Merge branch 'develop' of https://github.com/HBAI-Ltd/Toonflow-app into develop

.github/workflows/release.yml

@@ -183,15 +183,14 @@ jobs:
 
             | 操作系统 | 架构 | 文件 | 说明 |
             |---------|------|------|------|
-            | 🪟 Windows | 通用 | `ToonFlow-*-win-setup.exe` | **推荐**，适用于大多数 Windows 电脑 |
+            | 🪟 Windows | x64 | `ToonFlow-*-win-x64-setup.exe` | **推荐**，适用于大多数 Windows 电脑 |
-            | 🪟 Windows | x64 | `ToonFlow-*-win-x64-setup.exe` | 适用于 64 位 Windows 电脑 |
             | 🪟 Windows | ARM64 | `ToonFlow-*-win-arm64-setup.exe` | 适用于 ARM 架构 Windows 设备 |
             | 🍎 macOS | Apple Silicon | `ToonFlow-*-mac-arm64.dmg` | 适用于 M1/M2/M3/M4 芯片的 Mac |
             | 🍎 macOS | Intel | `ToonFlow-*-mac-x64.dmg` | 适用于 Intel 芯片的 Mac |
-            | 🐧 Linux | x86_64 | `ToonFlow-*-linux-x86_64.AppImage` | 适用于大多数 Linux 发行版 |
+            | 🐧 Linux | x64 | `ToonFlow-*-linux-x86_64.AppImage` | 适用于大多数 Linux 发行版 |
             | 🐧 Linux | ARM64 | `ToonFlow-*-linux-arm64.AppImage` | 适用于 ARM 架构的 Linux 设备 |
 
-            > 💡 **不确定选哪个？** Windows 用户通常选 **win-setup.exe**；Mac 用户查看「关于本机」：M 系列芯片选 **arm64.dmg**，Intel 选 **x64.dmg**。
+            > 💡 **不确定选哪个？** Windows 用户通常选 **win-x64-setup.exe**；Mac 用户查看「关于本机」：M 系列芯片选 **arm64.dmg**，Intel 选 **x64.dmg**。
 
             ## 🚀 安装说明
 

data/vendor/klingai.ts

@@ -465,7 +465,7 @@ const videoRequest = async (config: VideoConfig, model: VideoModel): Promise<str
   const isStartEndRequired = currentMode.includes("startEndRequired");
   const isEndFrameOptional = currentMode.includes("endFrameOptional");
   const isStartFrameOptional = currentMode.includes("startFrameOptional");
-  const hasMultiRef = currentMode.some((m) => Array.isArray(m));
+  const hasMultiRef = Array.isArray(currentMode) && currentMode.some((m) => Array.isArray(m));
 
   // 提取不同类型的引用
   const imageRefs = (config.referenceList || []).filter((r) => r.type === "image");
@@ -635,4 +635,4 @@ exports.videoRequest = videoRequest;
 exports.ttsRequest = ttsRequest;
 
 // 这行代码用于确保当前文件被识别为模块，避免全局变量冲突
-export {};
+export {};

