refactor(*): 平台架构 - 拆分后端领域模块与画布前端组件并补充测试
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// 渠道模型选项的编码、解析与展示：channelId + 模型名组合成选择值，按能力过滤可选模型。
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import { type AiConfig, type ModelChannel, resolveModelChannel } from "./use-config-store";
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export const CHANNEL_MODEL_SEPARATOR = "::";
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export function uniqueModelOptions(models: string[]) {
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return Array.from(
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new Set(
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(models || [])
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.filter((model): model is string => typeof model === "string")
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.map((model) => model.trim())
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.filter(Boolean),
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),
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);
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}
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export function normalizeRawModelName(value: unknown) {
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if (typeof value !== "string") return "";
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const model = modelOptionName(value).trim();
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return model && model !== "undefined" && model !== "null" ? model : "";
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}
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export function encodeChannelModel(channelId: string, model: string) {
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return `${channelId}${CHANNEL_MODEL_SEPARATOR}${model.trim()}`;
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}
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export function isChannelModelValue(value: string) {
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return value.includes(CHANNEL_MODEL_SEPARATOR);
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}
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export function decodeChannelModel(value: string) {
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const index = value.indexOf(CHANNEL_MODEL_SEPARATOR);
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if (index < 0) return null;
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return { channelId: value.slice(0, index), model: value.slice(index + CHANNEL_MODEL_SEPARATOR.length) };
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}
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export function modelOptionName(value: string) {
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return decodeChannelModel(value)?.model || value;
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}
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export function modelDisplayName(config: AiConfig, value: string) {
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const model = modelOptionName(value);
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const channel = resolveModelChannel(config, value);
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const displayName = channel.modelCosts?.find((item) => item.model === model)?.displayName?.trim();
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if (displayName) return displayName;
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return channel.scope === "system" ? "系统模型" : model;
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}
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export function modelIcon(config: AiConfig, value: string) {
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const model = modelOptionName(value);
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return resolveModelChannel(config, value).modelCosts?.find((item) => item.model === model)?.icon || "";
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}
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export function modelOptionLabel(config: AiConfig, value: string) {
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const decoded = decodeChannelModel(value);
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if (!decoded) return modelDisplayName(config, value);
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const channel = config.channels.find((item) => item.id === decoded.channelId);
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const displayName = modelDisplayName(config, value);
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// 平台前台模型只展示公开名称；供应来源和内部目录适配器不属于创作端信息。
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if (!channel || channel.scope === "system") return displayName;
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return `${displayName}（${channel.name}）`;
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}
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export function modelOptionsFromChannels(channels: ModelChannel[]) {
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return uniqueModelOptions(
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channels.flatMap((channel) =>
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channel.models
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.map(normalizeRawModelName)
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.filter(Boolean)
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.filter((model) => channel.scope !== "system" || hasSystemModelPrice(channel, model))
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.map((model) => encodeChannelModel(channel.id, model)),
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),
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);
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}
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export function hasSystemModelPrice(channel: ModelChannel, model: string) {
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if (channel.scope !== "system") return true;
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// 价格字段已由后端按“非负数”校验；0 表示免费模型，不能在目录重建时被过滤。
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const configured = (value: number | undefined) => typeof value === "number" && Number.isFinite(value) && value >= 0;
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return (
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channel.modelCosts?.some((item) => {
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if (item.model !== model) return false;
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const tiers = item.logicalPriceTiers || [];
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if (tiers.length) {
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return tiers.some((tier) => (tier.billingMode === "token" ? [tier.inputTokenPriceMicrocredits, tier.outputTokenPriceMicrocredits, tier.cachedTokenPriceMicrocredits].every(configured) : configured(tier.unitPriceMicrocredits)));
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}
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if (item.billingMode === "token") {
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return [item.inputTokenPriceMicrocredits, item.outputTokenPriceMicrocredits, item.cachedTokenPriceMicrocredits].every(configured);
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}
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return configured(item.unitPriceMicrocredits);
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}) === true
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);
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}
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export function normalizeModelOptionValue(value: unknown, channels: ModelChannel[]) {
