fix: stabilize canvas agent mode for kimi
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@ -8310,6 +8310,12 @@ export function AdminSettingsClient() {
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/>
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</div>
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<div className="rounded-lg border border-amber-200 bg-amber-50 px-3 py-2 text-xs leading-5 text-amber-900">
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无限画布里“对话框”的 Agent 对话/识图默认模型，不在本区域设置；
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它取自 AI 模型管理里被标记为“Agent模式”的多模态模型。
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这里的配置只影响 AI详情生成专用模型，以及 Agent模式自动出图时的默认生图模型。
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</div>
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<div className="space-y-2 mt-4">
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<Label htmlFor="canvasDetailAgentModelId">
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AI详情生成 - Agent专用模型
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@ -8352,8 +8358,7 @@ export function AdminSettingsClient() {
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</Select>
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<div className="text-xs text-muted-foreground">
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用于 AI详情生成 的分镜规划与排版复审。未设置时沿用
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Agent 对话默认模型；建议选择官方 Kimi K2.5
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多模态模型。
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Agent 对话默认模型；仅影响 AI详情生成，不影响普通无限画布对话框的 Agent 对话。建议选择官方 Kimi K2.5 多模态模型。
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</div>
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</div>
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@ -8465,7 +8470,7 @@ export function AdminSettingsClient() {
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</SelectContent>
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</Select>
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<div className="text-xs text-muted-foreground">
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当用户选择"Agent模式"时，系统实际使用的生图模型。"系统智能选择"会根据用户意图（如抠图、换背景等）自动路由到最佳模型。
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当用户选择"Agent模式"并进入执行出图时，系统实际使用的默认生图模型。"系统智能选择"会根据用户意图（如抠图、换背景等）自动路由到最佳模型；这里不控制无限画布对话框里的 Agent 对话/识图模型。
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</div>
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</div>
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</>
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@ -45,6 +45,7 @@ import {
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} from "@/lib/ai/rmbg/service";
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import { getLanguageModelById } from "@/lib/ai/dynamic-provider";
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import { resolveDetailAgentLocalMoonshotModel } from "@/lib/ai/detail-agent-model-runtime";
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import { shouldForceInlineVisionInputs } from "@/lib/ai/model-compat";
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import { inlineVisionImages } from "@/lib/ai/vision-inline";
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import { createDocument } from "@/lib/ai/tools/create-document";
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import { getWeather } from "@/lib/ai/tools/get-weather";
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@ -140,6 +141,7 @@ import {
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resolveClarificationBypass,
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resolveClarificationMessage,
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} from "@/lib/ai/runtime/clarification-interruption";
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import { resolveCanvasAgentClarificationGate } from "@/lib/ai/runtime/middlewares/canvas-agent-clarification-gate";
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import {
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pruneTaskBoundaryContext,
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pruneTaskBoundaryHistoryMessages,
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@ -2089,18 +2091,188 @@ function ensureImageAnalysisSummarySection(message: string, summary: string): st
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* 注意：gemini-image 和 huanke-gemini 都需要 URL 格式（不是 Base64）
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* 因为这些 provider 内部会自行处理图片上传到 S3
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*/
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async function convertMessagesWithImages(messages: ChatMessage[], providerType: string) {
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async function convertMessagesWithImages(
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messages: ChatMessage[],
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providerType:
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| string
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| {
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providerType?: string;
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providerBaseUrl?: string | null;
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modelId?: string | null;
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displayName?: string | null;
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forceInlineImageUrls?: boolean;
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loggerPrefix?: string;
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},
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) {
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type ImageConversionTarget = {
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providerType?: string;
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providerBaseUrl?: string | null;
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modelId?: string | null;
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displayName?: string | null;
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forceInlineImageUrls?: boolean;
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loggerPrefix?: string;
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};
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const normalizeTarget = (
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target: string | ImageConversionTarget,
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): Required<Pick<ImageConversionTarget, "providerType" | "forceInlineImageUrls" | "loggerPrefix">> &
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Omit<ImageConversionTarget, "providerType" | "forceInlineImageUrls" | "loggerPrefix"> => {
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if (typeof target === "string") {
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return {
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providerType: target,
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providerBaseUrl: null,
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modelId: null,
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displayName: null,
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forceInlineImageUrls: false,
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loggerPrefix: "[Chat API] Vision inline",
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};
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}
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return {
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providerType: String(target.providerType || ""),
