施工员andmultica-agent
3eaa36a8d5
主站 - 新增 m0006 invite_codes 迁移；注册强制邀请码（首个管理员除外），消码与建号 同一事务，并发提交只有一个能成功 - 新增 /api/hub-admin/* 服务端点（共享 HUB_ADMIN_TOKEN，先于鉴权校验），供数据 中枢桥接读写会话/密码/模型池/会员/邀请码，并提供供应商模型列表拉取 - 前端：注册表单加邀请码（桌面 login、index.html、移动端）；「系统管理」改为 「数据中枢」入口指向 8766，原模型池与会员管理分区移除，仅留「行情管理」； 随之清理陈旧 CSS 数据中枢 - 取消独立账号：删除 hub_admin/hub_sessions 与登录、改密、锁定逻辑，改为校验 主站 xiaobai_session，仅管理员可进，CSRF 由会话派生，危险操作二次确认走主站 - 控制台新增数据源凭证可编辑区（原有内容一项不删）、供应商制模型池（自动拉取 /models，失败退回卡内手动录入）、会员管理与邀请码页 - 日夜双主题：颜色收敛为同名 token 换值，SVG 改用 inline style 以吃到变量 自测 - 主站 verify_baseline 通过（498 项）；数据中枢 235 项通过 - tools/verify_datahub_console.py 端到端跑通两服务真实对话； tools/verify_datahub_console_ui.py 浏览器跑通门禁/凭证/模型池/会员/主题/1030 窄屏 Co-authored-by: multica-agent <github@multica.ai>
265 lines
10 KiB
Python
265 lines
10 KiB
Python
from __future__ import annotations
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from datetime import datetime, timezone
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from typing import Any
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from urllib.parse import urlparse
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from backend.bootstrap.config import validate_text
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from backend.features.screener.compiler import LLMCompilerError, test_llm_connection
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from backend.llm import transport as llm_transport
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class LLMServiceMixin:
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def _personal_llm_profile(self) -> dict[str, Any]:
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credentials = self._credentials()
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return {
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"source": "personal",
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"primary": {
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"api_key": credentials["llm_primary_api_key"],
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"base_url": credentials["llm_primary_base_url"],
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"model": credentials["llm_primary_model"],
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},
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"fallback": {
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"api_key": credentials["llm_fallback_api_key"],
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"base_url": credentials["llm_fallback_base_url"],
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"model": credentials["llm_fallback_model"],
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},
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}
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def _platform_llm_profile(self) -> dict[str, Any]:
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models = {
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str(item.get("id") or ""): item
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for item in self._system_credentials.get("llm_models") or []
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if isinstance(item, dict) and item.get("id")
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}
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def selected(role: str) -> dict[str, str]:
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item = models.get(str(self._system_credentials.get(f"{role}_model_id") or ""), {})
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return {
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"id": str(item.get("id") or ""),
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"name": str(item.get("name") or ""),
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"api_key": str(item.get("api_key") or ""),
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"base_url": str(item.get("base_url") or ""),
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"model": str(item.get("model") or ""),
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}
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return {
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"source": "platform",
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"primary": selected("primary"),
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"fallback": selected("fallback"),
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}
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@staticmethod
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def _profile_configured(profile: dict[str, str]) -> bool:
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return bool(profile.get("api_key") and profile.get("base_url") and profile.get("model"))
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def _resolved_llm_profile(self) -> dict[str, Any]:
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platform = self._platform_llm_profile()
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platform_ready = self.membership()["active"] and self._profile_configured(platform["primary"])
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if platform_ready:
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return platform
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return {"source": "none", "primary": {}, "fallback": {}}
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@property
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def llm_primary_api_key(self) -> str:
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return str(self._resolved_llm_profile()["primary"].get("api_key") or "")
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@property
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def llm_primary_base_url(self) -> str:
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return str(self._resolved_llm_profile()["primary"].get("base_url") or "")
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@property
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def llm_primary_model(self) -> str:
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return str(self._resolved_llm_profile()["primary"].get("model") or "")
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@property
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def llm_fallback_api_key(self) -> str:
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return str(self._resolved_llm_profile()["fallback"].get("api_key") or "")
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@property
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def llm_fallback_base_url(self) -> str:
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return str(self._resolved_llm_profile()["fallback"].get("base_url") or "")
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@property
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def llm_fallback_model(self) -> str:
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return str(self._resolved_llm_profile()["fallback"].get("model") or "")
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@property
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def llm_source(self) -> str:
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return str(self._resolved_llm_profile().get("source") or "none")
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@property
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def llm_configured(self) -> bool:
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return bool(self.llm_primary_api_key and self.llm_primary_model)
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@property
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def llm_fallback_configured(self) -> bool:
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return bool(
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self.llm_fallback_api_key
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and self.llm_fallback_base_url
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and self.llm_fallback_model
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)
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def save_llm_settings(
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self,
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primary: dict[str, Any],
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fallback: dict[str, Any],
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fallback_enabled: bool,
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) -> None:
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personal = self._personal_llm_profile()
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primary_profile = self._validate_llm_profile(
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primary,
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personal["primary"],
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required=True,
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label="主模型",
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)
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if fallback_enabled:
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fallback_profile = self._validate_llm_profile(
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fallback,
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personal["fallback"],
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required=True,
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label="辅助模型",
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)
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else:
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fallback_profile = {"api_key": "", "base_url": "", "model": ""}
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credentials = self._credentials()
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credentials.update(
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{
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"llm_primary_api_key": primary_profile["api_key"],
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"llm_primary_base_url": primary_profile["base_url"],
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"llm_primary_model": primary_profile["model"],
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"llm_fallback_api_key": fallback_profile["api_key"],
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"llm_fallback_base_url": fallback_profile["base_url"],
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"llm_fallback_model": fallback_profile["model"],
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}
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)
