feat: admin add custom plugin (#2582)

* feat: admin add custom plugin

* refresh plugins

* plugin input box ui

* fix: run plugin varialbes error

* perf: comment

* fix: ts

docSite/content/zh-cn/docs/course/feishu.md

@@ -86,3 +86,12 @@ Verification Token 默认生成的这个 Token 用于校验来源。但我们使
 然后就可以在工作台里找到你的机器人啦。接下来就是把机器人拉进群组，或者单独与它对话。
 
 ![图片](/imgs/feishu-bot-9.png)
+
+## FAQ
+
+### 发送了消息，没响应
+
+1. 检查飞书机器人回调地址、权限等是否正确。
+2. 查看 FastGPT 对话日志，是否有对应的提问记录
+3. 如果有记录，飞书没回应，则是没给机器人开权限。
+4. 如果没记录，则可能是应用运行报错了，可以先试试最简单的机器人。（飞书机器人无法输入全局变量、文件、图片内容）

docSite/content/zh-cn/docs/development/upgrading/4810.md

@@ -11,37 +11,74 @@ weight: 814
 
 ### 1. 做好数据备份
 
-### 2. 更新商业版环境变量
+
+### 2. 商业版 —— 修改环境变量
 
 1. 需要给`fastgpt-pro`镜像，增加沙盒的环境变量：`SANDBOX_URL=http://xxxxx:3000`
-2. 给两个镜像增加环境变量，以便更好的存储系统日志：
+2. 给`fastgpt-pro`镜像和`fastgpt`镜像增加环境变量，以便更好的存储系统日志：
 
 ```
 LOG_LEVEL=debug
 STORE_LOG_LEVEL=warn
 ```
 
+### 3. 修改镜像tag
+
+- 更新 FastGPT 镜像 tag: v4.8.10-alpha
+- 更新 FastGPT 商业版镜像 tag: v4.8.10-alpha
+- Sandbox 镜像，可以不更新
+
+## 4. 执行初始化
+
+从任意终端，发起 1 个 HTTP 请求。其中 {{rootkey}} 替换成环境变量里的 `rootkey`；{{host}} 替换成**FastGPT 域名**。
+
+```bash
+curl --location --request POST 'https://{{host}}/api/admin/initv4810' \
+--header 'rootkey: {{rootkey}}' \
+--header 'Content-Type: application/json'
+```
+
+1. 初始化发布记录版本标记
+2. 初始化开票记录
+
 -------
 
 ## V4.8.10 更新说明
 
+完整内容请见：[4.8.10 release](https://github.com/labring/FastGPT/releases/tag/v4.8.10-alpha)
+
 1. 新增 - 模板市场
 2. 新增 - 工作流节点拖动自动对齐吸附
 3. 新增 - 用户选择节点（Debug 模式暂未支持）
 4. 新增 - 工作流撤销和重做
 5. 新增 - 工作流本次编辑记录，取代自动保存
 6. 新增 - 工作流版本支持重命名
-7. 商业版新增 - 飞书机器人接入
-8. 商业版新增 - 公众号接入接入
-9. 商业版新增 - 自助开票申请
-10. 商业版新增 - SSO 定制
-11. 优化 - 知识库集合禁用，目录禁用会递归修改其下所有 children 的禁用状态。
-12. 优化 - 节点选择，避免切换 tab 时候，path 加载报错。
-13. 优化 - 最新 React Markdown 组件，支持 Base64 图片。
-14. 优化 - 知识库列表 UI。
-15. 优化 - 支持无网络配置情况下运行。
-16. 修复 - Prompt 模式调用工具，stream=false 模式下，会携带 0: 开头标记。
-17. 修复 - 对话日志鉴权问题：仅为 APP 管理员的用户，无法查看对话日志详情。
-18. 修复 - 选择 Milvus 部署时，无法导出知识库。 
-19. 修复 - 创建 APP 副本，无法复制系统配置。
-20. 修复 - 图片识别模式下，自动解析图片链接正则不够严谨问题。
+7. 新增 - 应用调用迁移成单独节点，同时可以传递全局变量和用户的文件。
+8. 新增 - 插件增加使用说明配置。
+9. 商业版新增 - 飞书机器人接入
+10. 商业版新增 - 公众号接入接入
+11. 商业版新增 - 自助开票申请
+12. 商业版新增 - SSO 定制
+13. 优化 - SSE 响应优化。
+14. 优化 - 无 SSL 证书情况下，优化复制。
+15. 优化 - 单选框打开后自动滚动到选中的位置。
+16. 优化 - 知识库集合禁用，目录禁用会递归修改其下所有 children 的禁用状态。
+17. 优化 - 节点选择，避免切换 tab 时候，path 加载报错。
+18. 优化 - 最新 React Markdown 组件，支持 Base64 图片。
+19. 优化 - 知识库列表 UI。
+20. 优化 - 知识库详情页 UI。
+21. 优化 - 支持无网络配置情况下运行。
+22. 优化 - 部分全局变量，增加数据类型约束。
+23. 修复 - 全局变量 key 可能重复。
+24. 修复 - Prompt 模式调用工具，stream=false 模式下，会携带 0: 开头标记。
+25. 修复 - 对话日志鉴权问题：仅为 APP 管理员的用户，无法查看对话日志详情。
+26. 修复 - 选择 Milvus 部署时，无法导出知识库。 
+27. 修复 - 创建 APP 副本，无法复制系统配置。
+28. 修复 - 图片识别模式下，自动解析图片链接正则不够严谨问题。
+29. 修复 - 内容提取的数据类型与输出数据类型未一致。
+30. 修复 - 工作流运行时间统计错误。
+31. 修复 - stream 模式下，工具调用有可能出现 undefined
+32. 修复 - 全局变量在 API 中无法持久化。
+33. 修复 - OpenAPI，detail=false模式下，不应该返回 tool 调用结果，仅返回文字。（可解决 cow 不适配问题）
+34. 修复 - 知识库标签重复加载。
+35. 修复 - Debug 模式下，循环调用边问题。