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providerBaseUrl: target.providerBaseUrl || null,
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modelId: target.modelId || null,
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displayName: target.displayName || null,
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forceInlineImageUrls: target.forceInlineImageUrls === true,
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loggerPrefix: target.loggerPrefix || "[Chat API] Vision inline",
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};
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};
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const extractImagePartUrl = (part: unknown): string | null => {
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const partAny = part as any;
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const candidateUrl =
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partAny?.type === "file"
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? partAny.url || partAny.data
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: partAny?.type === "image"
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? partAny.image || partAny.url
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: partAny?.type === "image_url"
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? partAny.image_url?.url
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: undefined;
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if (typeof candidateUrl !== "string") return null;
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const normalized = candidateUrl.trim();
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if (!/^https?:\/\//i.test(normalized)) return null;
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return normalized;
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};
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const replaceImagePartUrl = (part: unknown, nextUrl: string): unknown => {
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const partAny = part as any;
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if (partAny?.type === "file") {
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return {
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...partAny,
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url: nextUrl,
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data: nextUrl,
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mediaType: String(partAny.mediaType || "").startsWith("image/")
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? partAny.mediaType
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: "image/jpeg",
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mimeType: String(partAny.mimeType || "").startsWith("image/")
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? partAny.mimeType
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: "image/jpeg",
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};
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}
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if (partAny?.type === "image") {
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return {
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...partAny,
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image: nextUrl,
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url: nextUrl,
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};
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}
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if (partAny?.type === "image_url") {
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return {
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...partAny,
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image_url: {
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...(partAny.image_url || {}),
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url: nextUrl,
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},
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};
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}
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return part;
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};
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const inlineImageUrlsInMessages = async (
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sourceMessages: ChatMessage[],
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target: ReturnType<typeof normalizeTarget>,
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) => {
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const uniqueUrls = Array.from(
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new Set(
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sourceMessages
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.flatMap((message) => message.parts || [])
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.map((part) => extractImagePartUrl(part))
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.filter((url): url is string => Boolean(url)),
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),
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);
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if (uniqueUrls.length === 0) {
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return sourceMessages;
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}
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const { inlineDataUrls } = await inlineVisionImages(uniqueUrls, {
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maxImages: Math.min(8, uniqueUrls.length),
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maxSide: 1200,
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timeoutMs: 12000,
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retries: 2,
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loggerPrefix: target.loggerPrefix,
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}).catch((error) => {
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console.warn(`${target.loggerPrefix} failed to inline message images:`, error);
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return { inlineDataUrls: [], skipped: [] };
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});
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if (inlineDataUrls.length === 0) {
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return sourceMessages;
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}
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const inlineMap = new Map<string, string>();
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for (let index = 0; index < inlineDataUrls.length; index += 1) {
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const originalUrl = uniqueUrls[index];
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const inlineUrl = inlineDataUrls[index];
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if (originalUrl && inlineUrl) {
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inlineMap.set(originalUrl, inlineUrl);
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}
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}
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if (inlineMap.size === 0) {
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return sourceMessages;
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}
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console.log(`${target.loggerPrefix} inlined chat message images:`, {
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total: uniqueUrls.length,
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inlined: inlineMap.size,
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providerType: target.providerType,