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const model = typeof value === "string" ? value.trim() : "";
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if (!normalizeRawModelName(model)) return "";
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const decoded = decodeChannelModel(model);
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if (decoded) {
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const channel = channels.find((item) => item.id === decoded.channelId);
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const resolved = channel?.modelAliases?.[decoded.model] || decoded.model;
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return channel && channel.models.includes(resolved) ? encodeChannelModel(channel.id, resolved) : "";
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}
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const channel = channels.find((item) => item.models.includes(model) || Boolean(item.modelAliases?.[model])) || channels[0];
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const resolved = channel?.modelAliases?.[model] || model;
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return channel && channel.models.includes(resolved) ? encodeChannelModel(channel.id, resolved) : "";
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}
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@@ -0,0 +1,294 @@
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// RunningHub 工作流字段映射的规范化与合并，兼容历史配置里的字段来源写法。
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import { workflowFieldRole, workflowFieldSafeToOverride, workflowVideoFieldsFromJson } from "@/lib/model-capabilities";
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import type { RunningHubCapability, RunningHubWorkflowKind } from "./use-config-store";
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export type WorkflowFieldMapping = {
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id?: string;
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nodeId: string;
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classType?: string;
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fieldName: string;
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fieldValue?: unknown;
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value?: unknown;
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default?: unknown;
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defaultValue?: unknown;
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fieldType?: string;
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label?: string;
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role?: string;
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safeToOverride?: boolean;
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optionsSource?: "workflow" | "manual" | "preset";
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options?: unknown[];
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min?: unknown;
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max?: unknown;
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step?: unknown;
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enabled?: boolean;
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source?: string;
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sourceIndex?: number;
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imageOrder?: number;
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required?: boolean;
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randomEnabled?: boolean;
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bindPrompt?: boolean;
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sourceFromUpstream?: boolean;
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sourceAutomatic?: boolean;
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};
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export function normalizeRunningHubCapability(value: unknown, fallback: RunningHubCapability = "image"): RunningHubCapability {
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return value === "image" || value === "video" || value === "audio" ? value : fallback;
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}
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export function normalizeRunningHubWorkflowKind(value: unknown): RunningHubWorkflowKind {
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return value === "app" ? "app" : "workflow";
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}
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export function normalizeWorkflowFieldSourceName(value: unknown, capability?: RunningHubCapability) {
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const source = String(value || "").trim();
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const normalized = source.toLowerCase().replace(/[\s_-]/g, "");
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const aliases: Record<string, string> = {
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text: "prompt",
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positive: "prompt",
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positiveprompt: "prompt",
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image: "referenceImage",
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referenceimage: "referenceImage",
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referenceimages: "referenceImage",
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video: "referenceVideo",
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referencevideo: "referenceVideo",
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referencevideos: "referenceVideo",
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audio: "referenceAudio",
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referenceaudio: "referenceAudio",
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referenceaudios: "referenceAudio",
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sizewidth: "width",
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imagewidth: "width",
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videowidth: "width",
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sizeheight: "height",
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imageheight: "height",
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videoheight: "height",
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ratio: "aspectRatio",
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aspectratio: "aspectRatio",
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imageaspectratio: "aspectRatio",
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imageratio: "aspectRatio",
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videoaspectratio: "aspectRatio",
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videoratio: "aspectRatio",
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videoresolution: "vquality",
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batch: "count",
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batchsize: "count",
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duration: "videoSeconds",
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videoseconds: "videoSeconds",
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videoquality: "vquality",
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generateaudio: "videoGenerateAudio",
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videogenerateaudio: "videoGenerateAudio",
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watermark: "videoWatermark",
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videowatermark: "videoWatermark",
