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https://gitee.com/wanwujie/deer-flow
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When using thinking-enabled models (like Kimi K2.5, DeepSeek), the API expects reasoning_content on all assistant messages. The original ChatDeepSeek stores reasoning_content in additional_kwargs but doesn't include it when making subsequent API calls, causing "reasoning_content is missing" errors. This adds PatchedChatDeepSeek which overrides _get_request_payload to restore reasoning_content from additional_kwargs into the payload. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
72 lines
3.0 KiB
Python
72 lines
3.0 KiB
Python
"""Patched ChatDeepSeek that preserves reasoning_content in multi-turn conversations.
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This module provides a patched version of ChatDeepSeek that properly handles
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reasoning_content when sending messages back to the API. The original implementation
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stores reasoning_content in additional_kwargs but doesn't include it when making
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subsequent API calls, which causes errors with APIs that require reasoning_content
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on all assistant messages when thinking mode is enabled.
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"""
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from typing import Any
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from langchain_core.language_models import LanguageModelInput
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from langchain_core.messages import AIMessage
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from langchain_deepseek import ChatDeepSeek
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class PatchedChatDeepSeek(ChatDeepSeek):
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"""ChatDeepSeek with proper reasoning_content preservation.
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When using thinking/reasoning enabled models, the API expects reasoning_content
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to be present on ALL assistant messages in multi-turn conversations. This patched
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version ensures reasoning_content from additional_kwargs is included in the
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request payload.
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"""
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def _get_request_payload(
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self,
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input_: LanguageModelInput,
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*,
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stop: list[str] | None = None,
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**kwargs: Any,
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) -> dict:
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"""Get request payload with reasoning_content preserved.
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Overrides the parent method to inject reasoning_content from
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additional_kwargs into assistant messages in the payload.
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"""
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# Get the original messages before conversion
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original_messages = self._convert_input(input_).to_messages()
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# Call parent to get the base payload
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payload = super()._get_request_payload(input_, stop=stop, **kwargs)
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# Match payload messages with original messages to restore reasoning_content
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payload_messages = payload.get("messages", [])
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# The payload messages and original messages should be in the same order
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# Iterate through both and match by position
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if len(payload_messages) == len(original_messages):
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for payload_msg, orig_msg in zip(payload_messages, original_messages):
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if (
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payload_msg.get("role") == "assistant"
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and isinstance(orig_msg, AIMessage)
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):
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reasoning_content = orig_msg.additional_kwargs.get("reasoning_content")
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if reasoning_content is not None:
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payload_msg["reasoning_content"] = reasoning_content
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else:
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# Fallback: match by counting assistant messages
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ai_messages = [m for m in original_messages if isinstance(m, AIMessage)]
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assistant_payloads = [
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(i, m) for i, m in enumerate(payload_messages)
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if m.get("role") == "assistant"
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]
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for (idx, payload_msg), ai_msg in zip(assistant_payloads, ai_messages):
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reasoning_content = ai_msg.additional_kwargs.get("reasoning_content")
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if reasoning_content is not None:
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payload_messages[idx]["reasoning_content"] = reasoning_content
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return payload
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