mirror of
https://github.com/hansjone/oclaw.git
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oliver
faeb067856
- Image expert: DashScope-style /chat/completions via image_legacy_client; early exit in direct_loop when skill_binding_role is image; shared placeholder helper; docs/IMAGE_SPECIALIST_LANE.md. - Strict attachment ACL: link_attachment_acl on assistant chat_message rows (sqlite_store); chat attachment rate limit when user_id empty; admin chat tests updated. - Admin chat UI: aggregate bubbles render assistant_text attachments (image_ref); WS expand path. - turn_runner: persisted_chat_attachments_nonempty for final_msg selection. - OpenAI Responses transport + agent_messages/agent_core_attempt adjustments; env docs and tests. Co-authored-by: Cursor <cursoragent@cursor.com>
730 lines
32 KiB
Python
730 lines
32 KiB
Python
from __future__ import annotations
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import json
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import logging
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import os
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import sys
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import uuid
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from typing import Any, Optional
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from collections.abc import Callable, Iterable
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from oclaw.platform.llm.transports.base import ChatModel, LLMResponse, LLMToolCall, normalize_image_b64_payload
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logger = logging.getLogger(__name__)
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def _redact_nested_json_preview(obj: Any, *, max_chars: int = 4000) -> str:
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"""Best-effort JSON preview for logs (truncate; redact mega data URLs)."""
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try:
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s = json.dumps(obj, ensure_ascii=False, default=str)
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except Exception:
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s = str(obj)
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def _clip_data_urls(t: str) -> str:
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out_parts: list[str] = []
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i = 0
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needle = "data:"
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while i < len(t):
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j = t.find(needle, i)
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if j < 0:
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out_parts.append(t[i:])
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break
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out_parts.append(t[i:j])
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k = j + len(needle)
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while k < len(t) and t[k] not in "?;, \n\r\t\"]":
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k += 1
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if k < len(t) and t[k:k + 8] == ";base64,":
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end = k + 8
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while end < len(t) and t[end] not in "\"}] \n\r\t":
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end += 1
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seg = end - (k + 8)
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out_parts.append(f"data:<redacted base64 ~{seg} chars>")
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i = end
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continue
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out_parts.append(t[j:k])
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i = k
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return "".join(out_parts)
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s = _clip_data_urls(s)
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if len(s) > max_chars:
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return s[: max_chars - 40] + "\n...<<truncated>>\n..."
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return s
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def _deep_redact_for_debug(obj: Any) -> Any:
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"""Recursive copy for logs: shorten giant ``data:...;base64,...`` and very long strings."""
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if isinstance(obj, dict):
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return {str(k): _deep_redact_for_debug(v) for k, v in obj.items()}
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if isinstance(obj, list):
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return [_deep_redact_for_debug(x) for x in obj]
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if isinstance(obj, str):
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s = obj
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if ";base64," in s and s.strip().startswith("data:") and len(s) > 160:
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head, _, _tail = s.partition(";base64,")
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return f"{head};base64,<redacted ~{len(s) - len(head) - 8} b64 chars>"
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if len(s) > 16_000:
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return s[:12_000] + f"...<truncated {len(s) - 12_000} chars>"
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return s
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return obj
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def _safe_int(raw: str | None, default: int, *, max_value: int = 2_000_000) -> int:
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try:
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value = int(str(raw or "").strip())
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except Exception:
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return default
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if value < 1:
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return default
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return min(value, max_value)
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def _log_openai_responses_wire_kwargs(payload: dict[str, Any], *, variant: str, phase: str) -> None:
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"""Temporary diagnostics: log kwargs passed into ``OpenAI.responses.create`` (after our assembly).
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Enable: ``AIA_RESPONSES_DEBUG_PRINT_PAYLOAD=1`` — emits ``logging.warning`` **and** the same JSON on **stderr**
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(gateway控制台默认可见，不依赖 logging level)。
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Notes:
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- OpenAI SDK may still apply minor JSON transforms on send; this matches **our** arguments.
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- Base64 / long ``data:`` URLs are redacted to keep logs readable.