docSite/content/zh-cn/docs/development/upgrading/4811.md

@@ -0,0 +1,23 @@
+---
+title: 'V4.8.11（进行中）'
+description: 'FastGPT V4.8.11 更新说明'
+icon: 'upgrade'
+draft: false
+toc: true
+weight: 813
+---
+
+## 更新指南
+
+### 1. 做好数据备份
+
+-------
+
+## V4.8.11 更新预告
+
+1. 
+2. 新增 - 插件自定义输入支持单选框
+3. 新增 - 插件输出，支持指定某些字段为工具调用结果
+4. 新增 - 插件支持配置使用引导、全局变量和文件输入
+5. 优化 - SSE 响应代码。
+6. 优化 - 非 HTTPS 环境下支持复制（除非 textarea 复制也不支持）

docSite/hugo.toml

@@ -1,4 +1,4 @@
-baseURL = "https://doc.fastgpt.in"
+baseURL = "https://doc.tryfastgpt.ai"
 languageCode = "en-GB"
 contentDir = "content"
 enableEmoji = true

package.json

@@ -10,7 +10,8 @@
     "postinstall": "sh ./scripts/postinstall.sh",
     "initIcon": "node ./scripts/icon/init.js",
     "previewIcon": "node ./scripts/icon/index.js",
-    "checkI18n": "node ./scripts/i18n/delete-unused-keys.js"
+    "i18n:delete-unused-keys": "node ./scripts/i18n/delete-unused-keys.js",
+    "i18n:query": "node ./scripts/i18n/query.js"
   },
   "devDependencies": {
     "@chakra-ui/cli": "^2.4.1",
@@ -30,4 +31,4 @@
     "node": ">=18.16.0",
     "pnpm": ">=9.0.0"
   }
-}
+}

packages/global/core/ai/prompt/agent.ts

@@ -4,6 +4,7 @@ export const Prompt_AgentQA = {
 - 答案需详细完整，尽可能保留原文描述，可以适当扩展答案描述。
 - 答案可以包含普通文字、链接、代码、表格、公示、媒体链接等 Markdown 元素。
 - 最多提出 50 个问题。
+- 生成的问题和答案和源文本语言相同。
 `,
   fixedText: `请按以下格式整理学习成果:
 <Context>

packages/global/core/app/type.d.ts

@@ -1,7 +1,11 @@
 import type { FlowNodeTemplateType, StoreNodeItemType } from '../workflow/type/node';
 import { AppTypeEnum } from './constants';
 import { PermissionTypeEnum } from '../../support/permission/constant';
-import { NodeInputKeyEnum, VariableInputEnum } from '../workflow/constants';
+import {
+  NodeInputKeyEnum,
+  VariableInputEnum,
+  WorkflowIOValueTypeEnum
+} from '../workflow/constants';
 import { SelectedDatasetType } from '../workflow/api';
 import { DatasetSearchModeEnum } from '../dataset/constants';
 import { TeamTagSchema as TeamTagsSchemaType } from '@fastgpt/global/support/user/team/type.d';
@@ -92,6 +96,9 @@ export type AppChatConfigType = {
   scheduledTriggerConfig?: AppScheduledTriggerConfigType;
   chatInputGuide?: ChatInputGuideConfigType;
   fileSelectConfig?: AppFileSelectConfigType;
+
+  // plugin
+  instruction?: string;
 };
 export type SettingAIDataType = {
   model: string;
@@ -111,6 +118,7 @@ export type VariableItemType = {
   required: boolean;
   maxLen: number;
   enums: { value: string }[];
+  valueType: WorkflowIOValueTypeEnum;
 };
 // tts
 export type AppTTSConfigType = {

