_name = "my_adder_tool"
         function_args = {"a": "1", "b": "2"}
 
-        human_message = HumanMessage(content="What is 1 + 2")
-        tool_message = ToolMessage(
-            name=function_name,
-            content=json.dumps({"result": 3}),
-            tool_call_id="abc123",
-        )
-
-        # String content (e.g., OpenAI)
-        string_content_msg = AIMessage(
-            content="",
-            tool_calls=[
-                {
-                    "name": function_name,
-                    "args": function_args,
-                    "id": "abc123",
-                },
-            ],
-        )
-        messages = [
-            human_message,
-            string_content_msg,
-            tool_message,
+        messages_string_content = [
+            HumanMessage(content="What is 1 + 2"),
+            # string content (e.g. OpenAI)
+            AIMessage(
+                content="",
+                tool_calls=[
+                    {
+                        "name": function_name,
+                        "args": function_args,
+                        "id": "abc123",
+                    },
+                ],
+            ),
+            ToolMessage(
+                name=function_name,
+                content=json.dumps({"result": 3}),
+                tool_call_id="abc123",
+            ),
         ]
-        result = model_with_tools.invoke(messages)
-        assert isinstance(result, AIMessage)
+        result_string_content = model_with_tools.invoke(messages_string_content)
+        assert isinstance(result_string_content, AIMessage)
 
-        # List content (e.g., Anthropic)
-        list_content_msg = AIMessage(
-            content=[
-                {"type": "text", "text": "some text"},
-                {
-                    "type": "tool_use",
-                    "id": "abc123",
-                    "name": function_name,
-                    "input": function_args,
-                },
-            ],
-            tool_calls=[
-                {
-                    "name": function_name,
-                    "args": function_args,
-                    "id": "abc123",
-                },
-            ],
-        )
-        messages = [
-            human_message,
-            list_content_msg,
-            tool_message,
+    def test_tool_message_histories_list_content(
+        self,
+        chat_model_class: Type[BaseChatModel],
+        chat_model_params: dict,
+        chat_model_has_tool_calling: bool,
+    ) -> None:
+        """
+        Test that message histories are compatible with list tool contents
+        (e.g. Anthropic).
+        """
+        if not chat_model_has_tool_calling:
+            pytest.skip("Test requires tool calling.")
+        model = chat_model_class(**chat_model_params)
+        model_with_tools = model.bind_tools([my_adder_tool])
+        function_name = "my_adder_tool"
+        function_args = {"a": 1, "b": 2}
+
+        messages_list_content = [
+            HumanMessage(content="What is 1 + 2"),
+            # List content (e.g., Anthropic)
+            AIMessage(
+                content=[
+                    {"type": "text", "text": "some text"},
+                    {
+                        "type": "tool_use",
+                        "id": "abc123",
+                        "name": function_name,
+                        "input": function_args,
+                    },
+                ],
+                tool_calls=[
+                    {
+                        "name": function_name,
+                        "args": function_args,
+                        "id": "abc123",
+                    },
+                ],
+            ),
+            ToolMessage(
+                name=function_name,
+                content=json.dumps({"result": 3}),
+                tool_call_id="abc123",
+            ),
         ]
-        result = model_with_tools.invoke(messages)
-        assert isinstance(result, AIMessage)
+        result_list_content = model_with_tools.invoke(messages_list_content)
+        assert isinstance(result_list_content, AIMessage)