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modelId: target.modelId,
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displayName: target.displayName,
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});
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return sourceMessages.map((message) => ({
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...message,
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parts: (message.parts || []).map((part) => {
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const originalUrl = extractImagePartUrl(part);
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if (!originalUrl) return part;
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const inlineUrl = inlineMap.get(originalUrl);
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return inlineUrl ? replaceImagePartUrl(part, inlineUrl) : part;
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}) as typeof message.parts,
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})) as ChatMessage[];
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};
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const target = normalizeTarget(providerType);
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let preparedMessages = messages;
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if (target.forceInlineImageUrls) {
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preparedMessages = await inlineImageUrlsInMessages(preparedMessages, target);
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}
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const normalizedProviderType = target.providerType;
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if (
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providerType === "bfl-image" ||
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providerType === "ruxa-image" ||
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providerType === "fal-image" ||
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providerType === "openai-image" ||
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providerType === "midjourney" ||
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providerType === "runway"
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normalizedProviderType === "bfl-image" ||
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normalizedProviderType === "ruxa-image" ||
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normalizedProviderType === "fal-image" ||
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normalizedProviderType === "openai-image" ||
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normalizedProviderType === "midjourney" ||
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normalizedProviderType === "runway"
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) {
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const convertedMessages = [];
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for (const message of messages) {
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for (const message of preparedMessages) {
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const content: any[] = [];
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for (const part of message.parts) {
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@ -2149,8 +2321,8 @@ async function convertMessagesWithImages(messages: ChatMessage[], providerType:
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// 所有 provider 都使用 URL 格式传输图片
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// gemini-image 和 huanke-gemini 内部会将 Base64 转换为 S3 URL
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// 这里不再进行 Base64 转换，直接返回标准格式
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if (providerType !== "gemini") {
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return convertToModelMessages(messages);
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if (normalizedProviderType !== "gemini") {
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return convertToModelMessages(preparedMessages);
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}
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// 🆕 检测是否有标注图片
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@ -2162,7 +2334,7 @@ async function convertMessagesWithImages(messages: ChatMessage[], providerType:
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// 对于 Gemini，需要特殊处理图片
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const convertedMessages = [];
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for (const message of messages) {
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for (const message of preparedMessages) {
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const content: any[] = [];
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// 🆕 如果是用户消息且有标注图片，在文本前添加系统提示
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@ -6407,6 +6579,34 @@ export async function POST(request: Request) {
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}
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}
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if (shouldRunAgentConversation) {
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const canvasClarificationGate = resolveCanvasAgentClarificationGate({
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text: requestTextContent,
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composerMode,
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contextPayload: effectiveContextPayload,
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mediaInput: requestResolvedMediaInput,
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targetResolution: requestTargetResolution,
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taskBoundaryDecision,
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executionMode: smartAutoExecution?.mode,
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actionKind: resolvedConversationAction.kind,
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});
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if (canvasClarificationGate.shouldClarify) {
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smartAutoNeedsClarification = true;
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smartAutoClarificationQuestion =
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canvasClarificationGate.clarificationMessage || smartAutoClarificationQuestion;
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smartAutoFriendlyMessage =
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canvasClarificationGate.clarificationMessage || smartAutoFriendlyMessage;
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console.log("[Smart Auto Mode] 🧭 Canvas clarification gate triggered:", {
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reason: canvasClarificationGate.reason,
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executionMode: smartAutoExecution?.mode,
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resolutionMode: requestTargetResolution?.resolutionMode,
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selectedCount: requestResolvedMediaInput?.selectedImageRefs.length || 0,
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candidateCount: requestTargetResolution?.candidateRefs.length || 0,
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});
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}
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}
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if (shouldRunAgentConversation && smartAutoNeedsClarification) {
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const clarificationMessage = resolveClarificationMessage({