packages/global/core/chat/type.d.ts

@@ -151,6 +151,7 @@ export type ChatHistoryItemType = HistoryItemType & {
 /* ------- response data ------------ */
 export type ChatHistoryItemResType = DispatchNodeResponseType & {
   nodeId: string;
+  id: string;
   moduleType: FlowNodeTypeEnum;
   moduleName: string;
 };

packages/global/core/dataset/constants.ts

@@ -12,17 +12,17 @@ export const DatasetTypeMap = {
     collectionLabel: 'common.Folder'
   },
   [DatasetTypeEnum.dataset]: {
-    icon: 'core/dataset/commonDataset',
+    icon: 'core/dataset/commonDatasetOutline',
     label: 'common_dataset',
     collectionLabel: 'common.File'
   },
   [DatasetTypeEnum.websiteDataset]: {
-    icon: 'core/dataset/websiteDataset',
+    icon: 'core/dataset/websiteDatasetOutline',
     label: 'website_dataset',
     collectionLabel: 'common.Website'
   },
   [DatasetTypeEnum.externalFile]: {
-    icon: 'core/dataset/externalDataset',
+    icon: 'core/dataset/externalDatasetOutline',
     label: 'external_file',
     collectionLabel: 'common.File'
   }

packages/global/core/dataset/type.d.ts

@@ -51,6 +51,7 @@ export type DatasetCollectionSchemaType = {
   chunkSize: number;
   chunkSplitter?: string;
   qaPrompt?: string;
+  ocrParse?: boolean;
 
   tags?: string[];
 

packages/global/core/workflow/constants.ts

@@ -52,6 +52,9 @@ export enum NodeInputKeyEnum {
   scheduleTrigger = 'scheduleTrigger',
   chatInputGuide = 'chatInputGuide',
 
+  // plugin config
+  instruction = 'instruction',
+
   // entry
   userChatInput = 'userChatInput',
   inputFiles = 'inputFiles',
@@ -128,6 +131,7 @@ export enum NodeInputKeyEnum {
 
   // read files
   fileUrlList = 'fileUrlList',
+
   // user select
   userSelectOptions = 'userSelectOptions'
 }

packages/global/core/workflow/node/constant.ts

@@ -15,6 +15,7 @@ export enum FlowNodeInputTypeEnum { // render ui
 
   // special input
   selectApp = 'selectApp',
+  customVariable = 'customVariable',
 
   // ai model select
   selectLLMModel = 'selectLLMModel',
@@ -44,7 +45,7 @@ export const FlowNodeInputMap: Record<
     icon: 'core/workflow/inputType/numberInput'
   },
   [FlowNodeInputTypeEnum.select]: {
-    icon: 'core/workflow/inputType/input'
+    icon: 'core/workflow/inputType/option'
   },
   [FlowNodeInputTypeEnum.switch]: {
     icon: 'core/workflow/inputType/switch'
@@ -79,8 +80,11 @@ export const FlowNodeInputMap: Record<
   [FlowNodeInputTypeEnum.hidden]: {
     icon: 'core/workflow/inputType/select'
   },
+  [FlowNodeInputTypeEnum.customVariable]: {
+    icon: 'core/workflow/inputType/customVariable'
+  },
   [FlowNodeInputTypeEnum.custom]: {
-    icon: 'core/workflow/inputType/select'
+    icon: 'core/workflow/inputType/custom'
   }
 };
 
@@ -94,6 +98,7 @@ export enum FlowNodeOutputTypeEnum {
 export enum FlowNodeTypeEnum {
   emptyNode = 'emptyNode',
   systemConfig = 'userGuide',
+  pluginConfig = 'pluginConfig',
   globalVariable = 'globalVariable',
   workflowStart = 'workflowStart',
   chatNode = 'chatNode',
@@ -106,6 +111,7 @@ export enum FlowNodeTypeEnum {
   contentExtract = 'contentExtract',
   httpRequest468 = 'httpRequest468',
   runApp = 'app',
+  appModule = 'appModule',
   pluginModule = 'pluginModule',
   pluginInput = 'pluginInput',
   pluginOutput = 'pluginOutput',

packages/global/core/workflow/runtime/type.d.ts

@@ -19,6 +19,7 @@ import { RuntimeNodeItemType } from '../runtime/type';
 import { RuntimeEdgeItemType } from './edge';
 import { ReadFileNodeResponse } from '../template/system/readFiles/type';
 import { UserSelectOptionType } from '../template/system/userSelect/type';
+import { WorkflowResponseType } from '../../../../service/core/workflow/dispatch/type';
 