data/vendor/yunwu.ts

@@ -1,427 +0,0 @@
-// ==================== 类型定义 ====================
-// 文本模型
-interface TextModel {
-  name: string;
-  modelName: string;
-  type: "text";
-  think: boolean;
-}
-
-// 图像模型
-interface ImageModel {
-  name: string;
-  modelName: string;
-  type: "image";
-  mode: ("text" | "singleImage" | "multiReference")[];
-  associationSkills?: string;
-}
-
-// 视频模型
-interface VideoModel {
-  name: string;
-  modelName: string;
-  type: "video";
-  mode: (
-    | "singleImage"
-    | "startEndRequired"
-    | "endFrameOptional"
-    | "startFrameOptional"
-    | "text"
-    | ("videoReference" | "imageReference" | "audioReference" | "textReference")[]
-  )[];
-  associationSkills?: string;
-  audio: "optional" | false | true;
-  durationResolutionMap: { duration: number[]; resolution: string[] }[];
-}
-
-interface TTSModel {
-  name: string;
-  modelName: string;
-  type: "tts";
-  voices: { title: string; voice: string }[];
-}
-
-interface VendorConfig {
-  id: string;
-  author: string;
-  description?: string;
-  name: string;
-  icon?: string;
-  inputs: { key: string; label: string; type: "text" | "password" | "url"; required: boolean; placeholder?: string }[];
-  inputValues: Record<string, string>;
-  models: (TextModel | ImageModel | VideoModel)[];
-}
-
-// ==================== 全局工具函数声明 ====================
-declare const zipImage: (completeBase64: string, size: number) => Promise<string>;
-declare const zipImageResolution: (completeBase64: string, width: number, height: number) => Promise<string>;
-declare const mergeImages: (completeBase64: string[], maxSize?: string) => Promise<string>;
-declare const urlToBase64: (url: string) => Promise<string>;
-declare const pollTask: (
-  fn: () => Promise<{ completed: boolean; data?: string; error?: string }>,
-  interval?: number,
-  timeout?: number,
-) => Promise<{ completed: boolean; data?: string; error?: string }>;
-declare const axios: any;
-declare const createOpenAI: any;
-declare const createDeepSeek: any;
-declare const createZhipu: any;
-declare const createQwen: any;
-declare const createAnthropic: any;
-declare const createOpenAICompatible: any;
-declare const createXai: any;
-declare const createMinimax: any;
-declare const createGoogleGenerativeAI: any;
-declare const logger: (logstring: string) => void;
-declare const jsonwebtoken: any;
-
-// ==================== 供应商数据 ====================
-const vendor: VendorConfig = {
-  id: "yunwu",
-  author: "Toonflow",
-  description: "OpenAI标准格式接口，您可以修改请求地址并手动添加缺失的模型。",
-  name: "云雾中转",
-  icon: "",
-  inputs: [
-    { key: "apiKey", label: "API密钥", type: "password", required: true },
-    { key: "baseUrl", label: "请求地址", type: "url", required: true, placeholder: "以v1结束，示例：https://yunwu.ai/v1" },
-  ],
-  inputValues: {
-    apiKey: "",
-    baseUrl: "https://yunwu.ai/v1",
-  },
-  models: [
-    {
-      name: "Doubao-Seedream-5.0-lite",
-      type: "image",
-      modelName: "doubao-seedream-5-0-260128",
-      mode: ["text", "singleImage", "multiReference"],
-    },
-    {
-      name: "Gemini-3-Pro-Image-Preview",
-      type: "image",
-      modelName: "gemini-3.1-flash-image-preview",
-      mode: ["text", "singleImage", "multiReference"],
-      associationSkills: "高质量图像生成，支持文本生成图像、图像编辑",
-    },
-    {
-      name: "Claude-sonnet-4.6",
-      type: "text",
-      modelName: "claude-sonnet-4-6",
-      think: false,
-    },
-    {
-      name: "Claude-haiku-4.5-20251001",
-      type: "text",
-      modelName: "claude-haiku-4-5-20251001",
-      think: false,
-    },
-    {
-      name: "Grok-Video-3",
-      type: "video",
-      modelName: "grok-video-3",
-      mode: ["text", "singleImage"],
-      audio: false,
-      durationResolutionMap: [
-        { duration: [6, 10], resolution: ["720P", "1080P"] }
-      ],
-      associationSkills: "文本生成视频,支持图片垫图"
-    }
-  ],
-};
-exports.vendor = vendor;
-
-// ==================== 适配器函数 ====================
-
-// 文本请求函数
-const textRequest = (textModel: TextModel) => {
-  if (!vendor.inputValues.apiKey) throw new Error("缺少API Key");
-  if (!vendor.inputValues.baseUrl) throw new Error("缺少请求地址(baseUrl)");
-
-  const apiKey = vendor.inputValues.apiKey.replace(/^Bearer\s+/i, "");
-  const baseURL = vendor.inputValues.baseUrl;
-
-  return createOpenAI({
-    baseURL: baseURL,
-    apiKey: apiKey,
-  }).chat(textModel.modelName);
-};
-exports.textRequest = textRequest;
-
-// 图片请求函数（修正版：使用 /v1/chat/completions 兼容接口）
-interface ImageConfig {
-  prompt: string;
-  imageBase64: string[];
-  size: "1K" | "2K" | "4K";
-  aspectRatio: `${number}:${number}`;
-}
-
-const imageRequest = async (imageConfig: ImageConfig, imageModel: ImageModel) => {
-  const { apiKey, baseUrl } = vendor.inputValues;
-  if (!apiKey) throw new Error("缺少API Key");
-  if (!baseUrl) throw new Error("缺少请求地址(baseUrl)");
-
-  const cleanApiKey = apiKey.replace(/^Bearer\s+/i, "");
-  const baseURL = baseUrl.replace(/\/$/, "");
-  const endpoint = baseURL + "/chat/completions";
-
-  // 构建用户消息内容（支持多图垫图）
-  const content: any[] = [
-    {
-      type: "text",
-      text: imageConfig.prompt,
-    },
-  ];
-
-  // 添加参考图片（垫图）
-  if (imageConfig.imageBase64 && imageConfig.imageBase64.length > 0) {
-    for (const imgBase64 of imageConfig.imageBase64) {
-      let dataUrl = imgBase64;
-      if (!imgBase64.startsWith("data:image")) {
-        dataUrl = `data:image/png;base64,${imgBase64}`;
-      }
-      content.push({
-        type: "image_url",
-        image_url: { url: dataUrl },
-      });
-    }
-  }
-
-  // 注意：云雾中转站可能支持通过额外参数传递图像尺寸/比例，
-  // 若不确定，可将 size 和 aspectRatio 拼接到 prompt 中（推荐）。
-  // 这里采用追加提示词的方式，确保模型理解期望的分辨率和比例。
-  let finalPrompt = imageConfig.prompt;
-  const sizeMap: Record<string, string> = { "1K": "1024x1024", "2K": "2048x2048", "4K": "4096x4096" };
-  const resolution = sizeMap[imageConfig.size] || "1024x1024";
-  finalPrompt += `\n请生成一张比例为 ${imageConfig.aspectRatio}、分辨率不低于 ${resolution} 的图片。`;
-  content[0].text = finalPrompt;
-
-  const requestBody = {
-    model: imageModel.modelName,
-    messages: [
-      {
-        role: "user",
-        content: content,
-      },
-    ],
-    max_tokens: 4096,       // 防止输出截断
-    response_format: { type: "json_object" }, // 部分中转站需要 JSON 输出
-  };
-
-  logger(`[图像生成] 请求URL: ${endpoint}`);
-  logger(`[图像生成] 模型: ${imageModel.modelName}`);
-  logger(`[图像生成] 参考图片数量: ${imageConfig.imageBase64?.length || 0}`);
-
-  try {
-    const response = await axios.post(endpoint, requestBody, {
-      headers: {
-        "Authorization": `Bearer ${cleanApiKey}`,
-        "Content-Type": "application/json",
-      },
-      timeout: 120000,
-    });
-