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voice: "audioVoice",
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systemprompt: "systemPrompt",
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transparentbackground: "transparentBackground",
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audiovoice: "audioVoice",
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audioformat: "audioFormat",
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audiospeed: "audioSpeed",
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audioinstructions: "audioInstructions",
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};
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if (normalized === "resolution") return capability === "video" ? "vquality" : "size";
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// 工作流的 quality 可能是连续数值（例如 0.1-3），不能按视频分辨率处理。
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if (normalized === "quality") return "quality";
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return aliases[normalized] || source;
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}
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export const workflowDimensionPrefixes = ["", "image", "video", "size", "output", "target", "latent", "frame", "canvas", "source", "resolution", "final"];
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export function workflowDimensionSource(key: string) {
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if (workflowDimensionPrefixes.some((prefix) => key === `${prefix}width`) || key === "pixelwidth" || key === "widthpixels") return "width";
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if (workflowDimensionPrefixes.some((prefix) => key === `${prefix}height`) || key === "pixelheight" || key === "heightpixels") return "height";
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return "";
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}
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export function inferWorkflowFieldSource(fieldName: string, fieldType: string, capability?: RunningHubCapability) {
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const key = fieldName.toLowerCase().replace(/[\s_-]/g, "");
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const normalizedFieldType = fieldType.trim().toLowerCase();
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if (["text", "prompt", "positive", "positiveprompt"].includes(key)) return "prompt";
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if (key.includes("mask")) return "mask";
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const dimensionSource = workflowDimensionSource(key);
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if (dimensionSource) return dimensionSource;
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if (["ratio", "aspectratio", "imageaspectratio", "imageratio", "videoaspectratio", "videoratio"].includes(key)) return "aspectRatio";
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if (["videoresolution", "videoquality", "vquality"].includes(key)) return "vquality";
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if (["size", "imagesize", "imageresolution"].includes(key)) return "size";
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if (key === "resolution") return capability === "video" ? "vquality" : capability === "image" ? "size" : "";
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if (["batch", "batchsize", "count", "numimages", "numberofimages", "imagecount", "imagescount"].includes(key)) return "count";
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if (key === "quality") return "quality";
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if (["duration", "seconds", "durationseconds", "videoseconds", "videoduration", "videodurationseconds", "videolength", "clipduration"].includes(key)) return "videoSeconds";
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if (["generateaudio", "videogenerateaudio"].includes(key)) return "videoGenerateAudio";
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if (["watermark", "videowatermark"].includes(key)) return "videoWatermark";
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if (key === "audioformat" || (key === "format" && normalizedFieldType === "audio")) return "audioFormat";
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if (["voice", "audiovoice"].includes(key)) return "audioVoice";
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if ((key === "speed" && normalizedFieldType === "audio") || key === "audiospeed") return "audioSpeed";
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if ((key === "instructions" && normalizedFieldType === "audio") || key === "audioinstructions") return "audioInstructions";
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if (["transparentbackground", "transparent"].includes(key)) return "transparentBackground";
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if (normalizedFieldType === "image") return "referenceImage";
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if (normalizedFieldType === "video") return "referenceVideo";
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if (normalizedFieldType === "audio") return "referenceAudio";
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return "";
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}
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export function shouldRepairLegacyWorkflowSource(fieldName: string, source: string, inferredSource: string, capability?: RunningHubCapability) {
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if (!source || !inferredSource || source === inferredSource) return false;
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const key = fieldName.toLowerCase().replace(/[\s_-]/g, "");
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const wasMistakenForMedia = ["referenceImage", "referenceVideo", "referenceAudio"].includes(source);
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if (wasMistakenForMedia && (workflowDimensionSource(key) || inferredSource === "aspectRatio" || inferredSource === "vquality")) return true;
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// 旧版把通用 resolution 固定识别为图片尺寸；视频条目必须恢复为视频清晰度。
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if (key === "resolution" && capability === "video" && source === "size") return true;
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if (key === "resolution" && capability === "image" && source === "vquality") return true;
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if (key === "quality" && source === "vquality") return true;
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return false;
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}
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export function normalizeWorkflowFieldMappings(value: unknown, capability?: RunningHubCapability): WorkflowFieldMapping[] {
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if (!Array.isArray(value)) return [];
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const fields = value.flatMap((item) => {
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if (!item || typeof item !== "object" || Array.isArray(item)) return [];
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const raw = item as Record<string, unknown>;
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const nodeId = String(raw.nodeId || raw.node || raw.node_id || "").trim();
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const fieldName = String(raw.fieldName || raw.input || raw.inputName || raw.input_name || "").trim();
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if (!nodeId || !fieldName) return [];
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// source 明确存在但为空，表示用户要求保留工作流默认值；只有旧数据完全缺少来源字段时才自动补绑定。