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"""
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raw = str(os.getenv("AIA_RESPONSES_DEBUG_PRINT_PAYLOAD") or "").strip().lower()
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if raw not in ("1", "true", "yes", "on"):
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return
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try:
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dbg = _deep_redact_for_debug(dict(payload))
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txt = json.dumps(dbg, ensure_ascii=False, indent=2, default=str)
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cap = _safe_int(os.getenv("AIA_RESPONSES_DEBUG_PRINT_MAX_CHARS"), 120_000, max_value=500_000)
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if len(txt) > cap:
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txt = txt[: max(cap - 80, 0)] + "\n...<<truncated>>\n"
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logger.warning(
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"openai_responses DEBUG wire kwargs [%s] variant=%s (%d chars):\n%s",
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phase,
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variant,
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len(txt),
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txt,
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)
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try:
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sys.stderr.write(
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f"\n[oclaw openai_responses DEBUG] phase={phase} variant={variant} chars={len(txt)}\n{txt}\n\n"
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)
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sys.stderr.flush()
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except Exception:
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pass
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except Exception as exc:
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logger.warning("openai_responses DEBUG payload serialization failed: %s", exc)
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def _openai_sdk_diagnostic_text(exc: BaseException) -> str:
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"""``str(APIStatusError)`` is often only ``Error code: 400``; validation detail lives in ``body['message']``."""
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chunks: list[str] = [str(exc)]
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msg = getattr(exc, "message", None)
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if isinstance(msg, str) and msg.strip() and msg not in chunks:
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chunks.append(msg)
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bod = getattr(exc, "body", None)
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if isinstance(bod, dict):
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bm = bod.get("message")
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if isinstance(bm, str) and bm.strip():
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chunks.append(bm)
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err = bod.get("error")
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if isinstance(err, dict):
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em = err.get("message")
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if isinstance(em, str) and em.strip():
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chunks.append(em)
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return "\n".join(chunks)
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def _is_input_messages_validation_error(exc: BaseException) -> bool:
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"""Third-party gateways often return 400/422 with Pydantic paths like ``input.messages.0.role``."""
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m = _openai_sdk_diagnostic_text(exc).lower()
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if "input.messages" in m:
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return True
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if "input should be 'user'" in m and "content" in m:
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return True
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return False
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def _as_dict(obj: Any) -> dict[str, Any] | None:
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if obj is None:
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return None
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if isinstance(obj, dict):
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return obj
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if hasattr(obj, "model_dump"):
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try:
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d = obj.model_dump(mode="python")
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return d if isinstance(d, dict) else None
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except Exception:
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return None
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return None
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def _collect_tool_calls_from_response_dict(resp: dict[str, Any]) -> list[LLMToolCall]:
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out: list[LLMToolCall] = []
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items = resp.get("output")
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if not isinstance(items, list):
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return out
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for it in items:
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d = _as_dict(it)
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if not d:
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continue
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if str(d.get("type") or "") in ("function_call", "tool_call"):
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call_id = str(d.get("call_id") or d.get("id") or "") or f"call_{uuid.uuid4().hex}"
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name = str(d.get("name") or "") or str((d.get("function") or {}).get("name") or "")
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args = d.get("arguments") or d.get("input") or {}
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if isinstance(args, str):
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try:
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args = json.loads(args)
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except Exception:
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args = {"_raw": args}
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if not isinstance(args, dict):
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args = {"_raw": args}
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out.append(LLMToolCall(id=call_id, name=name, arguments=args, thought_signature=None))
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return out
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def parse_openai_responses_stream_events(
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events: Iterable[Any],
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*,
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on_token: Optional[Callable[[str], None]] = None,
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) -> tuple[str, list[LLMToolCall], dict[str, Any] | None]:
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"""Pure stream parser (offline-testable).
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Returns: (assembled_text, tool_calls, final_response_dict?)
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"""
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parts: list[str] = []
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final_resp: dict[str, Any] | None = None
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for ev in events:
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d = _as_dict(ev) or {}
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typ = str(d.get("type") or "")
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# Text deltas
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if typ in ("response.output_text.delta", "response.output_text"):
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delta = d.get("delta")
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if delta is None:
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delta = d.get("text")
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s = str(delta or "")
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if s:
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parts.append(s)
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if on_token:
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on_token(s)
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continue
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# Sometimes the SDK emits generic `response.delta` with nested segments.
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if typ == "response.delta":
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delta = d.get("delta")
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if isinstance(delta, dict):
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txt = delta.get("output_text")
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if isinstance(txt, str) and txt:
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parts.append(txt)
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if on_token:
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on_token(txt)
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continue
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# Terminal response object
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if typ in ("response.completed", "response.complete", "response.done"):
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resp = d.get("response") or d.get("data") or d.get("result")
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final_resp = _as_dict(resp) or final_resp
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continue
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if typ == "response.created":
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continue
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# Some SDK versions yield the response object directly (no explicit event type).
