langchain Architecture Docs
Home / Function/ test__format_messages_with_tool_calls() — langchain Function Reference

test__format_messages_with_tool_calls() — langchain Function Reference

Architecture documentation for the test__format_messages_with_tool_calls() function in test_chat_models.py from the langchain codebase.

Function python LangChainCore LanguageModelBase

Entity Profile

Dependency Diagram

graph TD
  caf73c73_7793_c348_561c_9c429ec2a8f8["test__format_messages_with_tool_calls()"]
  18428dc5_a41b_90c6_88ad_615296ee3311["test_chat_models.py"]
  caf73c73_7793_c348_561c_9c429ec2a8f8 -->|defined in| 18428dc5_a41b_90c6_88ad_615296ee3311
  style caf73c73_7793_c348_561c_9c429ec2a8f8 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/anthropic/tests/unit_tests/test_chat_models.py lines 530–670

def test__format_messages_with_tool_calls() -> None:
    system = SystemMessage("fuzz")  # type: ignore[misc]
    human = HumanMessage("foo")  # type: ignore[misc]
    ai = AIMessage(
        "",  # with empty string
        tool_calls=[{"name": "bar", "id": "1", "args": {"baz": "buzz"}}],
    )
    ai2 = AIMessage(
        [],  # with empty list
        tool_calls=[{"name": "bar", "id": "2", "args": {"baz": "buzz"}}],
    )
    tool = ToolMessage(
        "blurb",
        tool_call_id="1",
    )
    tool_image_url = ToolMessage(
        [{"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,...."}}],
        tool_call_id="2",
    )
    tool_image = ToolMessage(
        [
            {
                "type": "image",
                "source": {
                    "data": "....",
                    "type": "base64",
                    "media_type": "image/jpeg",
                },
            },
        ],
        tool_call_id="3",
    )
    messages = [system, human, ai, tool, ai2, tool_image_url, tool_image]
    expected = (
        "fuzz",
        [
            {"role": "user", "content": "foo"},
            {
                "role": "assistant",
                "content": [
                    {
                        "type": "tool_use",
                        "name": "bar",
                        "id": "1",
                        "input": {"baz": "buzz"},
                    },
                ],
            },
            {
                "role": "user",
                "content": [
                    {
                        "type": "tool_result",
                        "content": "blurb",
                        "tool_use_id": "1",
                        "is_error": False,
                    },
                ],
            },
            {
                "role": "assistant",
                "content": [
                    {
                        "type": "tool_use",
                        "name": "bar",
                        "id": "2",
                        "input": {"baz": "buzz"},
                    },
                ],
            },
            {
                "role": "user",
                "content": [
                    {
                        "type": "tool_result",
                        "content": [
                            {
                                "type": "image",
                                "source": {
                                    "data": "....",
                                    "type": "base64",

Domain

Subdomains

Defined In

Source

Frequently Asked Questions

What does test__format_messages_with_tool_calls() do?
test__format_messages_with_tool_calls() is a function in the langchain codebase, defined in libs/partners/anthropic/tests/unit_tests/test_chat_models.py.
Where is test__format_messages_with_tool_calls() defined?
test__format_messages_with_tool_calls() is defined in libs/partners/anthropic/tests/unit_tests/test_chat_models.py at line 530.

Analyze Your Own Codebase

Get architecture documentation, dependency graphs, and domain analysis for your codebase in minutes.

Try Supermodel Free