-    if (response.status !== 200) {
-      throw new Error(`HTTP ${response.status}: ${JSON.stringify(response.data)}`);
-    }
-
-    const assistantMessage = response.data?.choices?.[0]?.message?.content;
-    if (!assistantMessage) {
-      throw new Error("响应中没有 assistant 消息内容");
-    }
-
-    // 提取图像数据（支持直接返回 base64 data URL 或普通 URL）
-    let imageBase64: string | null = null;
-    // 情况1：消息内容本身就是 data:image 开头
-    if (assistantMessage.startsWith("data:image")) {
-      imageBase64 = assistantMessage;
-    }
-    // 情况2：包含 Markdown 图片链接 ![alt](url)
-    else {
-      const markdownMatch = assistantMessage.match(/!\[.*?\]\((.*?)\)/);
-      if (markdownMatch && markdownMatch[1]) {
-        const url = markdownMatch[1];
-        if (url.startsWith("data:image")) {
-          imageBase64 = url;
-        } else {
-          imageBase64 = await urlToBase64(url);
-        }
-      }
-      // 情况3：直接是纯文本 URL
-      else if (assistantMessage.match(/^https?:\/\/[^\s]+\.(png|jpg|jpeg|gif|webp)/i)) {
-        imageBase64 = await urlToBase64(assistantMessage);
-      }
-    }
-
-    if (!imageBase64) {
-      // 最后尝试：也许整个 content 就是 base64 字符串（无前缀）
-      if (/^[A-Za-z0-9+/=]+$/.test(assistantMessage) && assistantMessage.length > 100) {
-        imageBase64 = `data:image/png;base64,${assistantMessage}`;
-      } else {
-        throw new Error(`无法从响应中提取图像数据: ${assistantMessage.substring(0, 200)}`);
-      }
-    }
-
-    logger(`[图像生成] 成功，图片大小: ${(imageBase64.length / 1024).toFixed(2)} KB`);
-    return imageBase64;
-  } catch (error: any) {
-    logger(`[图像生成] 失败: ${error.message}`);
-    if (error.response) {
-      logger(`[图像生成] API 错误详情: ${JSON.stringify(error.response.data)}`);
-      throw new Error(`图像生成失败: ${error.response.data?.error?.message || error.message}`);
-    }
-    throw error;
-  }
-};
-exports.imageRequest = imageRequest;
-
-// 视频请求函数（保持原有实现，若云雾中转站有专用视频接口可按需修改）
-interface VideoConfig {
-  duration: number;
-  resolution: string;
-  aspectRatio: "16:9" | "9:16";
-  prompt: string;
-  imageBase64?: string[];
-  audio?: boolean;
-  mode:
-  | "singleImage"
-  | "multiImage"
-  | "gridImage"
-  | "startEndRequired"
-  | "endFrameOptional"
-  | "startFrameOptional"
-  | "text"
-  | ("video" | "image" | "audio" | "text")[];
-}
-
-const videoRequest = async (videoConfig: VideoConfig, videoModel: VideoModel) => {
-  const { apiKey, baseUrl: rawBaseUrl } = vendor.inputValues;
-  if (!apiKey) throw new Error("缺少API Key");
-  const baseUrl = rawBaseUrl?.trim();
-  if (!baseUrl) throw new Error("缺少请求地址(baseUrl)");
-
-  const createEndpoint = baseUrl.replace(/\/$/, "") + "/video/create";
-  const queryEndpoint = baseUrl.replace(/\/$/, "") + "/video/query";
-
-  let images: string[] | undefined;
-  if (videoConfig.imageBase64 && videoConfig.imageBase64.length > 0) {
-    logger(`[视频生成] 原始图片数组: ${JSON.stringify(videoConfig.imageBase64)}`);
-    images = videoConfig.imageBase64
-      .filter(img => img && typeof img === 'string' && img.length > 0)
-      .map(img => {
-        if (img.startsWith("data:image")) return img;
-        return `data:image/png;base64,${img}`;
-      });
-    if (images.length === 0) {
-      logger(`[视频生成] 警告: 所有图片都无效，将忽略图片参数`);
-      images = undefined;
-    } else {
-      logger(`[视频生成] 有效图片数量: ${images.length}`);
-    }
-  }
-
-  let aspectRatioParam: string;
-  switch (videoConfig.aspectRatio) {
-    case "16:9": aspectRatioParam = "3:2"; break;
-    case "9:16": aspectRatioParam = "2:3"; break;
-    default: aspectRatioParam = "1:1";
-  }
-
-  let sizeParam: string = "720P";
-  if (videoConfig.resolution && videoConfig.resolution.includes("1080")) sizeParam = "1080P";
-
-  const createBody: any = {
-    model: videoModel.modelName,
-    prompt: videoConfig.prompt,
-    aspect_ratio: aspectRatioParam,
-    size: sizeParam,
-  };
-  if (images && images.length > 0) createBody.images = images;
-
-  try {
-    logger(`[视频生成] 创建请求体: ${JSON.stringify({ ...createBody, images: images ? `${images.length}张图片` : undefined })}`);
-    const createResp = await axios.post(createEndpoint, createBody, {
-      headers: {
-        "Authorization": `Bearer ${apiKey.replace(/^Bearer\s+/i, "")}`,
-        "Content-Type": "application/json",
-      },
-      timeout: 30000,
-    });
-
-    if (createResp.status !== 200 || !createResp.data?.id) {
-      throw new Error(`创建任务失败: ${JSON.stringify(createResp.data)}`);
-    }
-
-    const taskId = createResp.data.id;
-    logger(`[视频生成] 任务已创建，ID: ${taskId}`);
-
-    const pollResult = await pollTask(
-      async () => {
-        try {
-          const queryResp = await axios.get(queryEndpoint, {
-            params: { id: taskId },
-            headers: {
-              "Authorization": `Bearer ${apiKey.replace(/^Bearer\s+/i, "")}`,
-              "Content-Type": "application/json",
-            },
-            timeout: 15000,
-          });
-
-          if (queryResp.status !== 200) {
-            return { completed: false, error: `查询失败: HTTP ${queryResp.status}` };
-          }
-
-          const data = queryResp.data;
-          const status = data.status;
-          logger(`[视频生成] 任务状态: ${status}`);
-
-          if (status === "succeeded" || status === "completed" || status === "success") {
-            if (data.video_url) {
-              return { completed: true, data: data.video_url };
-            } else {
-              return { completed: false, error: "任务成功但未返回视频URL" };
-            }
-          } else if (status === "failed" || status === "error") {
-            return { completed: false, error: `视频生成失败: ${data.error || "未知错误"}` };
-          } else {
-            return { completed: false };
-          }
-        } catch (err: any) {
-          logger(`[视频生成] 轮询出错: ${err.message}`);
-          return { completed: false, error: err.message };
-        }
-      },
-      3000,
-      300000
-    );
-
-    if (!pollResult.completed) {
-      throw new Error(pollResult.error || "视频生成超时或失败");
-    }
-
-    const videoUrl = pollResult.data;
-    logger(`[视频生成] 成功，视频URL: ${videoUrl}`);
-    return videoUrl;
-  } catch (error: any) {
-    logger(`[视频生成] 失败: ${error.message}`);
-    if (error.response) {
-      logger(`[视频生成] API 错误详情: ${JSON.stringify(error.response.data)}`);
-      throw new Error(`视频生成失败: ${error.response.data?.error?.message || error.message}`);
-    }
-    throw error;
-  }
-};
-exports.videoRequest = videoRequest;
-
-// TTS 请求函数（占位）
-interface TTSConfig {
-  text: string;
-  voice: string;
-  speechRate: number;
-  pitchRate: number;
-  volume: number;
-}
-const ttsRequest = async (ttsConfig: TTSConfig, ttsModel: TTSModel) => {
-  return null;
-};
-exports.ttsRequest = ttsRequest;