 /* workflow props */
 export type ChatDispatchProps = {
@@ -36,9 +37,9 @@ export type ChatDispatchProps = {
   query: UserChatItemValueItemType[]; // trigger query
   chatConfig: AppSchema['chatConfig'];
   stream: boolean;
-  detail: boolean; // response detail
   maxRunTimes: number;
   isToolCall?: boolean;
+  workflowStreamResponse?: WorkflowResponseType;
 };
 
 export type ModuleDispatchProps<T> = ChatDispatchProps & {
@@ -95,6 +96,8 @@ export type DispatchNodeResponseType = {
   error?: Record<string, any>;
   customInputs?: Record<string, any>;
   customOutputs?: Record<string, any>;
+  nodeInputs?: Record<string, any>;
+  nodeOutputs?: Record<string, any>;
 
   // bill
   tokens?: number;
@@ -158,15 +161,18 @@ export type DispatchNodeResponseType = {
 
   // user select
   userSelectResult?: string;
+
+  // update var
+  updateVarResult?: any[];
 };
 
 export type DispatchNodeResultType<T> = {
   [DispatchNodeResponseKeyEnum.skipHandleId]?: string[]; // skip some edge handle id
   [DispatchNodeResponseKeyEnum.nodeResponse]?: DispatchNodeResponseType; // The node response detail
-  [DispatchNodeResponseKeyEnum.nodeDispatchUsages]?: ChatNodeUsageType[]; //
-  [DispatchNodeResponseKeyEnum.childrenResponses]?: DispatchNodeResultType[];
-  [DispatchNodeResponseKeyEnum.toolResponses]?: ToolRunResponseItemType;
-  [DispatchNodeResponseKeyEnum.assistantResponses]?: ChatItemValueItemType[];
+  [DispatchNodeResponseKeyEnum.nodeDispatchUsages]?: ChatNodeUsageType[]; // Node total usage
+  [DispatchNodeResponseKeyEnum.childrenResponses]?: DispatchNodeResultType[]; // Children node response
+  [DispatchNodeResponseKeyEnum.toolResponses]?: ToolRunResponseItemType; // Tool response
+  [DispatchNodeResponseKeyEnum.assistantResponses]?: ChatItemValueItemType[]; // Assistant response(Store to db)
 } & T;
 
 /* Single node props */

packages/global/core/workflow/runtime/utils.ts

@@ -117,39 +117,6 @@ export const filterWorkflowEdges = (edges: RuntimeEdgeItemType[]) => {
   );
 };
 
-/* 
-  区分普通连线和递归连线
-  递归连线：可以通过往上查询 nodes，最终追溯到自身
-*/
-export const splitEdges2WorkflowEdges = ({
-  edges,
-  allEdges,
-  currentNode
-}: {
-  edges: RuntimeEdgeItemType[];
-  allEdges: RuntimeEdgeItemType[];
-  currentNode: RuntimeNodeItemType;
-}) => {
-  const commonEdges: RuntimeEdgeItemType[] = [];
-  const recursiveEdges: RuntimeEdgeItemType[] = [];
-
-  edges.forEach((edge) => {
-    const checkIsCurrentNode = (edge: RuntimeEdgeItemType): boolean => {
-      const sourceEdge = allEdges.find((item) => item.target === edge.source);
-      if (!sourceEdge) return false;
-      if (sourceEdge.source === currentNode.nodeId) return true;
-      return checkIsCurrentNode(sourceEdge);
-    };
-    if (checkIsCurrentNode(edge)) {
-      recursiveEdges.push(edge);
-    } else {
-      commonEdges.push(edge);
-    }
-  });
-
-  return { commonEdges, recursiveEdges };
-};
-
 /* 
   1. 输入线分类：普通线和递归线（可以追溯到自身）
   2. 起始线全部非 waiting 执行，或递归线全部非 waiting 执行
@@ -161,31 +128,72 @@ export const checkNodeRunStatus = ({
   node: RuntimeNodeItemType;
   runtimeEdges: RuntimeEdgeItemType[];
 }) => {
-  const workflowEdges = filterWorkflowEdges(runtimeEdges).filter(
+  /* 
+    区分普通连线和递归连线
+    递归连线：可以通过往上查询 nodes，最终追溯到自身
+  */
+  const splitEdges2WorkflowEdges = ({
+    sourceEdges,
+    allEdges,
+    currentNode
+  }: {
+    sourceEdges: RuntimeEdgeItemType[];
+    allEdges: RuntimeEdgeItemType[];
+    currentNode: RuntimeNodeItemType;
+  }) => {
+    const commonEdges: RuntimeEdgeItemType[] = [];
+    const recursiveEdges: RuntimeEdgeItemType[] = [];
+
+    const checkIsCircular = (edge: RuntimeEdgeItemType, visited: Set<string>): boolean => {
+      if (edge.source === currentNode.nodeId) {
+        return true; // 检测到环,并且环中包含当前节点
+      }
+      if (visited.has(edge.source)) {
+        return false; // 检测到环,但不包含当前节点(子节点成环)
+      }
+      visited.add(edge.source);
+
+      const nextEdges = allEdges.filter((item) => item.target === edge.source);
+      return nextEdges.some((nextEdge) => checkIsCircular(nextEdge, new Set(visited)));
+    };
+
+    sourceEdges.forEach((edge) => {
+      if (checkIsCircular(edge, new Set([currentNode.nodeId]))) {
+        recursiveEdges.push(edge);
+      } else {
+        commonEdges.push(edge);
+      }
+    });
+
+    return { commonEdges, recursiveEdges };
+  };
+
+  const runtimeNodeSourceEdge = filterWorkflowEdges(runtimeEdges).filter(
     (item) => item.target === node.nodeId
   );
 
