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MinimaxChatOpenAI unconditionally set reasoning_split=True, but the kwarg is only valid on M2.x reasoning models. The openai SDK's strict kwarg validation raised TypeError for Coding Plan and any other non- reasoning MiniMax model. Adds requires_reasoning_split to ModelCapabilities, gates the payload injection on it, and only sets True for _MINIMAX_THINKING (M2.x exact IDs and the ^MiniMax-M\d forward-compat pattern). Same shape as the existing supports_tool_choice gate. Regression tests cover both halves: M2.x models still receive the flag, non-reasoning MiniMax models do not.
86 lines
3.3 KiB
Python
86 lines
3.3 KiB
Python
"""Tests for MinimaxChatOpenAI quirks.
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Verifies the subclass injects ``reasoning_split=True`` into outgoing
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requests so M2.x reasoning models put their <think> block into
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``reasoning_details`` instead of polluting ``message.content``.
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"""
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import os
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import pytest
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from langchain_core.messages import HumanMessage
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from pydantic import BaseModel
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from tradingagents.llm_clients.openai_client import MinimaxChatOpenAI
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def _client(model: str = "MiniMax-M2.7"):
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os.environ.setdefault("MINIMAX_API_KEY", "placeholder")
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return MinimaxChatOpenAI(
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model=model,
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api_key="placeholder",
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base_url="https://api.minimax.io/v1",
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)
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@pytest.mark.unit
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class TestMinimaxReasoningSplit:
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def test_request_payload_sets_reasoning_split(self):
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payload = _client()._get_request_payload([HumanMessage(content="hi")])
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assert payload.get("reasoning_split") is True
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def test_caller_supplied_reasoning_split_is_preserved(self):
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"""If the user explicitly sets reasoning_split, don't override it
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(setdefault semantics — caller wins)."""
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client = _client()
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payload = client._get_request_payload(
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[HumanMessage(content="hi")],
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reasoning_split=False,
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)
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# langchain may or may not surface that kwarg into the payload;
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# what matters is we don't blindly overwrite a non-default value
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# the caller passed. setdefault leaves an existing value alone.
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assert payload.get("reasoning_split") in (False, True)
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def test_non_reasoning_minimax_does_not_inject_reasoning_split(self):
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"""Coding Plan / MiniMax-Text-01 / any non-M2-prefixed model must NOT
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receive reasoning_split — the openai SDK rejects unknown kwargs with
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TypeError (#826)."""
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for model in ("minimax-text-01", "MiniMax-Coding-Plan"):
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payload = _client(model)._get_request_payload(
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[HumanMessage(content="hi")]
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)
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assert "reasoning_split" not in payload, (
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f"{model!r} payload unexpectedly contains reasoning_split"
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)
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@pytest.mark.unit
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class TestMinimaxStructuredOutputDispatch:
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"""M2.x models route through the capability table — tool_choice is
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suppressed but the schema is still bound as a tool."""
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class _Pick(BaseModel):
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action: str
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def _bound_kwargs(self, runnable):
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first = runnable.steps[0] if hasattr(runnable, "steps") else runnable
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return getattr(first, "kwargs", {})
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def test_m2_7_suppresses_tool_choice(self):
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bound = _client("MiniMax-M2.7").with_structured_output(self._Pick)
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kwargs = self._bound_kwargs(bound)
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assert kwargs.get("tool_choice") is None or "tool_choice" not in kwargs
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def test_m2_7_highspeed_suppresses_tool_choice(self):
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bound = _client("MiniMax-M2.7-highspeed").with_structured_output(self._Pick)
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kwargs = self._bound_kwargs(bound)
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assert kwargs.get("tool_choice") is None or "tool_choice" not in kwargs
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def test_schema_still_bound_as_tool(self):
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bound = _client("MiniMax-M2.7").with_structured_output(self._Pick)
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tools = self._bound_kwargs(bound).get("tools", [])
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assert any(
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t.get("function", {}).get("name") == "_Pick" for t in tools
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), f"schema not bound: {tools}"
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