"""Test chat model integration.""" from __future__ import annotations import os from collections.abc import Callable from typing import Any, Literal, cast from unittest.mock import MagicMock, patch import anthropic import pytest from anthropic.types import Message, TextBlock, Usage from blockbuster import blockbuster_ctx from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage from langchain_core.runnables import RunnableBinding from langchain_core.tools import BaseTool from langchain_core.tracers.base import BaseTracer from langchain_core.tracers.schemas import Run from pydantic import BaseModel, Field, SecretStr, ValidationError from pytest import CaptureFixture, MonkeyPatch from langchain_anthropic import ChatAnthropic from langchain_anthropic.chat_models import ( _create_usage_metadata, _format_image, _format_messages, _is_builtin_tool, _merge_messages, convert_to_anthropic_tool, ) os.environ["ANTHROPIC_API_KEY"] = "foo" MODEL_NAME = "claude-sonnet-4-5-20250929" def test_initialization() -> None: """Test chat model initialization.""" for model in [ ChatAnthropic(model_name=MODEL_NAME, api_key="xyz", timeout=2), # type: ignore[arg-type, call-arg] ChatAnthropic( # type: ignore[call-arg, call-arg, call-arg] model=MODEL_NAME, anthropic_api_key="xyz", default_request_timeout=2, base_url="https://api.anthropic.com", ), ]: assert model.model == MODEL_NAME assert cast("SecretStr", model.anthropic_api_key).get_secret_value() == "xyz" assert model.default_request_timeout == 2.0 assert model.anthropic_api_url == "https://api.anthropic.com" @pytest.mark.parametrize("async_api", [True, False]) def test_streaming_attribute_should_stream(async_api: bool) -> None: # noqa: FBT001 llm = ChatAnthropic(model=MODEL_NAME, streaming=True) assert llm._should_stream(async_api=async_api) def test_anthropic_client_caching() -> None: """Test that the OpenAI client is cached.""" llm1 = ChatAnthropic(model=MODEL_NAME) llm2 = ChatAnthropic(model=MODEL_NAME) assert llm1._client._client is llm2._client._client llm3 = ChatAnthropic(model=MODEL_NAME, base_url="foo") assert llm1._client._client is not llm3._client._client llm4 = ChatAnthropic(model=MODEL_NAME, timeout=None) assert llm1._client._client is llm4._client._client llm5 = ChatAnthropic(model=MODEL_NAME, timeout=3) assert llm1._client._client is not llm5._client._client def test_anthropic_proxy_support() -> None: """Test that both sync and async clients support proxy configuration.""" proxy_url = "http://proxy.example.com:8080" # Test sync client with proxy llm_sync = ChatAnthropic(model=MODEL_NAME, anthropic_proxy=proxy_url) sync_client = llm_sync._client assert sync_client is not None # Test async client with proxy - this should not raise TypeError async_client = llm_sync._async_client assert async_client is not None # Test that clients with different proxy settings are not cached together llm_no_proxy = ChatAnthropic(model=MODEL_NAME) llm_with_proxy = ChatAnthropic(model=MODEL_NAME, anthropic_proxy=proxy_url) # Different proxy settings should result in different cached clients assert llm_no_proxy._client._client is not llm_with_proxy._client._client def test_anthropic_proxy_from_environment() -> None: """Test that proxy can be set from ANTHROPIC_PROXY environment variable.""" proxy_url = "http://env-proxy.example.com:8080" # Test with environment variable set with patch.dict(os.environ, {"ANTHROPIC_PROXY": proxy_url}): llm = ChatAnthropic(model=MODEL_NAME) assert llm.anthropic_proxy == proxy_url # Should be able to create clients successfully sync_client = llm._client async_client = llm._async_client assert sync_client is not None assert async_client is not None # Test that explicit parameter overrides environment variable with patch.dict(os.environ, {"ANTHROPIC_PROXY": "http://env-proxy.com"}): explicit_proxy = "http://explicit-proxy.com" llm = ChatAnthropic(model=MODEL_NAME, anthropic_proxy=explicit_proxy) assert llm.anthropic_proxy == explicit_proxy def test_set_default_max_tokens() -> None: """Test the set_default_max_tokens function.""" # Test claude-sonnet-4-5 models llm = ChatAnthropic(model="claude-sonnet-4-5-20250929", anthropic_api_key="test") assert llm.max_tokens == 64000 # Test claude-opus-4 models llm = ChatAnthropic(model="claude-opus-4-20250514", anthropic_api_key="test") assert llm.max_tokens == 32000 # Test claude-sonnet-4 models llm = ChatAnthropic(model="claude-sonnet-4-20250514", anthropic_api_key="test") assert llm.max_tokens == 64000 # Test claude-3-7-sonnet models llm = ChatAnthropic(model="claude-3-7-sonnet-20250219", anthropic_api_key="test") assert llm.max_tokens == 64000 # Test claude-3-5-haiku models llm = ChatAnthropic(model="claude-3-5-haiku-20241022", anthropic_api_key="test") assert llm.max_tokens == 8192 # Test claude-3-haiku models (should default to 4096) llm = ChatAnthropic(model="claude-3-haiku-20240307", anthropic_api_key="test") assert llm.max_tokens == 4096 # Test that existing max_tokens values are preserved llm = ChatAnthropic(model=MODEL_NAME, max_tokens=2048, anthropic_api_key="test") assert llm.max_tokens == 2048 # Test that explicitly set max_tokens values are preserved llm = ChatAnthropic(model=MODEL_NAME, max_tokens=4096, anthropic_api_key="test") assert llm.max_tokens == 4096 @pytest.mark.requires("anthropic") def test_anthropic_model_name_param() -> None: llm = ChatAnthropic(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg] assert llm.model == MODEL_NAME @pytest.mark.requires("anthropic") def test_anthropic_model_param() -> None: llm = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg] assert llm.model == MODEL_NAME @pytest.mark.requires("anthropic") def test_anthropic_model_kwargs() -> None: llm = ChatAnthropic(model_name=MODEL_NAME, model_kwargs={"foo": "bar"}) # type: ignore[call-arg, call-arg] assert llm.model_kwargs == {"foo": "bar"} @pytest.mark.requires("anthropic") def test_anthropic_fields_in_model_kwargs() -> None: """Test that for backwards compatibility fields can be passed in as model_kwargs.""" llm = ChatAnthropic(model=MODEL_NAME, model_kwargs={"max_tokens_to_sample": 5}) # type: ignore[call-arg] assert llm.max_tokens == 5 llm = ChatAnthropic(model=MODEL_NAME, model_kwargs={"max_tokens": 5}) # type: ignore[call-arg] assert llm.max_tokens == 5 @pytest.mark.requires("anthropic") def test_anthropic_incorrect_field() -> None: with pytest.warns(match="not default parameter"): llm = ChatAnthropic(model=MODEL_NAME, foo="bar") # type: ignore[call-arg, call-arg] assert llm.model_kwargs == {"foo": "bar"} @pytest.mark.requires("anthropic") def test_anthropic_initialization() -> None: """Test anthropic initialization.""" # Verify that chat anthropic can be initialized using a secret key provided # as a parameter rather than an environment variable. ChatAnthropic(model=MODEL_NAME, anthropic_api_key="test") # type: ignore[call-arg, call-arg] def test__format_output() -> None: anthropic_msg = Message( id="foo", content=[TextBlock(type="text", text="bar")], model="baz", role="assistant", stop_reason=None, stop_sequence=None, usage=Usage(input_tokens=2, output_tokens=1), type="message", ) expected = AIMessage( # type: ignore[misc] "bar", usage_metadata={ "input_tokens": 2, "output_tokens": 1, "total_tokens": 3, "input_token_details": {}, }, response_metadata={"model_provider": "anthropic"}, ) llm = ChatAnthropic(model=MODEL_NAME, anthropic_api_key="test") # type: ignore[call-arg, call-arg] actual = llm._format_output(anthropic_msg) assert actual.generations[0].message == expected def test__format_output_cached() -> None: anthropic_msg = Message( id="foo", content=[TextBlock(type="text", text="bar")], model="baz", role="assistant", stop_reason=None, stop_sequence=None, usage=Usage( input_tokens=2, output_tokens=1, cache_creation_input_tokens=3, cache_read_input_tokens=4, ), type="message", ) expected = AIMessage( # type: ignore[misc] "bar", usage_metadata={ "input_tokens": 9, "output_tokens": 1, "total_tokens": 10, "input_token_details": {"cache_creation": 3, "cache_read": 4}, }, response_metadata={"model_provider": "anthropic"}, ) llm = ChatAnthropic(model=MODEL_NAME, anthropic_api_key="test") # type: ignore[call-arg, call-arg] actual = llm._format_output(anthropic_msg) assert actual.generations[0].message == expected def test__merge_messages() -> None: messages = [ SystemMessage("foo"), # type: ignore[misc] HumanMessage("bar"), # type: ignore[misc] AIMessage( # type: ignore[misc] [ {"text": "baz", "type": "text"}, { "tool_input": {"a": "b"}, "type": "tool_use", "id": "1", "text": None, "name": "buz", }, {"text": "baz", "type": "text"}, { "tool_input": {"a": "c"}, "type": "tool_use", "id": "2", "text": None, "name": "blah", }, { "tool_input": {"a": "c"}, "type": "tool_use", "id": "3", "text": None, "name": "blah", }, ], ), ToolMessage("buz output", tool_call_id="1", status="error"), # type: ignore[misc] ToolMessage( content=[ { "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": "fake_image_data", }, }, ], tool_call_id="2", ), # type: ignore[misc] ToolMessage([], tool_call_id="3"), # type: ignore[misc] HumanMessage("next thing"), # type: ignore[misc] ] expected = [ SystemMessage("foo"), # type: ignore[misc] HumanMessage("bar"), # type: ignore[misc] AIMessage( # type: ignore[misc] [ {"text": "baz", "type": "text"}, { "tool_input": {"a": "b"}, "type": "tool_use", "id": "1", "text": None, "name": "buz", }, {"text": "baz", "type": "text"}, { "tool_input": {"a": "c"}, "type": "tool_use", "id": "2", "text": None, "name": "blah", }, { "tool_input": {"a": "c"}, "type": "tool_use", "id": "3", "text": None, "name": "blah", }, ], ), HumanMessage( # type: ignore[misc] [ { "type": "tool_result", "content": "buz output", "tool_use_id": "1", "is_error": True, }, { "type": "tool_result", "content": [ { "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": "fake_image_data", }, }, ], "tool_use_id": "2", "is_error": False, }, { "type": "tool_result", "content": [], "tool_use_id": "3", "is_error": False, }, {"type": "text", "text": "next thing"}, ], ), ] actual = _merge_messages(messages) assert expected == actual # Test tool message case messages = [ ToolMessage("buz output", tool_call_id="1"), # type: ignore[misc] ToolMessage( # type: ignore[misc] content=[ {"type": "tool_result", "content": "blah output", "tool_use_id": "2"}, ], tool_call_id="2", ), ] expected = [ HumanMessage( # type: ignore[misc] [ { "type": "tool_result", "content": "buz output", "tool_use_id": "1", "is_error": False, }, {"type": "tool_result", "content": "blah output", "tool_use_id": "2"}, ], ), ] actual = _merge_messages(messages) assert expected == actual def test__merge_messages_mutation() -> None: original_messages = [ HumanMessage([{"type": "text", "text": "bar"}]), # type: ignore[misc] HumanMessage("next thing"), # type: ignore[misc] ] messages = [ HumanMessage([{"type": "text", "text": "bar"}]), # type: ignore[misc] HumanMessage("next thing"), # type: ignore[misc] ] expected = [ HumanMessage( # type: ignore[misc] [{"type": "text", "text": "bar"}, {"type": "text", "text": "next thing"}], ), ] actual = _merge_messages(messages) assert expected == actual assert messages == original_messages def test__format_image() -> None: url = "dummyimage.com/600x400/000/fff" with pytest.raises(ValueError): _format_image(url) @pytest.fixture def pydantic() -> type[BaseModel]: class dummy_function(BaseModel): # noqa: N801 """Dummy function.""" arg1: int = Field(..., description="foo") arg2: Literal["bar", "baz"] = Field(..., description="one of 'bar', 'baz'") return dummy_function @pytest.fixture def function() -> Callable: def dummy_function(arg1: int, arg2: Literal["bar", "baz"]) -> None: """Dummy function. Args: arg1: foo arg2: one of 'bar', 'baz' """ return dummy_function @pytest.fixture def dummy_tool() -> BaseTool: class Schema(BaseModel): arg1: int = Field(..., description="foo") arg2: Literal["bar", "baz"] = Field(..., description="one of 'bar', 'baz'") class DummyFunction(BaseTool): # type: ignore[override] args_schema: type[BaseModel] = Schema name: str = "dummy_function" description: str = "Dummy function." def _run(self, *args: Any, **kwargs: Any) -> Any: pass return DummyFunction() @pytest.fixture def json_schema() -> dict: return { "title": "dummy_function", "description": "Dummy function.", "type": "object", "properties": { "arg1": {"description": "foo", "type": "integer"}, "arg2": { "description": "one of 'bar', 'baz'", "enum": ["bar", "baz"], "type": "string", }, }, "required": ["arg1", "arg2"], } @pytest.fixture def openai_function() -> dict: return { "name": "dummy_function", "description": "Dummy function.", "parameters": { "type": "object", "properties": { "arg1": {"description": "foo", "type": "integer"}, "arg2": { "description": "one of 'bar', 'baz'", "enum": ["bar", "baz"], "type": "string", }, }, "required": ["arg1", "arg2"], }, } def test_convert_to_anthropic_tool( pydantic: type[BaseModel], function: Callable, dummy_tool: BaseTool, json_schema: dict, openai_function: dict, ) -> None: expected = { "name": "dummy_function", "description": "Dummy function.", "input_schema": { "type": "object", "properties": { "arg1": {"description": "foo", "type": "integer"}, "arg2": { "description": "one of 'bar', 'baz'", "enum": ["bar", "baz"], "type": "string", }, }, "required": ["arg1", "arg2"], }, } for fn in (pydantic, function, dummy_tool, json_schema, expected, openai_function): actual = convert_to_anthropic_tool(fn) assert actual == expected 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", "media_type": "image/jpeg", }, }, ], "tool_use_id": "2", "is_error": False, }, { "type": "tool_result", "content": [ { "type": "image", "source": { "data": "....", "type": "base64", "media_type": "image/jpeg", }, }, ], "tool_use_id": "3", "is_error": False, }, ], }, ], ) actual = _format_messages(messages) assert expected == actual # Check handling of empty AIMessage empty_contents: list[str | list[str | dict]] = ["", []] for empty_content in empty_contents: ## Permit message in final position _, anthropic_messages = _format_messages([human, AIMessage(empty_content)]) expected_messages = [ {"role": "user", "content": "foo"}, {"role": "assistant", "content": empty_content}, ] assert expected_messages == anthropic_messages ## Remove message otherwise _, anthropic_messages = _format_messages( [human, AIMessage(empty_content), human] ) expected_messages = [ {"role": "user", "content": "foo"}, {"role": "user", "content": "foo"}, ] assert expected_messages == anthropic_messages actual = _format_messages( [system, human, ai, tool, AIMessage(empty_content), human] ) assert actual[0] == "fuzz" assert [message["role"] for message in actual[1]] == [ "user", "assistant", "user", "user", ] def test__format_tool_use_block() -> None: # Test we correctly format tool_use blocks when there is no corresponding tool_call. message = AIMessage( [ { "type": "tool_use", "name": "foo_1", "id": "1", "input": {"bar_1": "baz_1"}, }, { "type": "tool_use", "name": "foo_2", "id": "2", "input": {}, "partial_json": '{"bar_2": "baz_2"}', "index": 1, }, ] ) result = _format_messages([message]) expected = { "role": "assistant", "content": [ { "type": "tool_use", "name": "foo_1", "id": "1", "input": {"bar_1": "baz_1"}, }, { "type": "tool_use", "name": "foo_2", "id": "2", "input": {"bar_2": "baz_2"}, }, ], } assert result == (None, [expected]) def test__format_messages_with_str_content_and_tool_calls() -> None: system = SystemMessage("fuzz") # type: ignore[misc] human = HumanMessage("foo") # type: ignore[misc] # If content and tool_calls are specified and content is a string, then both are # included with content first. ai = AIMessage( # type: ignore[misc] "thought", tool_calls=[{"name": "bar", "id": "1", "args": {"baz": "buzz"}}], ) tool = ToolMessage("blurb", tool_call_id="1") # type: ignore[misc] messages = [system, human, ai, tool] expected = ( "fuzz", [ {"role": "user", "content": "foo"}, { "role": "assistant", "content": [ {"type": "text", "text": "thought"}, { "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, }, ], }, ], ) actual = _format_messages(messages) assert expected == actual def test__format_messages_with_list_content_and_tool_calls() -> None: system = SystemMessage("fuzz") # type: ignore[misc] human = HumanMessage("foo") # type: ignore[misc] ai = AIMessage( # type: ignore[misc] [{"type": "text", "text": "thought"}], tool_calls=[{"name": "bar", "id": "1", "args": {"baz": "buzz"}}], ) tool = ToolMessage( # type: ignore[misc] "blurb", tool_call_id="1", ) messages = [system, human, ai, tool] expected = ( "fuzz", [ {"role": "user", "content": "foo"}, { "role": "assistant", "content": [ {"type": "text", "text": "thought"}, { "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, }, ], }, ], ) actual = _format_messages(messages) assert expected == actual def test__format_messages_with_tool_use_blocks_and_tool_calls() -> None: """Show that tool_calls are preferred to tool_use blocks when both have same id.""" system = SystemMessage("fuzz") # type: ignore[misc] human = HumanMessage("foo") # type: ignore[misc] # NOTE: tool_use block in contents and tool_calls have different arguments. ai = AIMessage( # type: ignore[misc] [ {"type": "text", "text": "thought"}, { "type": "tool_use", "name": "bar", "id": "1", "input": {"baz": "NOT_BUZZ"}, }, ], tool_calls=[{"name": "bar", "id": "1", "args": {"baz": "BUZZ"}}], ) tool = ToolMessage("blurb", tool_call_id="1") # type: ignore[misc] messages = [system, human, ai, tool] expected = ( "fuzz", [ {"role": "user", "content": "foo"}, { "role": "assistant", "content": [ {"type": "text", "text": "thought"}, { "type": "tool_use", "name": "bar", "id": "1", "input": {"baz": "BUZZ"}, # tool_calls value preferred. }, ], }, { "role": "user", "content": [ { "type": "tool_result", "content": "blurb", "tool_use_id": "1", "is_error": False, }, ], }, ], ) actual = _format_messages(messages) assert expected == actual def test__format_messages_with_cache_control() -> None: messages = [ SystemMessage( [ {"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}}, ], ), HumanMessage( [ {"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}}, { "type": "text", "text": "foo", }, ], ), ] expected_system = [ {"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}}, ] expected_messages = [ { "role": "user", "content": [ {"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}}, {"type": "text", "text": "foo"}, ], }, ] actual_system, actual_messages = _format_messages(messages) assert expected_system == actual_system assert expected_messages == actual_messages # Test standard multi-modal format (v0) messages = [ HumanMessage( [ { "type": "text", "text": "Summarize this document:", }, { "type": "file", "source_type": "base64", "mime_type": "application/pdf", "data": "", "cache_control": {"type": "ephemeral"}, }, ], ), ] actual_system, actual_messages = _format_messages(messages) assert actual_system is None expected_messages = [ { "role": "user", "content": [ { "type": "text", "text": "Summarize this document:", }, { "type": "document", "source": { "type": "base64", "media_type": "application/pdf", "data": "", }, "cache_control": {"type": "ephemeral"}, }, ], }, ] assert actual_messages == expected_messages # Test standard multi-modal format (v1) messages = [ HumanMessage( [ { "type": "text", "text": "Summarize this document:", }, { "type": "file", "mime_type": "application/pdf", "base64": "", "extras": {"cache_control": {"type": "ephemeral"}}, }, ], ), ] actual_system, actual_messages = _format_messages(messages) assert actual_system is None expected_messages = [ { "role": "user", "content": [ { "type": "text", "text": "Summarize this document:", }, { "type": "document", "source": { "type": "base64", "media_type": "application/pdf", "data": "", }, "cache_control": {"type": "ephemeral"}, }, ], }, ] assert actual_messages == expected_messages # Test standard multi-modal format (v1, unpacked extras) messages = [ HumanMessage( [ { "type": "text", "text": "Summarize this document:", }, { "type": "file", "mime_type": "application/pdf", "base64": "", "cache_control": {"type": "ephemeral"}, }, ], ), ] actual_system, actual_messages = _format_messages(messages) assert actual_system is None expected_messages = [ { "role": "user", "content": [ { "type": "text", "text": "Summarize this document:", }, { "type": "document", "source": { "type": "base64", "media_type": "application/pdf", "data": "", }, "cache_control": {"type": "ephemeral"}, }, ], }, ] assert actual_messages == expected_messages # Also test file inputs ## Images for block in [ # v1 { "type": "image", "file_id": "abc123", }, # v0 { "type": "image", "source_type": "id", "id": "abc123", }, ]: messages = [ HumanMessage( [ { "type": "text", "text": "Summarize this image:", }, block, ], ), ] actual_system, actual_messages = _format_messages(messages) assert actual_system is None expected_messages = [ { "role": "user", "content": [ { "type": "text", "text": "Summarize this image:", }, { "type": "image", "source": { "type": "file", "file_id": "abc123", }, }, ], }, ] assert actual_messages == expected_messages ## Documents for block in [ # v1 { "type": "file", "file_id": "abc123", }, # v0 { "type": "file", "source_type": "id", "id": "abc123", }, ]: messages = [ HumanMessage( [ { "type": "text", "text": "Summarize this document:", }, block, ], ), ] actual_system, actual_messages = _format_messages(messages) assert actual_system is None expected_messages = [ { "role": "user", "content": [ { "type": "text", "text": "Summarize this document:", }, { "type": "document", "source": { "type": "file", "file_id": "abc123", }, }, ], }, ] assert actual_messages == expected_messages def test__format_messages_with_citations() -> None: input_messages = [ HumanMessage( content=[ { "type": "file", "source_type": "text", "text": "The grass is green. The sky is blue.", "mime_type": "text/plain", "citations": {"enabled": True}, }, {"type": "text", "text": "What color is the grass and sky?"