   // Entry
-  if (workflowEdges.length === 0) {
+  if (runtimeNodeSourceEdge.length === 0) {
     return 'run';
   }
 
+  // Classify edges
   const { commonEdges, recursiveEdges } = splitEdges2WorkflowEdges({
-    edges: workflowEdges,
+    sourceEdges: runtimeNodeSourceEdge,
     allEdges: runtimeEdges,
     currentNode: node
   });
 
-  // check skip
-  if (commonEdges.every((item) => item.status === 'skipped')) {
+  // check skip（其中一组边，全 skip）
+  if (commonEdges.length > 0 && commonEdges.every((item) => item.status === 'skipped')) {
     return 'skip';
   }
   if (recursiveEdges.length > 0 && recursiveEdges.every((item) => item.status === 'skipped')) {
     return 'skip';
   }
 
-  // check active
-  if (commonEdges.every((item) => item.status !== 'waiting')) {
+  // check active（有一类边，不全是 wait 即可运行）
+  if (commonEdges.length > 0 && commonEdges.every((item) => item.status !== 'waiting')) {
     return 'run';
   }
   if (recursiveEdges.length > 0 && recursiveEdges.every((item) => item.status !== 'waiting')) {
@@ -236,7 +244,7 @@ export const textAdaptGptResponse = ({
   finish_reason?: null | 'stop';
   extraData?: Object;
 }) => {
-  return JSON.stringify({
+  return {
     ...extraData,
     id: '',
     object: '',
@@ -252,7 +260,7 @@ export const textAdaptGptResponse = ({
         finish_reason
       }
     ]
-  });
+  };
 };
 
 /* Update runtimeNode's outputs with interactive data from history */

packages/global/core/workflow/template/constants.ts

@@ -1,4 +1,5 @@
 import { SystemConfigNode } from './system/systemConfig';
+import { PluginConfigNode } from './system/pluginConfig';
 import { EmptyNode } from './system/emptyNode';
 import { WorkflowStart } from './system/workflowStart';
 import { AiChatModule } from './system/aiChat';
@@ -12,10 +13,11 @@ import { HttpNode468 } from './system/http468';
 import { ToolModule } from './system/tools';
 import { StopToolNode } from './system/stopTool';
 
-import { RunAppModule } from './system/runApp/index';
+import { RunAppModule } from './system/abandoned/runApp/index';
 import { PluginInputModule } from './system/pluginInput';
 import { PluginOutputModule } from './system/pluginOutput';
 import { RunPluginModule } from './system/runPlugin';
+import { RunAppPluginModule } from './system/runAppPlugin';
 import { AiQueryExtension } from './system/queryExtension';
 
 import type { FlowNodeTemplateType } from '../type/node';
@@ -44,8 +46,7 @@ const systemNodes: FlowNodeTemplateType[] = [
   LafModule,
   IfElseNode,
   VariableUpdateNode,
-  CodeNode,
-  RunAppModule
+  CodeNode
 ];
 /* app flow module templates */
 export const appSystemModuleTemplates: FlowNodeTemplateType[] = [
@@ -57,6 +58,7 @@ export const appSystemModuleTemplates: FlowNodeTemplateType[] = [
 ];
 /* plugin flow module templates */
 export const pluginSystemModuleTemplates: FlowNodeTemplateType[] = [
+  PluginConfigNode,
   PluginInputModule,
   PluginOutputModule,
   ...systemNodes
@@ -70,5 +72,7 @@ export const moduleTemplatesFlat: FlowNodeTemplateType[] = [
     )
   ),
   EmptyNode,
-  RunPluginModule
+  RunPluginModule,
+  RunAppPluginModule,
+  RunAppModule
 ];

packages/global/core/workflow/template/input.ts

@@ -9,8 +9,9 @@ export const Input_Template_History: FlowNodeInputItemType = {
   key: NodeInputKeyEnum.history,
   renderTypeList: [FlowNodeInputTypeEnum.numberInput, FlowNodeInputTypeEnum.reference],
   valueType: WorkflowIOValueTypeEnum.chatHistory,
-  label: 'core.module.input.label.chat history',
-  description: '最多携带多少轮对话记录',
+  label: i18nT('common:core.module.input.label.chat history'),
+  description: i18nT('workflow:max_dialog_rounds'),
+
   required: true,
   min: 0,
   max: 50,
@@ -21,7 +22,7 @@ export const Input_Template_UserChatInput: FlowNodeInputItemType = {
   key: NodeInputKeyEnum.userChatInput,
   renderTypeList: [FlowNodeInputTypeEnum.reference, FlowNodeInputTypeEnum.textarea],
   valueType: WorkflowIOValueTypeEnum.string,
-  label: '用户问题',
+  label: i18nT('workflow:user_question'),
   required: true
 };
 