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if d.get("output") is not None and d.get("id") is not None:
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final_resp = d
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text = "".join(parts)
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tool_calls = _collect_tool_calls_from_response_dict(final_resp) if final_resp else []
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return text, tool_calls, final_resp
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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__(
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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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from openai import OpenAI
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except Exception as e:
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raise RuntimeError("未安装 openai 依赖，请先 pip install -r requirements.txt") from e
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kw: dict[str, Any] = {"api_key": self.api_key}
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if self.base_url:
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kw["base_url"] = self.base_url
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self._client = OpenAI(**kw)
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@staticmethod
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def _strip_leading_system_messages(messages: list[dict[str, Any]]) -> tuple[str, list[dict[str, Any]]]:
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"""Separate leading ``role=system`` rows for ``instructions=` (Responses API expects user-shaped ``input.messages``)."""
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msgs = messages or []
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sys_chunks: list[str] = []
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i = 0
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while i < len(msgs):
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m = msgs[i]
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if not isinstance(m, dict):
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break
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if str(m.get("role") or "").strip().lower() != "system":
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break
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c = m.get("content")
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if isinstance(c, str) and c.strip():
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sys_chunks.append(c.strip())
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elif isinstance(c, list):
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texts: list[str] = []
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for it in c:
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if not isinstance(it, dict):
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continue
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tt = str(it.get("type") or "").strip().lower()
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if tt in ("text", "input_text"):
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xs = str(it.get("text") or "").strip()
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if xs:
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texts.append(xs)
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elif isinstance(it.get("text"), str) and str(it.get("text")).strip():
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texts.append(str(it.get("text")).strip())
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if texts:
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sys_chunks.append("\n".join(texts))
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i += 1
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joined = "\n\n".join(sys_chunks).strip()
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return joined, list(msgs[i:])
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@staticmethod
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def _normalize_messages(
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messages: list[dict[str, Any]],
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*,
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envelope_openai_message: bool = True,
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content_chat_completions_parts: bool = False,
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image_detail_auto: bool = True,
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) -> list[dict[str, Any]]:
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"""
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Build Response input rows used under ``input`` (flat array) or ``input.messages``.
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- **Responses content** (default when ``content_chat_completions_parts=False``): each part is
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``{"type":"input_text",...}`` / ``{"type":"input_image","image_url":"<str>","detail":"auto"}``
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(`OpenAI response_input_*` TypedDict surface).
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- **Chat completions content** (``content_chat_completions_parts=True``): ``text`` /
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``image_url`` multimodal blocks.
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- **Envelope** ``envelope_openai_message=True``: ``{"type":"message","role","content":[...]}``;
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``False``: ``{"role","content":[...]}`` (per some Bailian curls).
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"""
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out: list[dict[str, Any]] = []
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for m in messages or []:
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if not isinstance(m, dict):
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continue
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role = str(m.get("role") or "user").strip().lower() or "user"
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content = m.get("content")
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norm_content: list[dict[str, Any]] = []
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def _append_image_part(url_value: str) -> None:
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if content_chat_completions_parts:
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norm_content.append({"type": "image_url", "image_url": {"url": url_value}})
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else:
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p: dict[str, Any] = {"type": "input_image", "image_url": url_value}
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if image_detail_auto:
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p["detail"] = "auto"
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norm_content.append(p)
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def _append_text_part(txt: str) -> None:
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if content_chat_completions_parts:
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norm_content.append({"type": "text", "text": txt})
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else:
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norm_content.append({"type": "input_text", "text": txt})
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if isinstance(content, list):
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for item in content:
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if isinstance(item, dict) and item.get("type") == "input_image":
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mime = str(item.get("mime") or "image/jpeg").strip() or "image/jpeg"
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b64 = normalize_image_b64_payload(item.get("image_base64") or item.get("data"))
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if not b64:
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continue
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_append_image_part(f"data:{mime};base64,{b64}")
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continue
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if isinstance(item, dict) and str(item.get("type") or "").strip().lower() == "image_url":
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iu = item.get("image_url")
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url = ""
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if isinstance(iu, dict):
