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- 在 oclaw/ 下重新初始化 Git 仓库 - 补齐子仓库 .gitignore，避免提交本地运行态数据（_local、node_modules、logs 等） - 提交当前工程代码与配置 Made-with: Cursor
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309
platform/llm/transports/openai_chat_completions.py
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309
platform/llm/transports/openai_chat_completions.py
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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 re
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import uuid
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from typing import Any, Optional
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from collections.abc import Callable
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from oclaw.platform.llm.transports.base import ChatModel, LLMResponse, LLMToolCall, normalize_image_b64_payload, coerce_thought_signature_for_storage
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logger = logging.getLogger(__name__)
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def _likely_gemini_openai_compat_base_url(url: str | None) -> bool:
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if not url:
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return False
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u = url.lower()
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return "generativelanguage.googleapis.com" in u or "aiplatform.googleapis.com" in u
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def _model_id_suggests_gemini(model: str | None) -> bool:
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return "gemini" in (model or "").lower()
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def _find_thought_signature_in_obj(o: Any) -> str | None:
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if isinstance(o, dict):
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for k in ("thought_signature", "thoughtSignature"):
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v = o.get(k)
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if v is not None:
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coerced = coerce_thought_signature_for_storage(v)
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if coerced is not None:
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return coerced
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for v in o.values():
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found = _find_thought_signature_in_obj(v)
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if found is not None:
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return found
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elif isinstance(o, list):
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for v in o:
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found = _find_thought_signature_in_obj(v)
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if found is not None:
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return found
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return None
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def _extract_thought_signature_from_message_tool_call(tc: Any) -> str | None:
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if hasattr(tc, "model_dump"):
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try:
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d = tc.model_dump(mode="python")
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ec = d.get("extra_content")
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if isinstance(ec, dict):
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g = ec.get("google")
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if isinstance(g, dict) and "thought_signature" in g:
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return coerce_thought_signature_for_storage(g.get("thought_signature"))
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except Exception:
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pass
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ec = getattr(tc, "extra_content", None)
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if isinstance(ec, dict):
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g = ec.get("google")
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if isinstance(g, dict) and "thought_signature" in g:
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return coerce_thought_signature_for_storage(g.get("thought_signature"))
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return _extract_thought_signature_from_tool_delta(tc)
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def _extract_thought_signature_from_tool_delta(tc: Any) -> str | None:
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for attr in ("thought_signature", "thoughtSignature"):
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v = getattr(tc, attr, None)
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if v is not None:
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return coerce_thought_signature_for_storage(v)
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extra = getattr(tc, "model_extra", None)
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if isinstance(extra, dict):
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for k in ("thought_signature", "thoughtSignature"):
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ev = extra.get(k)
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if ev is not None:
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return coerce_thought_signature_for_storage(ev)
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pyd_ex = getattr(tc, "__pydantic_extra__", None)
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if isinstance(pyd_ex, dict):
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for k in ("thought_signature", "thoughtSignature"):
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ev = pyd_ex.get(k)
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if ev is not None:
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return coerce_thought_signature_for_storage(ev)
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if hasattr(tc, "model_dump"):
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try:
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found = _find_thought_signature_in_obj(tc.model_dump(mode="python"))
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if found is not None:
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return found
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except Exception:
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pass
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return None
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class OpenAIChatModel(ChatModel):
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def __init__(
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self,
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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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):
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self.model = model or os.getenv("OPENAI_MODEL") or "gpt-4o-mini"
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self.api_key = api_key or os.getenv("OPENAI_API_KEY")
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self.base_url = base_url or os.getenv("OPENAI_BASE_URL")
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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 模型")
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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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client_kwargs: dict[str, Any] = {"api_key": self.api_key}
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if self.base_url:
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client_kwargs["base_url"] = self.base_url
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self._client = OpenAI(**client_kwargs)
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self._skip_tools: bool = False
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def _create_chat_completion(self, norm_msgs: list[dict[str, Any]], tools: list[dict[str, Any]], *, stream: bool):
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# replay_policy/tool_wire_policy live in sibling modules; import lazily to avoid cycles.