data/version.txt

@@ -1 +1 @@
-1.1.5
+1.1.6

package.json

@@ -1,6 +1,6 @@
 {
   "name": "toonflow",
-  "version": "1.1.5",
+  "version": "1.1.6",
   "description": "Toonflow 是一款 AI 短剧漫剧工具，能够利用 AI 技术将小说自动转化为剧本，并结合 AI 生成的图片和视频，实现高效的短剧创作。",
   "author": "HBAI-Ltd <ltlctools@outlook.com>",
   "license": "Apache-2.0",

src/agents/productionAgent/index.ts

@@ -1,6 +1,6 @@
 import { Socket } from "socket.io";
-import { tool } from "ai";
 import { z } from "zod";
+import { tool, jsonSchema } from "ai";
 import u from "@/utils";
 import Memory from "@/utils/agent/memory";
 import { createSkillTools, parseFrontmatter, scanSkills, useSkill } from "@/utils/agent/skillsTools";
@@ -138,9 +138,11 @@ async function createSubAgent(parentCtx: AgentContext) {
     return fullResponse;
   }
 
-  const promptInput = z.object({
+  const promptInput = z
-    prompt: z.string().describe("交给子Agent的任务简约描述，100字以内"),
+    .object({
-  });
+      prompt: z.string().describe("交给子Agent的任务简约描述，100字以内"),
+    })
+    .toJSONSchema();
 
   const projectInfo = await u.db("o_project").where("id", resTool.data.projectId).first();
   if (!projectInfo) throw new Error(`项目不存在，ID: ${resTool.data.projectId}`);
@@ -195,7 +197,7 @@ async function createSubAgent(parentCtx: AgentContext) {
   //衍生资产分析与信息写入
   const run_sub_agent_derive_assets = tool({
     description: "运行执行subAgent来完成衍生资产分析与信息写入相关任务",
-    inputSchema: promptInput,
+    inputSchema: jsonSchema<{ prompt: string }>(promptInput),
     execute: async ({ prompt }) => {
       const skill = path.join(u.getPath("skills"), "production_execution_derive_assets.md");
       const systemPrompt = await fs.promises.readFile(skill, "utf-8");
@@ -217,7 +219,7 @@ async function createSubAgent(parentCtx: AgentContext) {
   //衍生资产图片生成
   const run_sub_agent_generate_assets = tool({
     description: "运行执行subAgent来完成衍生资产图片生成相关任务",
-    inputSchema: promptInput,
+    inputSchema: jsonSchema<{ prompt: string }>(promptInput),
     execute: async ({ prompt }) => {
       const skill = path.join(u.getPath("skills"), "production_execution_generate_assets.md");
       const systemPrompt = await fs.promises.readFile(skill, "utf-8");
@@ -239,7 +241,7 @@ async function createSubAgent(parentCtx: AgentContext) {
   //拍摄计划
   const run_sub_agent_director_plan = tool({
     description: "运行执行subAgent来完成导演规划相关任务",
-    inputSchema: promptInput,
+    inputSchema: jsonSchema<{ prompt: string }>(promptInput),
     execute: async ({ prompt }) => {
       const skill = path.join(u.getPath("skills"), "production_execution_director_plan.md");
       const systemPrompt = await fs.promises.readFile(skill, "utf-8");
@@ -264,7 +266,7 @@ async function createSubAgent(parentCtx: AgentContext) {
   //分镜图生成
   const run_sub_agent_storyboard_gen = tool({
     description: "运行执行subAgent来完成分镜图生成相关任务",
-    inputSchema: promptInput,
+    inputSchema: jsonSchema<{ prompt: string }>(promptInput),
     execute: async ({ prompt }) => {
       const skill = path.join(u.getPath("skills"), "production_execution_storyboard_gen.md");
       const systemPrompt = await fs.promises.readFile(skill, "utf-8");
@@ -298,7 +300,7 @@ async function createSubAgent(parentCtx: AgentContext) {
   //分镜面板写入
   const run_sub_agent_storyboard_panel = tool({
     description: "运行执行subAgent来完成分镜面板写入相关任务",
-    inputSchema: promptInput,
+    inputSchema: jsonSchema<{ prompt: string }>(promptInput),
     execute: async ({ prompt }) => {
       const skill = path.join(u.getPath("skills"), "production_execution_storyboard_panel.md");
       const systemPrompt = await fs.promises.readFile(skill, "utf-8");
@@ -324,7 +326,7 @@ async function createSubAgent(parentCtx: AgentContext) {
   //分镜表写入
   const run_sub_agent_storyboard_table = tool({
     description: "运行执行subAgent来完成分镜表构建相关任务",
-    inputSchema: promptInput,
+    inputSchema: jsonSchema<{ prompt: string }>(promptInput),
     execute: async ({ prompt }) => {
       const skill = path.join(u.getPath("skills"), "production_execution_storyboard_table.md");
       const systemPrompt = await fs.promises.readFile(skill, "utf-8");
@@ -348,7 +350,7 @@ async function createSubAgent(parentCtx: AgentContext) {
 