}, ], ), ] expected_messages = [ { "role": "user", "content": [ { "type": "document", "source": { "type": "text", "media_type": "text/plain", "data": "The grass is green. The sky is blue.", }, "citations": {"enabled": True}, }, {"type": "text", "text": "What color is the grass and sky?"}, ], }, ] actual_system, actual_messages = _format_messages(input_messages) assert actual_system is None assert actual_messages == expected_messages def test__format_messages_openai_image_format() -> None: message = HumanMessage( content=[ { "type": "text", "text": "Can you highlight the differences between these two images?", }, { "type": "image_url", "image_url": {"url": "data:image/jpeg;base64,"}, }, { "type": "image_url", "image_url": {"url": "https://"}, }, ], ) actual_system, actual_messages = _format_messages([message]) assert actual_system is None expected_messages = [ { "role": "user", "content": [ { "type": "text", "text": ( "Can you highlight the differences between these two images?" ), }, { "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": "", }, }, { "type": "image", "source": { "type": "url", "url": "https://", }, }, ], }, ] assert actual_messages == expected_messages def test__format_messages_with_multiple_system() -> None: messages = [ HumanMessage("baz"), SystemMessage("bar"), SystemMessage("baz"), SystemMessage( [ {"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}}, ], ), ] expected_system = [ {"type": "text", "text": "bar"}, {"type": "text", "text": "baz"}, {"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}}, ] expected_messages = [{"role": "user", "content": "baz"}] actual_system, actual_messages = _format_messages(messages) assert expected_system == actual_system assert expected_messages == actual_messages def test_anthropic_api_key_is_secret_string() -> None: """Test that the API key is stored as a SecretStr.""" chat_model = ChatAnthropic( # type: ignore[call-arg, call-arg] model=MODEL_NAME, anthropic_api_key="secret-api-key", ) assert isinstance(chat_model.anthropic_api_key, SecretStr) def test_anthropic_api_key_masked_when_passed_from_env( monkeypatch: MonkeyPatch, capsys: CaptureFixture, ) -> None: """Test that the API key is masked when passed from an environment variable.""" monkeypatch.setenv("ANTHROPIC_API_KEY ", "secret-api-key") chat_model = ChatAnthropic( # type: ignore[call-arg] model=MODEL_NAME, ) print(chat_model.anthropic_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" def test_anthropic_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: """Test that the API key is masked when passed via the constructor.""" chat_model = ChatAnthropic( # type: ignore[call-arg, call-arg] model=MODEL_NAME, anthropic_api_key="secret-api-key", ) print(chat_model.anthropic_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" def test_anthropic_uses_actual_secret_value_from_secretstr() -> None: """Test that the actual secret value is correctly retrieved.""" chat_model = ChatAnthropic( # type: ignore[call-arg, call-arg] model=MODEL_NAME, anthropic_api_key="secret-api-key", ) assert ( cast("SecretStr", chat_model.anthropic_api_key).get_secret_value() == "secret-api-key" ) class GetWeather(BaseModel): """Get the current weather in a given location.""" location: str = Field(..., description="The city and state, e.g. San Francisco, CA") def test_anthropic_bind_tools_tool_choice() -> None: chat_model = ChatAnthropic( # type: ignore[call-arg, call-arg] model=MODEL_NAME, anthropic_api_key="secret-api-key", ) chat_model_with_tools = chat_model.bind_tools( [GetWeather], tool_choice={"type": "tool", "name": "GetWeather"}, ) assert cast("RunnableBinding", chat_model_with_tools).kwargs["tool_choice"] == { "type": "tool", "name": "GetWeather", } chat_model_with_tools = chat_model.bind_tools( [GetWeather], tool_choice="GetWeather", ) assert cast("RunnableBinding", chat_model_with_tools).kwargs["tool_choice"] == { "type": "tool", "name": "GetWeather", } chat_model_with_tools = chat_model.bind_tools([GetWeather], tool_choice="auto") assert cast("RunnableBinding", chat_model_with_tools).kwargs["tool_choice"] == { "type": "auto", } chat_model_with_tools = chat_model.bind_tools([GetWeather], tool_choice="any") assert cast("RunnableBinding", chat_model_with_tools).kwargs["tool_choice"] == { "type": "any", } def test_fine_grained_tool_streaming_beta() -> None: """Test that fine-grained tool streaming beta can be enabled.""" # Test with betas parameter at initialization model = ChatAnthropic( model=MODEL_NAME, betas=["fine-grained-tool-streaming-2025-05-14"] ) # Create a simple tool def get_weather(city: str) -> str: """Get the weather for a city.""" return f"Weather in {city}" model_with_tools = model.bind_tools([get_weather]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "What's the weather in SF?", stream=True, **model_with_tools.kwargs, # type: ignore[attr-defined] ) # Verify beta header is in payload assert "fine-grained-tool-streaming-2025-05-14" in payload["betas"] assert payload["stream"] is True # Test combining with other betas model = ChatAnthropic( model=MODEL_NAME, betas=["context-1m-2025-08-07", "fine-grained-tool-streaming-2025-05-14"], ) model_with_tools = model.bind_tools([get_weather]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "What's the weather?", stream=True, **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert set(payload["betas"]) == { "context-1m-2025-08-07", "fine-grained-tool-streaming-2025-05-14", } # Test that _create routes to beta client when betas are present model = ChatAnthropic( model=MODEL_NAME, betas=["fine-grained-tool-streaming-2025-05-14"] ) payload = {"betas": ["fine-grained-tool-streaming-2025-05-14"], "stream": True} with patch.object(model._client.beta.messages, "create") as mock_beta_create: model._create(payload) mock_beta_create.assert_called_once_with(**payload) def test_optional_description() -> None: llm = ChatAnthropic(model=MODEL_NAME) class SampleModel(BaseModel): sample_field: str _ = llm.with_structured_output(SampleModel.model_json_schema()) def test_get_num_tokens_from_messages_passes_kwargs() -> None: """Test that get_num_tokens_from_messages passes kwargs to the model.""" llm = ChatAnthropic(model=MODEL_NAME) with patch.object(anthropic, "Client") as _client: llm.get_num_tokens_from_messages([HumanMessage("foo")], foo="bar") assert _client.return_value.messages.count_tokens.call_args.kwargs["foo"] == "bar" llm = ChatAnthropic( model=MODEL_NAME, betas=["context-management-2025-06-27"], context_management={"edits": [{"type": "clear_tool_uses_20250919"}]}, ) with patch.object(anthropic, "Client") as _client: