628 lines
21 KiB
TypeScript
628 lines
21 KiB
TypeScript
import { Annotation, StateGraph, START, END } from "@langchain/langgraph";
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import { generateText } from "ai";
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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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interface HistoryMessage {
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role: "user" | "assistant";
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content: string;
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}
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export interface AgentRecommendedImageParams {
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aspectRatio?: string;
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imageSize?: string;
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imageCount?: number;
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negativePrompt?: string;
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promptExtend?: boolean;
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}
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export interface AutoAgentInput {
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text: string;
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hasImage: boolean;
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imageUrls?: string[];
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imageFromHistory?: boolean;
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historyMessages?: HistoryMessage[];
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context?: string;
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}
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export interface AutoAgentResult {
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analysis?: AnalysisResult;
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friendlyMessage?: string;
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optimizedPrompt?: string;
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recommendedImageParams?: AgentRecommendedImageParams;
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route?: RouteResult;
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}
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const AutoAgentState = Annotation.Root({
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text: Annotation<string>,
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hasImage: Annotation<boolean>,
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imageUrls: Annotation<string[]>,
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imageFromHistory: Annotation<boolean>,
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historyMessages: Annotation<HistoryMessage[]>,
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context: Annotation<string | null>,
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analysis: Annotation<AnalysisResult | null>,
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friendlyMessage: Annotation<string | null>,
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optimizedPrompt: Annotation<string | null>,
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recommendedImageParams: Annotation<AgentRecommendedImageParams | null>,
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route: Annotation<RouteResult | null>,
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});
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type AutoAgentStateType = typeof AutoAgentState.State;
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function compactText(text: string, max = 48) {
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const normalized = text.replace(/\s+/g, " ").trim();
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if (normalized.length <= max) return normalized;
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return `${normalized.slice(0, max)}...`;
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}
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function normalizeAspectRatio(value?: string): string | undefined {
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if (!value) return undefined;
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const normalized = value.trim().toLowerCase();
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if (normalized === "auto") return "auto";
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const raw = value.trim();
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const allowed = new Set([
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"1:1",
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"2:3",
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"3:2",
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"3:4",
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"4:3",
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"4:5",
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"5:4",
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"9:16",
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"16:9",
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"21:9",
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]);
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return allowed.has(raw) ? raw : undefined;
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}
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function normalizeImageSize(value?: string): string | undefined {
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if (!value) return undefined;
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const normalized = value.trim().toUpperCase();
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if (normalized === "1K" || normalized === "2K" || normalized === "4K") {
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return normalized;
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}
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return undefined;
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}
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function sanitizeRecommendedImageParams(
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params: AgentRecommendedImageParams | undefined,
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intent?: AnalysisResult["intent"],
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): AgentRecommendedImageParams | undefined {
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if (!params) return undefined;
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const safe: AgentRecommendedImageParams = {};
