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Revert "deepseek: temporarily bypass tests" (#30424)
Reverts langchain-ai/langchain#30423
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"""Test ChatDeepSeek chat model."""
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from typing import Optional, Type
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import pytest
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from langchain_core.language_models import BaseChatModel
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from langchain_core.messages import AIMessageChunk, BaseMessageChunk
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from langchain_core.tools import BaseTool
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from langchain_tests.integration_tests import ChatModelIntegrationTests
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from langchain_deepseek.chat_models import ChatDeepSeek
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class TestChatDeepSeek(ChatModelIntegrationTests):
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@property
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def chat_model_class(self) -> Type[ChatDeepSeek]:
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return ChatDeepSeek
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@property
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def chat_model_params(self) -> dict:
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# These should be parameters used to initialize your integration for testing
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return {
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"model": "deepseek-chat",
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"temperature": 0,
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}
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@property
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def supports_json_mode(self) -> bool:
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"""(bool) whether the chat model supports JSON mode."""
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return True
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@pytest.mark.xfail(reason="Not yet supported.")
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def test_tool_message_histories_list_content(
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self, model: BaseChatModel, my_adder_tool: BaseTool
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) -> None:
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super().test_tool_message_histories_list_content(model, my_adder_tool)
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@pytest.mark.xfail(reason="Takes > 30s to run.")
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def test_reasoning_content() -> None:
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"""Test reasoning content."""
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chat_model = ChatDeepSeek(model="deepseek-reasoner")
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response = chat_model.invoke("What is 3^3?")
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assert response.content
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assert response.additional_kwargs["reasoning_content"]
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raise ValueError()
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@pytest.mark.xfail(reason="Takes > 30s to run.")
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def test_reasoning_content_streaming() -> None:
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chat_model = ChatDeepSeek(model="deepseek-reasoner")
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full: Optional[BaseMessageChunk] = None
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for chunk in chat_model.stream("What is 3^3?"):
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full = chunk if full is None else full + chunk
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assert isinstance(full, AIMessageChunk)
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assert full.additional_kwargs["reasoning_content"]
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