ai生成任务字段单独拿出来

src/lib/initDB.ts

@@ -263,8 +263,7 @@ export default async (knex: Knex, forceInit: boolean = false): Promise<void> =>
           {
             name: "剧本资产提取",
             type: "scriptAssetExtraction",
-            data: `
+            data: `---
-            ---
 name: universal_agent
 description: 专注于从剧本内容中提取所使用的资产（角色、场景、道具）并生成结构化资产列表的助手。
 ---

src/routes/assetsGenerate/batchGenerateImageAssets.ts

@@ -110,16 +110,20 @@ export default router.post("/", validateFields(requestSchema), async (req, res)
 
       try {
         const aiImage = u.Ai.Image(model);
-        await aiImage.run({
+        await aiImage.run(
+          {
             prompt: userPrompt,
             imageBase64: item.base64 ? [item.base64] : [],
             size: resolution,
             aspectRatio: "16:9",
+          },
+          {
             taskClass: cfg.taskClass,
             describe,
             projectId,
             relatedObjects: JSON.stringify(relatedObjects),
-        });
+          },
+        );
         aiImage.save(imagePath);
 
         const imageData = await u.db("o_image").where("id", imageId).select("*").first();

src/routes/assetsGenerate/generateAssets.ts

@@ -98,16 +98,20 @@ export default router.post("/", validateFields(requestSchema), async (req, res)
   try {
     // 4. 调用 AI 生成图片
     const aiImage = u.Ai.Image(model);
-    await aiImage.run({
+    await aiImage.run(
+      {
         prompt: userPrompt,
         imageBase64: base64 ? [base64] : [],
         size: resolution,
         aspectRatio: "16:9",
+      },
+      {
         taskClass: cfg.taskClass,
         describe,
         projectId,
         relatedObjects: JSON.stringify(relatedObjects),
-    });
+      },
+    );
     aiImage.save(imagePath);
 
     // 5. 更新记录 & 返回结果

src/routes/production/workbench/generateVideo.ts

@@ -105,8 +105,8 @@ export default router.post(
         console.log("%c Line:110 🍑 prompt", "background:#b03734", prompt);
 
         const aiVideo = u.Ai.Video(model);
-        await aiVideo.run({
+        await aiVideo.run(
-          projectId,
+          {
             prompt,
             imageBase64: base64.filter((item) => item !== null) as string[],
             mode,
@@ -114,10 +114,14 @@ export default router.post(
             aspectRatio: (ratio?.videoRatio as `${number}:${number}`) || "16:9",
             resolution,
             audio,
+          },
+          {
+            projectId,
             taskClass: "视频生成",
             describe: "根据提示词生成视频",
             relatedObjects: JSON.stringify(relatedObjects),
-        });
+          },
+        );
         await aiVideo.save(videoPath);
         await u.db("o_video").where("id", videoId).update({ state: "生成成功" });
         // await u.db("o_videoConfig").where("storyboardId", storyboardId).update({ videoId, updateTime: Date.now() });

src/types/database.d.ts

@@ -1,8 +1,4 @@
-<<<<<<< HEAD
-// @db-hash 93b2462070c45c2b449e9a18c4e88763
-=======
 // @db-hash 24748d4ef971381a79c720c846f83847
->>>>>>> 796947cef173e7fe2f96e21fa8aeae23ff0fdf4a
 //该文件由脚本自动生成，请勿手动修改
 
 export interface memories {
@@ -182,6 +178,7 @@ export interface o_storyboard {
   'sound'?: string | null;
   'state'?: string | null;
   'title'?: string | null;
+  'videoPrompt'?: string | null;
 }
 export interface o_tasks {
   'describe'?: string | null;