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const sourceKey = ["source", "bind", "from"].find((key) => Object.prototype.hasOwnProperty.call(raw, key));
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const sourceConfigured = Boolean(sourceKey);
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const configuredSource = sourceKey ? raw[sourceKey] : raw.bindPrompt === true || raw.bind_prompt === true ? "prompt" : "";
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let source = normalizeWorkflowFieldSourceName(configuredSource, capability);
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const rawSourceAutomatic = raw.sourceAutomatic ?? raw.source_automatic;
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let sourceAutomatic = typeof rawSourceAutomatic === "boolean" ? rawSourceAutomatic : !sourceConfigured && Boolean(source);
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const upstreamAllowed = raw.sourceFromUpstream !== false && raw.source_from_upstream !== false;
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const upstreamExplicit = raw.sourceFromUpstream === true || raw.source_from_upstream === true;
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const fieldType = String(raw.fieldType || raw.type || "").trim();
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const inferredSource = inferWorkflowFieldSource(fieldName, fieldType, capability);
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if (!source && !sourceConfigured && upstreamAllowed) {
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source = inferredSource;
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sourceAutomatic = Boolean(source);
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if (!source && upstreamExplicit && ["text", "prompt", "positiveprompt", "positive"].includes(fieldName.toLowerCase().replace(/[\s_-]/g, ""))) {
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source = "prompt";
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sourceAutomatic = true;
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}
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} else if (shouldRepairLegacyWorkflowSource(fieldName, source, inferredSource, capability)) {
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source = inferredSource;
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}
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// 比例、清晰度、时长等标量必须按 nodeId + fieldName 独立配置。
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// 旧版自动来源会把两个同类字段折叠到同一个全局参数，重新归一化为直接字段值。
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if (sourceAutomatic && !["prompt", "referenceImage", "referenceVideo", "referenceAudio", "mask"].includes(source)) {
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source = "";
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sourceAutomatic = false;
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}
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const fieldValue = raw.fieldValue ?? raw.defaultValue ?? raw.default_value ?? raw.default ?? raw.value;
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const rawOptions = raw.options ?? raw.values ?? raw.choices ?? raw.enum ?? raw.fieldOptions ?? raw.field_options;
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const options = Array.isArray(rawOptions)
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? rawOptions
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: rawOptions && typeof rawOptions === "object"
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? ((rawOptions as Record<string, unknown>).choices ?? (rawOptions as Record<string, unknown>).options ?? (rawOptions as Record<string, unknown>).values)
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: undefined;
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const range = workflowFieldRange(rawOptions);
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const min = raw.min ?? raw.minValue ?? raw.min_value ?? range.min;
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const max = raw.max ?? raw.maxValue ?? raw.max_value ?? range.max;
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const step = raw.step ?? raw.stepValue ?? raw.step_value ?? range.step;
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const required = raw.required ?? raw.isRequired ?? raw.is_required;
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const randomEnabled = raw.randomEnabled ?? raw.random_enabled;
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const sourceIndex = Number(raw.sourceIndex ?? raw.source_index ?? raw.index);
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const imageOrder = Number(raw.imageOrder ?? raw.image_order);
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const candidate: WorkflowFieldMapping = {
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...raw,
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nodeId,
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classType: String(raw.classType || raw.class_type || "").trim() || undefined,
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fieldName,
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id: String(raw.id || `${nodeId}::${fieldName}`),
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source,
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...(fieldValue !== undefined ? { fieldValue } : {}),
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...(Array.isArray(options) ? { options } : {}),
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...(min !== undefined ? { min } : {}),
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...(max !== undefined ? { max } : {}),
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...(step !== undefined ? { step } : {}),
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...(typeof required === "boolean" ? { required } : {}),
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...(typeof randomEnabled === "boolean" ? { randomEnabled } : {}),
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sourceAutomatic,
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...(Number.isFinite(sourceIndex) ? { sourceIndex } : {}),
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...(Number.isFinite(imageOrder) ? { imageOrder } : {}),
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} as WorkflowFieldMapping;
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const safeToOverride = raw.safeToOverride !== false && raw.safe_to_override !== false && workflowFieldSafeToOverride(candidate);
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const role = workflowFieldRole({ ...candidate, role: String(raw.role || "") });
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const configuredEnabled = typeof raw.enabled === "boolean" ? raw.enabled : role !== "internal";
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const optionsSource = ["workflow", "manual", "preset"].includes(String(raw.optionsSource || raw.options_source || ""))
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? (String(raw.optionsSource || raw.options_source) as WorkflowFieldMapping["optionsSource"])
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: Array.isArray(options)
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? "workflow"
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: undefined;
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return [{ ...candidate, role, safeToOverride, enabled: safeToOverride && configuredEnabled, ...(optionsSource ? { optionsSource } : {}) }];
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});
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let imageOrder = 0;
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let videoOrder = 0;
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let audioOrder = 0;