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clarificationQuestion: smartAutoClarificationQuestion,
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@ -9054,6 +9254,10 @@ export async function POST(request: Request) {
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const currentModelConfig = isUuidModelId
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? await getAiModelById({ id: finalModelId }).catch(() => undefined)
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: undefined;
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const currentProviderConfig =
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currentModelConfig?.providerId
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? await getAiProviderById({ id: currentModelConfig.providerId }).catch(() => null)
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: null;
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const runtimeProviderType = String(
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resolvedProviderType || (languageModel as any).provider || ""
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);
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@ -10878,7 +11082,19 @@ export async function POST(request: Request) {
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// 转换消息，对于 Gemini 和千问视频将图片 URL 转换为 base64
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const convertedMessages = await convertMessagesWithImages(
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injectHistoryImagesToLastUserMessage(uiMessagesForModel, smartAutoHistoryImages),
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(languageModel as any).provider || ""
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{
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providerType: (languageModel as any).provider || "",
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providerBaseUrl: currentProviderConfig?.baseUrl || null,
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modelId: currentModelConfig?.modelId || actualModelId,
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displayName: currentModelConfig?.displayName || actualModelId,
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forceInlineImageUrls: shouldForceInlineVisionInputs({
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providerType: resolvedProviderType || currentProviderConfig?.type || runtimeProviderType,
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providerBaseUrl: currentProviderConfig?.baseUrl || null,
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modelId: currentModelConfig?.modelId || actualModelId,
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displayName: currentModelConfig?.displayName || actualModelId,
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}),
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loggerPrefix: "[Chat API] Stream text vision inline",
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}
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);
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console.log("[Chat API] Messages converted, count:", convertedMessages.length); // 调试日志
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@ -1,6 +1,7 @@
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import { Annotation, StateGraph, START, END } from "@langchain/langgraph";
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import { generateText } from "ai";
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import sharp from "sharp";
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import { getCompatibleTextGenerationTemperature } from "@/lib/ai/model-compat";
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import type { AnalysisResult } from "@/lib/ai/smart-analyzer";
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import type { RouteResult } from "@/lib/ai/model-router";
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import type { AgentSkillRouteResult } from "@/lib/ai/skill-router";
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@ -1359,7 +1360,7 @@ async function buildExecutionPlanWithAgent(
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]
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.filter(Boolean)
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.join("\n\n"),
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temperature: 0.1,
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temperature: getCompatibleTextGenerationTemperature(plannerModelConfig, 0.1),
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maxTokens: 420,
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abortSignal: AbortSignal.timeout(AGENT_OPERATION_TIMEOUT_MS),
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} as any);
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@ -1482,7 +1483,7 @@ async function generateAgentConversation(
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content: [{ type: "text" as const, text: visionPrompt }, ...visionInputs],
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},
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],
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temperature: 0.2,
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temperature: getCompatibleTextGenerationTemperature(visionModelConfig, 0.2),
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maxTokens: 360,
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abortSignal: AbortSignal.timeout(AGENT_OPERATION_TIMEOUT_MS),
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} as any);
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@ -1544,7 +1545,7 @@ async function generateAgentConversation(
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model: reasoningModel,
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system: systemPrompt,
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prompt,
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temperature: 0.35,
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temperature: getCompatibleTextGenerationTemperature(reasoningModelConfig, 0.35),
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maxTokens: 520,
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abortSignal: AbortSignal.timeout(AGENT_OPERATION_TIMEOUT_MS),
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} as any);
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@ -1626,7 +1627,7 @@ async function recommendImageParamsWithAgent(
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- 若用户未指定比例，优先 auto。
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- negativePrompt 仅在明显需要避坑时填写。`,
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prompt: requestText,
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temperature: 0.15,
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temperature: getCompatibleTextGenerationTemperature(plannerModelConfig, 0.15),
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maxTokens: 220,
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abortSignal: AbortSignal.timeout(AGENT_OPERATION_TIMEOUT_MS),
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} as any);
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@ -1719,7 +1720,7 @@ async function recommendVideoPlanWithAgent(
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- 有参考图时优先 first-frame 或 first-last-frame。
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- 避免抽象空话，必须可执行。`,
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prompt,
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temperature: 0.2,
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temperature: getCompatibleTextGenerationTemperature(plannerModelConfig, 0.2),