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const normalizedAspectRatio = normalizeAspectRatio(params.aspectRatio);
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const normalizedImageSize = normalizeImageSize(params.imageSize);
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if (normalizedAspectRatio) safe.aspectRatio = normalizedAspectRatio;
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if (normalizedImageSize) safe.imageSize = normalizedImageSize;
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if (typeof params.imageCount === "number" && Number.isFinite(params.imageCount)) {
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const min = 1;
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const max = intent === "image_edit" ? 1 : 4;
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safe.imageCount = Math.min(Math.max(Math.round(params.imageCount), min), max);
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}
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if (intent === "image_edit") {
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safe.imageCount = 1;
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}
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if (typeof params.negativePrompt === "string" && params.negativePrompt.trim()) {
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safe.negativePrompt = params.negativePrompt.trim().slice(0, 500);
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}
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if (typeof params.promptExtend === "boolean") {
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safe.promptExtend = params.promptExtend;
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}
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return Object.keys(safe).length > 0 ? safe : undefined;
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}
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function heuristicImageParams(
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text: string,
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analysis: AnalysisResult,
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): AgentRecommendedImageParams | undefined {
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const normalized = text.toLowerCase();
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const params: AgentRecommendedImageParams = {
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imageSize: "2K",
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promptExtend: true,
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};
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if (analysis.intent === "image_edit") {
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params.imageCount = 1;
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} else if (/多张|四张|4张|三张|3张/.test(normalized)) {
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params.imageCount = /四张|4张/.test(normalized) ? 4 : 3;
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} else if (/两张|2张|双图/.test(normalized)) {
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params.imageCount = 2;
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} else {
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params.imageCount = 1;
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}
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if (/横图|横版|宽屏|封面|banner|海报/.test(normalized)) {
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params.aspectRatio = "16:9";
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} else if (/竖图|竖版|手机壁纸|全身|海报竖版/.test(normalized)) {
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params.aspectRatio = "9:16";
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} else if (/方图|方形|头像|logo|电商主图/.test(normalized)) {
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params.aspectRatio = "1:1";
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} else {
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params.aspectRatio = "auto";
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}
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return sanitizeRecommendedImageParams(params, analysis.intent);
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}
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function buildStrategicFriendlyMessage(
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analysis: AnalysisResult,
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state: AutoAgentStateType,
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): Promise<string | null> {
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const fallbackQuestion = analysis.clarifyingQuestions?.[0];
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if (analysis.needsClarification) {
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return Promise.resolve(
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analysis.conversationalReply || fallbackQuestion || "我先确认一下，你希望我优先调整哪一部分？"
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);
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}
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return generateAgentConversation(analysis, state);
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}
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type EnabledModelWithProvider = Awaited<
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ReturnType<typeof import("@/lib/db/queries")["getEnabledAiModelsWithProvider"]>
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>[number];
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function pickQwenVisionModel(models: EnabledModelWithProvider[]) {
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const visionModels = models.filter((item) => item.model.supportsVision && item.model.isEnabled);
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const qwenByName = visionModels.find(
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(item) =>
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item.provider.type === "alibaba" &&
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/(qwen|通义|vl)/i.test(`${item.model.displayName} ${item.model.modelId}`)
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);
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if (qwenByName) return qwenByName;
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const alibabaVision = visionModels.find((item) => item.provider.type === "alibaba");
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if (alibabaVision) return alibabaVision;
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return visionModels[0];