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return fields.map((field) => {
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if (field.source === "referenceImage") {
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const configuredOrder = Number(field.imageOrder);
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const nextOrder = Number.isInteger(configuredOrder) && configuredOrder > 0 ? configuredOrder : imageOrder + 1;
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imageOrder = Math.max(imageOrder, nextOrder);
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return { ...field, imageOrder: nextOrder, sourceIndex: nextOrder - 1, required: field.required ?? nextOrder === 1 };
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}
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if (field.source === "referenceVideo") {
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const configuredIndex = Number(field.sourceIndex);
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const nextIndex = Number.isInteger(configuredIndex) && configuredIndex >= 0 ? configuredIndex : videoOrder;
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videoOrder = Math.max(videoOrder, nextIndex + 1);
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return { ...field, sourceIndex: nextIndex, required: field.required ?? nextIndex === 0 };
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}
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if (field.source === "referenceAudio") {
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const configuredIndex = Number(field.sourceIndex);
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const nextIndex = Number.isInteger(configuredIndex) && configuredIndex >= 0 ? configuredIndex : audioOrder;
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audioOrder = Math.max(audioOrder, nextIndex + 1);
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return { ...field, sourceIndex: nextIndex, required: field.required ?? nextIndex === 0 };
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}
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if (field.source === "prompt") return { ...field, required: field.required ?? true };
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return field;
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});
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}
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export function mergeWorkflowFieldMappings(current: unknown, incoming: unknown, capability?: RunningHubCapability) {
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const previousFields = normalizeWorkflowFieldMappings(current, capability);
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const nextFields = normalizeWorkflowFieldMappings(incoming, capability);
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const previousByKey = new Map(previousFields.map((field) => [`${field.nodeId}::${field.fieldName}`, field]));
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const policyKeys: Array<keyof WorkflowFieldMapping> = ["label", "enabled", "randomEnabled", "source", "sourceAutomatic", "sourceFromUpstream", "sourceIndex", "imageOrder", "required", "bindPrompt"];
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return nextFields.map((field) => {
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const previous = previousByKey.get(`${field.nodeId}::${field.fieldName}`);
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if (!previous) return field;
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const merged = { ...field } as WorkflowFieldMapping;
|
||||
const target = merged as unknown as Record<string, unknown>;
|
||||
const source = previous as unknown as Record<string, unknown>;
|
||||
policyKeys.forEach((key) => {
|
||||
if (Object.prototype.hasOwnProperty.call(source, key)) target[key] = source[key];
|
||||
});
|
||||
// 字段类型、枚举和数值范围属于上游协议。只有本次拉取未返回时，才沿用本地补充配置。
|
||||
if (!field.fieldType && previous.fieldType) merged.fieldType = previous.fieldType;
|
||||
if (!field.options?.length && previous.options?.length) {
|
||||
merged.options = previous.options;
|
||||
merged.optionsSource = previous.optionsSource;
|
||||
}
|
||||
if (field.min === undefined && previous.min !== undefined) merged.min = previous.min;
|
||||
if (field.max === undefined && previous.max !== undefined) merged.max = previous.max;
|
||||
if (field.step === undefined && previous.step !== undefined) merged.step = previous.step;
|
||||
// 首次获得角色信息时采用服务端的内部参数默认值；之后才保留用户明确的启用选择。
|
||||
if (field.role === "internal" && previous.role !== "internal") merged.enabled = field.enabled;
|
||||
if (field.safeToOverride === false) merged.enabled = false;
|
||||
return merged;
|
||||
});
|
||||
}
|
||||
|
||||
export function normalizeSavedWorkflowFields(workflow: { fields?: unknown; workflowJson?: Record<string, unknown> }, capability: RunningHubCapability) {
|
||||
const schemaFields = workflowVideoFieldsFromJson(workflow.workflowJson);
|
||||
return schemaFields.length ? mergeWorkflowFieldMappings(workflow.fields, schemaFields, capability) : normalizeWorkflowFieldMappings(workflow.fields, capability);
|
||||
}
|
||||
|
||||
export function workflowFieldRange(value: unknown) {
|
||||
const candidates: Record<string, unknown>[] = [];
|
||||
const add = (item: unknown) => {
|
||||
if (!item || typeof item !== "object" || Array.isArray(item)) return;
|
||||
const record = item as Record<string, unknown>;
|
||||
candidates.push(record);
|
||||
add(record.range);
|
||||
};
|
||||
add(value);
|
||||
if (Array.isArray(value)) value.forEach(add);
|
||||
const read = (keys: string[]) => candidates.map((item) => keys.map((key) => item[key]).find((candidate) => candidate !== undefined)).find((candidate) => candidate !== undefined);
|
||||
return { min: read(["min", "minValue", "min_value"]), max: read(["max", "maxValue", "max_value"]), step: read(["step", "stepValue", "step_value"]) };
|
||||
}
|
||||
@@ -1,312 +1,27 @@
|
||||
import { scopedLocalStorage } from "@/lib/user-scope";
|
||||
import { useMemo } from "react";
|
||||
import type { ModelTag } from "@/lib/model-tags";
|
||||
import { create } from "zustand";
|
||||
import { createJSONStorage, persist } from "zustand/middleware";
|
||||
import { persist, createJSONStorage } from "zustand/middleware";
|
||||
import { nanoid } from "nanoid";
|
||||
|
||||
import { scopedLocalStorage } from "@/lib/user-scope";
|
||||
import { modelProtocolCapability, normalizeModelProtocol, type ModelProtocol } from "@/lib/model-protocols";
|
||||
import { normalizeVideoDuration, normalizeVideoResolution } from "@/lib/video-generation-options";
|
||||
import { workflowFieldRole, workflowFieldSafeToOverride, workflowVideoFieldsFromJson, type ModelCapabilityConfig } from "@/lib/model-capabilities";
|
||||
import { type ModelCapabilityConfig } from "@/lib/model-capabilities";
|
||||
import { useUserStore } from "@/stores/use-user-store";
|
||||
import type { CapabilitySpec, PublicLogicalModelPriceTier } from "@/services/api/logical-models";
|
||||
import { type WorkflowFieldMapping, normalizeRunningHubCapability, normalizeRunningHubWorkflowKind, normalizeSavedWorkflowFields, mergeWorkflowFieldMappings, normalizeWorkflowFieldMappings } from "./config-workflow-fields";
|
||||
import { decodeChannelModel, modelOptionName, modelOptionsFromChannels, normalizeModelOptionValue, CHANNEL_MODEL_SEPARATOR, uniqueModelOptions, encodeChannelModel, hasSystemModelPrice, isChannelModelValue, modelDisplayName, modelIcon, modelOptionLabel } from "./config-model-options";
|
||||
import { normalizeRawModelName } from "./config-model-options";
|
||||
|
||||
export { CHANNEL_MODEL_SEPARATOR, normalizeRawModelName, uniqueModelOptions } from "./config-model-options";