@@ -36,14 +37,14 @@ export const Input_Template_DynamicInput: FlowNodeInputItemType = {
 export const Input_Template_SelectAIModel: FlowNodeInputItemType = {
   key: NodeInputKeyEnum.aiModel,
   renderTypeList: [FlowNodeInputTypeEnum.selectLLMModel, FlowNodeInputTypeEnum.reference],
-  label: 'core.module.input.label.aiModel',
+  label: i18nT('common:core.module.input.label.aiModel'),
   required: true,
   valueType: WorkflowIOValueTypeEnum.string
 };
 export const Input_Template_SettingAiModel: FlowNodeInputItemType = {
   key: NodeInputKeyEnum.aiModel,
   renderTypeList: [FlowNodeInputTypeEnum.settingLLMModel, FlowNodeInputTypeEnum.reference],
-  label: 'core.module.input.label.aiModel',
+  label: i18nT('common:core.module.input.label.aiModel'),
   valueType: WorkflowIOValueTypeEnum.string
 };
 
@@ -52,7 +53,7 @@ export const Input_Template_System_Prompt: FlowNodeInputItemType = {
   renderTypeList: [FlowNodeInputTypeEnum.textarea, FlowNodeInputTypeEnum.reference],
   max: 3000,
   valueType: WorkflowIOValueTypeEnum.string,
-  label: 'core.ai.Prompt',
+  label: i18nT('common:core.ai.Prompt'),
   description: chatNodeSystemPromptTip,
   placeholder: chatNodeSystemPromptTip
 };
@@ -61,7 +62,7 @@ export const Input_Template_Dataset_Quote: FlowNodeInputItemType = {
   key: NodeInputKeyEnum.aiChatDatasetQuote,
   renderTypeList: [FlowNodeInputTypeEnum.settingDatasetQuotePrompt],
   label: '',
-  debugLabel: '知识库引用',
+  debugLabel: i18nT('workflow:knowledge_base_reference'),
   description: '',
   valueType: WorkflowIOValueTypeEnum.datasetQuote
 };
@@ -73,3 +74,12 @@ export const Input_Template_Text_Quote: FlowNodeInputItemType = {
   description: i18nT('app:document_quote_tip'),
   valueType: WorkflowIOValueTypeEnum.string
 };
+export const Input_Template_File_Link: FlowNodeInputItemType = {
+  key: NodeInputKeyEnum.fileUrlList,
+  renderTypeList: [FlowNodeInputTypeEnum.reference],
+  required: true,
+  label: i18nT('app:workflow.user_file_input'),
+  debugLabel: i18nT('app:workflow.user_file_input'),
+  description: i18nT('app:workflow.user_file_input_desc'),
+  valueType: WorkflowIOValueTypeEnum.arrayString
+};

packages/global/core/workflow/template/system/abandoned/runApp/index.ts

@@ -3,16 +3,17 @@ import {
   FlowNodeInputTypeEnum,
   FlowNodeOutputTypeEnum,
   FlowNodeTypeEnum
-} from '../../../node/constant';
-import { FlowNodeTemplateType } from '../../../type/node.d';
+} from '../../../../node/constant';
+import { FlowNodeTemplateType } from '../../../../type/node';
 import {
   WorkflowIOValueTypeEnum,
   NodeInputKeyEnum,
   NodeOutputKeyEnum,
   FlowNodeTemplateTypeEnum
-} from '../../../constants';
-import { Input_Template_History, Input_Template_UserChatInput } from '../../input';
-import { getHandleConfig } from '../../utils';
+} from '../../../../constants';
+import { Input_Template_History, Input_Template_UserChatInput } from '../../../input';
+import { getHandleConfig } from '../../../utils';
+import { i18nT } from '../../../../../../../web/i18n/utils';
 
