644 lines
21 KiB
TypeScript
644 lines
21 KiB
TypeScript
import { generateText } from "ai";
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import { execFile } from "node:child_process";
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import { promisify } from "node:util";
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import { mkdtemp, readFile, rm } from "node:fs/promises";
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import os from "node:os";
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import path from "node:path";
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import { NextResponse } from "next/server";
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import sharp from "sharp";
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import { z } from "zod";
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import { auth } from "@/app/(auth)/auth";
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import { getLanguageModelById } from "@/lib/ai/dynamic-provider";
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import { inlineVisionImages } from "@/lib/ai/vision-inline";
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import { getAiModelAgentCodingModelId, isAiModelMultimodal } from "@/lib/db/ai-model-flags";
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import {
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getAgentAiModelWithProvider,
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getEnabledAiModelsWithProvider,
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} from "@/lib/db/queries";
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export const runtime = "nodejs";
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export const maxDuration = 180;
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const execFileAsync = promisify(execFile);
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const VIDEO_ANALYSIS_FRAME_COUNT = 4;
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const VIDEO_ANALYSIS_MAX_SIDE = 960;
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const VIDEO_ANALYSIS_TIMEOUT_MS = 15000;
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const requestSchema = z.object({
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mode: z.enum(["video-analysis", "character-scene-extraction", "agent-plan"]),
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prompt: z.string().trim().optional(),
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imageUrls: z.array(z.string().trim()).optional(),
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videoUrls: z.array(z.string().trim()).optional(),
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nodeTitle: z.string().trim().optional(),
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preferredModelRef: z.string().trim().optional(),
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model: z.string().trim().optional(),
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});
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function sanitizeUrls(value: unknown, limit = 6) {
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if (!Array.isArray(value)) return [];
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return value
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.map((item) => String(item || "").trim())
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.filter((url) => /^https?:\/\//i.test(url))
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.slice(0, limit);
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}
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function supportsVision(model: {
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supportsVision?: boolean | null;
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capabilities?: unknown;
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defaultParams?: unknown;
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}) {
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return (
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Boolean(model.supportsVision) ||
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isAiModelMultimodal({
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capabilities: model.capabilities,
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defaultParams: model.defaultParams,
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})
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);
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}
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function extractJsonBlock<T = Record<string, unknown>>(text: string): T | null {
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const matched = String(text || "").match(/\{[\s\S]*\}/);
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if (!matched) return null;
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try {
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return JSON.parse(matched[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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function normalizeText(value: unknown) {
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return String(value || "").trim();
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}
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function normalizeStringList(value: unknown, limit = 8) {
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if (!Array.isArray(value)) return [];
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return Array.from(
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new Set(
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value
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.map((item) => String(item || "").trim())
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.filter(Boolean)
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.slice(0, limit)
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)
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);
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}
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function normalizeShotIdeas(value: unknown) {
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if (!Array.isArray(value)) return [];
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return value
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.map((item, index) => {
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const raw =
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item && typeof item === "object" && !Array.isArray(item)
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? (item as Record<string, unknown>)
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: {};