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self._save_credentials(credentials)
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def save_llm_mode(self, mode: str) -> None:
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raise ValueError("LLM 算力由管理员统一配置，会员账号自动使用平台模型。")
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def test_llm_profile(self, role: str, payload: dict[str, Any]) -> dict[str, Any]:
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personal = self._personal_llm_profile()
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if role == "primary":
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current = personal["primary"]
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label = "主模型"
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elif role == "fallback":
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current = personal["fallback"]
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label = "辅助模型"
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else:
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raise ValueError("模型角色不支持。")
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profile = self._validate_llm_profile(payload, current, required=True, label=label)
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try:
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return self.llm_gateway.probe(
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profile,
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lambda model: test_llm_connection(
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model.api_key, model.base_url, model.model
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),
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)
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except LLMCompilerError as exc:
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raise ValueError(str(exc)) from exc
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@staticmethod
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def _validate_llm_profile(
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payload: dict[str, Any],
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current: dict[str, str],
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required: bool,
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label: str,
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) -> dict[str, str]:
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api_key = str(payload.get("api_key") or current.get("api_key") or "").strip()
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base_url = str(payload.get("base_url") or current.get("base_url") or "").strip().rstrip("/")
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model = str(payload.get("model") or current.get("model") or "").strip()
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if not required and not any((api_key, base_url, model)):
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return {"api_key": "", "base_url": "", "model": ""}
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parsed = urlparse(base_url)
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if parsed.scheme not in {"http", "https"} or not parsed.netloc:
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raise ValueError(f"{label} Base URL 格式不正确。")
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if not api_key or len(api_key) > 300:
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raise ValueError(f"{label} API Key 不能为空或过长。")
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if not model or len(model) > 100:
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raise ValueError(f"{label}模型名称不能为空或过长。")
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return {"api_key": api_key, "base_url": base_url, "model": model}
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def llm_access_status(self) -> dict[str, Any]:
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platform = self._platform_llm_profile()
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membership = self.membership()
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limit = max(1, int(self._system_credentials.get("member_daily_limit") or 50))
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used = self._platform_usage_today() if membership["active"] else 0
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resolved = self._resolved_llm_profile()
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return {
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"mode": "platform" if membership["active"] else "locked",
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"resolved_source": resolved.get("source") or "none",
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"resolved_model": str(resolved.get("primary", {}).get("model") or ""),
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"platform_configured": self._profile_configured(platform["primary"]),
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"membership": membership,
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"daily_limit": limit,
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"used_today": used,
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"remaining_calls": None if membership["is_admin"] else max(0, limit - used),
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}
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def _platform_usage_today(self) -> int:
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return self._platform_usage_today_for_user(self.current_user_id)
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def _platform_usage_today_for_user(self, user_id: int) -> int:
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now = datetime.now().astimezone()
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start = now.replace(hour=0, minute=0, second=0, microsecond=0).astimezone(timezone.utc)
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return self.database.count_llm_usage_since(
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user_id,
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"platform",
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start.isoformat(timespec="seconds"),
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)
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def fetch_llm_models(self, base_url: str, api_key: str) -> list[str]:
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base_url = str(base_url or "").strip().rstrip("/")
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parsed = urlparse(base_url)
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if parsed.scheme not in {"http", "https"} or not parsed.netloc:
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raise ValueError("Base URL 格式不正确。")
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key = str(api_key or "").strip()
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if not key:
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key = self._stored_api_key(base_url)
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if not key:
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raise ValueError("该供应商尚未保存 API Key，请先填写后再拉取模型列表。")
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try:
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return llm_transport.list_models(
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api_key=key,
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base_url=base_url,
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timeout=15,
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user_agent="XiaobaiReviewWeb/0.5",
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)
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except llm_transport.OpenAIHTTPError as exc:
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raise ValueError(exc.describe("模型列表拉取失败")) from exc
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except llm_transport.OpenAITransportError as exc:
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raise ValueError(f"模型列表拉取失败：{exc}") from exc
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def _stored_api_key(self, base_url: str) -> str:
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for item in self._system_credentials.get("llm_models") or []:
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if not isinstance(item, dict):
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continue
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stored = str(item.get("base_url") or "").strip().rstrip("/")
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if stored == base_url and item.get("api_key"):
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return str(item["api_key"])
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return ""
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def test_system_llm_profile(self, model_id: str, payload: dict[str, Any]) -> dict[str, Any]:
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current = next(
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(
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item
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for item in self._system_credentials.get("llm_models") or []
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if str(item.get("id") or "") == model_id
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),
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{},
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)
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label = validate_text(payload.get("name") or current.get("name"), "模型名称", 50, required=True)
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profile = self._validate_llm_profile(
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payload, current, required=True, label=label
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)
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try:
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return self.llm_gateway.probe(
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profile,
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lambda model: test_llm_connection(
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model.api_key, model.base_url, model.model
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),
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)
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except LLMCompilerError as exc:
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raise ValueError(str(exc)) from exc
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