 export const RunAppModule: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.runApp,
@@ -21,8 +22,8 @@ export const RunAppModule: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, true, true),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/runApp',
-  name: '应用调用',
-  intro: '可以选择一个其他应用进行调用',
+  name: i18nT('workflow:application_call'),
+  intro: i18nT('workflow:select_another_application_to_call'),
   showStatus: true,
   version: '481',
   isTool: true,
@@ -31,22 +32,22 @@ export const RunAppModule: FlowNodeTemplateType = {
       key: NodeInputKeyEnum.runAppSelectApp,
       renderTypeList: [FlowNodeInputTypeEnum.selectApp, FlowNodeInputTypeEnum.reference],
       valueType: WorkflowIOValueTypeEnum.selectApp,
-      label: '选择一个应用',
-      description: '选择一个其他应用进行调用',
+      label: i18nT('workflow:select_an_application'),
+      description: i18nT('workflow:choose_another_application_to_call'),
       required: true
     },
     Input_Template_History,
     {
       ...Input_Template_UserChatInput,
-      toolDescription: '用户问题'
+      toolDescription: i18nT('workflow:user_question')
     }
   ],
   outputs: [
     {
       id: NodeOutputKeyEnum.history,
       key: NodeOutputKeyEnum.history,
-      label: '新的上下文',
-      description: '将该应用回复内容拼接到历史记录中，作为新的上下文返回',
+      label: i18nT('workflow:new_context'),
+      description: i18nT('workflow:append_application_reply_to_history_as_new_context'),
       valueType: WorkflowIOValueTypeEnum.chatHistory,
       valueDesc: chatHistoryValueDesc,
       required: true,
@@ -55,8 +56,8 @@ export const RunAppModule: FlowNodeTemplateType = {
     {
       id: NodeOutputKeyEnum.answerText,
       key: NodeOutputKeyEnum.answerText,
-      label: '回复的文本',
-      description: '将在应用完全结束后触发',
+      label: i18nT('workflow:reply_text'),
+      description: i18nT('workflow:trigger_after_application_completion'),
       valueType: WorkflowIOValueTypeEnum.string,
       type: FlowNodeOutputTypeEnum.static
     }

packages/global/core/workflow/template/system/aiChat.ts

@@ -81,22 +81,23 @@ export const AiChatModule: FlowNodeTemplateType = {
     // settings modal ---
     {
       ...Input_Template_System_Prompt,
-      label: 'core.ai.Prompt',
+      label: i18nT('common:core.ai.Prompt'),
       description: chatNodeSystemPromptTip,
       placeholder: chatNodeSystemPromptTip
     },
     Input_Template_History,
     Input_Template_Dataset_Quote,
     Input_Template_Text_Quote,
-    { ...Input_Template_UserChatInput, toolDescription: '用户问题' }
+
+    { ...Input_Template_UserChatInput, toolDescription: i18nT('workflow:user_question') }
   ],
   outputs: [
     {
       id: NodeOutputKeyEnum.history,
       key: NodeOutputKeyEnum.history,
       required: true,
-      label: 'core.module.output.label.New context',
-      description: 'core.module.output.description.New context',
+      label: i18nT('common:core.module.output.label.New context'),
+      description: i18nT('common:core.module.output.description.New context'),
       valueType: WorkflowIOValueTypeEnum.chatHistory,
       valueDesc: chatHistoryValueDesc,
       type: FlowNodeOutputTypeEnum.static
@@ -105,8 +106,8 @@ export const AiChatModule: FlowNodeTemplateType = {
       id: NodeOutputKeyEnum.answerText,
       key: NodeOutputKeyEnum.answerText,
       required: true,
-      label: 'core.module.output.label.Ai response content',
-      description: 'core.module.output.description.Ai response content',
+      label: i18nT('common:core.module.output.label.Ai response content'),
+      description: i18nT('common:core.module.output.description.Ai response content'),
       valueType: WorkflowIOValueTypeEnum.string,
       type: FlowNodeOutputTypeEnum.static
     }

packages/global/core/workflow/template/system/assignedAnswer.ts

@@ -6,6 +6,7 @@ import {
   FlowNodeTemplateTypeEnum
 } from '../../constants';
 import { getHandleConfig } from '../utils';
+import { i18nT } from '../../../../../web/i18n/utils';
 
 export const AssignedAnswerModule: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.answerNode,
@@ -14,9 +15,9 @@ export const AssignedAnswerModule: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, true, true),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/reply',
-  name: '指定回复',
-  intro:
-    '该模块可以直接回复一段指定的内容。常用于引导、提示。非字符串内容传入时，会转成字符串进行输出。',
+  name: i18nT('workflow:assigned_reply'),
+  intro: i18nT('workflow:intro_assigned_reply'),
+
   version: '481',
   isTool: true,
   inputs: [
@@ -25,9 +26,9 @@ export const AssignedAnswerModule: FlowNodeTemplateType = {
       renderTypeList: [FlowNodeInputTypeEnum.textarea, FlowNodeInputTypeEnum.reference],
       valueType: WorkflowIOValueTypeEnum.any,
       required: true,
-      label: 'core.module.input.label.Response content',
-      description: 'core.module.input.description.Response content',
-      placeholder: 'core.module.input.description.Response content'
+      label: i18nT('common:core.module.input.label.Response content'),
+      description: i18nT('common:core.module.input.description.Response content'),
+      placeholder: i18nT('common:core.module.input.description.Response content')
     }
   ],
   outputs: []

