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https://github.com/TauricResearch/TradingAgents.git
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fix(llm): structured output for DeepSeek V4 and reasoner
DeepSeek V4 and reasoner reject tool_choice but accept tools. Route via a per-model capability table that suppresses tool_choice for thinking-mode models. #678 #689
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79
tests/test_capabilities.py
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79
tests/test_capabilities.py
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"""Unit tests for the LLM capability table."""
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import pytest
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from tradingagents.llm_clients.capabilities import (
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ModelCapabilities,
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get_capabilities,
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)
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@pytest.mark.unit
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class TestExactIdMatches:
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def test_deepseek_chat_supports_tool_choice(self):
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caps = get_capabilities("deepseek-chat")
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assert caps.supports_tool_choice is True
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def test_deepseek_reasoner_rejects_tool_choice(self):
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caps = get_capabilities("deepseek-reasoner")
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assert caps.supports_tool_choice is False
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assert caps.requires_reasoning_content_roundtrip is True
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def test_deepseek_v4_flash_rejects_tool_choice(self):
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caps = get_capabilities("deepseek-v4-flash")
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assert caps.supports_tool_choice is False
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assert caps.requires_reasoning_content_roundtrip is True
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def test_deepseek_v4_pro_rejects_tool_choice(self):
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caps = get_capabilities("deepseek-v4-pro")
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assert caps.supports_tool_choice is False
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assert caps.requires_reasoning_content_roundtrip is True
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@pytest.mark.unit
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class TestPatternMatches:
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"""Forward-compat regex patterns catch unknown DeepSeek variants."""
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def test_future_deepseek_v5_inherits_thinking_quirks(self):
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caps = get_capabilities("deepseek-v5-flash")
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assert caps.supports_tool_choice is False
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assert caps.requires_reasoning_content_roundtrip is True
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def test_future_deepseek_v9_inherits_thinking_quirks(self):
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caps = get_capabilities("deepseek-v9-anything")
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assert caps.supports_tool_choice is False
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def test_reasoner_variant_inherits_thinking_quirks(self):
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caps = get_capabilities("deepseek-reasoner-pro")
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assert caps.supports_tool_choice is False
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@pytest.mark.unit
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class TestDefault:
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"""Unknown / non-DeepSeek models get the permissive default."""
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def test_gpt_default(self):
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caps = get_capabilities("gpt-4.1")
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assert caps.supports_tool_choice is True
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assert caps.preferred_structured_method == "function_calling"
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def test_grok_default(self):
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caps = get_capabilities("grok-4-0709")
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assert caps.supports_tool_choice is True
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def test_unknown_model_default(self):
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caps = get_capabilities("totally-made-up-model-id")
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assert caps.supports_tool_choice is True
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def test_exact_match_precedes_pattern(self):
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"""deepseek-chat must NOT match the v\\d regex."""
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caps = get_capabilities("deepseek-chat")
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assert caps.supports_tool_choice is True
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@pytest.mark.unit
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def test_capabilities_dataclass_is_frozen():
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"""Capability rows are immutable so they can be safely shared."""
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caps = get_capabilities("deepseek-chat")
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with pytest.raises(Exception):
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caps.supports_tool_choice = False # type: ignore[misc]
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@@ -5,9 +5,10 @@ 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`` raises NotImplementedError for
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``deepseek-reasoner`` so the agent factories' free-text fallback
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handles the request instead of failing at runtime.
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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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@@ -15,6 +16,7 @@ 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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@@ -115,42 +117,111 @@ class TestDeepSeekReasoningContent:
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# ---------------------------------------------------------------------------
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# deepseek-reasoner: structured output unavailable, falls through to free-text
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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 TestDeepSeekReasonerStructuredOutput:
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def test_with_structured_output_raises_for_reasoner(self):
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client = DeepSeekChatOpenAI(
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model="deepseek-reasoner",
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api_key="placeholder",
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base_url="https://api.deepseek.com",
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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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from pydantic import BaseModel
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class _Sample(BaseModel):
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answer: str
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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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with pytest.raises(NotImplementedError):
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client.with_structured_output(_Sample)
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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_with_structured_output_works_for_v4(self):
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"""V4 models (non-reasoner) accept tool_choice; structured output works."""
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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="placeholder",
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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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from pydantic import BaseModel
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class _Sample(BaseModel):
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answer: str
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# Should return a Runnable, not raise. (The actual API call would
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# require a real key; we only assert binding succeeds.)
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wrapped = client.with_structured_output(_Sample)
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assert wrapped is not None
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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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