Files
dify-plugin-sdks/python/tests/interfaces/model/test_wrap_think.py
呆萌闷油瓶 2409ade20e enh: The addition of the think closure tag supports multi-turn thin… (#267)
* 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
2025-12-25 00:12:09 +08:00

112 lines
4.1 KiB
Python

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