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* refactor(agent): ground runtime decisions in structured evidence Use producer status and structured errors for tool recovery, preserve memory context without lexical confirmation, and remove predetermined receipt and skill conclusions. Require explicit MCP discovery and authenticated schedule trial actions. Preserve authorization, ownership, isolation, and bounded execution; update documentation and regression coverage. * test(sandbox): expect read_file initialization error callback * fix(agent): address evidence review follow-ups * fix(agent): preserve retry budgets for OpenAI transport errors * fix(agent): expose tool execution status in model requests Project validated execution metadata and exact tool text into request-only JSON so status survives provider serialization. Preserve stored results and receipt hashes, cover SDK and graph boundaries, and migrate replay input hashes without changing recorded responses. --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
125 lines
4.9 KiB
Python
125 lines
4.9 KiB
Python
"""Patched ChatOpenAI adapter for Xiaomi MiMo reasoning_content replay.
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MiMo's OpenAI-compatible API returns ``reasoning_content`` in thinking mode and
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requires that value to be replayed on historical assistant messages in
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multi-turn agent conversations. Standard ``langchain_openai.ChatOpenAI`` drops
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that provider-specific field, which can cause HTTP 400 errors once tool calls
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enter the conversation history.
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"""
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from __future__ import annotations
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from collections.abc import Mapping
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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, AIMessageChunk
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from deerflow.models.assistant_payload_replay import restore_assistant_payloads, restore_reasoning_content
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from deerflow.models.openai_response import GuardedChatOpenAI
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from deerflow.models.reasoning_capture import typed_choice_message, with_reasoning_content
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_MISSING = object()
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def _extract_reasoning_content(value: Any) -> str | object:
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"""Return reasoning_content from a dict/Pydantic object, preserving empty strings."""
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if isinstance(value, Mapping):
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if "reasoning_content" in value and value["reasoning_content"] is not None:
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return value["reasoning_content"]
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return _MISSING
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reasoning = getattr(value, "reasoning_content", _MISSING)
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if reasoning is not _MISSING and reasoning is not None:
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return reasoning
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model_extra = getattr(value, "model_extra", None)
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if isinstance(model_extra, Mapping) and "reasoning_content" in model_extra and model_extra["reasoning_content"] is not None:
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return model_extra["reasoning_content"]
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return _MISSING
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class PatchedChatMiMo(GuardedChatOpenAI):
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"""ChatOpenAI with ``reasoning_content`` preservation for MiMo thinking mode."""
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@classmethod
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def is_lc_serializable(cls) -> bool:
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return True
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@property
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def lc_secrets(self) -> dict[str, str]:
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return {"api_key": "MIMO_API_KEY", "openai_api_key": "MIMO_API_KEY"}
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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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original_messages = self._convert_input(input_).to_messages()
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payload = super()._get_request_payload(input_, stop=stop, **kwargs)
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restore_assistant_payloads(
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payload.get("messages", []),
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original_messages,
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restore_reasoning_content,
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)
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return payload
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def _convert_chunk_to_generation_chunk(
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self,
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chunk: dict,
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default_chunk_class: type,
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base_generation_info: dict | None,
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) -> ChatGenerationChunk | None:
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generation_chunk = super()._convert_chunk_to_generation_chunk(
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chunk,
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default_chunk_class,
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base_generation_info,
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)
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if generation_chunk is None:
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return None
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choices = chunk.get("choices", [])
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if choices:
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delta = choices[0].get("delta") or {}
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reasoning = _extract_reasoning_content(delta)
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if reasoning is not _MISSING and isinstance(generation_chunk.message, AIMessageChunk):
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generation_chunk = ChatGenerationChunk(
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message=with_reasoning_content(generation_chunk.message, reasoning),
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generation_info=generation_chunk.generation_info,
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)
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return generation_chunk
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def _create_chat_result(
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self,
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response: dict | Any,
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generation_info: dict | None = None,
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) -> ChatResult:
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result = super()._create_chat_result(response, generation_info)
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response_dict = response if isinstance(response, dict) else response.model_dump()
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choices = response_dict.get("choices", [])
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patched_generations: list[ChatGeneration] | None = None
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for index, generation in enumerate(result.generations):
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choice = choices[index] if index < len(choices) else {}
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choice_message = choice.get("message", {}) if isinstance(choice, Mapping) else {}
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reasoning = _extract_reasoning_content(choice_message)
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if reasoning is _MISSING and not isinstance(response, dict):
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reasoning = _extract_reasoning_content(typed_choice_message(response, index))
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message = generation.message
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if reasoning is not _MISSING and isinstance(message, AIMessage):
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if patched_generations is None:
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patched_generations = list(result.generations)
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patched_generations[index] = ChatGeneration(
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message=with_reasoning_content(message, reasoning),
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generation_info=generation.generation_info,
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)
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return ChatResult(generations=patched_generations or result.generations, llm_output=result.llm_output)
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