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e3bc872982
Clear the deferred full-repo lint backlog so the whole tree passes the strict ruff select (E,W,F,I,B,UP,C4,SIM). Mechanical fixes dominate: import sorting, pep585/604 annotations, dropped dead imports, and whitespace. The few semantic changes are behavior-preserving: declare __all__ on the agent_utils and alpha_vantage re-export hubs; expand 'from x import *' to explicit names; use immutable tuple defaults instead of mutable list defaults; contextlib.suppress for try/except/pass; and narrow an over-broad assertRaises.
240 lines
9.7 KiB
Python
240 lines
9.7 KiB
Python
"""Tests for DeepSeekChatOpenAI thinking-mode behaviour.
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Two pieces verified:
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1. ``reasoning_content`` is captured on receive into the AIMessage's
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``additional_kwargs`` and re-attached on send so DeepSeek's API
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sees the same value across turns.
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2. ``with_structured_output`` consults the capability table and
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suppresses ``tool_choice`` for models that reject it (V4 + reasoner),
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matching DeepSeek's official tool-calling pattern at
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https://api-docs.deepseek.com/guides/tool_calls.
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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 AIMessage, HumanMessage
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from langchain_core.prompt_values import ChatPromptValue
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from pydantic import BaseModel
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from tradingagents.llm_clients.openai_client import (
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DeepSeekChatOpenAI,
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NormalizedChatOpenAI,
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_input_to_messages,
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)
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# ---------------------------------------------------------------------------
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# _input_to_messages — the helper that handles list / ChatPromptValue / other
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# (Gemini bot review note: non-list inputs must also work)
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# ---------------------------------------------------------------------------
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@pytest.mark.unit
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class TestInputToMessages:
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def test_list_input_returned_as_is(self):
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msgs = [HumanMessage(content="hi")]
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assert _input_to_messages(msgs) is msgs
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def test_chat_prompt_value_unwrapped(self):
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msgs = [HumanMessage(content="hi")]
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prompt_value = ChatPromptValue(messages=msgs)
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assert _input_to_messages(prompt_value) == msgs
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def test_string_input_yields_empty_list(self):
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# A bare string isn't a message-bearing input; the caller's normal
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# langchain conversion happens upstream of _get_request_payload.
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assert _input_to_messages("hello") == []
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# ---------------------------------------------------------------------------
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# Reasoning content propagation across turns
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# ---------------------------------------------------------------------------
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@pytest.mark.unit
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class TestDeepSeekReasoningContent:
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def _client(self):
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os.environ.setdefault("DEEPSEEK_API_KEY", "placeholder")
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return DeepSeekChatOpenAI(
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model="deepseek-v4-flash",
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api_key="placeholder",
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base_url="https://api.deepseek.com",
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)
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def test_capture_on_receive(self):
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"""When the response carries reasoning_content, it lands on the
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AIMessage's additional_kwargs so the next turn can echo it back."""
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client = self._client()
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result = client._create_chat_result(
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{
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"model": "deepseek-v4-flash",
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": "Plan: buy NVDA.",
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"reasoning_content": "Step 1: trend is up. Step 2: ...",
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},
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"finish_reason": "stop",
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}
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],
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"usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
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}
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)
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ai = result.generations[0].message
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assert ai.additional_kwargs["reasoning_content"] == "Step 1: trend is up. Step 2: ..."
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def test_propagate_on_send(self):
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"""When an outgoing AIMessage carries reasoning_content, the request
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payload echoes it on the corresponding message dict."""
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client = self._client()
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prior = AIMessage(
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content="Plan",
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additional_kwargs={"reasoning_content": "weighed bull case"},
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)
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new_user = HumanMessage(content="Refine.")
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payload = client._get_request_payload([prior, new_user])
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# Find the assistant message in the payload
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assistant_dicts = [m for m in payload["messages"] if m.get("role") == "assistant"]
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assert assistant_dicts, "assistant message missing from outgoing payload"
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assert assistant_dicts[0]["reasoning_content"] == "weighed bull case"
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def test_propagate_through_chat_prompt_value(self):
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"""Gemini bot review note: non-list inputs (ChatPromptValue) must
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also propagate reasoning_content."""
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client = self._client()
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prior = AIMessage(
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content="Plan",
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additional_kwargs={"reasoning_content": "weighed bull case"},
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)
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prompt_value = ChatPromptValue(messages=[prior, HumanMessage(content="Refine.")])
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payload = client._get_request_payload(prompt_value)
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assistant_dicts = [m for m in payload["messages"] if m.get("role") == "assistant"]
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assert assistant_dicts[0]["reasoning_content"] == "weighed bull case"
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# ---------------------------------------------------------------------------
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# Capability-driven structured output: tool_choice suppressed for V4 + reasoner
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# ---------------------------------------------------------------------------
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def _bound_kwargs(runnable):
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"""Extract bind() kwargs from a with_structured_output result."""