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}
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function pickAgentReasoningModel(
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agentModel: EnabledModelWithProvider | undefined,
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models: EnabledModelWithProvider[],
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) {
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const isReasoningCompatibleProvider = (providerType: string) =>
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["zhipu", "alibaba", "openai", "custom", "local"].includes(providerType);
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if (agentModel && isReasoningCompatibleProvider(agentModel.provider.type)) return agentModel;
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const zhipuModel = models.find(
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(item) =>
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item.provider.type === "zhipu" &&
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item.model.isEnabled &&
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!item.model.isSubModel &&
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isReasoningCompatibleProvider(item.provider.type)
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);
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if (zhipuModel) return zhipuModel;
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return models.find(
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(item) =>
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item.model.isEnabled &&
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!item.model.isSubModel &&
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isReasoningCompatibleProvider(item.provider.type)
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);
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}
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function parseJsonBlock<T = Record<string, unknown>>(text: string): T | null {
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const match = text.match(/\{[\s\S]*\}/);
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if (!match) return null;
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try {
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return JSON.parse(match[0]) as T;
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} catch {
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return null;
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}
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}
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async function generateAgentConversation(
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analysis: AnalysisResult,
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state: AutoAgentStateType
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): Promise<string | null> {
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try {
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const {
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getEnabledAiModelsWithProvider,
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getAgentAiModelWithProvider,
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} = await import("@/lib/db/queries");
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const { getLanguageModelById } = await import("./dynamic-provider");
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const allModels = await getEnabledAiModelsWithProvider();
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const agentModel = await getAgentAiModelWithProvider();
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const reasoningModelConfig = pickAgentReasoningModel(agentModel, allModels);
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if (!reasoningModelConfig) {
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return analysis.conversationalReply || null;
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}
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const reasoningModel = await getLanguageModelById(reasoningModelConfig.model.id);
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if (!reasoningModel) {
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return analysis.conversationalReply || null;
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}
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const history = state.historyMessages
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.slice(-6)
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.map((msg) => `${msg.role === "user" ? "用户" : "助手"}: ${compactText(msg.content, 120)}`)
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.join("\n");
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const validImageUrls = state.imageUrls
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.filter((url) => /^https?:\/\//i.test(url))
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.slice(0, 4);
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const imageInfo = state.hasImage
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? `用户本轮可用图片数量：${state.imageUrls.length || 1}。图片来源：${state.imageFromHistory ? "历史消息复用" : "本轮上传"}。`
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: "用户本轮没有上传图片。";
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let visualAnalysis = "";
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if (state.hasImage && validImageUrls.length > 0) {
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try {
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const visionModelConfig = pickQwenVisionModel(allModels);
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if (!visionModelConfig) {
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throw new Error("missing_vision_model");
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}
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const visionModel = await getLanguageModelById(visionModelConfig.model.id);
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if (!visionModel) {
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throw new Error("missing_vision_runtime");
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}
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const visionPrompt = `请分析以下参考图片，给出可用于生图/修图执行前决策的结果。
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要求：
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1) 每张图输出 1 行，格式：图X：主体...；风格...；构图...；光影与色调...；材质/氛围...
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2) 只写客观可见信息，不要编造品牌、尺寸、镜头参数。