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const title = normalizeText(raw.title || `镜头 ${index + 1}`);
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const description = normalizeText(raw.description || raw.prompt || raw.summary);
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if (!title && !description) return null;
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return {
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title: title || `镜头 ${index + 1}`,
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description,
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};
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})
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.filter(
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(item): item is { title: string; description: string } => Boolean(item)
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)
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.slice(0, 6);
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}
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function normalizeVideoAnalysisRows(
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value: unknown,
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framePreviews: string[],
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) {
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if (!Array.isArray(value)) return [];
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return value
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.map((item, index) => {
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const raw =
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item && typeof item === "object" && !Array.isArray(item)
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? (item as Record<string, unknown>)
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: {};
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const sceneDescription = normalizeText(
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raw.sceneDescription || raw.scene || raw.title || raw.description,
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);
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const backgroundMusic = normalizeText(
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raw.backgroundMusic || raw.music || raw.bgm,
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);
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const voiceAndSfx = normalizeText(
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raw.voiceAndSfx || raw.sound || raw.audio || raw.sfx,
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);
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const imagePrompt = normalizeText(
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raw.imagePrompt || raw.image_generation_prompt || raw.image,
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);
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const motionPrompt = normalizeText(
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raw.motionPrompt || raw.motion || raw.videoPrompt || raw.video_motion_prompt,
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);
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const keyframeIndex = Number(
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raw.keyframeIndex || raw.frameIndex || raw.key_frame_index || index + 1,
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);
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if (
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!sceneDescription &&
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!backgroundMusic &&
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!voiceAndSfx &&
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!imagePrompt &&
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!motionPrompt
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) {
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return null;
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}
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const normalizedFrameIndex =
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Number.isFinite(keyframeIndex) && keyframeIndex > 0
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? Math.max(0, Math.min(framePreviews.length - 1, Math.round(keyframeIndex) - 1))
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: index % Math.max(framePreviews.length, 1);
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return {
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sceneDescription: sceneDescription || `镜头 ${index + 1}`,
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backgroundMusic: backgroundMusic || "无",
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voiceAndSfx: voiceAndSfx || "无",
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imagePrompt: imagePrompt || sceneDescription || "延续当前视频气质生成关键帧",
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motionPrompt: motionPrompt || "保持镜头运动自然、连贯、符合前后节奏。",
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keyframeUrl: framePreviews[normalizedFrameIndex] || "",
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};
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})
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.filter(
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(
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item,
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): item is {
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sceneDescription: string;
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backgroundMusic: string;
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voiceAndSfx: string;
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imagePrompt: string;
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motionPrompt: string;
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keyframeUrl: string;
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} => Boolean(item),
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)
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.slice(0, 8);
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}
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function normalizeCharacters(value: unknown) {
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if (!Array.isArray(value)) return [];
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return value
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.map((item) => {
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const raw =
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item && typeof item === "object" && !Array.isArray(item)
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? (item as Record<string, unknown>)