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maxTokens: 720,
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abortSignal: AbortSignal.timeout(AGENT_OPERATION_TIMEOUT_MS),
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} as any);
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@ -1799,7 +1800,7 @@ async function refineRouteWithAgent(
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意图：${state.analysis.intent}/${state.analysis.subIntent || "none"}
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候选模型：
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${candidatesText}`,
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temperature: 0.1,
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temperature: getCompatibleTextGenerationTemperature(plannerModelConfig, 0.1),
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maxTokens: 120,
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abortSignal: AbortSignal.timeout(AGENT_OPERATION_TIMEOUT_MS),
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} as any);
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81
lib/ai/model-compat.ts
Normal file
81
lib/ai/model-compat.ts
Normal file
@ -0,0 +1,81 @@
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type ModelCompatDescriptor = {
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providerType?: string | null;
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providerBaseUrl?: string | null;
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modelId?: string | null;
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displayName?: string | null;
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provider?: {
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type?: string | null;
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baseUrl?: string | null;
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} | null;
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model?: {
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modelId?: string | null;
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displayName?: string | null;
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} | null;
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};
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const KIMI_MODEL_PATTERN = /\bkimi(?:[-_.\s]?[a-z0-9.]+)?\b/i;
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function normalizeProviderType(input: ModelCompatDescriptor): string {
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return String(input.providerType || input.provider?.type || "")
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.trim()
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.toLowerCase();
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}
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function normalizeProviderBaseUrl(input: ModelCompatDescriptor): string {
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return String(input.providerBaseUrl || input.provider?.baseUrl || "")
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.trim()
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.toLowerCase();
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}
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function normalizeModelText(input: ModelCompatDescriptor): string {
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return `${String(input.displayName || input.model?.displayName || "")} ${String(input.modelId || input.model?.modelId || "")}`
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.trim()
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.toLowerCase();
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}
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export function isMoonshotKimiModel(
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input?: ModelCompatDescriptor | null,
|
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): boolean {
|
||||
if (!input) {
|
||||
return false;
|
||||
}
|
||||
|
||||
const providerType = normalizeProviderType(input);
|
||||
const providerBaseUrl = normalizeProviderBaseUrl(input);
|
||||
const modelText = normalizeModelText(input);
|
||||
|
||||
if (providerType.includes("moonshot")) {
|
||||
return true;
|
||||
}
|
||||
|
||||
if (providerBaseUrl.includes("moonshot.cn")) {
|
||||
return true;
|
||||
}
|
||||
|
||||
if (modelText.includes("moonshot")) {
|
||||
return true;
|
||||
}
|
||||
|
||||
if (KIMI_MODEL_PATTERN.test(modelText)) {
|
||||
return true;
|
||||
}
|
||||
|
||||
return false;
|
||||
}
|
||||
|
||||
export function getCompatibleTextGenerationTemperature(
|
||||
input: ModelCompatDescriptor | null | undefined,
|
||||
preferredTemperature?: number,
|
||||
): number | undefined {
|
||||
if (isMoonshotKimiModel(input)) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
return preferredTemperature;
|
||||
}
|
||||
|
||||
export function shouldForceInlineVisionInputs(
|
||||
input: ModelCompatDescriptor | null | undefined,
|
||||
): boolean {
|
||||
return isMoonshotKimiModel(input);
|
||||
}
|
||||
@ -170,6 +170,7 @@ const EDIT_TEMPLATES: Record<
|
||||
};
|
||||
|
||||
import { generateText } from "ai";
|
||||
import { getCompatibleTextGenerationTemperature } from "@/lib/ai/model-compat";
|
||||
|
||||
function inferStyleTransferTarget(originalPrompt: string, extracted?: string): string {
|
||||
const text = (originalPrompt || "").trim();
|
||||
@ -674,7 +675,7 @@ export async function optimizePromptWithLLM(params: {
|
||||
model,
|
||||
system: optimizerSystemPrompt,
|
||||
prompt: finalUserMessage,
|
||||
temperature: 0.2,
|
||||
temperature: getCompatibleTextGenerationTemperature(selectedOptimizerModel, 0.2),
|
||||
maxTokens: 200,
|
||||
} as any);
|
||||
|
||||
|
||||
@ -6,6 +6,7 @@
|
||||
*/
|
||||
|
||||
import { generateText } from "ai";
|
||||
import { getCompatibleTextGenerationTemperature } from "@/lib/ai/model-compat";
|
||||
import sharp from "sharp";
|
||||
import { isAiModelMultimodal } from "@/lib/db/ai-model-flags";
|
||||
// 使用动态导入避免循环依赖问题
|
||||
@ -1505,6 +1506,7 @@ export async function summarizeImageForExecutionReport(params: {
|
||||
if (fallbackVisionImages.length === 0) continue;
|
||||
|
||||
try {
|
||||
const compatibleTemperature = getCompatibleTextGenerationTemperature(candidate, 0.1);
|
||||
const { text: summaryText } = await generateText({
|
||||
model,
|
||||
messages: [
|
||||
@ -1519,7 +1521,9 @@ export async function summarizeImageForExecutionReport(params: {
|
||||
],
|
||||
},
|
||||
],
|
||||
temperature: 0.1,
|
||||
...(compatibleTemperature !== undefined
|
||||
? { temperature: compatibleTemperature }
|
||||
: {}),
|
||||
maxTokens: 120,
|
||||
abortSignal: AbortSignal.timeout(ANALYZER_TIMEOUT_MS),
|
||||
} as any);
|
||||
@ -2107,6 +2111,7 @@ ${historyText}
|
||||
);
|
||||
|
||||
// 调用模型分析（有图时携带图片，确保真实视觉分析）
|
||||
const analyzerTemperature = getCompatibleTextGenerationTemperature(selectedAnalyzerModel, 0.1);
|
||||
const requestPayload = hasImage && visionImages.length > 0
|
||||
&& !cachedVisualSummary
|
||||
? {
|
||||
@ -2121,7 +2126,7 @@ ${historyText}
|
||||
],
|
||||
},
|
||||
],
|
||||
temperature: 0.1,
|
||||
...(analyzerTemperature !== undefined ? { temperature: analyzerTemperature } : {}),
|
||||
maxTokens: 360,
|
||||
abortSignal: AbortSignal.timeout(ANALYZER_TIMEOUT_MS),
|
||||
}
|
||||
@ -2129,7 +2134,7 @@ ${historyText}
|
||||
model,
|
||||
system: ANALYZER_SYSTEM_PROMPT,
|
||||
prompt: userMessage,
|
||||
temperature: 0.1,
|
||||
...(analyzerTemperature !== undefined ? { temperature: analyzerTemperature } : {}),
|
||||
maxTokens: 300,
|
||||
abortSignal: AbortSignal.timeout(ANALYZER_TIMEOUT_MS),
|
||||
};
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user