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refactor(tests): reorganize tests into unittest/ and e2e/ directories
- Move all unit tests from tests/ to tests/unittest/ - Add tests/e2e/ directory for end-to-end tests - Update conftest.py for new test structure - Add new tests for auth dependencies, policies, route injection - Add new tests for run callbacks, create store, execution artifacts - Remove obsolete tests for deleted persistence layer Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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from langchain_core.messages import AIMessageChunk, HumanMessage
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from deerflow.models.patched_minimax import PatchedChatMiniMax
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def _make_model(**kwargs) -> PatchedChatMiniMax:
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return PatchedChatMiniMax(
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model="MiniMax-M2.5",
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api_key="test-key",
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base_url="https://example.com/v1",
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**kwargs,
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)
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def test_get_request_payload_preserves_thinking_and_forces_reasoning_split():
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model = _make_model(extra_body={"thinking": {"type": "disabled"}})
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payload = model._get_request_payload([HumanMessage(content="hello")])
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assert payload["extra_body"]["thinking"]["type"] == "disabled"
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assert payload["extra_body"]["reasoning_split"] is True
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def test_create_chat_result_maps_reasoning_details_to_reasoning_content():
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model = _make_model()
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response = {
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"choices": [
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{
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"message": {
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"role": "assistant",
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"content": "最终答案",
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"reasoning_details": [
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{
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"type": "reasoning.text",
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"id": "reasoning-text-1",
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"format": "MiniMax-response-v1",
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"index": 0,
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"text": "先分析问题,再给出答案。",
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}
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],
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},
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"finish_reason": "stop",
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}
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],
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"model": "MiniMax-M2.5",
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}
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result = model._create_chat_result(response)
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message = result.generations[0].message
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assert message.content == "最终答案"
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assert message.additional_kwargs["reasoning_content"] == "先分析问题,再给出答案。"
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assert result.generations[0].text == "最终答案"
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def test_create_chat_result_strips_inline_think_tags():
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model = _make_model()
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response = {
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"choices": [
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{
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"message": {
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"role": "assistant",
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"content": "<think>\n这是思考过程。\n</think>\n\n真正回答。",
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},
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"finish_reason": "stop",
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}
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],
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"model": "MiniMax-M2.5",
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}
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result = model._create_chat_result(response)
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message = result.generations[0].message
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assert message.content == "真正回答。"
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assert message.additional_kwargs["reasoning_content"] == "这是思考过程。"
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assert result.generations[0].text == "真正回答。"
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def test_convert_chunk_to_generation_chunk_preserves_reasoning_deltas():
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model = _make_model()
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first = model._convert_chunk_to_generation_chunk(
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{
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"choices": [
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{
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"delta": {
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"role": "assistant",
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"content": "",
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"reasoning_details": [
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{
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"type": "reasoning.text",
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"id": "reasoning-text-1",
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"format": "MiniMax-response-v1",
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"index": 0,
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"text": "The user",
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}
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],
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}
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}
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]
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},
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AIMessageChunk,
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{},
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)
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second = model._convert_chunk_to_generation_chunk(
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{
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"choices": [
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{
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"delta": {
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"content": "",
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"reasoning_details": [
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{
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"type": "reasoning.text",
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"id": "reasoning-text-1",
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"format": "MiniMax-response-v1",
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"index": 0,
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"text": " asks.",
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}
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],
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}
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}
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]
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},
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AIMessageChunk,
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{},
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)
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answer = model._convert_chunk_to_generation_chunk(
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{
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"choices": [
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{
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"delta": {
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"content": "最终答案",
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},
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"finish_reason": "stop",
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}
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],
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"model": "MiniMax-M2.5",
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},
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AIMessageChunk,
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{},
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)
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assert first is not None
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assert second is not None
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assert answer is not None
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combined = first.message + second.message + answer.message
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assert combined.additional_kwargs["reasoning_content"] == "The user asks."
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assert combined.content == "最终答案"
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