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url = str(iu.get("url") or "").strip()
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elif isinstance(iu, str):
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url = iu.strip()
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if url.startswith(("http://", "https://", "data:")):
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_append_image_part(url)
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continue
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if isinstance(item, dict) and item.get("type") in ("input_text", "text"):
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t = str(item.get("text") or "").strip()
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if t:
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_append_text_part(t)
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continue
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if isinstance(item, dict):
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itype = str(item.get("type") or "").strip().lower()
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img_key = item.get("image")
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if isinstance(img_key, str) and img_key.strip() and itype != "input_image":
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uu = img_key.strip()
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if uu.startswith(("http://", "https://", "data:")):
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_append_image_part(uu)
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continue
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if isinstance(item.get("text"), str) and str(item.get("text") or "").strip():
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_append_text_part(str(item.get("text") or "").strip())
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continue
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s = str(item.get("text") or "").strip()
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if s:
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_append_text_part(s)
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continue
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s = str(item or "").strip()
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if s:
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_append_text_part(s)
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elif isinstance(content, str):
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txt = content.strip()
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if txt:
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_append_text_part(txt)
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elif content is not None:
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s = str(content).strip()
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if s:
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_append_text_part(s)
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if not norm_content:
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continue
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coerced_role = "user"
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if role != "user":
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prefix = "assistant" if role == "assistant" else ("system" if role == "system" else role)
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norm_content.insert(
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0,
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OpenAIResponsesModel._role_prefix_part(prefix, content_chat_completions_parts),
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)
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if envelope_openai_message:
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out.append({"type": "message", "role": coerced_role, "content": norm_content})
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else:
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out.append({"role": coerced_role, "content": norm_content})
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return out
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@staticmethod
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def _role_prefix_part(prefix: str, chat_parts: bool) -> dict[str, Any]:
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t = f"[{prefix}]"
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if chat_parts:
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return {"type": "text", "text": t}
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return {"type": "input_text", "text": t}
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@staticmethod
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def _responses_input_candidates(
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msgs: list[dict[str, Any]],
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*,
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flat_responses: bool,
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prefer_envelope: bool,
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prefer_chat_parts: bool,
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) -> list[tuple[str, Any]]:
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"""Several gateways validate ``input.messages`` differently; try a small deterministic set."""
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def N(env: bool, chat: bool) -> list[dict[str, Any]]:
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return OpenAIResponsesModel._normalize_messages(
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msgs,
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envelope_openai_message=env,
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content_chat_completions_parts=chat,
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)
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keys_seen: set[str] = set()
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out: list[tuple[str, Any]] = []
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def push(tag: str, inp: Any) -> None:
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try:
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sk = json.dumps(inp, ensure_ascii=False, sort_keys=True, default=str)
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except Exception:
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sk = repr(inp)
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if sk in keys_seen:
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return
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keys_seen.add(sk)
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out.append((tag, inp))
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if flat_responses:
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for env in (prefer_envelope, not prefer_envelope):
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push(f"flat_envelope_{env}", N(env, False))
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return out
|
||
|
||
# Prefer profile/env defaults first (``AIA_RESPONSES_NESTED_CHAT_PARTS`` / envelope toggles), then a fixed
|
||
# fallback ring so picky gateways still get Chat-shaped ``input.messages`` without dropping multimodal pixels.
|
||
primary_combo = (prefer_envelope, prefer_chat_parts)
|
||
fallback_ring = [(False, True), (False, False), (True, False), (True, True)]
|
||
combos = [primary_combo] + [p for p in fallback_ring if p != primary_combo]
|
||
for env, chat in combos:
|
||
norm = N(env, chat)
|
||
push(f"e{int(env)}c{int(chat)}_messages", {"messages": norm})
|
||
push(f"e{int(env)}c{int(chat)}_flat_input", norm)
|
||
return out
|
||
|
||
def chat(
|
||
self,
|
||
messages: list[dict[str, Any]],
|
||
tools: list[dict[str, Any]],
|
||
*,
|
||
on_token: Optional[Callable[[str], None]] = None,
|
||
) -> LLMResponse:
|
||
sys_instructions, rest = OpenAIResponsesModel._strip_leading_system_messages(messages)
|
||
def _env_truthy(name: str) -> bool:
|
||
return str(os.getenv(name) or "").strip().lower() in ("1", "true", "yes", "on")
|
||
|
||
if _env_truthy("AIA_RESPONSES_DEBUG_PRINT_PAYLOAD"):
|
||
try:
|
||
sys.stderr.write(
|
||
"[oclaw openai_responses] chat() ENTERED "
|
||
f"model={self.model!r} base_url={self.base_url!r} "
|
||
f"messages={len(messages)} tools={len(tools or [])}\n"
|
||
)
|
||
sys.stderr.flush()
|
||
except Exception:
|
||
pass
|
||
|
||
# Default: nested ``input.messages`` rows use **Responses** parts (``input_text`` / ``input_image``).
|
||
# Flat Bailian ``input`` array: ``AIA_RESPONSES_INPUT_IS_FLAT_LIST=1`` (optional ``AIA_RESPONSES_FLAT_OPENAI_ENVELOPE``).