   const run_sub_agent_supervision = tool({
     description: "运行监督层subAgent执行独立任务，完成后返回结果",
-    inputSchema: promptInput,
+    inputSchema: jsonSchema<{ prompt: string }>(promptInput),
     execute: async ({ prompt }) => {
       const skill = path.join(u.getPath("skills"), "production_agent_supervision.md");
       const systemPrompt = await fs.promises.readFile(skill, "utf-8");

src/agents/productionAgent/tools.ts

@@ -1,4 +1,4 @@
-import { tool, Tool } from "ai";
+import { tool, jsonSchema, Tool } from "ai";
 import { z } from "zod";
 import _ from "lodash";
 import ResTool from "@/socket/resTool";
@@ -69,9 +69,13 @@ export default (toolCpnfig: ToolConfig) => {
   const tools: Record<string, Tool> = {
     get_flowData: tool({
       description: "获取工作区数据",
-      inputSchema: z.object({
+      inputSchema: jsonSchema<{ key: keyof FlowData }>(
-        key: keySchema.describe("数据key"),
+        z
-      }),
+          .object({
+            key: keySchema.describe("数据key"),
+          })
+          .toJSONSchema(),
+      ),
       execute: async ({ key }) => {
         const thinking = msg.thinking(`正在获取${flowDataKeyLabels[key]}工作区数据...`);
         console.log("[tools] get_flowData", key);
@@ -84,18 +88,22 @@ export default (toolCpnfig: ToolConfig) => {
     }),
     add_deriveAsset: tool({
       description: "新增或更新衍生资产",
-      inputSchema: z.object({
+      inputSchema: jsonSchema<{ assetsId: number; id: number | null; name: string; desc: string }>(
-        assetsId: z.number().describe("关联的资产ID"),
+        z
-        id: z.preprocess(
+          .object({
-          (val) => {
+            assetsId: z.number().describe("关联的资产ID"),
-            if (val === "null" || val === "" || val === undefined) return null;
+            id: z.number().nullable().describe("衍生资产ID,如果新增则为空"),
-            return val;
+            name: z.string().describe("衍生资产名称"),
-          },
+            desc: z.string().describe("衍生资产描述"),
-          z.number().nullable().describe("衍生资产ID,如果新增则为空")),
+          })
-        name: z.string().describe("衍生资产名称"),
+          .toJSONSchema(),
-        desc: z.string().describe("衍生资产描述"),
+      ),
-      }),
+      execute: async (raw) => {
-      execute: async (deriveAsset) => {
+        // 容错：LLM 偶尔传 "null" 字符串或空串，统一规范为 null
+        const idRaw = raw.id as unknown;
+        const normalizedId = idRaw === "null" || idRaw === "" || idRaw === undefined ? null : (idRaw as number | null);
+        const deriveAsset = { ...raw, id: normalizedId };
+
         const thinking = msg.thinking("正在操作资产...");
         const { projectId, scriptId } = resTool.data;
         const startTime = Date.now();
@@ -128,10 +136,14 @@ export default (toolCpnfig: ToolConfig) => {
     }),
     del_deriveAsset: tool({
       description: "删除衍生资产",
-      inputSchema: z.object({
+      inputSchema: jsonSchema<{ assetsId: number; id: number }>(
-        assetsId: z.number().describe("关联的资产ID"),
+        z
-        id: z.number().describe("衍生资产ID"),
+          .object({
-      }),
+            assetsId: z.number().describe("关联的资产ID"),
+            id: z.number().describe("衍生资产ID"),
+          })
+          .toJSONSchema(),
+      ),
       execute: async ({ assetsId, id }) => {
         const thinking = msg.thinking("正在操作资产...");
         const { scriptId } = resTool.data;
@@ -146,9 +158,13 @@ export default (toolCpnfig: ToolConfig) => {
     }),
     generate_deriveAsset: tool({
       description: "生成衍生资产图片",
-      inputSchema: z.object({
+      inputSchema: jsonSchema<{ ids: number[] }>(
-        ids: z.array(z.number()).describe("需要生成的 衍生资产ID"),
+        z
-      }),
+          .object({
+            ids: z.array(z.number()).describe("需要生成的 衍生资产ID"),
+          })
+          .toJSONSchema(),
+      ),
       execute: async ({ ids }) => {
         const thinking = msg.thinking("正在生成衍生资产...");
         new Promise((resolve) => socket.emit("generateDeriveAsset", { ids }, (res: any) => resolve(res)))
@@ -168,9 +184,13 @@ export default (toolCpnfig: ToolConfig) => {
     }),
     generate_storyboard: tool({
       description: "生成分镜图片",
-      inputSchema: z.object({
+      inputSchema: jsonSchema<{ ids: number[] }>(
-        ids: z.array(z.number()).describe("必须获取真实的分镜ID，支持批量生成"),
+        z
-      }),
+          .object({
+            ids: z.array(z.number()).describe("必须获取真实的分镜ID，支持批量生成"),
+          })
+          .toJSONSchema(),
+      ),
       execute: async ({ ids }) => {
         const thinking = msg.thinking("正在生成分镜...");
         new Promise((resolve) => socket.emit("generateStoryboard", { ids }, (res: any) => resolve(res)))

src/agents/scriptAgent/index.ts

@@ -1,5 +1,5 @@
 import { Socket } from "socket.io";
-import { tool } from "ai";
+import { tool, jsonSchema } from "ai";
 import { z } from "zod";
 import u from "@/utils";
 import Memory from "@/utils/agent/memory";
@@ -133,13 +133,15 @@ function createSubAgent(parentCtx: AgentContext) {
     return fullResponse;
   }
 