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: {};
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const name = normalizeText(raw.name || raw.title);
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const description = normalizeText(raw.description || raw.summary);
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const prompt = normalizeText(raw.prompt);
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if (!name && !description && !prompt) return null;
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return {
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name: name || "未命名角色",
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description,
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prompt,
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look: normalizeText(raw.look) || undefined,
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wardrobe: normalizeText(raw.wardrobe) || undefined,
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signatureAction: normalizeText(raw.signatureAction) || undefined,
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traits: normalizeStringList(raw.traits, 6),
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};
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})
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.filter((item): item is NonNullable<typeof item> => Boolean(item))
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.slice(0, 8);
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}
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function normalizeScenes(value: unknown) {
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if (!Array.isArray(value)) return [];
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return value
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.map((item) => {
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const raw =
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item && typeof item === "object" && !Array.isArray(item)
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? (item as Record<string, unknown>)
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: {};
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const name = normalizeText(raw.name || raw.title);
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const description = normalizeText(raw.description || raw.summary);
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const prompt = normalizeText(raw.prompt);
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if (!name && !description && !prompt) return null;
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return {
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name: name || "未命名场景",
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description,
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prompt,
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mood: normalizeText(raw.mood) || undefined,
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lighting: normalizeText(raw.lighting) || undefined,
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props: normalizeStringList(raw.props, 6),
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};
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})
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.filter((item): item is NonNullable<typeof item> => Boolean(item))
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.slice(0, 8);
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}
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function buildVideoAnalysisPrompt(params: {
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nodeTitle?: string;
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prompt: string;
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frameCount: number;
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imageCount: number;
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videoCount: number;
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}) {
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return [
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"你是视频画布工作流里的视频分析节点，需要为后续分镜和生成节点提供结构化输出。",
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params.nodeTitle ? `当前节点：${params.nodeTitle}` : "",
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`上游参考图数量：${params.imageCount}`,
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`上游视频数量：${params.videoCount}`,
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`已提供按时间顺序抽取的视频关键帧数量：${params.frameCount}`,
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params.prompt ? `上游脚本/说明：\n${params.prompt}` : "上游没有额外脚本，请重点依据参考画面分析。",
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"",
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"请只输出 JSON，不要输出 markdown，不要输出解释。",
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"JSON 结构如下：",
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`{
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"summary": "一句话总结当前视频/镜头风格，不超过120字",
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"highlights": ["3-5条镜头分析要点"],
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"analysisRows": [
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{
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"sceneDescription": "镜头内容与景别描述",
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"backgroundMusic": "这一段适合的背景音乐氛围",
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"voiceAndSfx": "人声/环境声/音效建议，没有就写无",
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"imagePrompt": "后续生成关键帧时可直接使用的图像提示词",
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"motionPrompt": "后续生成视频时可直接使用的运动提示词",
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"keyframeIndex": 1
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}
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],
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"shotIdeas": [
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{ "title": "镜头 1", "description": "一句话描述镜头目标、景别或动作" }
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],
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"visualMotifs": ["2-4个可复用的视觉母题"],
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"recommendedNextSteps": ["2-4条建议的下游动作"]
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}`,
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"analysisRows 请尽量输出 2-6 行；keyframeIndex 使用 1 开始的序号，对应你看到的关键帧顺序。",
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"如果没有真实视频帧，只能依据参考图和脚本推断，也要正常输出，但措辞要稳健。",
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]
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.filter(Boolean)
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.join("\n");