|
||
flat_raw = os.getenv("AIA_RESPONSES_INPUT_IS_FLAT_LIST")
|
||
if flat_raw is not None and str(flat_raw).strip():
|
||
flat_responses = _env_truthy("AIA_RESPONSES_INPUT_IS_FLAT_LIST")
|
||
else:
|
||
legacy = os.getenv("AIA_RESPONSES_INPUT_USE_MESSAGES_ARRAY")
|
||
if legacy is None or not str(legacy).strip():
|
||
flat_responses = False
|
||
else:
|
||
flat_responses = not _env_truthy("AIA_RESPONSES_INPUT_USE_MESSAGES_ARRAY")
|
||
|
||
# Nested ``input.messages`` defaults to **Responses** content parts (``input_*``) + ``type:message``,
|
||
# matching OpenAI ``EasyInputMessageParam`` / Model Studio expanded examples. Some proxies wrongly
|
||
# expect Chat ``text``/``image_url`` parts → ``AIA_RESPONSES_NESTED_CHAT_PARTS=1``.
|
||
if flat_responses:
|
||
envelope_openai_message = _env_truthy("AIA_RESPONSES_FLAT_OPENAI_ENVELOPE")
|
||
nested_chat_parts = False
|
||
else:
|
||
envelope_openai_message = True
|
||
nested_chat_parts = _env_truthy("AIA_RESPONSES_NESTED_CHAT_PARTS")
|
||
|
||
def _normalize_batch(msgs: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||
return OpenAIResponsesModel._normalize_messages(
|
||
msgs,
|
||
envelope_openai_message=envelope_openai_message,
|
||
content_chat_completions_parts=nested_chat_parts,
|
||
)
|
||
|
||
msgs_for_norm: list[dict[str, Any]]
|
||
if rest:
|
||
msgs_for_norm = rest
|
||
norm = _normalize_batch(rest)
|
||
inst_kw: dict[str, Any] = {}
|
||
if sys_instructions.strip():
|
||
si = sys_instructions.strip()
|
||
if len(si) > 80_000:
|
||
si = si[:80_000] + "\n...[truncated]"
|
||
inst_kw = {"instructions": si}
|
||
if not norm:
|
||
msgs_for_norm = messages
|
||
norm = _normalize_batch(messages)
|
||
inst_kw = {}
|
||
else:
|
||
msgs_for_norm = messages
|
||
norm = _normalize_batch(messages)
|
||
inst_kw = {}
|
||
|
||
explicit_variants = os.getenv("AIA_RESPONSES_INPUT_VARIANTS")
|
||
bu_norm = (self.base_url or "").strip().lower()