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3) 全部输出控制在 6 行内。`;
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const visionInputs = validImageUrls.map((url) => ({ type: "image" as const, image: url }));
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const { text: visionText } = await generateText({
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model: visionModel,
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messages: [
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{
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role: "user" as const,
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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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maxTokens: 360,
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} as any);
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visualAnalysis = visionText.trim();
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console.log(
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`[Auto Agent] Vision analysis model: ${visionModelConfig.model.displayName} (${visionModelConfig.provider.type})`
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);
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} catch (error) {
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console.warn("[Auto Agent] Vision analysis failed, continue without visual summary");
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visualAnalysis = "";
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}
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}
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const systemPrompt = `你是一个真实执行中的 AI 生图/修图 Agent。你的核心目标是提升用户一次生成成功率。
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你必须按以下 AGENT SKILLS 执行：
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1) 需求拆解：把用户目标拆成可执行指令，优先满足用户明确要求。
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2) 风险预判：主动识别可能导致失败的歧义（主体数量、视角、风格冲突、文字内容）。
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3) 提示词策略：给出可落地的正向约束和必要的负向约束，避免空泛描述。
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4) 参数策略：在输出设置中明确建议宽高比、清晰度档位、出图数量（若用户未指定）。
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输出要求：
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- 不要说“看不到图片/无法访问图片”。
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- 不暴露内部实现、模型路由、算法细节。
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- 输出 Markdown，控制在 8-14 行。
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- 有图片时必须按顺序输出以下 Markdown H3 标题（逐字一致）：
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### 需求理解
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### 图片分析
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### 执行计划
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### 输出设置
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- “图片分析”必须基于视觉分析结果写具体观察，不允许写泛化空话。
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- 只有明确拿到原图分辨率时，才能写具体像素尺寸；否则只写宽高比/清晰度目标。`;
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const prompt = [
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`【用户原始需求】${state.text || "（无文本）"}`,
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`【意图分析】intent=${analysis.intent}; subIntent=${analysis.subIntent || "none"}; confidence=${analysis.confidence}`,
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`【图片信息】${imageInfo}`,
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visualAnalysis ? `【视觉模型预分析】\n${visualAnalysis}` : "",
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state.context ? `【画布上下文】${compactText(state.context, 300)}` : "",
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history ? `【最近对话】\n${history}` : "",
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state.hasImage
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? "请先做本轮图片分析，再输出执行前汇报。"
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: "请输出本轮执行前汇报。",
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]
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.filter(Boolean)
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.join("\n\n");
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const { text } = await generateText({
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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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maxTokens: 520,
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} as any);
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const cleaned = text.trim();
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if (cleaned.length < 10) {
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return analysis.conversationalReply || null;
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}
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console.log(
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`[Auto Agent] Conversation model: ${reasoningModelConfig.model.displayName} (${reasoningModelConfig.provider.type})`
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);
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return cleaned;
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} catch (error) {
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return analysis.conversationalReply || null;
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}
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}
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async function recommendImageParamsWithAgent(
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analysis: AnalysisResult,
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state: AutoAgentStateType,
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): Promise<AgentRecommendedImageParams | undefined> {
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if (analysis.intent !== "image_edit" && analysis.intent !== "image_gen") {
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return undefined;
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}
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try {
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const {
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getEnabledAiModelsWithProvider,
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getAgentAiModelWithProvider,
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} = await import("@/lib/db/queries");
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const { getLanguageModelById } = await import("./dynamic-provider");
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const allModels = await getEnabledAiModelsWithProvider();
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const agentModel = await getAgentAiModelWithProvider();