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}
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async function getVideoDurationSeconds(videoUrl: string) {
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try {
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const { stdout } = await execFileAsync(
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"ffprobe",
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[
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"-v",
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"error",
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"-show_entries",
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"format=duration",
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"-of",
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"default=noprint_wrappers=1:nokey=1",
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videoUrl,
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],
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{
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timeout: VIDEO_ANALYSIS_TIMEOUT_MS,
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maxBuffer: 1024 * 1024,
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},
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);
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const duration = Number.parseFloat(String(stdout || "").trim());
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return Number.isFinite(duration) && duration > 0 ? duration : null;
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} catch {
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return null;
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}
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}
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function buildVideoFrameTimestamps(durationSeconds: number | null, frameCount: number) {
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const safeCount = Math.max(1, frameCount);
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if (!durationSeconds || !Number.isFinite(durationSeconds) || durationSeconds <= 0.5) {
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return Array.from({ length: safeCount }).map((_, index) =>
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Number((0.3 + index * 0.45).toFixed(2)),
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);
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}
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const start = Math.min(0.35, durationSeconds * 0.12);
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const end = Math.max(start + 0.6, durationSeconds * 0.88);
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if (safeCount === 1) return [Number(((start + end) / 2).toFixed(2))];
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const step = (end - start) / (safeCount - 1);
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return Array.from({ length: safeCount }).map((_, index) =>
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Number((start + step * index).toFixed(2)),
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);
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}
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async function downscaleVideoFrameForVision(inputBuffer: Buffer) {
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return sharp(inputBuffer, { failOn: "none" })
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.rotate()
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.resize({
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width: VIDEO_ANALYSIS_MAX_SIDE,
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height: VIDEO_ANALYSIS_MAX_SIDE,
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fit: "inside",
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withoutEnlargement: true,
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})
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.jpeg({ quality: 82, mozjpeg: true })
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.toBuffer();
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}
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async function extractVideoFrameDataUrls(videoUrl: string, frameCount = VIDEO_ANALYSIS_FRAME_COUNT) {
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const tempDir = await mkdtemp(path.join(os.tmpdir(), "canvas-video-analysis-"));
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try {
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const durationSeconds = await getVideoDurationSeconds(videoUrl);
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const timestamps = buildVideoFrameTimestamps(durationSeconds, frameCount);
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const frames = await Promise.all(
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timestamps.map(async (timestamp, index) => {
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const outputPath = path.join(tempDir, `frame-${index + 1}.jpg`);
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await execFileAsync(
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"ffmpeg",
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[
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"-hide_banner",
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"-loglevel",
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"error",
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"-ss",
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String(timestamp),
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"-i",
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videoUrl,
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"-frames:v",
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"1",
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"-q:v",
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"4",
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"-y",
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outputPath,
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],
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{
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timeout: VIDEO_ANALYSIS_TIMEOUT_MS,
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maxBuffer: 2 * 1024 * 1024,
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},
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);
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const buffer = await readFile(outputPath);
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const processed = await downscaleVideoFrameForVision(buffer);
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return `data:image/jpeg;base64,${processed.toString("base64")}`;