|
||||
export { mergeWorkflowFieldMappings, normalizeRunningHubCapability, normalizeRunningHubWorkflowKind, normalizeWorkflowFieldMappings, type WorkflowFieldMapping } from "./config-workflow-fields";
|
||||
export { decodeChannelModel, encodeChannelModel, hasSystemModelPrice, isChannelModelValue, modelDisplayName, modelIcon, modelOptionLabel, modelOptionName, modelOptionsFromChannels, normalizeModelOptionValue } from "./config-model-options";
|
||||
|
||||
export type ApiCallFormat = "openai" | "gemini" | "claude";
|
||||
export type ChannelInterfaceType = ModelProtocol;
|
||||
export type ChannelHeader = { name: string; value: string };
|
||||
export type RunningHubCapability = "image" | "video" | "audio";
|
||||
export type RunningHubWorkflowKind = "workflow" | "app";
|
||||
|
||||
export type WorkflowFieldMapping = {
|
||||
id?: string;
|
||||
nodeId: string;
|
||||
classType?: string;
|
||||
fieldName: string;
|
||||
fieldValue?: unknown;
|
||||
value?: unknown;
|
||||
default?: unknown;
|
||||
defaultValue?: unknown;
|
||||
fieldType?: string;
|
||||
label?: string;
|
||||
role?: string;
|
||||
safeToOverride?: boolean;
|
||||
optionsSource?: "workflow" | "manual" | "preset";
|
||||
options?: unknown[];
|
||||
min?: unknown;
|
||||
max?: unknown;
|
||||
step?: unknown;
|
||||
enabled?: boolean;
|
||||
source?: string;
|
||||
sourceIndex?: number;
|
||||
imageOrder?: number;
|
||||
required?: boolean;
|
||||
randomEnabled?: boolean;
|
||||
bindPrompt?: boolean;
|
||||
sourceFromUpstream?: boolean;
|
||||
sourceAutomatic?: boolean;
|
||||
};
|
||||
|
||||
export function normalizeRunningHubCapability(value: unknown, fallback: RunningHubCapability = "image"): RunningHubCapability {
|
||||
return value === "image" || value === "video" || value === "audio" ? value : fallback;
|
||||
}
|
||||
|
||||
export function normalizeRunningHubWorkflowKind(value: unknown): RunningHubWorkflowKind {
|
||||
return value === "app" ? "app" : "workflow";
|
||||
}
|
||||
|
||||
function normalizeWorkflowFieldSourceName(value: unknown, capability?: RunningHubCapability) {
|
||||
const source = String(value || "").trim();
|
||||
const normalized = source.toLowerCase().replace(/[\s_-]/g, "");
|
||||
const aliases: Record<string, string> = {
|
||||
text: "prompt",
|
||||
positive: "prompt",
|
||||
positiveprompt: "prompt",
|
||||
image: "referenceImage",
|
||||
referenceimage: "referenceImage",
|
||||
referenceimages: "referenceImage",
|
||||
video: "referenceVideo",
|
||||
referencevideo: "referenceVideo",
|
||||
referencevideos: "referenceVideo",
|
||||
audio: "referenceAudio",
|
||||
referenceaudio: "referenceAudio",
|
||||
referenceaudios: "referenceAudio",
|
||||
sizewidth: "width",
|
||||
imagewidth: "width",
|
||||
videowidth: "width",
|
||||
sizeheight: "height",
|
||||
imageheight: "height",
|
||||
videoheight: "height",
|
||||
ratio: "aspectRatio",
|
||||
aspectratio: "aspectRatio",
|
||||
imageaspectratio: "aspectRatio",
|
||||
imageratio: "aspectRatio",
|
||||
videoaspectratio: "aspectRatio",
|
||||
videoratio: "aspectRatio",
|
||||
videoresolution: "vquality",
|
||||
batch: "count",
|
||||
batchsize: "count",
|
||||
duration: "videoSeconds",
|
||||
videoseconds: "videoSeconds",
|
||||
videoquality: "vquality",
|
||||
generateaudio: "videoGenerateAudio",
|
||||
videogenerateaudio: "videoGenerateAudio",
|
||||
watermark: "videoWatermark",
|
||||
videowatermark: "videoWatermark",
|
||||
voice: "audioVoice",
|
||||
systemprompt: "systemPrompt",
|
||||
transparentbackground: "transparentBackground",
|
||||
audiovoice: "audioVoice",
|
||||
audioformat: "audioFormat",
|
||||
audiospeed: "audioSpeed",
|
||||
audioinstructions: "audioInstructions",
|
||||
};
|
||||
if (normalized === "resolution") return capability === "video" ? "vquality" : "size";
|
||||
// 工作流的 quality 可能是连续数值（例如 0.1-3），不能按视频分辨率处理。
|
||||
if (normalized === "quality") return "quality";
|
||||
return aliases[normalized] || source;
|
||||
}
|
||||
|
||||
const workflowDimensionPrefixes = ["", "image", "video", "size", "output", "target", "latent", "frame", "canvas", "source", "resolution", "final"];
|
||||
|
||||
function workflowDimensionSource(key: string) {
|
||||
if (workflowDimensionPrefixes.some((prefix) => key === `${prefix}width`) || key === "pixelwidth" || key === "widthpixels") return "width";
|
||||
if (workflowDimensionPrefixes.some((prefix) => key === `${prefix}height`) || key === "pixelheight" || key === "heightpixels") return "height";
|
||||
return "";
|
||||
}
|
||||
|
||||
function inferWorkflowFieldSource(fieldName: string, fieldType: string, capability?: RunningHubCapability) {
|
||||
const key = fieldName.toLowerCase().replace(/[\s_-]/g, "");
|
||||
const normalizedFieldType = fieldType.trim().toLowerCase();
|
||||
if (["text", "prompt", "positive", "positiveprompt"].includes(key)) return "prompt";
|
||||
if (key.includes("mask")) return "mask";
|
||||
const dimensionSource = workflowDimensionSource(key);
|
||||
if (dimensionSource) return dimensionSource;
|
||||
if (["ratio", "aspectratio", "imageaspectratio", "imageratio", "videoaspectratio", "videoratio"].includes(key)) return "aspectRatio";
|
||||
if (["videoresolution", "videoquality", "vquality"].includes(key)) return "vquality";
|
||||
if (["size", "imagesize", "imageresolution"].includes(key)) return "size";
|
||||
if (key === "resolution") return capability === "video" ? "vquality" : capability === "image" ? "size" : "";
|
||||
if (["batch", "batchsize", "count", "numimages", "numberofimages", "imagecount", "imagescount"].includes(key)) return "count";
|
||||
if (key === "quality") return "quality";
|
||||
if (["duration", "seconds", "durationseconds", "videoseconds", "videoduration", "videodurationseconds", "videolength", "clipduration"].includes(key)) return "videoSeconds";
|
||||
if (["generateaudio", "videogenerateaudio"].includes(key)) return "videoGenerateAudio";
|
||||
if (["watermark", "videowatermark"].includes(key)) return "videoWatermark";
|
||||
if (key === "audioformat" || (key === "format" && normalizedFieldType === "audio")) return "audioFormat";
|
||||
if (["voice", "audiovoice"].includes(key)) return "audioVoice";
|
||||
if ((key === "speed" && normalizedFieldType === "audio") || key === "audiospeed") return "audioSpeed";
|
||||
if ((key === "instructions" && normalizedFieldType === "audio") || key === "audioinstructions") return "audioInstructions";
|
||||
if (["transparentbackground", "transparent"].includes(key)) return "transparentBackground";
|
||||
if (normalizedFieldType === "image") return "referenceImage";
|
||||
if (normalizedFieldType === "video") return "referenceVideo";
|
||||
if (normalizedFieldType === "audio") return "referenceAudio";
|
||||
return "";
|
||||
}
|
||||
|
||||
function shouldRepairLegacyWorkflowSource(fieldName: string, source: string, inferredSource: string, capability?: RunningHubCapability) {
|
||||
if (!source || !inferredSource || source === inferredSource) return false;
|
||||
const key = fieldName.toLowerCase().replace(/[\s_-]/g, "");
|
||||
const wasMistakenForMedia = ["referenceImage", "referenceVideo", "referenceAudio"].includes(source);
|
||||
if (wasMistakenForMedia && (workflowDimensionSource(key) || inferredSource === "aspectRatio" || inferredSource === "vquality")) return true;
|
||||
// 旧版把通用 resolution 固定识别为图片尺寸；视频条目必须恢复为视频清晰度。
|
||||
if (key === "resolution" && capability === "video" && source === "size") return true;
|
||||
if (key === "resolution" && capability === "image" && source === "vquality") return true;
|
||||
if (key === "quality" && source === "vquality") return true;
|
||||
return false;
|
||||
}
|
||||
|
||||
export function normalizeWorkflowFieldMappings(value: unknown, capability?: RunningHubCapability): WorkflowFieldMapping[] {
|
||||
if (!Array.isArray(value)) return [];