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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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@pytest.mark.unit
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class TestStructuredOutputCapabilityDispatch:
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"""DeepSeek V4 and reasoner reject the tool_choice parameter
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(official guide: api-docs.deepseek.com/guides/tool_calls passes
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tools=[...] without tool_choice). Verify the capability dispatch
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suppresses tool_choice for those models and sends it for chat."""
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class _Sample(BaseModel):
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answer: str
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def _client(self, model):
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return DeepSeekChatOpenAI(
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model=model, api_key="placeholder", base_url="https://api.deepseek.com",
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)
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def test_chat_sends_tool_choice(self):
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bound = self._client("deepseek-chat").with_structured_output(self._Sample)
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assert _bound_kwargs(bound).get("tool_choice") is not None
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def test_reasoner_suppresses_tool_choice(self):
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bound = self._client("deepseek-reasoner").with_structured_output(self._Sample)
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# tool_choice is either absent or explicitly None — both are valid
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# signals that langchain's bind_tools will skip the parameter.
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assert _bound_kwargs(bound).get("tool_choice") in (None, ...) or \
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"tool_choice" not in _bound_kwargs(bound)
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def test_v4_flash_suppresses_tool_choice(self):
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bound = self._client("deepseek-v4-flash").with_structured_output(self._Sample)
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assert _bound_kwargs(bound).get("tool_choice") is None or \
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"tool_choice" not in _bound_kwargs(bound)
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def test_v4_pro_suppresses_tool_choice(self):
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bound = self._client("deepseek-v4-pro").with_structured_output(self._Sample)
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assert _bound_kwargs(bound).get("tool_choice") is None or \
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"tool_choice" not in _bound_kwargs(bound)
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def test_future_v_variant_via_regex(self):
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"""Forward-compat: unknown deepseek-v\\d-* IDs inherit V4 quirks."""
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bound = self._client("deepseek-v5-hypothetical").with_structured_output(self._Sample)
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assert _bound_kwargs(bound).get("tool_choice") is None or \
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"tool_choice" not in _bound_kwargs(bound)
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def test_schema_is_still_bound_as_tool(self):
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"""tool_choice is suppressed, but the schema is still bound as a tool —
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exactly matching DeepSeek's official tool-calling examples."""
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bound = self._client("deepseek-reasoner").with_structured_output(self._Sample)
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kwargs = _bound_kwargs(bound)
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tools = kwargs.get("tools", [])
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assert any(
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t.get("function", {}).get("name") == "_Sample" for t in tools
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), f"schema not bound as a tool: {tools}"
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# ---------------------------------------------------------------------------
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# Live API: structured output round-trips against the real DeepSeek backend
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# ---------------------------------------------------------------------------
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def _has_real_deepseek_key():
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key = os.environ.get("DEEPSEEK_API_KEY", "")
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return bool(key) and key != "placeholder"
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@pytest.mark.integration
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@pytest.mark.skipif(
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not _has_real_deepseek_key(),
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reason="DEEPSEEK_API_KEY not set (or placeholder); skipping live API call",
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)
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class TestDeepSeekLiveStructuredOutput:
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"""End-to-end: a real DeepSeek V4-flash call returns a typed instance.
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Verifies the no-tool_choice path doesn't trigger the 400 reported in
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issue #678 and that the structured-output binding still parses to a
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Pydantic instance.
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"""
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class _Pick(BaseModel):
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action: str
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confidence: float
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def test_v4_flash_returns_structured_output(self):
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client = DeepSeekChatOpenAI(
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model="deepseek-v4-flash",
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api_key=os.environ["DEEPSEEK_API_KEY"],
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base_url="https://api.deepseek.com",
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timeout=60,
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)
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bound = client.with_structured_output(self._Pick)
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result = bound.invoke(
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"Pick BUY or SELL or HOLD for a tech stock with strong earnings. "
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"Confidence is a float between 0 and 1."
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)
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assert isinstance(result, self._Pick)
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assert result.action in {"BUY", "SELL", "HOLD"}
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assert 0.0 <= result.confidence <= 1.0
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# ---------------------------------------------------------------------------
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# Base class isolation: NormalizedChatOpenAI does NOT have DeepSeek behaviour
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# ---------------------------------------------------------------------------
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@pytest.mark.unit
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class TestBaseClassIsolation:
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def test_normalized_does_not_propagate_reasoning_content(self):
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"""The general-purpose NormalizedChatOpenAI must not carry
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DeepSeek-specific behaviour. Only the subclass does."""
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assert not hasattr(NormalizedChatOpenAI, "_get_request_payload") or (
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NormalizedChatOpenAI._get_request_payload
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is NormalizedChatOpenAI.__bases__[0]._get_request_payload
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)
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