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}),
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);
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return frames.filter(Boolean);
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} catch (error) {
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console.warn("[Canvas Workflow Analyze] extract video frames failed:", {
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message: error instanceof Error ? error.message : String(error),
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videoUrl,
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});
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return [];
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} finally {
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await rm(tempDir, { force: true, recursive: true }).catch(() => undefined);
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}
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}
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function buildCharacterScenePrompt(params: {
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nodeTitle?: string;
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prompt: string;
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imageCount: number;
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}) {
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return [
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"你是视频画布工作流里的角色场景提取节点，需要从脚本、设定和参考图里提取角色与场景资产。",
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params.nodeTitle ? `当前节点：${params.nodeTitle}` : "",
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`参考图数量：${params.imageCount}`,
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params.prompt ? `输入内容：\n${params.prompt}` : "没有额外脚本，请根据参考图提取角色和场景线索。",
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"",
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"请只输出 JSON，不要输出 markdown，不要输出解释。",
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"JSON 结构如下：",
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`{
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"summary": "一句话总结当前故事世界与资产线索",
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"characters": [
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{
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"name": "角色名",
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"description": "角色外观与身份简介",
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"look": "外貌特征",
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"wardrobe": "服装/道具",
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"traits": ["性格关键词"],
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"signatureAction": "代表动作",
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"prompt": "可直接用于生成角色图/角色视频的提示词"
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}
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],
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"scenes": [
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{
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"name": "场景名",
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"description": "空间、布景和氛围简介",
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"mood": "情绪与气氛",
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"lighting": "布光特征",
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"props": ["核心道具"],
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"prompt": "可直接用于生成场景图/场景视频的提示词"
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}
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],
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"relationships": ["角色与场景或角色之间的关系"],
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"recommendedNextSteps": ["建议连接的后续节点动作"]
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}`,
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"角色和场景都要尽量具体，避免空泛形容词。",
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]
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.filter(Boolean)
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.join("\n");
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}
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function buildAgentPlanPrompt(params: {
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nodeTitle?: string;
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prompt: string;
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imageCount: number;
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videoCount: number;
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}) {
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return [
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"你是视频画布里的自定义代理节点，需要把当前创作目标拆成可执行的工作流计划。",
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params.nodeTitle ? `当前节点：${params.nodeTitle}` : "",
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`参考图数量：${params.imageCount}`,
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`视频数量：${params.videoCount}`,
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params.prompt ? `上游需求：\n${params.prompt}` : "没有额外脚本，请基于当前上下文给出稳妥计划。",
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"",
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"请只输出 JSON，不要输出 markdown，不要输出解释。",
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"JSON 结构如下：",
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`{
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"summary": "一句话说明这个代理链的目标",
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"agentPlan": ["3-6个执行步骤"],
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"deliverables": ["2-4个预期交付物"],
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"recommendedNodes": ["建议继续连接的节点类型或能力名"]
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}`,
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"步骤要偏工作流视角，而不是泛泛而谈。",
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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: Request) {
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try {
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const session = await auth();
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if (!session?.user?.id) {
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return NextResponse.json({ success: false, error: "未登录" }, { status: 401 });
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}
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const body = await request.json().catch(() => ({}));
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const parsed = requestSchema.safeParse(body);
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if (!parsed.success) {