|
||
# CRITICAL: do **not** treat empty ``base_url`` as "official" here. Clients often omit storing the
|
||
# default URL in profiles while still hitting third-party gateways via env/SDK defaults—but then we
|
||
# would wrongly disable alternate ``input`` shapes and only ever send ``primary``.
|
||
host_is_explicit_official_api = bool(bu_norm) and ("api.openai.com" in bu_norm)
|
||
if explicit_variants is not None and str(explicit_variants).strip():
|
||
multi_shape = _env_truthy("AIA_RESPONSES_INPUT_VARIANTS")
|
||
else:
|
||
multi_shape = not host_is_explicit_official_api
|
||
|
||
if flat_responses:
|
||
primary_input: Any = norm
|
||
else:
|
||
primary_input = {"messages": norm}
|
||
|
||
input_candidates: list[tuple[str, Any]]
|
||
if multi_shape and not _env_truthy("AIA_RESPONSES_DISABLE_INPUT_VARIANTS"):
|
||
input_candidates = OpenAIResponsesModel._responses_input_candidates(
|
||
msgs_for_norm,
|
||
flat_responses=flat_responses,
|
||
prefer_envelope=envelope_openai_message,
|
||
prefer_chat_parts=nested_chat_parts,
|
||
)
|
||
else:
|
||
input_candidates = [("primary", primary_input)]
|
||
|
||
stream_errors: list[str] = []
|
||
b = str(self.base_url or "").strip().lower()
|
||
force_disable = str(os.getenv("AIA_LLM_THINKING_FORCE_DISABLED") or "").strip().lower() in ("1", "true", "yes", "on")
|
||
force_enable = str(os.getenv("AIA_LLM_THINKING_FORCE_ENABLED") or "").strip().lower() in ("1", "true", "yes", "on")
|
||
mode_enabled = bool(getattr(self, "thinking_mode_enabled", False))
|
||
extra_body: dict[str, Any] = {}
|
||
if b and ("api.openai.com" not in b):
|
||
if force_disable:
|
||
extra_body["thinking"] = {"type": "disabled"}
|
||
elif force_enable or mode_enabled:
|
||
extra_body["thinking"] = {"type": "enabled"}
|
||
else:
|
||
# Default: disable thinking for non-official gateways unless explicitly enabled.
|
||
if str(os.getenv("AIA_LLM_THINKING_DISABLED") or "1").strip().lower() in ("1", "true", "yes", "on"):
|
||
extra_body["thinking"] = {"type": "disabled"}
|
||
thinking = {"extra_body": extra_body} if extra_body else {}
|
||
reasoning_effort = str(getattr(self, "reasoning_effort", "") or "").strip().lower()
|
||
if reasoning_effort not in ("low", "medium", "high"):
|
||
reasoning_effort = ""
|
||
base_stream_template: dict[str, Any] = {
|
||
**thinking,
|
||
**inst_kw,
|
||
**({"reasoning_effort": reasoning_effort} if reasoning_effort else {}),
|
||
"model": self.model,
|
||
"tools": tools or None,
|
||
"stream": True,
|
||
}
|
||
try:
|
||
for cand_label, responses_input in input_candidates:
|
||
stream_payload = {**base_stream_template, "input": responses_input}
|
||
if _env_truthy("AIA_RESPONSES_LOG_PAYLOAD_SUMMARY"):
|
||
logger.warning(
|
||
"openai_responses [%s] payload summary (redacted): %s",
|
||
cand_label,
|
||
_redact_nested_json_preview(
|
||
{
|
||
"model": self.model,
|
||
"input": responses_input,
|
||
"has_instructions": bool(inst_kw.get("instructions")),
|
||
"tools_n": len(tools or []),
|
||
"stream": True,
|
||
}
|
||
),
|
||
)
|
||
payload = stream_payload
|
||
_log_openai_responses_wire_kwargs(payload, variant=str(cand_label), phase="stream")
|
||
try:
|
||
stream = self._client.responses.create(**payload)
|
||
text, tool_calls, final_resp = parse_openai_responses_stream_events(stream, on_token=on_token)
|
||
if (not text.strip()) and final_resp:
|
||
ot = final_resp.get("output_text")
|
||
if isinstance(ot, str) and ot.strip():
|
||
text = ot
|
||
if on_token:
|
||
on_token(text)
|
||
if cand_label != input_candidates[0][0]:
|
||
logger.info("openai_responses: succeeded with input variant %s", cand_label)
|
||
return LLMResponse(content=text, tool_calls=tool_calls)
|
||
except Exception as exc:
|
||
emsg = str(exc)