-  const promptInput = z.object({
+  const promptInput = z
-    prompt: z.string().describe("交给子Agent的任务简约描述，100字以内"),
+    .object({
-  });
+      prompt: z.string().describe("交给子Agent的任务简约描述，100字以内"),
+    })
+    .toJSONSchema();
 
   const run_sub_agent_storySkeleton = tool({
     description: "运行执行subAgent来完成故事骨架相关任务",
-    inputSchema: promptInput,
+    inputSchema: jsonSchema<{ prompt: string }>(promptInput),
     execute: async ({ prompt }) => {
       const skill = path.join(u.getPath("skills"), "script_execution_skeleton.md");
       const systemPrompt = await fs.promises.readFile(skill, "utf-8");
@@ -159,7 +161,7 @@ function createSubAgent(parentCtx: AgentContext) {
 
   const run_sub_agent_adaptationStrategy = tool({
     description: "运行执行subAgent来完成改编策略相关任务",
-    inputSchema: promptInput,
+    inputSchema: jsonSchema<{ prompt: string }>(promptInput),
     execute: async ({ prompt }) => {
       const skill = path.join(u.getPath("skills"), "script_execution_adaptation.md");
       const systemPrompt = await fs.promises.readFile(skill, "utf-8");

src/agents/scriptAgent/tools.ts

@@ -1,4 +1,4 @@
-import { tool, Tool } from "ai";
+import { tool, jsonSchema, Tool } from "ai";
 import u from "@/utils";
 import { z } from "zod";
 import _ from "lodash";
@@ -33,9 +33,13 @@ export default (toolCpnfig: ToolConfig) => {
   const tools: Record<string, Tool> = {
     get_novel_events: tool({
       description: "获取章节事件",
-      inputSchema: z.object({
+      inputSchema: jsonSchema<{ chapterIndexs: number[] }>(
-        chapterIndexs: z.array(z.number()).describe("章节的编号"),
+        z
-      }),
+          .object({
+            chapterIndexs: z.array(z.number()).describe("章节的编号"),
+          })
+          .toJSONSchema(),
+      ),
       execute: async ({ chapterIndexs }) => {
         console.log("[tools] get_novel_events", chapterIndexs);
         const thinking = msg.thinking("正在查询章节事件...");
@@ -45,7 +49,7 @@ export default (toolCpnfig: ToolConfig) => {
           .select("id", "chapterIndex as index", "reel", "chapter", "chapterData", "event", "eventState")
           .whereIn("chapterIndex", chapterIndexs);
         thinking.appendText("正在查询章节编号: " + chapterIndexs.join(","));
-        const eventString = data.map((i: any) => [`第${i.index}章，标题：${i.chapter}，事件：${i.event}`].join("\n")).join("\n");
+        const eventString = data.map((i: any) => [`第${i.index}章，标题:${i.chapter}，事件:${i.event}`].join("\n")).join("\n");
         thinking.appendText("查询结果:\n" + eventString);
         thinking.updateTitle("查询章节事件完成");
         thinking.complete();
@@ -54,9 +58,13 @@ export default (toolCpnfig: ToolConfig) => {
     }),
     get_planData: tool({
       description: "获取工作区数据",
-      inputSchema: z.object({
+      inputSchema: jsonSchema<{ key: keyof planData }>(
-        key: keySchema.describe("数据key"),
+        z
-      }),
+          .object({
+            key: keySchema.describe("数据key"),
+          })
+          .toJSONSchema(),
+      ),
       execute: async ({ key }) => {
         console.log("[tools] get_planData", key);
         const thinking = msg.thinking(`正在获取${planDataKeyLabels[key]}工作区数据...`);
@@ -69,9 +77,13 @@ export default (toolCpnfig: ToolConfig) => {
     }),
     get_novel_text: tool({
       description: "获取小说章节原始文本内容",
-      inputSchema: z.object({
+      inputSchema: jsonSchema<{ chapterIndex: string }>(
-        chapterIndex: z.string().describe("章节编号"),
+        z
-      }),
+          .object({
+            chapterIndex: z.string().describe("章节编号"),
+          })
+          .toJSONSchema(),
+      ),
       execute: async ({ chapterIndex }) => {
         console.log("[tools] get_novel_text", "[tools] get_novel_text", chapterIndex);
         const thinking = msg.thinking(`正在获取小说章节原文...`);
@@ -85,9 +97,13 @@ export default (toolCpnfig: ToolConfig) => {
     }),
     get_script_content: tool({
       description: "获取剧本本内容",
-      inputSchema: z.object({
+      inputSchema: jsonSchema<{ ids: string[] }>(
-        ids: z.array(z.string()).describe("脚本id"),
+        z
-      }),
+          .object({
+            ids: z.array(z.string()).describe("脚本id"),
+          })
+          .toJSONSchema(),
+      ),
       execute: async ({ ids }) => {
         console.log("[tools] get_script_content", "[tools] get_script_content", ids);
         const thinking = msg.thinking(`正在获取脚本内容...`);

src/routes/cornerScape/batchBindAudio.ts

@@ -3,7 +3,7 @@ import u from "@/utils";
 import { z } from "zod";
 import { success } from "@/lib/responseFormat";
 import { validateFields } from "@/middleware/middleware";
-import { tool } from "ai";
+import { tool, jsonSchema } from "ai";
 const router = express.Router();
 
 // 获取资产
@@ -22,26 +22,37 @@ export default router.post(
       .whereIn("id", assetsIds)
       .andWhere("projectId", projectId)
       .select("id", "name", "describe");
-    console.log("%c Line:20 🍎 assetsData", "background:#b03734", assetsData);
 