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return NextResponse.json(
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{ success: false, error: "请求参数不合法" },
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{ status: 400 }
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);
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}
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const prompt = normalizeText(parsed.data.prompt);
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const imageUrls = sanitizeUrls(parsed.data.imageUrls, 4);
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const videoUrls = sanitizeUrls(parsed.data.videoUrls, 4);
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const extractedVideoFrames =
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parsed.data.mode === "video-analysis" && videoUrls.length > 0
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? await extractVideoFrameDataUrls(videoUrls[0], VIDEO_ANALYSIS_FRAME_COUNT)
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: [];
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const preferredModelRef = normalizeText(parsed.data.preferredModelRef || parsed.data.model);
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if (!prompt && imageUrls.length === 0 && videoUrls.length === 0) {
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return NextResponse.json(
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{ success: false, error: "请至少提供脚本、图片或视频上下文" },
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{ status: 400 }
|
||
);
|
||
}
|
||
|
||
const [allModels, agentModel] = await Promise.all([
|
||
getEnabledAiModelsWithProvider(false),
|
||
getAgentAiModelWithProvider(),
|
||
]);
|
||
const compatibleModels = allModels.filter(
|
||
(item) => !item.model.isSubModel && item.model.isEnabled
|
||
);
|
||
|
||
const preferredModel =
|
||
preferredModelRef
|
||
? compatibleModels.find(
|
||
(item) =>
|
||
item.model.id === preferredModelRef ||
|
||
item.model.modelId === preferredModelRef,
|
||
)
|
||
: null;
|
||
|
||
const hasVisionInputs = imageUrls.length > 0 || extractedVideoFrames.length > 0;
|
||
|
||
let selectedModel =
|
||
preferredModel && (!hasVisionInputs || supportsVision(preferredModel.model))
|
||
? preferredModel
|
||
: hasVisionInputs &&
|
||
agentModel &&
|
||
supportsVision(agentModel.model)
|
||
? agentModel
|
||
: hasVisionInputs
|
||
? compatibleModels.find((item) => supportsVision(item.model))
|
||
: agentModel || compatibleModels[0];
|
||
|
||
if (!selectedModel) {
|
||
selectedModel = compatibleModels[0];
|
||
}
|
||
|
||
if (!selectedModel) {
|
||
return NextResponse.json(
|
||
{ success: false, error: "当前站点没有可用的分析模型" },
|
||
{ status: 500 }
|
||
);
|
||
}
|
||
|
||
const useVision = hasVisionInputs && supportsVision(selectedModel.model);
|
||
const codingModelId = getAiModelAgentCodingModelId(selectedModel.model);
|
||
const model = await getLanguageModelById(selectedModel.model.id, codingModelId);
|
||
if (!model) {
|
||
return NextResponse.json(
|
||
{ success: false, error: "分析模型初始化失败" },
|
||
{ status: 500 }
|
||
);
|
||
}
|
||
|
||
const promptText =
|
||
parsed.data.mode === "video-analysis"
|
||
? buildVideoAnalysisPrompt({
|
||
frameCount: extractedVideoFrames.length,
|
||
nodeTitle: normalizeText(parsed.data.nodeTitle),
|
||
prompt,
|
||
imageCount: imageUrls.length,
|
||
videoCount: videoUrls.length,
|
||
})
|
||
: parsed.data.mode === "character-scene-extraction"
|
||
? buildCharacterScenePrompt({
|
||
nodeTitle: normalizeText(parsed.data.nodeTitle),
|
||
prompt,
|
||
imageCount: imageUrls.length,
|
||
})
|
||
: buildAgentPlanPrompt({
|
||
nodeTitle: normalizeText(parsed.data.nodeTitle),
|
||
prompt,
|
||
imageCount: imageUrls.length,
|
||
videoCount: videoUrls.length,
|
||
});
|
||
|
||
const inlinedImageReferences = useVision
|
||
? (
|
||
await inlineVisionImages(imageUrls, {
|
||
maxImages: 4,
|
||
maxSide: 960,
|
||
timeoutMs: 12000,
|
||
retries: 1,
|
||
loggerPrefix: "[Canvas Workflow Analyze]",
|
||
})
|
||
).inlineDataUrls
|
||
: [];
|
||
const referenceImages = useVision
|
||
? [...inlinedImageReferences, ...extractedVideoFrames].slice(0, 6)
|
||
: [];
|
||
|
||
const { text } = await generateText({
|
||
model,
|
||
messages: [
|
||
{
|
||
role: "user",
|
||
content: [
|
||
{ type: "text", text: promptText },
|
||
...referenceImages.map((image) => ({ type: "image" as const, image })),
|
||
],
|
||
},
|
||
],
|
||
temperature: 0.2,
|
||
maxTokens: 1600,
|
||
} as any);
|
||
|
||
const parsedJson = extractJsonBlock<Record<string, unknown>>(text) || {};
|
||
const summary =
|
||
normalizeText(parsedJson.summary) ||
|
||
normalizeText(text).slice(0, 160);
|
||
|
||
if (parsed.data.mode === "video-analysis") {
|
||
const analysisRows = normalizeVideoAnalysisRows(
|
||
parsedJson.analysisRows,
|
||
extractedVideoFrames,
|
||
);
|
||
const shotIdeas =
|
||
normalizeShotIdeas(parsedJson.shotIdeas).length > 0
|
||
? normalizeShotIdeas(parsedJson.shotIdeas)
|
||
: analysisRows.map((row, index) => ({
|
||
title: `镜头 ${index + 1}`,
|
||
description: row.sceneDescription,
|
||
}));
|
||
return NextResponse.json({
|
||
success: true,
|
||
data: {
|
||
summary,
|
||
analysisRows,
|
||
framePreviews: extractedVideoFrames,
|
||
highlights: normalizeStringList(parsedJson.highlights, 6),
|
||
shotIdeas,
|
||
visualMotifs: normalizeStringList(parsedJson.visualMotifs, 6),
|
||
recommendedNextSteps: normalizeStringList(parsedJson.recommendedNextSteps, 6),
|
||
model: selectedModel.model.displayName,
|
||
raw: text,
|
||
},
|
||
});
|
||
}
|
||
|
||
if (parsed.data.mode === "character-scene-extraction") {
|
||
return NextResponse.json({
|
||
success: true,
|
||
data: {
|
||
summary,
|
||
characters: normalizeCharacters(parsedJson.characters),
|
||
scenes: normalizeScenes(parsedJson.scenes),
|
||
relationships: normalizeStringList(parsedJson.relationships, 8),
|
||
recommendedNextSteps: normalizeStringList(parsedJson.recommendedNextSteps, 6),
|
||
model: selectedModel.model.displayName,
|
||
raw: text,
|
||
},
|
||
});
|
||
}
|
||
|
||
return NextResponse.json({
|
||
success: true,
|
||
data: {
|
||
summary,
|
||
agentPlan: normalizeStringList(parsedJson.agentPlan, 8),
|
||
deliverables: normalizeStringList(parsedJson.deliverables, 6),
|
||
recommendedNodes: normalizeStringList(parsedJson.recommendedNodes, 6),
|
||
model: selectedModel.model.displayName,
|
||
raw: text,
|
||
},
|
||
});
|
||
} catch (error) {
|
||
console.error("[Canvas Workflow Analyze] Error:", error);
|
||
return NextResponse.json(
|
||
{
|
||
success: false,
|
||
error: error instanceof Error ? error.message : "工作流分析失败",
|
||
},
|
||
{ status: 500 }
|
||
);
|
||
}
|
||
}
|