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try:
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from oclaw.platform.llm.replay_policy import apply_replay_policy_to_messages, resolve_replay_policy
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policy = resolve_replay_policy(self.base_url, self.model)
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if policy.enabled:
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norm_msgs = apply_replay_policy_to_messages(norm_msgs, policy)
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except Exception as exc:
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logger.warning("replay_policy apply failed (%s); continuing without rewrite", exc)
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use_tools = bool(tools)
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cleaned_msgs: list[dict[str, Any]] = []
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for m in norm_msgs or []:
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if not isinstance(m, dict):
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continue
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if str(m.get("role") or "") == "tool":
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mm = dict(m)
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for k in ("tool_call_id", "call_id"):
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if k in mm:
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v = mm.get(k)
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try:
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sv = str(v).strip() if v is not None else ""
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except Exception:
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sv = ""
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if not sv:
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mm.pop(k, None)
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else:
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mm[k] = sv
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cleaned_msgs.append(mm)
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else:
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cleaned_msgs.append(m)
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kwargs: dict[str, Any] = {"model": self.model, "messages": cleaned_msgs, "stream": stream}
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if use_tools:
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try:
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from oclaw.platform.config.paths import db_path
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from oclaw.platform.persistence.sqlite_store import SqliteStore
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from oclaw.tools.exposure_plan import build_llm_tools_plan
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plan = build_llm_tools_plan(
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store=SqliteStore(db_path()),
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role="",
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base_url=self.base_url,
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max_json_bytes=_default_max_openai_tools_json_bytes(self.base_url),
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include_mcp=False,
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preview_internal=False,
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raw_openai_tools_override=tools,
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)
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kwargs["tools"] = plan.tools_wired
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except Exception:
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kwargs["tools"] = tools
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return self._client.chat.completions.create(**kwargs)
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def _llm_response_from_completion(self, completion: Any, *, on_token: Optional[Callable[[str], None]]) -> LLMResponse:
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msg = completion.choices[0].message
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reasoning_parts = getattr(msg, "reasoning_content", None) or ""
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reasoning_text = str(reasoning_parts).strip() if reasoning_parts else ""
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content = msg.content or ""
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if on_token and content:
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on_token(content)
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tool_calls: list[LLMToolCall] = []
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if msg.tool_calls:
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for tc in msg.tool_calls:
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tid = str(getattr(tc, "id", "") or "")
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fn = getattr(tc, "function", None)
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name = str(fn.name) if fn and getattr(fn, "name", None) else ""
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args_raw = str(fn.arguments) if fn and getattr(fn, "arguments", None) is not None else "{}"
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if not name and not tid:
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continue
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if not tid:
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tid = f"call_{uuid.uuid4().hex}"
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try:
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args = json.loads(args_raw)
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except json.JSONDecodeError:
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args = {"_raw": args_raw}
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sig = _extract_thought_signature_from_message_tool_call(tc)
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if sig is None and fn is not None:
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sig = _extract_thought_signature_from_tool_delta(fn)
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tool_calls.append(LLMToolCall(id=tid, name=name, arguments=args, thought_signature=sig))
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return LLMResponse(content=content, tool_calls=tool_calls, reasoning_content=reasoning_text)
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def chat(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]],
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*,
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on_token: Optional[Callable[[str], None]] = None,
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) -> LLMResponse:
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# Normalize input_image to image_url.
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norm_msgs = []
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for m in messages:
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if m.get("role") == "user" and isinstance(m.get("content"), list):
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norm_content = []
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for item in m["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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norm_content.append({"type": "image_url", "image_url": {"url": f"data:{mime};base64,{b64}"}})
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else:
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norm_content.append(item)
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norm_msgs.append({"role": m["role"], "content": norm_content})
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else:
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norm_msgs.append(m)
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try:
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stream = self._create_chat_completion(norm_msgs, tools, stream=True)
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except Exception as exc:
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logger.info("Streaming chat completion failed; fallback to non-stream (%s)", exc)
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completion = self._create_chat_completion(norm_msgs, tools, stream=False)
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return self._llm_response_from_completion(completion, on_token=on_token)
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content_parts: list[str] = []
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tool_acc: dict[int, dict[str, Any]] = {}
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reasoning_parts: list[str] = []
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def _consume_stream(stream_obj: Any) -> None:
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nonlocal content_parts, tool_acc, reasoning_parts
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for chunk in stream_obj:
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if not chunk.choices:
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continue
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delta = chunk.choices[0].delta
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rc = getattr(delta, "reasoning_content", None) or ""
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if rc:
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reasoning_parts.append(rc)
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if delta.tool_calls:
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for tc in delta.tool_calls:
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idx = int(tc.index)
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slot = tool_acc.setdefault(idx, {"id": None, "name": None, "arguments": "", "thought_signature": None})
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if tc.id:
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slot["id"] = tc.id
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if tc.function:
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if tc.function.name:
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slot["name"] = tc.function.name
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if tc.function.arguments:
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slot["arguments"] = f"{slot['arguments']}{tc.function.arguments}"
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sig = _extract_thought_signature_from_message_tool_call(tc)
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if sig is None:
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sig = _extract_thought_signature_from_tool_delta(tc)
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if sig is None and tc.function is not None:
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sig = _extract_thought_signature_from_tool_delta(tc.function)
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if sig is not None:
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slot["thought_signature"] = sig
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if delta.content:
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content_parts.append(delta.content)
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if on_token:
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on_token(delta.content)
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try:
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_consume_stream(stream)
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except Exception:
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raise
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content = "".join(content_parts)
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reasoning_text = "".join(reasoning_parts).strip()
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tool_calls: list[LLMToolCall] = []
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for idx in sorted(tool_acc.keys()):
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slot = tool_acc[idx]
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tid = slot.get("id") or ""
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name = slot.get("name") or ""
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args_raw = slot.get("arguments") or "{}"
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if not name and not tid:
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continue
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if not tid:
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tid = f"call_{uuid.uuid4().hex}"
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try:
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args = json.loads(args_raw)
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except json.JSONDecodeError:
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args = {"_raw": args_raw}
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ts_raw = slot.get("thought_signature")
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tsig = coerce_thought_signature_for_storage(ts_raw) if ts_raw is not None else None
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tool_calls.append(LLMToolCall(id=str(tid), name=str(name), arguments=args, thought_signature=tsig))
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return LLMResponse(content=content, tool_calls=tool_calls, reasoning_content=reasoning_text)
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__all__ = ["OpenAIChatModel", "_likely_gemini_openai_compat_base_url", "_model_id_suggests_gemini"]
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def _default_max_openai_tools_json_bytes(base_url: str | None) -> int | None:
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u = str(base_url or "").strip().lower()
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if not u:
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return None
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# Conservative caps for some OpenAI-compatible gateways.
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if "dashscope" in u or "aliyuncs" in u:
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return 28_000
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return None
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