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const plannerModelConfig = pickAgentReasoningModel(agentModel, allModels);
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if (!plannerModelConfig) {
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return heuristicImageParams(state.text, analysis);
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}
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const plannerModel = await getLanguageModelById(plannerModelConfig.model.id);
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if (!plannerModel) {
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return heuristicImageParams(state.text, analysis);
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}
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const history = state.historyMessages
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.slice(-4)
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.map((msg) => `${msg.role === "user" ? "用户" : "助手"}: ${compactText(msg.content, 120)}`)
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.join("\n");
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const requestText = [
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`用户请求：${state.text || "（无文本）"}`,
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`识别意图：${analysis.intent}/${analysis.subIntent || "none"}`,
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`是否有参考图：${state.hasImage ? "是" : "否"}`,
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history ? `最近对话：\n${history}` : "",
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"请给出本次最稳妥的生图参数建议。",
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]
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.filter(Boolean)
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.join("\n\n");
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const { text } = await generateText({
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model: plannerModel,
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system: `你是生图参数规划 Agent。目标是提升首图命中率和稳定性。
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只输出 JSON，不要输出解释。格式如下：
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{
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"aspectRatio": "1:1|2:3|3:2|3:4|4:3|4:5|5:4|9:16|16:9|21:9|auto",
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"imageSize": "1K|2K|4K",
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"imageCount": 1,
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"negativePrompt": "可选，简洁",
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"promptExtend": true
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}
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规则：
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- image_edit 场景强制 imageCount=1。
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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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maxTokens: 220,
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} as any);
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const parsed = parseJsonBlock<AgentRecommendedImageParams>(text);
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const sanitized = sanitizeRecommendedImageParams(parsed || undefined, analysis.intent);
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if (sanitized) {
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console.log(
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`[Auto Agent] Parameter planner model: ${plannerModelConfig.model.displayName} (${plannerModelConfig.provider.type})`
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);
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return sanitized;
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}
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return heuristicImageParams(state.text, analysis);
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} catch {
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return heuristicImageParams(state.text, analysis);
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}
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}
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async function refineRouteWithAgent(
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state: AutoAgentStateType,
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baseRoute: RouteResult,
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): Promise<RouteResult> {
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if (!state.analysis || (state.analysis.intent !== "image_edit" && state.analysis.intent !== "image_gen")) {
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return baseRoute;
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}
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try {
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const { db } = await import("@/lib/db");
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const { siteSettings } = await import("@/lib/db/schema");
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const [settings] = await db.select().from(siteSettings).limit(1);
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const configuredDefaultModelId = settings?.canvasAutoModeDefaultModelId || null;
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if (configuredDefaultModelId && baseRoute.id === configuredDefaultModelId) {
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console.log(
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`[Auto Agent] Keep admin default model for auto mode: ${baseRoute.displayName}`
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);
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return baseRoute;
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}
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const {
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getEnabledAiModelsWithProvider,
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getAgentAiModelWithProvider,
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} = await import("@/lib/db/queries");
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const { getLanguageModelById } = await import("./dynamic-provider");
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const { isAiModelAgentAuto } = await import("@/lib/db/ai-model-flags");
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const allModelsRaw = await getEnabledAiModelsWithProvider(false);
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const allModels = allModelsRaw.filter((item) => !isAiModelAgentAuto(item.model));