src/utils/ai.ts

@@ -25,23 +25,10 @@ async function getVendorTemplateFn(fnName: FnName, modelName: `${string}:${strin
   const selectedModel = modelList.find((i: any) => i.modelName == name);
   if (!selectedModel) throw new Error(`未找到模型 ${name} id=${id}`);
   const jsCode = transform(vendorConfigData.code!, { transforms: ["typescript"] }).code;
-  const running = u.vm(jsCode);
+  const fn = u.vm(jsCode)[fnName];
-  Object.assign(running.vendor.inputValues, JSON.parse(vendorConfigData.inputValues ?? "{}"));
-  running.vendor.models = modelList;
-  const fn = running[fnName];
   if (!fn) throw new Error(`未找到供应商配置中的函数 ${fnName} id=${id}`);
-  if (fnName == "textRequest") {
+  if (fnName == "textRequest") return fn(selectedModel);
-    const model = fn(selectedModel);
+  else return <T>(input: T) => fn(input, selectedModel);
-    if (!model) throw new Error(`供应商 textRequest 返回无效模型 id=${id}`);
-    return model;
-  }
-  return async <T>(input: T) => {
-    const result = await fn(input, selectedModel);
-    if (result === undefined || result === null) {
-      throw new Error(`供应商函数 ${fnName} 未返回有效结果 id=${id}`);
-    }
-    return result;
-  };
 }
 
 async function withTaskRecord<T>(
@@ -113,6 +100,9 @@ interface ImageConfig {
   imageBase64: string[]; //输入的图片提示词
   size: "1K" | "2K" | "4K"; // 图片尺寸
   aspectRatio: `${number}:${number}`; // 长宽比
+}
+
+interface TaskRecord {
   taskClass: string; // 任务分类
   describe: string; // 任务描述
   relatedObjects: string; // 相关对象信息，便于后续分析和追踪
@@ -125,8 +115,8 @@ class AiImage {
   constructor(key: `${string}:${string}`) {
     this.key = key;
   }
-  async run(input: ImageConfig) {
+  async run(input: ImageConfig, taskRecord: TaskRecord) {
-    return withTaskRecord(this.key, input.taskClass, input.describe, input.relatedObjects, input.projectId, async (modelName) => {
+    return withTaskRecord(this.key, taskRecord.taskClass, taskRecord.describe, taskRecord.relatedObjects, taskRecord.projectId, async (modelName) => {
       const fn = await getVendorTemplateFn("imageRequest", modelName);
       this.result = await fn(input);
       if (this.result.startsWith("http")) this.result = await urlToBase64(this.result);
@@ -139,7 +129,6 @@ class AiImage {
   }
 }
 interface VideoConfig {
-  projectId: number; // 项目ID
   prompt: string; //视频提示词
   imageBase64: string[]; //输入的图片提示词
   aspectRatio: `${number}:${number}`; // 长宽比
@@ -147,9 +136,6 @@ interface VideoConfig {
   duration: number; // 视频时长，单位秒
   resolution: string; // 视频分辨率
   audio: boolean; // 是否需要配音
-  taskClass: string; // 任务分类
-  describe: string; // 任务描述
-  relatedObjects: string; // 相关对象信息，便于后续分析和追踪
 }
 
 class AiVideo {
@@ -158,8 +144,8 @@ class AiVideo {
   constructor(key: `${string}:${string}`) {
     this.key = key;
   }
-  async run(input: VideoConfig) {
+  async run(input: VideoConfig, taskRecord: TaskRecord) {
-    return withTaskRecord(this.key, input.taskClass, input.describe, input.relatedObjects, input.projectId, async (modelName) => {
+    return withTaskRecord(this.key, taskRecord.taskClass, taskRecord.describe, taskRecord.relatedObjects, taskRecord.projectId, async (modelName) => {
       const fn = await getVendorTemplateFn("videoRequest", modelName);
       this.result = await fn(input);
       if (this.result.startsWith("http")) this.result = await urlToBase64(this.result);
@@ -177,8 +163,8 @@ class AiAudio {
   constructor(key: `${string}:${string}`) {
     this.key = key;
   }
-  async run(input: VideoConfig) {
+  async run(input: VideoConfig, taskRecord: TaskRecord) {
-    return withTaskRecord(this.key, input.taskClass, input.describe, input.relatedObjects, input.projectId, async (modelName) => {
+    return withTaskRecord(this.key, taskRecord.taskClass, taskRecord.describe, taskRecord.relatedObjects, taskRecord.projectId, async (modelName) => {
       const fn = await getVendorTemplateFn("ttsRequest", modelName);
       this.result = await fn(input);
       if (this.result.startsWith("http")) this.result = await urlToBase64(this.result);