|
||||
const fields = value.flatMap((item) => {
|
||||
if (!item || typeof item !== "object" || Array.isArray(item)) return [];
|
||||
const raw = item as Record<string, unknown>;
|
||||
const nodeId = String(raw.nodeId || raw.node || raw.node_id || "").trim();
|
||||
const fieldName = String(raw.fieldName || raw.input || raw.inputName || raw.input_name || "").trim();
|
||||
if (!nodeId || !fieldName) return [];
|
||||
// source 明确存在但为空，表示用户要求保留工作流默认值；只有旧数据完全缺少来源字段时才自动补绑定。
|
||||
const sourceKey = ["source", "bind", "from"].find((key) => Object.prototype.hasOwnProperty.call(raw, key));
|
||||
const sourceConfigured = Boolean(sourceKey);
|
||||
const configuredSource = sourceKey ? raw[sourceKey] : raw.bindPrompt === true || raw.bind_prompt === true ? "prompt" : "";
|
||||
let source = normalizeWorkflowFieldSourceName(configuredSource, capability);
|
||||
const rawSourceAutomatic = raw.sourceAutomatic ?? raw.source_automatic;
|
||||
let sourceAutomatic = typeof rawSourceAutomatic === "boolean" ? rawSourceAutomatic : !sourceConfigured && Boolean(source);
|
||||
const upstreamAllowed = raw.sourceFromUpstream !== false && raw.source_from_upstream !== false;
|
||||
const upstreamExplicit = raw.sourceFromUpstream === true || raw.source_from_upstream === true;
|
||||
const fieldType = String(raw.fieldType || raw.type || "").trim();
|
||||
const inferredSource = inferWorkflowFieldSource(fieldName, fieldType, capability);
|
||||
if (!source && !sourceConfigured && upstreamAllowed) {
|
||||
source = inferredSource;
|
||||
sourceAutomatic = Boolean(source);
|
||||
if (!source && upstreamExplicit && ["text", "prompt", "positiveprompt", "positive"].includes(fieldName.toLowerCase().replace(/[\s_-]/g, ""))) {
|
||||
source = "prompt";
|
||||
sourceAutomatic = true;
|
||||
}
|
||||
} else if (shouldRepairLegacyWorkflowSource(fieldName, source, inferredSource, capability)) {
|
||||
source = inferredSource;
|
||||
}
|
||||
// 比例、清晰度、时长等标量必须按 nodeId + fieldName 独立配置。
|
||||
// 旧版自动来源会把两个同类字段折叠到同一个全局参数，重新归一化为直接字段值。
|
||||
if (sourceAutomatic && !["prompt", "referenceImage", "referenceVideo", "referenceAudio", "mask"].includes(source)) {
|
||||
source = "";
|
||||
sourceAutomatic = false;
|
||||
}
|
||||
const fieldValue = raw.fieldValue ?? raw.defaultValue ?? raw.default_value ?? raw.default ?? raw.value;
|
||||
const rawOptions = raw.options ?? raw.values ?? raw.choices ?? raw.enum ?? raw.fieldOptions ?? raw.field_options;
|
||||
const options = Array.isArray(rawOptions)
|
||||
? rawOptions
|
||||
: rawOptions && typeof rawOptions === "object"
|
||||
? ((rawOptions as Record<string, unknown>).choices ?? (rawOptions as Record<string, unknown>).options ?? (rawOptions as Record<string, unknown>).values)
|
||||
: undefined;
|
||||
const range = workflowFieldRange(rawOptions);
|
||||
const min = raw.min ?? raw.minValue ?? raw.min_value ?? range.min;
|
||||
const max = raw.max ?? raw.maxValue ?? raw.max_value ?? range.max;
|
||||
const step = raw.step ?? raw.stepValue ?? raw.step_value ?? range.step;
|
||||
const required = raw.required ?? raw.isRequired ?? raw.is_required;
|
||||
const randomEnabled = raw.randomEnabled ?? raw.random_enabled;
|
||||
const sourceIndex = Number(raw.sourceIndex ?? raw.source_index ?? raw.index);
|
||||
const imageOrder = Number(raw.imageOrder ?? raw.image_order);
|
||||
const candidate: WorkflowFieldMapping = {
|
||||
...raw,
|
||||
nodeId,
|
||||
classType: String(raw.classType || raw.class_type || "").trim() || undefined,
|
||||
fieldName,
|
||||
id: String(raw.id || `${nodeId}::${fieldName}`),
|
||||
source,
|
||||
...(fieldValue !== undefined ? { fieldValue } : {}),
|
||||
...(Array.isArray(options) ? { options } : {}),
|
||||
...(min !== undefined ? { min } : {}),
|
||||
...(max !== undefined ? { max } : {}),
|
||||
...(step !== undefined ? { step } : {}),
|
||||
...(typeof required === "boolean" ? { required } : {}),
|
||||
...(typeof randomEnabled === "boolean" ? { randomEnabled } : {}),
|
||||
sourceAutomatic,
|
||||
...(Number.isFinite(sourceIndex) ? { sourceIndex } : {}),
|
||||
...(Number.isFinite(imageOrder) ? { imageOrder } : {}),
|
||||
} as WorkflowFieldMapping;
|
||||
const safeToOverride = raw.safeToOverride !== false && raw.safe_to_override !== false && workflowFieldSafeToOverride(candidate);
|
||||
const role = workflowFieldRole({ ...candidate, role: String(raw.role || "") });
|
||||
const configuredEnabled = typeof raw.enabled === "boolean" ? raw.enabled : role !== "internal";
|
||||
const optionsSource = ["workflow", "manual", "preset"].includes(String(raw.optionsSource || raw.options_source || ""))
|
||||
? (String(raw.optionsSource || raw.options_source) as WorkflowFieldMapping["optionsSource"])
|
||||
: Array.isArray(options)
|
||||
? "workflow"
|
||||
: undefined;
|
||||
return [{ ...candidate, role, safeToOverride, enabled: safeToOverride && configuredEnabled, ...(optionsSource ? { optionsSource } : {}) }];
|
||||
});
|
||||
let imageOrder = 0;
|
||||
let videoOrder = 0;
|
||||
let audioOrder = 0;
|
||||
return fields.map((field) => {
|
||||
if (field.source === "referenceImage") {
|
||||
const configuredOrder = Number(field.imageOrder);
|
||||
const nextOrder = Number.isInteger(configuredOrder) && configuredOrder > 0 ? configuredOrder : imageOrder + 1;
|
||||
imageOrder = Math.max(imageOrder, nextOrder);
|
||||
return { ...field, imageOrder: nextOrder, sourceIndex: nextOrder - 1, required: field.required ?? nextOrder === 1 };
|
||||
}
|
||||
if (field.source === "referenceVideo") {
|
||||
const configuredIndex = Number(field.sourceIndex);
|
||||
const nextIndex = Number.isInteger(configuredIndex) && configuredIndex >= 0 ? configuredIndex : videoOrder;
|
||||
videoOrder = Math.max(videoOrder, nextIndex + 1);
|
||||
return { ...field, sourceIndex: nextIndex, required: field.required ?? nextIndex === 0 };
|
||||
}
|
||||
if (field.source === "referenceAudio") {
|
||||
const configuredIndex = Number(field.sourceIndex);
|
||||
const nextIndex = Number.isInteger(configuredIndex) && configuredIndex >= 0 ? configuredIndex : audioOrder;
|
||||
audioOrder = Math.max(audioOrder, nextIndex + 1);
|
||||
return { ...field, sourceIndex: nextIndex, required: field.required ?? nextIndex === 0 };
|
||||
}
|
||||
if (field.source === "prompt") return { ...field, required: field.required ?? true };
|
||||
return field;
|
||||
});
|
||||
}
|
||||
|
||||
export function mergeWorkflowFieldMappings(current: unknown, incoming: unknown, capability?: RunningHubCapability) {
|
||||
const previousFields = normalizeWorkflowFieldMappings(current, capability);
|
||||
const nextFields = normalizeWorkflowFieldMappings(incoming, capability);
|
||||
const previousByKey = new Map(previousFields.map((field) => [`${field.nodeId}::${field.fieldName}`, field]));
|
||||
const policyKeys: Array<keyof WorkflowFieldMapping> = ["label", "enabled", "randomEnabled", "source", "sourceAutomatic", "sourceFromUpstream", "sourceIndex", "imageOrder", "required", "bindPrompt"];
|
||||
return nextFields.map((field) => {
|
||||
const previous = previousByKey.get(`${field.nodeId}::${field.fieldName}`);
|
||||
if (!previous) return field;
|
||||
const merged = { ...field } as WorkflowFieldMapping;
|
||||
const target = merged as unknown as Record<string, unknown>;
|
||||
const source = previous as unknown as Record<string, unknown>;
|
||||
policyKeys.forEach((key) => {
|
||||
if (Object.prototype.hasOwnProperty.call(source, key)) target[key] = source[key];
|
||||
});
|
||||
// 字段类型、枚举和数值范围属于上游协议。只有本次拉取未返回时，才沿用本地补充配置。
|
||||
if (!field.fieldType && previous.fieldType) merged.fieldType = previous.fieldType;
|
||||
if (!field.options?.length && previous.options?.length) {
|
||||
merged.options = previous.options;
|
||||
merged.optionsSource = previous.optionsSource;
|
||||
}
|
||||
