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补充并收敛管理台与运行时改动，完成本轮代码提交。
包含模型配置/thinking 透传、MCP 运行时与安装体验增强、以及会话渲染与图片显示链路修复，确保流式与历史展示行为一致。 Made-with: Cursor
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10 changed files with 395 additions and 41 deletions
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@ -104,10 +104,21 @@ def parse_openai_responses_stream_events(
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class OpenAIResponsesModel(ChatModel):
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"""OpenAI Responses API transport (OpenAI-compatible gateways may implement this surface)."""
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def __init__(self, *, model: str | None = None, api_key: str | None = None, base_url: str | None = None):
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def __init__(
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self,
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*,
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model: str | None = None,
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api_key: str | None = None,
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base_url: str | None = None,
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thinking_mode_enabled: bool = False,
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reasoning_effort: str | None = None,
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):
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self.model = (model or os.getenv("OPENAI_MODEL") or "gpt-4o-mini").strip()
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self.api_key = (api_key or os.getenv("OPENAI_API_KEY") or "").strip()
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self.base_url = (base_url or os.getenv("OPENAI_BASE_URL") or "").strip() or None
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self.thinking_mode_enabled = bool(thinking_mode_enabled)
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eff = str(reasoning_effort or "").strip().lower()
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self.reasoning_effort = eff if eff in ("low", "medium", "high") else ""
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if not self.api_key:
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raise RuntimeError("未设置 OPENAI_API_KEY，无法使用 OpenAI Responses")
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try:
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@ -188,9 +199,41 @@ class OpenAIResponsesModel(ChatModel):
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norm = self._normalize_messages(messages)
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# OpenAI-compatible gateways differ: some require input={"messages":[...]} with role=user only.
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stream_errors: list[str] = []
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b = str(self.base_url or "").strip().lower()
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force_disable = str(os.getenv("AIA_LLM_THINKING_FORCE_DISABLED") or "").strip().lower() in ("1", "true", "yes", "on")
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force_enable = str(os.getenv("AIA_LLM_THINKING_FORCE_ENABLED") or "").strip().lower() in ("1", "true", "yes", "on")
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mode_enabled = bool(getattr(self, "thinking_mode_enabled", False))
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extra_body: dict[str, Any] = {}
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if b and ("api.openai.com" not in b):
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if force_disable:
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extra_body["thinking"] = {"type": "disabled"}
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elif force_enable or mode_enabled:
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extra_body["thinking"] = {"type": "enabled"}
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else:
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# Default: disable thinking for non-official gateways unless explicitly enabled.
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if str(os.getenv("AIA_LLM_THINKING_DISABLED") or "1").strip().lower() in ("1", "true", "yes", "on"):
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extra_body["thinking"] = {"type": "disabled"}
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thinking = {"extra_body": extra_body} if extra_body else {}
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reasoning_effort = str(getattr(self, "reasoning_effort", "") or "").strip().lower()
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if reasoning_effort not in ("low", "medium", "high"):
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reasoning_effort = ""
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stream_variants: list[dict[str, Any]] = [
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{"model": self.model, "input": {"messages": norm}, "tools": tools or None, "stream": True},
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{"model": self.model, "input": norm, "tools": tools or None, "stream": True},
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{
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**thinking,
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**({"reasoning_effort": reasoning_effort} if reasoning_effort else {}),
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"model": self.model,
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"input": {"messages": norm},
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"tools": tools or None,
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"stream": True,
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},
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{
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**thinking,
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**({"reasoning_effort": reasoning_effort} if reasoning_effort else {}),
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"model": self.model,
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"input": norm,
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"tools": tools or None,
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"stream": True,
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},
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]
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try:
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for payload in stream_variants:
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@ -205,15 +248,48 @@ class OpenAIResponsesModel(ChatModel):
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on_token(text)
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return LLMResponse(content=text, tool_calls=tool_calls)
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except Exception as exc:
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stream_errors.append(str(exc))
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emsg = str(exc)
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# Fallback: provider thinking-mode replay contract.
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if "reasoning_content" in emsg and "thinking mode" in emsg and "must be passed back" in emsg:
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try:
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forced = dict(payload)
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eb = forced.get("extra_body") if isinstance(forced.get("extra_body"), dict) else {}
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eb = dict(eb)
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eb["thinking"] = {"type": "disabled"}
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forced["extra_body"] = eb
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forced.pop("reasoning_effort", None)
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stream = self._client.responses.create(**forced)
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text, tool_calls, final_resp = parse_openai_responses_stream_events(stream, on_token=on_token)
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if (not text.strip()) and final_resp:
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ot = final_resp.get("output_text")
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if isinstance(ot, str) and ot.strip():
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text = ot
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if on_token:
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on_token(text)
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return LLMResponse(content=text, tool_calls=tool_calls)
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except Exception:
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pass
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stream_errors.append(emsg)
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continue
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raise RuntimeError("; ".join(stream_errors) or "responses_stream_all_variants_failed")
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except Exception as exc:
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logger.info("responses stream failed; fallback to non-stream (%s)", exc)
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nonstream_errors: list[str] = []
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for payload in (
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{"model": self.model, "input": {"messages": norm}, "tools": tools or None},
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{"model": self.model, "input": norm, "tools": tools or None},
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{
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**thinking,
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**({"reasoning_effort": reasoning_effort} if reasoning_effort else {}),
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"model": self.model,
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"input": {"messages": norm},
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"tools": tools or None,
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},
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{
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**thinking,
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**({"reasoning_effort": reasoning_effort} if reasoning_effort else {}),
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"model": self.model,
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"input": norm,
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"tools": tools or None,
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},
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):
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try:
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resp = self._client.responses.create(**payload)
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@ -224,7 +300,25 @@ class OpenAIResponsesModel(ChatModel):
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on_token(text)
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return LLMResponse(content=text, tool_calls=tool_calls)
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except Exception as e2:
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nonstream_errors.append(str(e2))
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emsg2 = str(e2)
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if "reasoning_content" in emsg2 and "thinking mode" in emsg2 and "must be passed back" in emsg2:
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try:
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forced = dict(payload)
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eb = forced.get("extra_body") if isinstance(forced.get("extra_body"), dict) else {}
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eb = dict(eb)
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eb["thinking"] = {"type": "disabled"}
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forced["extra_body"] = eb
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forced.pop("reasoning_effort", None)
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resp = self._client.responses.create(**forced)
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d = _as_dict(resp) or {}
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text = str(d.get("output_text") or "")
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tool_calls = _collect_tool_calls_from_response_dict(d)
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if on_token and text:
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on_token(text)
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return LLMResponse(content=text, tool_calls=tool_calls)
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except Exception:
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pass
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nonstream_errors.append(emsg2)
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continue
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raise RuntimeError(
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"openai_responses_request_failed: "
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