129 lines
3.8 KiB
TypeScript
129 lines
3.8 KiB
TypeScript
import { generateText } from "ai";
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import { NextRequest, NextResponse } from "next/server";
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import { z } from "zod";
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import { inlineVisionImages } from "@/lib/ai/vision-inline";
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import { createLogger } from "@/lib/observability/logger";
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import {
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PRODUCT_PLANNING_ADVANCED_DISABLED,
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PRODUCT_PLANNING_ADVANCED_DISABLED_MESSAGE,
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} from "@/lib/product-planning/advanced-disabled";
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import { resolveProductPlanningVisionModel } from "@/lib/product-planning/model-runtime";
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import {
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isProductPlanningEnabled,
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PRODUCT_PLANNING_DISABLED_MESSAGE,
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} from "@/lib/product-planning/server-feature";
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import { getProductPlanningToken } from "@/lib/product-planning/server-auth";
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import type { ProductPlanningAnalyzeResponse } from "@/lib/product-planning/types";
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export const runtime = "nodejs";
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export const maxDuration = 120;
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const logger = createLogger("product-planning-analyze-image-api");
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const requestSchema = z.object({
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itemId: z.string().trim().min(1),
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imageUrl: z.string().trim().url(),
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title: z.string().trim().optional(),
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});
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function jsonError(status: number, error: string) {
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const payload: ProductPlanningAnalyzeResponse = {
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success: false,
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error,
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};
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return NextResponse.json(payload, { status });
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}
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function buildImageAnalyzePrompt(params: { itemId: string; title?: string }) {
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return [
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"你是一名资深电商视觉策划顾问，请分析这张竞品主图，并输出结构化 markdown。",
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`商品ID：${params.itemId}`,
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params.title ? `商品标题：${params.title}` : "",
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"",
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"请至少包含以下部分：",
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"1. 画面场景与拍摄方式",
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"2. 产品外观与结构要点",
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"3. 当前主图正在强调的核心卖点",
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"4. 用户感知风险或信息缺口",
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"5. 给我们做同类商品主图时可复用的优化建议",
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"",
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"要求：",
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"- 用中文输出",
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"- 结论明确，避免空泛形容词",
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"- 结合电商转化场景给出建议",
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]
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.filter(Boolean)
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.join("\n");
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}
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export async function POST(request: NextRequest) {
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if (PRODUCT_PLANNING_ADVANCED_DISABLED) {
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return jsonError(503, PRODUCT_PLANNING_ADVANCED_DISABLED_MESSAGE);
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}
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const token = await getProductPlanningToken(request);
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if (!token?.sub) {
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return jsonError(401, "未登录");
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}
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if (!(await isProductPlanningEnabled())) {
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return jsonError(403, PRODUCT_PLANNING_DISABLED_MESSAGE);
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}
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let body: unknown;
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try {
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body = await request.json();
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} catch {
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return jsonError(400, "请求体不是合法 JSON");
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}
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const parsed = requestSchema.safeParse(body);
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if (!parsed.success) {
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return jsonError(400, "请求参数不合法");
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}
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const resolvedModel = await resolveProductPlanningVisionModel();
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if (!resolvedModel.model) {
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return jsonError(500, resolvedModel.error || "当前站点没有可用的图片分析模型");
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}
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try {
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const { inlineDataUrls } = await inlineVisionImages([parsed.data.imageUrl], {
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maxImages: 1,
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maxSide: 900,
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timeoutMs: 12000,
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retries: 1,
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loggerPrefix: "[ProductPlanning][AnalyzeImage]",
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});
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const effectiveImage = inlineDataUrls[0] || parsed.data.imageUrl;
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const { text } = await generateText({
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model: resolvedModel.model,
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messages: [
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{
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role: "user",
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content: [
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{
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type: "text",
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text: buildImageAnalyzePrompt(parsed.data),
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},
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{
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type: "image",
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image: effectiveImage,
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},
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],
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},
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],
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temperature: 0.2,
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maxTokens: 1200,
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} as any);
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return NextResponse.json({
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success: true,
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markdown: text,
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} satisfies ProductPlanningAnalyzeResponse);
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} catch (error) {
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logger.error("request_failed", error);
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return jsonError(502, "图片分析失败");
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}
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}
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