llm.get_num_tokens_from_messages([HumanMessage("foo")]) call_args = _client.return_value.beta.messages.count_tokens.call_args.kwargs assert call_args["betas"] == ["context-management-2025-06-27"] assert call_args["context_management"] == { "edits": [{"type": "clear_tool_uses_20250919"}] } def test_usage_metadata_standardization() -> None: class UsageModel(BaseModel): input_tokens: int = 10 output_tokens: int = 5 cache_read_input_tokens: int = 3 cache_creation_input_tokens: int = 2 # Happy path usage = UsageModel() result = _create_usage_metadata(usage) assert result["input_tokens"] == 15 # 10 + 3 + 2 assert result["output_tokens"] == 5 assert result["total_tokens"] == 20 assert result.get("input_token_details") == {"cache_read": 3, "cache_creation": 2} # Null input and output tokens class UsageModelNulls(BaseModel): input_tokens: int | None = None output_tokens: int | None = None cache_read_input_tokens: int | None = None cache_creation_input_tokens: int | None = None usage_nulls = UsageModelNulls() result = _create_usage_metadata(usage_nulls) assert result["input_tokens"] == 0 assert result["output_tokens"] == 0 assert result["total_tokens"] == 0 # Test missing fields class UsageModelMissing(BaseModel): pass usage_missing = UsageModelMissing() result = _create_usage_metadata(usage_missing) assert result["input_tokens"] == 0 assert result["output_tokens"] == 0 assert result["total_tokens"] == 0 class FakeTracer(BaseTracer): """Fake tracer to capture inputs to `chat_model_start`.""" def __init__(self) -> None: super().__init__() self.chat_model_start_inputs: list = [] def _persist_run(self, run: Run) -> None: """Persist a run.""" def on_chat_model_start(self, *args: Any, **kwargs: Any) -> Run: self.chat_model_start_inputs.append({"args": args, "kwargs": kwargs}) return super().on_chat_model_start(*args, **kwargs) def test_mcp_tracing() -> None: # Test we exclude sensitive information from traces mcp_servers = [ { "type": "url", "url": "https://mcp.deepwiki.com/mcp", "name": "deepwiki", "authorization_token": "PLACEHOLDER", }, ] llm = ChatAnthropic( model=MODEL_NAME, betas=["mcp-client-2025-04-04"], mcp_servers=mcp_servers, ) tracer = FakeTracer() mock_client = MagicMock() def mock_create(*args: Any, **kwargs: Any) -> Message: return Message( id="foo", content=[TextBlock(type="text", text="bar")], model="baz", role="assistant", stop_reason=None, stop_sequence=None, usage=Usage(input_tokens=2, output_tokens=1), type="message", ) mock_client.messages.create = mock_create input_message = HumanMessage("Test query") with patch.object(llm, "_client", mock_client): _ = llm.invoke([input_message], config={"callbacks": [tracer]}) # Test headers are not traced assert len(tracer.chat_model_start_inputs) == 1 assert "PLACEHOLDER" not in str(tracer.chat_model_start_inputs) # Test headers are correctly propagated to request payload = llm._get_request_payload([input_message]) assert payload["mcp_servers"][0]["authorization_token"] == "PLACEHOLDER" # noqa: S105 def test_cache_control_kwarg() -> None: llm = ChatAnthropic(model=MODEL_NAME) messages = [HumanMessage("foo"), AIMessage("bar"), HumanMessage("baz")] payload = llm._get_request_payload(messages) assert payload["messages"] == [ {"role": "user", "content": "foo"}, {"role": "assistant", "content": "bar"}, {"role": "user", "content": "baz"}, ] payload = llm._get_request_payload(messages, cache_control={"type": "ephemeral"}) assert payload["messages"] == [ {"role": "user", "content": "foo"}, {"role": "assistant", "content": "bar"}, { "role": "user", "content": [ {"type": "text", "text": "baz", "cache_control": {"type": "ephemeral"}} ], }, ] assert isinstance(messages[-1].content, str) # test no mutation messages = [ HumanMessage("foo"), AIMessage("bar"), HumanMessage( content=[ {"type": "text", "text": "baz"}, {"type": "text", "text": "qux"}, ] ), ] payload = llm._get_request_payload(messages, cache_control={"type": "ephemeral"}) assert payload["messages"] == [ {"role": "user", "content": "foo"}, {"role": "assistant", "content": "bar"}, { "role": "user", "content": [ {"type": "text", "text": "baz"}, {"type": "text", "text": "qux", "cache_control": {"type": "ephemeral"}}, ], }, ] assert "cache_control" not in messages[-1].content[-1] # test no mutation def test_cache_control_kwarg_skips_empty_messages() -> None: llm = ChatAnthropic(model=MODEL_NAME) messages = [HumanMessage("foo"), AIMessage(content=[])] payload = llm._get_request_payload(messages, cache_control={"type": "ephemeral"}) assert payload["messages"] == [ { "role": "user", "content": [ {"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}} ], }, {"role": "assistant", "content": []}, ] def test_context_management_in_payload() -> None: llm = ChatAnthropic( model=MODEL_NAME, # type: ignore[call-arg] betas=["context-management-2025-06-27"], context_management={"edits": [{"type": "clear_tool_uses_20250919"}]}, ) llm_with_tools = llm.bind_tools( [{"type": "web_search_20250305", "name": "web_search"}] ) input_message = HumanMessage("Search for recent developments in AI") payload = llm_with_tools._get_request_payload([input_message]) # type: ignore[attr-defined] assert payload["context_management"] == { "edits": [{"type": "clear_tool_uses_20250919"}] } def test_anthropic_model_params() -> None: llm = ChatAnthropic(model=MODEL_NAME) ls_params = llm._get_ls_params() assert ls_params == { "ls_provider": "anthropic", "ls_model_type": "chat", "ls_model_name": MODEL_NAME, "ls_max_tokens": 64000, "ls_temperature": None, } ls_params = llm._get_ls_params(model=MODEL_NAME) assert ls_params.get("ls_model_name") == MODEL_NAME def test_streaming_cache_token_reporting() -> None: """Test that cache tokens are properly reported in streaming events.""" from unittest.mock import MagicMock from anthropic.types import MessageDeltaUsage from langchain_anthropic.chat_models import _make_message_chunk_from_anthropic_event # Create a mock message_start event mock_message = MagicMock() mock_message.model = MODEL_NAME mock_message.usage.input_tokens = 100 mock_message.usage.output_tokens = 0 mock_message.usage.cache_read_input_tokens = 25 mock_message.usage.cache_creation_input_tokens = 10 message_start_event = MagicMock() message_start_event.type = "message_start" message_start_event.message = mock_message # Create a mock message_delta event with complete usage info mock_delta_usage = MessageDeltaUsage( output_tokens=50, input_tokens=100, cache_read_input_tokens=25, cache_creation_input_tokens=10, ) mock_delta = MagicMock() mock_delta.stop_reason = "end_turn" mock_delta.stop_sequence = None message_delta_event = MagicMock() message_delta_event.type = "message_delta" message_delta_event.usage = mock_delta_usage message_delta_event.delta = mock_delta # Test message_start event start_chunk, _ = _make_message_chunk_from_anthropic_event( message_start_event, stream_usage=True, coerce_content_to_string=True, block_start_event=None, ) # Test message_delta event - should contain complete usage metadata (w/ cache) delta_chunk, _ = _make_message_chunk_from_anthropic_event( message_delta_event, stream_usage=True, coerce_content_to_string=True, block_start_event=None, ) # Verify