if (field.min === undefined && previous.min !== undefined) merged.min = previous.min;
|
||||
if (field.max === undefined && previous.max !== undefined) merged.max = previous.max;
|
||||
if (field.step === undefined && previous.step !== undefined) merged.step = previous.step;
|
||||
// 首次获得角色信息时采用服务端的内部参数默认值；之后才保留用户明确的启用选择。
|
||||
if (field.role === "internal" && previous.role !== "internal") merged.enabled = field.enabled;
|
||||
if (field.safeToOverride === false) merged.enabled = false;
|
||||
return merged;
|
||||
});
|
||||
}
|
||||
|
||||
function normalizeSavedWorkflowFields(workflow: { fields?: unknown; workflowJson?: Record<string, unknown> }, capability: RunningHubCapability) {
|
||||
const schemaFields = workflowVideoFieldsFromJson(workflow.workflowJson);
|
||||
return schemaFields.length ? mergeWorkflowFieldMappings(workflow.fields, schemaFields, capability) : normalizeWorkflowFieldMappings(workflow.fields, capability);
|
||||
}
|
||||
|
||||
function workflowFieldRange(value: unknown) {
|
||||
const candidates: Record<string, unknown>[] = [];
|
||||
const add = (item: unknown) => {
|
||||
if (!item || typeof item !== "object" || Array.isArray(item)) return;
|
||||
const record = item as Record<string, unknown>;
|
||||
candidates.push(record);
|
||||
add(record.range);
|
||||
};
|
||||
add(value);
|
||||
if (Array.isArray(value)) value.forEach(add);
|
||||
const read = (keys: string[]) => candidates.map((item) => keys.map((key) => item[key]).find((candidate) => candidate !== undefined)).find((candidate) => candidate !== undefined);
|
||||
return { min: read(["min", "minValue", "min_value"]), max: read(["max", "maxValue", "max_value"]), step: read(["step", "stepValue", "step_value"]) };
|
||||
}
|
||||
|
||||
export type RunningHubWorkflow = {
|
||||
kind?: RunningHubWorkflowKind;
|
||||
capability?: RunningHubCapability;
|
||||
@@ -422,7 +137,6 @@ export type AiConfig = {
|
||||
|
||||
export const CONFIG_STORE_KEY = "open_ai_canvas:ai_config_store";
|
||||
export type ModelCapability = "image" | "video" | "text" | "audio";
|
||||
const CHANNEL_MODEL_SEPARATOR = "::";
|
||||
const OPENAI_BASE_URL = "https://api.openai.com";
|
||||
const GEMINI_BASE_URL = "https://generativelanguage.googleapis.com";
|
||||
const LEGACY_DEFAULT_MODEL_NAMES = new Set(["gpt-image-2", "grok-imagine-video", "gpt-5.5", "gpt-4o-mini-tts"]);
|
||||
@@ -749,92 +463,6 @@ export function createModelChannel(channel?: Partial<ModelChannel>): ModelChanne
|
||||
};
|
||||
}
|
||||
|
||||
export function encodeChannelModel(channelId: string, model: string) {
|
||||
return `${channelId}${CHANNEL_MODEL_SEPARATOR}${model.trim()}`;
|
||||
}
|
||||
|
||||
export function isChannelModelValue(value: string) {
|
||||
return value.includes(CHANNEL_MODEL_SEPARATOR);
|
||||
}
|
||||
|
||||
export function decodeChannelModel(value: string) {
|
||||
const index = value.indexOf(CHANNEL_MODEL_SEPARATOR);
|
||||
if (index < 0) return null;
|
||||
return { channelId: value.slice(0, index), model: value.slice(index + CHANNEL_MODEL_SEPARATOR.length) };
|
||||
}
|
||||
|
||||
export function modelOptionName(value: string) {
|
||||
return decodeChannelModel(value)?.model || value;
|
||||
}
|
||||
|
||||
export function modelDisplayName(config: AiConfig, value: string) {
|
||||
const model = modelOptionName(value);
|
||||
const channel = resolveModelChannel(config, value);
|
||||
const displayName = channel.modelCosts?.find((item) => item.model === model)?.displayName?.trim();
|
||||
if (displayName) return displayName;
|
||||
return channel.scope === "system" ? "系统模型" : model;
|
||||
}
|
||||
|
||||
export function modelIcon(config: AiConfig, value: string) {
|
||||
const model = modelOptionName(value);
|
||||
return resolveModelChannel(config, value).modelCosts?.find((item) => item.model === model)?.icon || "";
|
||||
}
|
||||
|
||||
export function modelOptionLabel(config: AiConfig, value: string) {
|
||||
const decoded = decodeChannelModel(value);
|
||||
if (!decoded) return modelDisplayName(config, value);
|
||||
const channel = config.channels.find((item) => item.id === decoded.channelId);
|
||||
const displayName = modelDisplayName(config, value);
|
||||
// 平台前台模型只展示公开名称；供应来源和内部目录适配器不属于创作端信息。
|
||||
if (!channel || channel.scope === "system") return displayName;
|
||||
return `${displayName}（${channel.name}）`;
|
||||
}
|
||||
|
||||
export function modelOptionsFromChannels(channels: ModelChannel[]) {
|
||||
return uniqueModelOptions(
|
||||
channels.flatMap((channel) =>
|
||||
channel.models
|
||||
.map(normalizeRawModelName)
|
||||
.filter(Boolean)
|
||||
.filter((model) => channel.scope !== "system" || hasSystemModelPrice(channel, model))
|
||||
.map((model) => encodeChannelModel(channel.id, model)),
|
||||
),
|
||||
);
|
||||
}
|
||||
|
||||
export function hasSystemModelPrice(channel: ModelChannel, model: string) {
|
||||
if (channel.scope !== "system") return true;
|
||||
// 价格字段已由后端按“非负数”校验；0 表示免费模型，不能在目录重建时被过滤。
|
||||
const configured = (value: number | undefined) => typeof value === "number" && Number.isFinite(value) && value >= 0;
|
||||
return (
|
||||
channel.modelCosts?.some((item) => {
|
||||
if (item.model !== model) return false;
|
||||
const tiers = item.logicalPriceTiers || [];
|
||||
if (tiers.length) {
|
||||
return tiers.some((tier) => (tier.billingMode === "token" ? [tier.inputTokenPriceMicrocredits, tier.outputTokenPriceMicrocredits, tier.cachedTokenPriceMicrocredits].every(configured) : configured(tier.unitPriceMicrocredits)));
|
||||
}
|
||||
if (item.billingMode === "token") {
|
||||
return [item.inputTokenPriceMicrocredits, item.outputTokenPriceMicrocredits, item.cachedTokenPriceMicrocredits].every(configured);
|
||||
}
|
||||
return configured(item.unitPriceMicrocredits);
|
||||
}) === true
|
||||
);
|
||||
}
|
||||
|
||||
export function normalizeModelOptionValue(value: unknown, channels: ModelChannel[]) {
|
||||
const model = typeof value === "string" ? value.trim() : "";
|
||||
if (!normalizeRawModelName(model)) return "";
|
||||
const decoded = decodeChannelModel(model);
|
||||
if (decoded) {
|
||||
const channel = channels.find((item) => item.id === decoded.channelId);
|
||||
const resolved = channel?.modelAliases?.[decoded.model] || decoded.model;
|
||||
return channel && channel.models.includes(resolved) ? encodeChannelModel(channel.id, resolved) : "";
|
||||
}
|
||||
const channel = channels.find((item) => item.models.includes(model) || Boolean(item.modelAliases?.[model])) || channels[0];
|
||||
const resolved = channel?.modelAliases?.[model] || model;
|
||||
return channel && channel.models.includes(resolved) ? encodeChannelModel(channel.id, resolved) : "";
|
||||
}
|
||||
|
||||
export function resolveModelChannel(config: AiConfig, value: string) {
|
||||
const decoded = decodeChannelModel(value);
|
||||
const model = decoded?.model || value;
|
||||
@@ -938,23 +566,6 @@ function uniqueRawModels(models: string[]) {
|
||||
return Array.from(new Set((models || []).map(normalizeRawModelName).filter(Boolean)));
|
||||
}
|
||||
|
||||
function uniqueModelOptions(models: string[]) {
|
||||
return Array.from(
|
||||
new Set(
|
||||
(models || [])
|
||||
.filter((model): model is string => typeof model === "string")
|
||||
.map((model) => model.trim())
|
||||
.filter(Boolean),
|
||||
),
|
||||
);
|
||||
}
|
||||
|
||||
function normalizeRawModelName(value: unknown) {
|
||||
if (typeof value !== "string") return "";
|
||||
const model = modelOptionName(value).trim();
|
||||
return model && model !== "undefined" && model !== "null" ? model : "";
|
||||
}
|
||||
|
||||
export function buildApiUrl(baseUrl: string, path: string) {
|
||||
let normalizedBaseUrl = resolveBackendApiUrl(baseUrl).replace(/\/+$/, "");
|
||||
normalizedBaseUrl = normalizeArkPlanBaseUrl(normalizedBaseUrl);
|
||||
|
||||
Reference in new issue
Block a user