-    const audioData = await u.db("o_assets").where("type", "audio").whereNull("assetsId").andWhere("projectId", projectId).select("id", "name", "describe");
+    const audioData = await u
-    console.log("%c Line:26 🍋 audioData", "background:#ea7e5c", audioData);
+      .db("o_assets")
+      .where("type", "audio")
+      .whereNull("assetsId")
+      .andWhere("projectId", projectId)
+      .select("id", "name", "describe");
     async function processGroup() {
       try {
         const resultTool = tool({
           description: "返回结果时必须调用这个工具",
-          inputSchema: z.object({
+          inputSchema: jsonSchema<{ result: { id: number; audioIds: number[] }[] }>(
-            result: z.array(z.object({
+            z
-              id: z.number(),
+              .object({
-              audioIds: z.array(z.number()).describe("适配的音频id 无适配内容可以为 空数组")
+                result: z
-            })).describe("适配的音色列表，id为资产id，audioIds为适配的音频id 无适配内容可以为 空数组")
+                  .array(
-          }),
+                    z.object({
+                      id: z.number(),
+                      audioIds: z.array(z.number()).describe("适配的音频id 无适配内容可以为 空数组"),
+                    }),
+                  )
+                  .describe("适配的音色列表，id为资产id，audioIds为适配的音频id 无适配内容可以为 空数组"),
+              })
+              .toJSONSchema(),
+          ),
           execute: async ({ result }) => {
             console.log("[tools] extractAssets result", result);
             for (const item of result) {
-              await u.db("o_assetsRole2Audio").where("assetsRoleId", item.id).delete()
+              await u.db("o_assetsRole2Audio").where("assetsRoleId", item.id).delete();
               if (item.audioIds.length)
-                await u.db("o_assetsRole2Audio").insert(item.audioIds.map(i => ({ assetsRoleId: item.id, assetsAudioId: i })))
+                await u.db("o_assetsRole2Audio").insert(item.audioIds.map((i) => ({ assetsRoleId: item.id, assetsAudioId: i })));
             }
             return "无需回复用户任何内容";
           },
@@ -55,21 +66,20 @@ export default router.post(
             },
             {
               role: "user",
-              content: `音频内容：${audioData.map(i => `Id:${i.id},音色名称:${i.name},描述:${i.describe}`).join("\n")}\n\n
+              content: `音频内容：${audioData.map((i) => `Id:${i.id},音色名称:${i.name},描述:${i.describe}`).join("\n")}\n\n
-                资产内容：${assetsData.map(i => `ID:${i.id},名称:${i.name},描述:${i.describe}`).join("\n")}\n\n
+                资产内容：${assetsData.map((i) => `ID:${i.id},名称:${i.name},描述:${i.describe}`).join("\n")}\n\n
                 请根据提供的资产内容描述 与 对应已有的音色 进行匹配，返回适配的音色`,
             },
           ],
           tools: { resultTool },
         });
         console.log("%c Line:44 🍞 text", "background:#f5ce50", text);
-
       } catch (e) {
         console.error(`提取失败:`, e);
         return;
       }
     }
-    await processGroup()
+    await processGroup();
     res.status(200).send(success());
   },
 );

src/routes/script/extractAssets.ts

@@ -4,7 +4,7 @@ import { z } from "zod";
 import { error, success } from "@/lib/responseFormat";
 import { validateFields } from "@/middleware/middleware";
 import { useSkill } from "@/utils/agent/skillsTools";
-import { tool } from "ai";
+import { tool, jsonSchema } from "ai";
 import { o_script } from "@/types/database";
 
 const router = express.Router();
@@ -131,10 +131,7 @@ export default router.post(
       }
 
       // 去重：相同 scriptId + assetId 只保留一条
-      const uniqueRows = [
+      const uniqueRows = [...new Map(scriptAssetRows.map((r) => [`${r.scriptId}_${r.assetId}`, r])).values()];
-        ...new Map(scriptAssetRows.map((r) => [`${r.scriptId}_${r.assetId}`, r])).values(),
-      ];
-
 
       // 先删除本批 scriptId 的旧关联，再插入新的
       await u.db("o_scriptAssets").whereIn("scriptId", batchScriptIds).delete();
@@ -188,16 +185,19 @@ export default router.post(
         try {
           const resultTool = tool({
             description: "返回结果时必须调用这个工具",
-            inputSchema: z.object({
+            inputSchema: jsonSchema<{ newAssets: NewAsset[]; existingAssetRefs: ExistingAssetRef[] }>(
-              newAssets: z
+              z
-                .array(NewAssetSchema)
+                .object({
-                .describe("新发现的资产列表（不在已有资产列表中的），需要完整的 prompt、name、desc、type 和使用该资产的 scriptIds"),
+                  newAssets: z
-              existingAssetRefs: z
+                    .array(NewAssetSchema)
-                .array(ExistingAssetRefSchema)
+                    .describe("新发现的资产列表（不在已有资产列表中的），需要完整的 prompt、name、desc、type 和使用该资产的 scriptIds"),
-                .describe("已有资产的引用列表（在已有资产列表中已存在的），只需给出资产名称和使用该资产的 scriptIds"),
+                  existingAssetRefs: z
-            }),
+                    .array(ExistingAssetRefSchema)
+                    .describe("已有资产的引用列表（在已有资产列表中已存在的），只需给出资产名称和使用该资产的 scriptIds"),
+                })
+                .toJSONSchema(),
+            ),
             execute: async ({ newAssets, existingAssetRefs }) => {
-
               if (newAssets?.length) collectedNew = newAssets;
               if (existingAssetRefs?.length) collectedExisting = existingAssetRefs;
               return "无需回复用户任何内容";

src/routes/setting/vendorConfig/getVendorList.ts

@@ -6,23 +6,29 @@ const router = express.Router();
 export default router.post("/", async (req, res) => {
   const data = await u.db("o_vendorConfig").select("*");
 
-  const list = await Promise.all(
+  const list = (
-    data.map(async (item) => {
+    await Promise.all(
-      const vendor = u.vendor.getVendor(item.id!);
+      data.map(async (item) => {
-      return {
+        const vendor = u.vendor.getVendor(item.id!);
-        ...item,
+        if (!vendor) {
-        inputValues: JSON.parse(item.inputValues ?? "{}"),
+          await u.db("o_vendorConfig").where("id", item.id).delete();
-        models: await u.vendor.getModelList(item.id!),
+          return null
-        code: u.vendor.getCode(item.id!),
+        };
-        description: vendor.description,
+        return {
-        inputs: vendor.inputs,
+          ...item,
-        author: vendor.author,
+          inputValues: JSON.parse(item.inputValues ?? "{}"),
-        name: vendor.name,
+          models: await u.vendor.getModelList(item.id!),
-        version: vendor.version ?? "1.0",
+          code: u.vendor.getCode(item.id!),
-      };
+          description: vendor.description ?? "",
-    }),
+          inputs: vendor.inputs,
-  );
+          author: vendor.author,
+          name: vendor.name,
+          version: vendor.version ?? "1.0",
+        };
+      }),
+    )
+  ).filter((i) => Boolean(i));
 
