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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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@@ -5,30 +5,45 @@ from langchain_core.messages import AIMessage
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from langchain_openai import ChatOpenAI
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from .base_client import BaseLLMClient, normalize_content
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from .capabilities import get_capabilities
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from .validators import validate_model
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class NormalizedChatOpenAI(ChatOpenAI):
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"""ChatOpenAI with normalized content output.
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"""ChatOpenAI with normalized content output and capability-aware binding.
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The Responses API returns content as a list of typed blocks
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(reasoning, text, etc.). ``invoke`` normalizes to string for
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consistent downstream handling. ``with_structured_output`` defaults
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to function-calling so the Responses-API parse path is avoided
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(langchain-openai's parse path emits noisy
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PydanticSerializationUnexpectedValue warnings per call without
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affecting correctness).
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consistent downstream handling.
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Provider-specific quirks (e.g. DeepSeek's thinking mode) live in
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purpose-built subclasses below so this base class stays small.
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``with_structured_output`` consults the per-model capability table
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(``capabilities.get_capabilities``) to pick the method and to decide
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whether ``tool_choice`` may be sent. Models that reject ``tool_choice``
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(e.g. DeepSeek V4 and reasoner — per their official tool-calling
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guide) still bind the schema as a tool, but no ``tool_choice``
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parameter is sent.
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Provider-specific quirks beyond structured-output (e.g. DeepSeek's
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reasoning_content roundtrip) live in subclasses so this base class
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stays small.
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"""
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def invoke(self, input, config=None, **kwargs):
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return normalize_content(super().invoke(input, config, **kwargs))
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def with_structured_output(self, schema, *, method=None, **kwargs):
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if method is None:
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method = "function_calling"
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caps = get_capabilities(self.model_name)
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if caps.preferred_structured_method == "none":
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raise NotImplementedError(
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f"{self.model_name} has no structured-output method available; "
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f"agent factories will fall back to free-text generation."
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)
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method = method or caps.preferred_structured_method
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# When the model rejects tool_choice, suppress langchain's hardcoded
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# value. The schema is still bound as a tool — exactly what
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# DeepSeek's official tool-calling examples do.
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if method == "function_calling" and not caps.supports_tool_choice:
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kwargs.setdefault("tool_choice", None)
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return super().with_structured_output(schema, method=method, **kwargs)
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@@ -52,18 +67,16 @@ def _input_to_messages(input_: Any) -> list:
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class DeepSeekChatOpenAI(NormalizedChatOpenAI):
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"""DeepSeek-specific overrides on top of the OpenAI-compatible client.
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Two quirks that don't apply to other OpenAI-compatible providers:
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Thinking-mode round-trip is the only DeepSeek-specific behavior that
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stays here. When DeepSeek's thinking models return a response with
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``reasoning_content``, that field must be echoed back as part of the
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assistant message on the next turn or the API fails with HTTP 400.
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``_create_chat_result`` captures it on receive and
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``_get_request_payload`` re-attaches it on send.
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1. **Thinking-mode round-trip.** When DeepSeek's thinking models return
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a response with ``reasoning_content``, that field must be echoed
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back as part of the assistant message on the next turn or the API
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fails with HTTP 400. ``_create_chat_result`` captures the field on
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receive and ``_get_request_payload`` re-attaches it on send.
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2. **deepseek-reasoner has no tool_choice.** Structured output via
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function-calling is unavailable, so we raise NotImplementedError
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and let the agent factories fall back to free-text generation
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(see ``tradingagents/agents/utils/structured.py``).
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Tool-choice handling for V4 and reasoner — those models reject the
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``tool_choice`` parameter — is handled by the capability dispatch in
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``NormalizedChatOpenAI.with_structured_output``, not here.
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"""
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def _get_request_payload(self, input_, *, stop=None, **kwargs):
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@@ -94,15 +107,6 @@ class DeepSeekChatOpenAI(NormalizedChatOpenAI):
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generation.message.additional_kwargs["reasoning_content"] = reasoning
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return chat_result
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def with_structured_output(self, schema, *, method=None, **kwargs):
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if self.model_name == "deepseek-reasoner":
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raise NotImplementedError(
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"deepseek-reasoner does not support tool_choice; structured "
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"output is unavailable. Agent factories fall back to "
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"free-text generation automatically."
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)
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return super().with_structured_output(schema, method=method, **kwargs)
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# Kwargs forwarded from user config to ChatOpenAI
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_PASSTHROUGH_KWARGS = (
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"timeout", "max_retries", "reasoning_effort",
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