refactor(ai_client): _send_minimax_result() returns Result[str]

src/ai_client.py

@@ -2299,46 +2299,49 @@ def _list_grok_models() -> list[str]:
  from src.vendor_capabilities import list_models_for_vendor
  return list_models_for_vendor("grok")
 
-def _send_minimax(md_content: str, user_message: str, base_dir: str,
+def _send_minimax_result(md_content: str, user_message: str, base_dir: str,
  file_items: list[dict[str, Any]] | None = None,
  discussion_history: str = "",
  stream: bool = False,
  pre_tool_callback: Optional[Callable[[str, str, Optional[Callable[[str], str]]], Optional[str]]] = None,
  qa_callback: Optional[Callable[[str], str]] = None,
  stream_callback: Optional[Callable[[str], None]] = None,
- patch_callback: Optional[Callable[[str, str], Optional[str]]] = None) -> str:
+ patch_callback: Optional[Callable[[str, str], Optional[str]]] = None) -> Result[str]:
  from src.openai_compatible import OpenAICompatibleRequest
- _ensure_minimax_client()
- tools: list[dict[str, Any]] | None = _get_deepseek_tools() or None
- _repair_minimax_history(_minimax_history)
- if discussion_history and not _minimax_history:
-  _minimax_history.append({"role": "user", "content": f"[DISCUSSION HISTORY]\n\n{discussion_history}\n\n---\n\n{user_message}"})
- else:
-  _minimax_history.append({"role": "user", "content": user_message})
- def _build_minimax_request(_round_idx: int) -> OpenAICompatibleRequest:
-  with _minimax_history_lock:
-   messages: list[dict[str, Any]] = [{"role": "system", "content": f"{_get_combined_system_prompt()}\n\n<context>\n{md_content}\n</context>"}]
-   messages.extend(_minimax_history)
-  return OpenAICompatibleRequest(
-   messages=messages, model=_model, temperature=_temperature, top_p=_top_p,
-   max_tokens=min(_max_tokens, 8192), stream=stream, stream_callback=stream_callback,
-   tools=tools, tool_choice="auto" if tools else "auto",
-  )
- def _extract_minimax_reasoning(raw_response: Any) -> str:
-  if raw_response and hasattr(raw_response, "choices"):
-   choice = raw_response.choices[0]
-   if hasattr(choice.message, "reasoning_details") and choice.message.reasoning_details:
-    return choice.message.reasoning_details[0].get("text", "") or ""
-  return ""
- caps = get_capabilities("minimax", _model)
- return run_with_tool_loop(
-  _minimax_client, _build_minimax_request, capabilities=caps,
-  pre_tool_callback=pre_tool_callback, qa_callback=qa_callback, stream_callback=stream_callback,
-  patch_callback=patch_callback, base_dir=base_dir, vendor_name="minimax",
-  history_lock=_minimax_history_lock, history=_minimax_history,
-  trim_func=lambda h: _trim_minimax_history(_build_minimax_request(0).messages, h),
-  reasoning_extractor=_extract_minimax_reasoning if caps.reasoning else None,
- )
+ try:
+  _ensure_minimax_client()
+  tools: list[dict[str, Any]] | None = _get_deepseek_tools() or None
+  _repair_minimax_history(_minimax_history)
+  if discussion_history and not _minimax_history:
+   _minimax_history.append({"role": "user", "content": f"[DISCUSSION HISTORY]\n\n{discussion_history}\n\n---\n\n{user_message}"})
+  else:
+   _minimax_history.append({"role": "user", "content": user_message})
+  def _build_minimax_request(_round_idx: int) -> OpenAICompatibleRequest:
+   with _minimax_history_lock:
+    messages: list[dict[str, Any]] = [{"role": "system", "content": f"{_get_combined_system_prompt()}\n\n<context>\n{md_content}\n</context>"}]
+    messages.extend(_minimax_history)
+   return OpenAICompatibleRequest(
+    messages=messages, model=_model, temperature=_temperature, top_p=_top_p,
+    max_tokens=min(_max_tokens, 8192), stream=stream, stream_callback=stream_callback,
+    tools=tools, tool_choice="auto" if tools else "auto",
+   )
+  def _extract_minimax_reasoning(raw_response: Any) -> str:
+   if raw_response and hasattr(raw_response, "choices"):
+    choice = raw_response.choices[0]
+    if hasattr(choice.message, "reasoning_details") and choice.message.reasoning_details:
+     return choice.message.reasoning_details[0].get("text", "") or ""
+   return ""
+  caps = get_capabilities("minimax", _model)
+  return Result(data=run_with_tool_loop(
+   _minimax_client, _build_minimax_request, capabilities=caps,
+   pre_tool_callback=pre_tool_callback, qa_callback=qa_callback, stream_callback=stream_callback,
+   patch_callback=patch_callback, base_dir=base_dir, vendor_name="minimax",
+   history_lock=_minimax_history_lock, history=_minimax_history,
+   trim_func=lambda h: _trim_minimax_history(_build_minimax_request(0).messages, h),
+   reasoning_extractor=_extract_minimax_reasoning if caps.reasoning else None,
+  ))
+ except Exception as exc:
+  return Result(data="", errors=[_classify_minimax_error(exc, source="ai_client.minimax")])
 
 #endregion: MiniMax Provider