-  list.sort((a, b) => (a.id === "toonflow" ? -1 : b.id === "toonflow" ? 1 : 0));
+  list.sort((a, b) => (a!.id === "toonflow" ? -1 : b!.id === "toonflow" ? 1 : 0));
   res.status(200).send(success(list));
 });

src/routes/setting/vendorConfig/modelTest.ts

@@ -3,7 +3,7 @@ import { success, error } from "@/lib/responseFormat";
 import { validateFields } from "@/middleware/middleware";
 import u from "@/utils";
 import { z } from "zod";
-import { tool } from "ai";
+import { tool, jsonSchema } from "ai";
 const router = express.Router();
 
 // 检查语言模型
@@ -57,9 +57,13 @@ export default router.post(
 
       const getWeatherTool = tool({
         description: "Get the weather in a location",
-        inputSchema: z.object({
+        inputSchema: jsonSchema<{ location: string }>(
-          location: z.string().describe("The location to get the weather for"),
+          z
-        }),
+            .object({
+              location: z.string().describe("The location to get the weather for"),
+            })
+            .toJSONSchema(),
+        ),
         execute: async ({ location }) => {
           return {
             location,

src/types/database.d.ts

@@ -1,4 +1,4 @@
-// @db-hash 88c167ba73e2771e7b043419ca5089dd
+// @db-hash 5364c2db0bf42b520761b813ce040489
 //该文件由脚本自动生成，请勿手动修改
 
 export interface memories {
@@ -23,6 +23,7 @@ export interface o_agentDeploy {
   'modelName'?: string | null;
   'name'?: string | null;
   'temperature'?: number | null;
+  'topP'?: number | null;
   'type'?: string | null;
   'vendorId'?: string | null;
 }
@@ -210,7 +211,6 @@ export interface o_user {
   'password'?: string | null;
 }
 export interface o_vendorConfig {
-  'code'?: string | null;
   'enable'?: number | null;
   'id'?: string;
   'inputValues'?: string | null;

src/utils/agent/memory.ts

@@ -2,7 +2,7 @@ import u from "@/utils";
 import { v4 as uuidv4 } from "uuid";
 import { getEmbedding, cosineSimilarity } from "./embedding";
 import type { memories as MemoryRow } from "@/types/database";
-import { tool } from "ai";
+import { tool, jsonSchema } from "ai";
 import { z } from "zod";
 
 // ── 可调配置默认值 ──
@@ -201,9 +201,13 @@ class Memory {
     return {
       deepRetrieve: tool({
         description: "深度检索记忆：当你需要回忆与某个关键词相关的详细历史信息时使用此工具",
-        inputSchema: z.object({
+        inputSchema: jsonSchema<{ keyword: string }>(
-          keyword: z.string().describe("要检索的关键词"),
+          z
-        }),
+            .object({
+              keyword: z.string().describe("要检索的关键词"),
+            })
+            .toJSONSchema(),
+        ),
         execute: async ({ keyword }) => {
           const results = await this.deepRetrieve(keyword);
           if (results.length === 0) return { found: false, message: "未找到相关记忆" };

src/utils/agent/skillsTools.ts

@@ -1,5 +1,5 @@
-import { tool } from "ai";
 import { z } from "zod";
+import { tool, jsonSchema } from "ai";
 import path from "path";
 import isPathInside from "is-path-inside";
 import getPath from "@/utils/getPath";
@@ -185,9 +185,13 @@ export function createSkillTools(skills: { name: string; description: string }[]
   return {
     activate_skill: tool({
       description: `激活一个技能，加载其完整指令和捆绑资源列表到上下文。可用技能：${skillNames.join(", ")}`,
-      inputSchema: z.object({
+      inputSchema: jsonSchema<{ name: string }>(
-        name: z.enum(skillNames as [string, ...string[]]).describe("要激活的技能名称"),
+        z
-      }),
+          .object({
+            name: z.enum(skillNames as [string, ...string[]]).describe("要激活的技能名称"),
+          })
+          .toJSONSchema(),
+      ),
       execute: async ({ name }) => {
         if (activated.has(name)) {
           console.log(`⚡[主技能] ℹ️ 技能 "${name}" 已激活，跳过重复注入`);
@@ -222,9 +226,13 @@ export function createSkillTools(skills: { name: string; description: string }[]
     }),
     read_skill_file: tool({
       description: "读取已激活技能目录下的资源文件。传入 activate_skill 返回的 skill_resources 中的文件路径。",
-      inputSchema: z.object({
+      inputSchema: jsonSchema<{ filePath: string }>(
-        filePath: z.string().describe("资源文件的相对路径，来自 activate_skill 返回的 skill_resources"),
+        z
-      }),
+          .object({
+            filePath: z.string().describe("资源文件的相对路径，来自 activate_skill 返回的 skill_resources"),
+          })
+          .toJSONSchema(),
+      ),
       execute: async ({ filePath }) => {
         const normalizedInputPath = toUnixPath(filePath).trim();
         if (!normalizedInputPath) {

src/utils/vendor.ts

@@ -25,6 +25,7 @@ export async function getModelList(id: string): Promise<Array<any>> {
   const code = getCode(id);
   const jsCode = transform(code, { transforms: ["typescript"] }).code;
   const vendorData = u.vm(jsCode);
+  if(!vendorData || !vendorData.vendor || !vendorData.vendor.models) return [];
   const combined = [...JSON.parse(JSON.stringify(vendorData.vendor.models)), ...JSON.parse(models?.models ?? "[]")];
   const map = new Map<string, any>();
   for (const m of combined) {