message_delta has complete usage_metadata including cache tokens assert start_chunk is not None, "message_start should produce a chunk" assert getattr(start_chunk, "usage_metadata", None) is None, ( "message_start should not have usage_metadata" ) assert delta_chunk is not None, "message_delta should produce a chunk" assert delta_chunk.usage_metadata is not None, ( "message_delta should have usage_metadata" ) assert "input_token_details" in delta_chunk.usage_metadata input_details = delta_chunk.usage_metadata["input_token_details"] assert input_details.get("cache_read") == 25 assert input_details.get("cache_creation") == 10 # Verify totals are correct: 100 base + 25 cache_read + 10 cache_creation = 135 assert delta_chunk.usage_metadata["input_tokens"] == 135 assert delta_chunk.usage_metadata["output_tokens"] == 50 assert delta_chunk.usage_metadata["total_tokens"] == 185 def test_strict_tool_use() -> None: model = ChatAnthropic( model=MODEL_NAME, # type: ignore[call-arg] betas=["structured-outputs-2025-11-13"], ) def get_weather(location: str, unit: Literal["C", "F"]) -> str: """Get the weather at a location.""" return "75 degrees Fahrenheit." model_with_tools = model.bind_tools([get_weather], strict=True) tool_definition = model_with_tools.kwargs["tools"][0] # type: ignore[attr-defined] assert tool_definition["strict"] is True def test_beta_merging_with_response_format() -> None: """Test that structured-outputs beta is merged with existing betas.""" class Person(BaseModel): """Person data.""" name: str age: int # Auto-inject structured-outputs beta with no others specified model = ChatAnthropic(model=MODEL_NAME) payload = model._get_request_payload( "Test query", response_format=Person.model_json_schema(), ) assert payload["betas"] == ["structured-outputs-2025-11-13"] # Merge structured-outputs beta if other betas are present model = ChatAnthropic( model=MODEL_NAME, betas=["mcp-client-2025-04-04"], ) payload = model._get_request_payload( "Test query", response_format=Person.model_json_schema(), ) assert payload["betas"] == [ "mcp-client-2025-04-04", "structured-outputs-2025-11-13", ] # Structured-outputs beta already present - don't duplicate model = ChatAnthropic( model=MODEL_NAME, betas=[ "mcp-client-2025-04-04", "structured-outputs-2025-11-13", ], ) payload = model._get_request_payload( "Test query", response_format=Person.model_json_schema(), ) assert payload["betas"] == [ "mcp-client-2025-04-04", "structured-outputs-2025-11-13", ] # No response_format - betas should not be modified model = ChatAnthropic( model=MODEL_NAME, betas=["mcp-client-2025-04-04"], ) payload = model._get_request_payload("Test query") assert payload["betas"] == ["mcp-client-2025-04-04"] def test_beta_merging_with_strict_tool_use() -> None: """Test beta merging for strict tools.""" def get_weather(location: str) -> str: """Get the weather at a location.""" return "Sunny" # Auto-inject structured-outputs beta with no others specified model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg] model_with_tools = model.bind_tools([get_weather], strict=True) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "What's the weather?", **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert payload["betas"] == ["structured-outputs-2025-11-13"] # Merge structured-outputs beta if other betas are present model = ChatAnthropic( model=MODEL_NAME, # type: ignore[call-arg] betas=["mcp-client-2025-04-04"], ) model_with_tools = model.bind_tools([get_weather], strict=True) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "What's the weather?", **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert payload["betas"] == [ "mcp-client-2025-04-04", "structured-outputs-2025-11-13", ] # Structured-outputs beta already present - don't duplicate model = ChatAnthropic( model=MODEL_NAME, # type: ignore[call-arg] betas=[ "mcp-client-2025-04-04", "structured-outputs-2025-11-13", ], ) model_with_tools = model.bind_tools([get_weather], strict=True) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "What's the weather?", **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert payload["betas"] == [ "mcp-client-2025-04-04", "structured-outputs-2025-11-13", ] # No strict tools - betas should not be modified model = ChatAnthropic( model=MODEL_NAME, # type: ignore[call-arg] betas=["mcp-client-2025-04-04"], ) model_with_tools = model.bind_tools([get_weather], strict=False) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "What's the weather?", **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert payload["betas"] == ["mcp-client-2025-04-04"] def test_auto_append_betas_for_tool_types() -> None: """Test that betas are automatically appended based on tool types.""" # Test web_fetch_20250910 auto-appends web-fetch-2025-09-10 model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg] tool = {"type": "web_fetch_20250910", "name": "web_fetch", "max_uses": 3} model_with_tools = model.bind_tools([tool]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert payload["betas"] == ["web-fetch-2025-09-10"] # Test code_execution_20250522 auto-appends code-execution-2025-05-22 model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg] tool = {"type": "code_execution_20250522", "name": "code_execution"} model_with_tools = model.bind_tools([tool]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert payload["betas"] == ["code-execution-2025-05-22"] # Test memory_20250818 auto-appends context-management-2025-06-27 model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg] tool = {"type": "memory_20250818", "name": "memory"} model_with_tools = model.bind_tools([tool]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert payload["betas"] == ["context-management-2025-06-27"] # Test merging with existing betas model = ChatAnthropic( model=MODEL_NAME, betas=["mcp-client-2025-04-04"], # type: ignore[call-arg] ) tool = {"type": "web_fetch_20250910", "name": "web_fetch"} model_with_tools = model.bind_tools([tool]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert payload["betas"] == ["mcp-client-2025-04-04", "web-fetch-2025-09-10"] # Test that it doesn't duplicate existing betas model = ChatAnthropic( model=MODEL_NAME, betas=["web-fetch-2025-09-10"], # type: ignore[call-arg] ) tool = {"type": "web_fetch_20250910", "name": "web_fetch"} model_with_tools = model.bind_tools([tool]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert payload["betas"] == ["web-fetch-2025-09-10"] # Test multiple tools with different beta requirements model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg] tools = [ {"type": "web_fetch_20250910", "name": "web_fetch"}, {"type": "code_execution_20250522", "name": "code_execution"}, ] model_with_tools = model.bind_tools(tools) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert set(payload["betas"]) == { "web-fetch-2025-09-10", "code-execution-2025-05-22", } def test_tool_search_is_builtin_tool() -> None: """Test that tool search tools are recognized as built-in tools.""" # Test regex variant regex_tool = { "type": "tool_search_tool_regex_20251119", "name": "tool_search_tool_regex", } assert _is_builtin_tool(regex_tool) # Test BM25 variant