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https://github.com/langgenius/dify-plugin-sdks.git
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2409ade20e
* ENH: The addition of the `think` closure tag supports multi-turn thinking scenarios. * fix lint * add test * fix lint * fix lint * fix lint * fix lint * add unittest * fix lint * fix lint
112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
import unittest
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from dify_plugin.entities.model import AIModelEntity, ModelPropertyKey, ModelType
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from dify_plugin.entities.model.llm import LLMMode, LLMResult
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from dify_plugin.interfaces.model.large_language_model import LargeLanguageModel
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class MockLLM(LargeLanguageModel):
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"""
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Concrete Mock class for testing non-abstract methods of LargeLanguageModel.
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"""
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def _invoke(
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self,
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model: str,
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credentials: dict,
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prompt_messages: list,
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model_parameters: dict,
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tools: list,
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stop: list,
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stream: bool,
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user: str,
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) -> LLMResult:
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pass
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def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list, tools: list) -> int:
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return 0
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def validate_credentials(self, model: str, credentials: dict) -> None:
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pass
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@property
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def _invoke_error_mapping(self) -> dict:
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return {}
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class TestWrapThinking(unittest.TestCase):
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def setUp(self):
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# Create a dummy model schema to satisfy AIModel.__init__
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dummy_schema = AIModelEntity(
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model="mock_model",
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label={"en_US": "Mock Model"},
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model_type=ModelType.LLM,
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features=[],
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model_properties={ModelPropertyKey.MODE: LLMMode.CHAT.value, ModelPropertyKey.CONTEXT_SIZE: 4096},
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parameter_rules=[],
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pricing=None,
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deprecated=False,
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)
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self.llm = MockLLM(model_schemas=[dummy_schema])
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def test_wrap_thinking_logic_closure(self):
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"""
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Test that when reasoning_content ends, even if content is empty (e.g. followed immediately by tool_calls),
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the <think> tag should be closed correctly.
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"""
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# Simulate simulated streaming data:
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# 1. Has reasoning_content
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# 2. reasoning_content ends, followed immediately by tool_calls (content is None)
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chunks = [
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# Chunk 1: Thinking started
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{"reasoning_content": "Thinking started.", "content": ""},
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# Chunk 2: Still thinking
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{"reasoning_content": " Still thinking.", "content": ""},
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# Chunk 3: Thinking ended, transitioned to Tool Call (reasoning_content=None, content=None/Empty)
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# This is a critical point, old logic would fail here because content is empty
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{"reasoning_content": None, "content": "", "tool_calls": [{"id": "call_1", "function": {}}]},
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# Chunk 4: Subsequent tool parameter stream
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{"reasoning_content": None, "content": "", "tool_calls": [{"function": {"arguments": "{"}}]},
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]
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# Use the "new logic" from PR for testing.
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# We can directly call self.llm._wrap_thinking_by_reasoning_content.
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# Assume we are testing the logic function itself:
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is_reasoning = False
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full_output = ""
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for chunk in chunks:
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# Directly call the implementation in SDK to verify real code logic
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output, is_reasoning = self.llm._wrap_thinking_by_reasoning_content(chunk, is_reasoning)
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full_output += output
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# Verify results
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print(f"DEBUG Output: {full_output!r}")
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assert "<think>" in full_output
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assert "Thinking started. Still thinking." in full_output
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assert "</think>" in full_output, "Should verify <think> tag is closed properly"
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# Verify the position of the closing tag: should be after the thinking content
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expected_part = "Thinking started. Still thinking.\n</think>"
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assert expected_part in full_output
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def test_standard_reasoning_flow(self):
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"""Test standard reasoning -> text flow"""
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chunks = [
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{"reasoning_content": "Thinking.", "content": ""},
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{"reasoning_content": None, "content": "Hello world."},
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]
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is_reasoning = False
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full_output = ""
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for chunk in chunks:
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# Directly call the implementation in SDK
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output, is_reasoning = self.llm._wrap_thinking_by_reasoning_content(chunk, is_reasoning)
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full_output += output
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assert full_output == "<think>\nThinking.\n</think>Hello world."
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