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const imageCandidates = allModels
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.filter((item) => item.model.supportsImageGen && item.model.isEnabled && !item.model.isSubModel)
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.slice(0, 12);
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if (imageCandidates.length <= 1) return baseRoute;
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const agentModel = await getAgentAiModelWithProvider();
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const plannerModelConfig = pickAgentReasoningModel(agentModel, allModelsRaw);
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if (!plannerModelConfig) return baseRoute;
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const plannerModel = await getLanguageModelById(plannerModelConfig.model.id);
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if (!plannerModel) return baseRoute;
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|
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const candidatesText = imageCandidates
|
||
.map((item, index) => `${index + 1}. id=${item.model.id}; 名称=${item.model.displayName}; provider=${item.provider.type}`)
|
||
.join("\n");
|
||
|
||
const { text } = await generateText({
|
||
model: plannerModel,
|
||
system: `你是生图模型选择 Agent。目标是优先提高一次成功率。
|
||
只输出 JSON：
|
||
{
|
||
"modelId": "候选里的id",
|
||
"reason": "一句话中文理由，不超过24字"
|
||
}
|
||
如果无法判断，也必须给出候选中的 modelId。`,
|
||
prompt: `用户请求：${state.text || "（无文本）"}
|
||
意图：${state.analysis.intent}/${state.analysis.subIntent || "none"}
|
||
候选模型：
|
||
${candidatesText}`,
|
||
temperature: 0.1,
|
||
maxTokens: 120,
|
||
} as any);
|
||
|
||
const parsed = parseJsonBlock<{ modelId?: string; reason?: string }>(text);
|
||
if (!parsed?.modelId) return baseRoute;
|
||
const chosen = imageCandidates.find((item) => item.model.id === parsed.modelId);
|
||
if (!chosen) return baseRoute;
|
||
|
||
return {
|
||
id: chosen.model.id,
|
||
displayName: chosen.model.displayName,
|
||
providerType: chosen.provider.type,
|
||
modelId: chosen.model.modelId,
|
||
reason: `Agent规划: ${(parsed.reason || chosen.model.displayName).slice(0, 40)}`,
|
||
};
|
||
} catch {
|
||
return baseRoute;
|
||
}
|
||
}
|
||
|
||
const analyzeNode = async (state: AutoAgentStateType) => {
|
||
const { analyzeIntent } = await import("./smart-analyzer");
|
||
const analysis = await analyzeIntent({
|
||
text: state.text,
|
||
hasImage: state.hasImage,
|
||
imageUrls: state.imageUrls,
|
||
imageFromHistory: state.imageFromHistory,
|
||
historyMessages: state.historyMessages,
|
||
context: state.context ?? undefined,
|
||
});
|
||
|
||
const fallbackQuestion = analysis.needsClarification
|
||
? analysis.clarifyingQuestions?.[0]
|
||
: undefined;
|
||
|
||
const strategicMessage = await buildStrategicFriendlyMessage(analysis, state);
|
||
|
||
return {
|
||
analysis,
|
||
friendlyMessage: strategicMessage || analysis.conversationalReply || fallbackQuestion || null,
|
||
};
|
||
};
|
||
|
||
const shouldOptimize = (state: AutoAgentStateType) => {
|
||
if (!state.analysis) return "route";
|
||
if (state.analysis.needsClarification) return "route";
|
||
if (state.analysis.intent === "image_gen") {
|
||
return "optimize";
|
||
}
|
||
if (state.analysis.intent === "image_edit" && state.analysis.subIntent) {
|
||
return "optimize";
|
||
}
|
||
return "route";
|
||
};
|
||
|
||
const optimizeNode = async (state: AutoAgentStateType) => {
|
||
if (!state.analysis) {
|
||
return {};
|
||
}
|
||
if (state.analysis.intent === "image_edit" && !state.analysis.subIntent) {
|
||
const recommendedImageParams = await recommendImageParamsWithAgent(state.analysis, state);
|
||
return { recommendedImageParams };
|
||
}
|
||
if (state.analysis.intent !== "image_edit" && state.analysis.intent !== "image_gen") {
|
||
return {};
|
||
}
|
||
const { optimizePromptWithLLM } = await import("./prompt-optimizer");
|
||
const optimized = await optimizePromptWithLLM({
|
||
originalPrompt: state.text,
|
||
intent: state.analysis.intent,
|
||
subIntent: state.analysis.subIntent,
|
||
historyMessages: state.historyMessages,
|
||
context: state.context ?? undefined,
|
||
});
|
||
const mergedParams = sanitizeRecommendedImageParams(
|
||
{
|
||
...optimized.recommendedImageParams,
|
||
negativePrompt:
|
||
optimized.negativePrompt && optimized.negativePrompt.trim().length > 0
|
||
? optimized.negativePrompt
|
||
: optimized.recommendedImageParams?.negativePrompt,
|
||
},
|
||
state.analysis.intent,
|
||
) ?? (await recommendImageParamsWithAgent(state.analysis, state));
|
||
|
||
return {
|
||
optimizedPrompt: optimized.optimizedPrompt,
|
||
recommendedImageParams: mergedParams,
|
||
};
|
||
};
|
||
|
||
const routeNode = async (state: AutoAgentStateType) => {
|
||
const { routeToModel } = await import("./model-router");
|
||
const baseRoute = await routeToModel({
|
||
intent: state.analysis?.intent || "chat",
|
||
subIntent: state.analysis?.subIntent,
|
||
hasImage: state.hasImage,
|
||
});
|
||
const route = await refineRouteWithAgent(state, baseRoute);
|
||
return { route };
|
||
};
|
||
|
||
let compiledGraph: ReturnType<typeof buildGraph> | null = null;
|
||
|
||
function buildGraph() {
|
||
return new StateGraph(AutoAgentState)
|
||
.addNode("analyze", analyzeNode)
|
||
.addNode("optimize", optimizeNode)
|
||
.addNode("routeModel", routeNode)
|
||
.addEdge(START, "analyze")
|
||
.addConditionalEdges("analyze", shouldOptimize, {
|
||
optimize: "optimize",
|
||
route: "routeModel",
|
||
})
|
||
.addEdge("optimize", "routeModel")
|
||
.addEdge("routeModel", END)
|
||
.compile();
|
||
}
|
||
|
||
function getGraph() {
|
||
if (!compiledGraph) {
|
||
compiledGraph = buildGraph();
|
||
}
|
||
return compiledGraph;
|
||
}
|
||
|
||
export async function runAutoAgentGraph(params: AutoAgentInput): Promise<AutoAgentResult> {
|
||
const graph = getGraph();
|
||
const result = await graph.invoke({
|
||
text: params.text,
|
||
hasImage: params.hasImage,
|
||
imageUrls: params.imageUrls ?? [],
|
||
imageFromHistory: params.imageFromHistory ?? false,
|
||
historyMessages: params.historyMessages ?? [],
|
||
context: params.context ?? null,
|
||
analysis: null,
|
||
friendlyMessage: null,
|
||
optimizedPrompt: null,
|
||
recommendedImageParams: null,
|
||
route: null,
|
||
});
|
||
|
||
return {
|
||
analysis: result.analysis ?? undefined,
|
||
friendlyMessage: result.friendlyMessage ?? undefined,
|
||
optimizedPrompt: result.optimizedPrompt ?? undefined,
|
||
recommendedImageParams: result.recommendedImageParams ?? undefined,
|
||
route: result.route ?? undefined,
|
||
};
|
||
}
|