bm25_tool = { "type": "tool_search_tool_bm25_20251119", "name": "tool_search_tool_bm25", } assert _is_builtin_tool(bm25_tool) # Test non-builtin tool regular_tool = { "name": "get_weather", "description": "Get weather", "input_schema": {"type": "object", "properties": {}}, } assert not _is_builtin_tool(regular_tool) def test_tool_search_beta_headers() -> None: """Test that tool search tools auto-append the correct beta headers.""" # Test regex variant model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg] regex_tool = { "type": "tool_search_tool_regex_20251119", "name": "tool_search_tool_regex", } model_with_tools = model.bind_tools([regex_tool]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert payload["betas"] == ["advanced-tool-use-2025-11-20"] # Test BM25 variant model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg] bm25_tool = { "type": "tool_search_tool_bm25_20251119", "name": "tool_search_tool_bm25", } model_with_tools = model.bind_tools([bm25_tool]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert payload["betas"] == ["advanced-tool-use-2025-11-20"] # Test merging with existing betas model = ChatAnthropic( model=MODEL_NAME, betas=["mcp-client-2025-04-04"], # type: ignore[call-arg] ) model_with_tools = model.bind_tools([regex_tool]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert payload["betas"] == [ "mcp-client-2025-04-04", "advanced-tool-use-2025-11-20", ] def test_tool_search_with_deferred_tools() -> None: """Test that `defer_loading` works correctly with tool search.""" llm = ChatAnthropic( model="claude-opus-4-5-20251101", # type: ignore[call-arg] ) # Create tools with defer_loading tools = [ { "type": "tool_search_tool_bm25_20251119", "name": "tool_search_tool_bm25", }, { "name": "calculator", "description": "Perform mathematical calculations", "input_schema": { "type": "object", "properties": { "expression": { "type": "string", "description": "Mathematical expression", }, }, "required": ["expression"], }, "defer_loading": True, }, ] llm_with_tools = llm.bind_tools(tools) # type: ignore[arg-type] # Verify the payload includes tools with defer_loading payload = llm_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **llm_with_tools.kwargs, # type: ignore[attr-defined] ) # Find the calculator tool in the payload calculator_tool = None for tool in payload["tools"]: if isinstance(tool, dict) and tool.get("name") == "calculator": calculator_tool = tool break assert calculator_tool is not None assert calculator_tool.get("defer_loading") is True def test_tool_search_result_formatting() -> None: """Test that `tool_result` blocks with `tool_reference` are handled correctly.""" # Tool search result with tool_reference blocks messages = [ HumanMessage("What tools can help with weather?"), # type: ignore[misc] AIMessage( # type: ignore[misc] [ { "type": "server_tool_use", "id": "srvtoolu_123", "name": "tool_search_tool_regex", "input": {"query": "weather"}, }, { "type": "tool_result", "tool_use_id": "srvtoolu_123", "content": [ {"type": "tool_reference", "tool_name": "get_weather"}, {"type": "tool_reference", "tool_name": "weather_forecast"}, ], }, ], ), ] _, formatted = _format_messages(messages) # Verify the tool_result block is preserved correctly assistant_msg = formatted[1] assert assistant_msg["role"] == "assistant" # Find the tool_result block tool_result_block = None for block in assistant_msg["content"]: if isinstance(block, dict) and block.get("type") == "tool_result": tool_result_block = block break assert tool_result_block is not None assert tool_result_block["tool_use_id"] == "srvtoolu_123" assert isinstance(tool_result_block["content"], list) assert len(tool_result_block["content"]) == 2 assert tool_result_block["content"][0]["type"] == "tool_reference" assert tool_result_block["content"][0]["tool_name"] == "get_weather" assert tool_result_block["content"][1]["type"] == "tool_reference" assert tool_result_block["content"][1]["tool_name"] == "weather_forecast" def test_auto_append_betas_for_mcp_servers() -> None: """Test that `mcp-client-2025-11-20` beta is automatically appended for `mcp_servers`.""" model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg] mcp_servers = [ { "type": "url", "url": "https://mcp.example.com/mcp", "name": "example", } ] payload = model._get_request_payload( "Test query", mcp_servers=mcp_servers, # type: ignore[arg-type] ) assert payload["betas"] == ["mcp-client-2025-11-20"] assert payload["mcp_servers"] == mcp_servers # Test merging with existing betas model = ChatAnthropic( model=MODEL_NAME, betas=["context-management-2025-06-27"], ) payload = model._get_request_payload( "Test query", mcp_servers=mcp_servers, # type: ignore[arg-type] ) assert payload["betas"] == [ "context-management-2025-06-27", "mcp-client-2025-11-20", ] # Test that it doesn't duplicate if beta already present model = ChatAnthropic( model=MODEL_NAME, betas=["mcp-client-2025-11-20"], ) payload = model._get_request_payload( "Test query", mcp_servers=mcp_servers, # type: ignore[arg-type] ) assert payload["betas"] == ["mcp-client-2025-11-20"] # Test with mcp_servers set on model initialization model = ChatAnthropic( model=MODEL_NAME, mcp_servers=mcp_servers, # type: ignore[arg-type] ) payload = model._get_request_payload("Test query") assert payload["betas"] == ["mcp-client-2025-11-20"] assert payload["mcp_servers"] == mcp_servers # Test with existing betas and mcp_servers on model initialization model = ChatAnthropic( model=MODEL_NAME, betas=["context-management-2025-06-27"], mcp_servers=mcp_servers, # type: ignore[arg-type] ) payload = model._get_request_payload("Test query") assert payload["betas"] == [ "context-management-2025-06-27", "mcp-client-2025-11-20", ] # Test that beta is not appended when mcp_servers is None model = ChatAnthropic(model=MODEL_NAME) payload = model._get_request_payload("Test query") assert "betas" not in payload or payload["betas"] is None # Test combining mcp_servers with tool types that require betas model = ChatAnthropic(model=MODEL_NAME) tool = {"type": "web_fetch_20250910", "name": "web_fetch"} model_with_tools = model.bind_tools([tool]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "Test query", mcp_servers=mcp_servers, **model_with_tools.kwargs, # type: ignore[attr-defined] ) assert set(payload["betas"]) == { "web-fetch-2025-09-10", "mcp-client-2025-11-20", } def test_profile() -> None: model = ChatAnthropic(model="claude-sonnet-4-20250514") assert model.profile assert not model.profile["structured_output"] model = ChatAnthropic(model="claude-sonnet-4-5") assert model.profile assert model.profile["structured_output"] assert model.profile["tool_calling"] # Test overwriting a field model.profile["tool_calling"] = False assert not model.profile["tool_calling"] # Test we didn't mutate model = ChatAnthropic(model="claude-sonnet-4-5") assert model.profile assert model.profile["tool_calling"] # Test passing in profile model = ChatAnthropic(model="claude-sonnet-4-5", profile={"tool_calling": False}) assert model.profile == {"tool_calling": False} async def test_model_profile_not_blocking() -> None: with blockbuster_ctx(): model = ChatAnthropic(model="claude-sonnet-4-5") _ = model.profile def test_effort_parameter_validation() -> None: """Test that effort parameter is validated correctly. The effort parameter is currently in beta and only supported by Claude Opus 4.5. """ # Valid effort values should work model = ChatAnthropic(model="claude-opus-4-5-20251101", effort="high") assert model.effort == "high" model = ChatAnthropic(model="claude-opus-4-5-20251101", effort="medium") assert model.effort == "medium" model = ChatAnthropic(model="claude-opus-4-5-20251101", effort="low") assert model.effort == "low" # Invalid effort values should raise ValidationError with pytest.raises(ValidationError, match="Input should be"): ChatAnthropic(model="claude-opus-4-5-20251101", effort="invalid") # type: ignore[arg-type] def test_effort_populates_betas() -> None: """Test that effort parameter auto-populates required betas.""" model = ChatAnthropic(model="claude-opus-4-5-20251101", effort="medium") assert model.effort == "medium" # Test that effort works with dated API ID payload = model._get_request_payload("Test query") assert payload["output_config"]["effort"] == "medium" assert "effort-2025-11-24" in payload["betas"] def test_effort_in_output_config() -> None: """Test that effort can be specified in `output_config`.""" # Test valid effort in output_config model = ChatAnthropic( model="claude-opus-4-5-20251101", output_config={"effort": "low"}, ) assert model.model_kwargs["output_config"] == {"effort": "low"} def test_effort_priority() -> None: """Test that top-level effort takes precedence over `output_config`.""" model = ChatAnthropic( model="claude-opus-4-5-20251101", effort="high", output_config={"effort": "low"}, ) # Top-level effort should take precedence in the payload payload = model._get_request_payload("Test query") assert payload["output_config"]["effort"] == "high" def test_effort_beta_header_auto_append() -> None: """Test that effort beta header is automatically appended.""" # Test with top-level effort parameter model = ChatAnthropic(model="claude-opus-4-5-20251101", effort="medium") payload = model._get_request_payload("Test query") assert "effort-2025-11-24" in payload["betas"] # Test with output_config model = ChatAnthropic( model="claude-opus-4-5-20251101", output_config={"effort": "low"}, ) payload = model._get_request_payload("Test query") assert "effort-2025-11-24" in payload["betas"] # Test that beta is not duplicated if already present model = ChatAnthropic( model="claude-opus-4-5-20251101", effort="high", betas=["effort-2025-11-24"], ) payload = model._get_request_payload("Test query") assert payload["betas"].count("effort-2025-11-24") == 1 # Test combining effort with other betas model = ChatAnthropic( model="claude-opus-4-5-20251101", effort="medium", betas=["context-1m-2025-08-07"], ) payload = model._get_request_payload("Test query") assert set(payload["betas"]) == { "context-1m-2025-08-07", "effort-2025-11-24", } def test_output_config_without_effort() -> None: """Test that output_config can be used without effort.""" # output_config might have other fields in the future model = ChatAnthropic( model=MODEL_NAME, output_config={"some_future_param": "value"}, ) payload = model._get_request_payload("Test query") assert payload["output_config"] == {"some_future_param": "value"} # No effort beta should be added assert payload.get("betas") is None or "effort-2025-11-24" not in payload.get( "betas", [] ) def test_extras_with_defer_loading() -> None: """Test that extras with `defer_loading` are merged into tool definitions.""" from langchain_core.tools import tool @tool(extras={"defer_loading": True}) def get_weather(location: str) -> str: """Get weather for a location.""" return f"Weather in {location}" model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg] model_with_tools = model.bind_tools([get_weather]) # Get the payload to check if defer_loading was merged payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **model_with_tools.kwargs, # type: ignore[attr-defined] ) # Find the get_weather tool in the payload weather_tool = None for tool_def in payload["tools"]: if isinstance(tool_def, dict) and tool_def.get("name") == "get_weather": weather_tool = tool_def break assert weather_tool is not None assert weather_tool.get("defer_loading") is True def test_extras_with_cache_control() -> None: """Test that extras with `cache_control` are merged into tool definitions.""" from langchain_core.tools import tool @tool(extras={"cache_control": {"type": "ephemeral"}}) def search_files(query: str) -> str: """Search files.""" return f"Results for {query}" model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg] model_with_tools = model.bind_tools([search_files]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **model_with_tools.kwargs, # type: ignore[attr-defined] ) search_tool = None for tool_def in payload["tools"]: if isinstance(tool_def, dict) and tool_def.get("name") == "search_files": search_tool = tool_def break assert search_tool is not None assert search_tool.get("cache_control") == {"type": "ephemeral"} def test_extras_with_input_examples() -> None: """Test that extras with `input_examples` are merged into tool definitions.""" from langchain_core.tools import tool @tool( extras={ "input_examples": [ {"location": "San Francisco, CA", "unit": "fahrenheit"}, {"location": "Tokyo, Japan", "unit": "celsius"}, ] } ) def get_weather(location: str, unit: str = "fahrenheit") -> str: """Get weather for a location.""" return f"Weather in {location}" model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg] model_with_tools = model.bind_tools([get_weather]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **model_with_tools.kwargs, # type: ignore[attr-defined] ) weather_tool = None for tool_def in payload["tools"]: if isinstance(tool_def, dict) and tool_def.get("name") == "get_weather": weather_tool = tool_def break assert weather_tool is not None assert "input_examples" in weather_tool assert len(weather_tool["input_examples"]) == 2 assert weather_tool["input_examples"][0] == { "location": "San Francisco, CA", "unit": "fahrenheit", } # Beta header is required assert "betas" in payload assert "advanced-tool-use-2025-11-20" in payload["betas"] def test_extras_with_multiple_fields() -> None: """Test that multiple extra fields can be specified together.""" from langchain_core.tools import tool @tool( extras={ "defer_loading": True, "cache_control": {"type": "ephemeral"}, "input_examples": [{"query": "python files"}], } ) def search_code(query: str) -> str: """Search code.""" return f"Code for {query}" model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg] model_with_tools = model.bind_tools([search_code]) payload = model_with_tools._get_request_payload( # type: ignore[attr-defined] "test", **model_with_tools.kwargs, # type: ignore[attr-defined] ) tool_def = None for t in payload["tools"]: if isinstance(t, dict) and t.get("name") == "search_code": tool_def = t break assert tool_def is not None assert tool_def.get("defer_loading") is True assert tool_def.get("cache_control") == {"type": "ephemeral"} assert "input_examples" in tool_def