|
||
# Fallback: provider thinking-mode replay contract.
|
||
if "reasoning_content" in emsg and "thinking mode" in emsg and "must be passed back" in emsg:
|
||
try:
|
||
forced = dict(payload)
|
||
eb = forced.get("extra_body") if isinstance(forced.get("extra_body"), dict) else {}
|
||
eb = dict(eb)
|
||
eb["thinking"] = {"type": "disabled"}
|
||
forced["extra_body"] = eb
|
||
forced.pop("reasoning_effort", None)
|
||
_log_openai_responses_wire_kwargs(
|
||
forced,
|
||
variant=f"{cand_label}_thinking_disabled",
|
||
phase="stream_retry_thinking",
|
||
)
|
||
stream = self._client.responses.create(**forced)
|
||
text, tool_calls, final_resp = parse_openai_responses_stream_events(stream, on_token=on_token)
|
||
if (not text.strip()) and final_resp:
|
||
ot = final_resp.get("output_text")
|
||
if isinstance(ot, str) and ot.strip():
|
||
text = ot
|
||
if on_token:
|
||
on_token(text)
|
||
if cand_label != input_candidates[0][0]:
|
||
logger.info("openai_responses: succeeded with input variant %s", cand_label)
|
||
return LLMResponse(content=text, tool_calls=tool_calls)
|
||
except Exception:
|
||
pass
|
||
if _env_truthy("AIA_RESPONSES_LOG_API_ERROR_DETAIL"):
|
||
eb = getattr(exc, "body", None)
|
||
if eb is not None:
|
||
logger.warning(
|
||
"responses.create stream [%s] body: %s", cand_label, eb
|
||
)
|
||
if len(input_candidates) > 1 and _is_input_messages_validation_error(exc):
|
||
stream_errors.append(f"{cand_label}:{emsg}")
|
||
continue
|
||
stream_errors.append(emsg)
|
||
break
|
||
raise RuntimeError("; ".join(stream_errors) or "responses_stream_all_variants_failed")
|
||
except Exception as exc:
|
||
logger.info("responses stream failed; fallback to non-stream (%s)", exc)
|
||
nonstream_errors: list[str] = []
|
||
base_nonstream = {
|
||
**thinking,
|
||
**inst_kw,
|
||
**({"reasoning_effort": reasoning_effort} if reasoning_effort else {}),
|
||
"model": self.model,
|
||
"tools": tools or None,
|
||
}
|
||
for cand_label, responses_input in input_candidates:
|
||
payload = {**base_nonstream, "input": responses_input}
|
||
_log_openai_responses_wire_kwargs(payload, variant=str(cand_label), phase="non_stream")
|
||
try:
|
||
resp = self._client.responses.create(**payload)
|
||
d = _as_dict(resp) or {}
|
||
text = str(d.get("output_text") or "")
|
||
tool_calls = _collect_tool_calls_from_response_dict(d)
|
||
if on_token and text:
|
||
on_token(text)
|
||
if cand_label != input_candidates[0][0]:
|
||
logger.info("openai_responses non-stream: succeeded with input variant %s", cand_label)
|
||
return LLMResponse(content=text, tool_calls=tool_calls)
|
||
except Exception as e2:
|
||
emsg2 = str(e2)
|
||
if "reasoning_content" in emsg2 and "thinking mode" in emsg2 and "must be passed back" in emsg2:
|
||
try:
|
||
forced = dict(payload)
|
||
eb = forced.get("extra_body") if isinstance(forced.get("extra_body"), dict) else {}
|
||
eb = dict(eb)
|
||
eb["thinking"] = {"type": "disabled"}
|
||
forced["extra_body"] = eb
|
||
forced.pop("reasoning_effort", None)
|
||
_log_openai_responses_wire_kwargs(
|
||
forced,
|
||
variant=f"{cand_label}_thinking_disabled",
|
||
phase="non_stream_retry_thinking",
|
||
)
|
||
resp = self._client.responses.create(**forced)
|
||
d = _as_dict(resp) or {}
|
||
text = str(d.get("output_text") or "")
|
||
tool_calls = _collect_tool_calls_from_response_dict(d)
|
||
if on_token and text:
|
||
on_token(text)
|
||
if cand_label != input_candidates[0][0]:
|
||
logger.info(
|
||
"openai_responses non-stream: succeeded with input variant %s",
|
||
cand_label,
|
||
)
|
||
return LLMResponse(content=text, tool_calls=tool_calls)
|
||
except Exception:
|
||
pass
|
||
if _env_truthy("AIA_RESPONSES_LOG_API_ERROR_DETAIL"):
|
||
eb = getattr(e2, "body", None)
|
||
if eb is not None:
|
||
logger.warning(
|
||
"responses.create non-stream [%s] body: %s", cand_label, eb
|
||
)
|
||
if len(input_candidates) > 1 and _is_input_messages_validation_error(e2):
|
||
nonstream_errors.append(f"{cand_label}:{emsg2}")
|
||
continue
|
||
nonstream_errors.append(emsg2)
|
||
break
|
||
raise RuntimeError(
|
||
"openai_responses_request_failed: "
|
||
+ " | ".join([str(exc)] + nonstream_errors[-2:])
|
||
) from exc
|
||
|
||
|
||
__all__ = ["OpenAIResponsesModel", "parse_openai_responses_stream_events"]
|
||
|