packages/global/core/workflow/template/system/classifyQuestion/index.ts

@@ -18,6 +18,7 @@ import {
 import { Input_Template_System_Prompt } from '../../input';
 import { LLMModelTypeEnum } from '../../../../ai/constants';
 import { getHandleConfig } from '../../utils';
+import { i18nT } from '../../../../../../web/i18n/utils';
 
 export const ClassifyQuestionModule: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.classifyQuestion,
@@ -26,8 +27,8 @@ export const ClassifyQuestionModule: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(false, false, false, false),
   targetHandle: getHandleConfig(true, false, true, true),
   avatar: 'core/workflow/template/questionClassify',
-  name: '问题分类',
-  intro: `根据用户的历史记录和当前问题判断该次提问的类型。可以添加多组问题类型，下面是一个模板例子：\n类型1: 打招呼\n类型2: 关于商品“使用”问题\n类型3: 关于商品“购买”问题\n类型4: 其他问题`,
+  name: i18nT('workflow:question_classification'),
+  intro: i18nT('workflow:intro_question_classification'),
   showStatus: true,
   version: '481',
   inputs: [
@@ -50,15 +51,15 @@ export const ClassifyQuestionModule: FlowNodeTemplateType = {
       label: '',
       value: [
         {
-          value: '打招呼',
+          value: i18nT('workflow:greeting'),
           key: 'wqre'
         },
         {
-          value: '关于 xxx 的问题',
+          value: i18nT('workflow:about_xxx_question'),
           key: 'sdfa'
         },
         {
-          value: '其他问题',
+          value: i18nT('workflow:other_questions'),
           key: 'agex'
         }
       ]
@@ -69,7 +70,7 @@ export const ClassifyQuestionModule: FlowNodeTemplateType = {
       id: NodeOutputKeyEnum.cqResult,
       key: NodeOutputKeyEnum.cqResult,
       required: true,
-      label: '分类结果',
+      label: i18nT('workflow:classification_result'),
       valueType: WorkflowIOValueTypeEnum.string,
       type: FlowNodeOutputTypeEnum.static
     }

packages/global/core/workflow/template/system/contextExtract/index.ts

@@ -13,6 +13,7 @@ import {
 import { Input_Template_SelectAIModel, Input_Template_History } from '../../input';
 import { LLMModelTypeEnum } from '../../../../ai/constants';
 import { getHandleConfig } from '../../utils';
+import { i18nT } from '../../../../../../web/i18n/utils';
 
 export const ContextExtractModule: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.contentExtract,
@@ -21,8 +22,8 @@ export const ContextExtractModule: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, true, true),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/extractJson',
-  name: '文本内容提取',
-  intro: '可从文本中提取指定的数据，例如：sql语句、搜索关键词、代码等',
+  name: i18nT('workflow:text_content_extraction'),
+  intro: i18nT('workflow:intro_text_content_extraction'),
   showStatus: true,
   isTool: true,
   version: '481',
@@ -35,27 +36,25 @@ export const ContextExtractModule: FlowNodeTemplateType = {
       key: NodeInputKeyEnum.description,
       renderTypeList: [FlowNodeInputTypeEnum.textarea, FlowNodeInputTypeEnum.reference],
       valueType: WorkflowIOValueTypeEnum.string,
-      label: '提取要求描述',
-      description:
-        '给AI一些对应的背景知识或要求描述，引导AI更好的完成任务。\n该输入框可使用全局变量。',
-      placeholder:
-        '例如: \n1. 当前时间为: {{cTime}}。你是一个实验室预约助手，你的任务是帮助用户预约实验室，从文本中获取对应的预约信息。\n2. 你是谷歌搜索助手，需要从文本中提取出合适的搜索词。'
+      label: i18nT('workflow:extraction_requirements_description'),
+      description: i18nT('workflow:extraction_requirements_description_detail'),
+      placeholder: i18nT('workflow:extraction_requirements_placeholder')
     },
     Input_Template_History,
     {
       key: NodeInputKeyEnum.contextExtractInput,
       renderTypeList: [FlowNodeInputTypeEnum.reference, FlowNodeInputTypeEnum.textarea],
-      label: '需要提取的文本',
+      label: i18nT('workflow:text_to_extract'),
       required: true,
       valueType: WorkflowIOValueTypeEnum.string,
-      toolDescription: '需要检索的内容'
+      toolDescription: i18nT('workflow:content_to_retrieve')
     },
     {
       key: NodeInputKeyEnum.extractKeys,
       renderTypeList: [FlowNodeInputTypeEnum.custom],
       label: '',
       valueType: WorkflowIOValueTypeEnum.any,
-      description: "由 '描述' 和 'key' 组成一个目标字段，可提取多个目标字段",
+      description: i18nT('workflow:target_fields_description'),
       value: [] // {valueType: string; desc: string; key: string; required: boolean; enum: string[]}[]
     }
   ],
@@ -63,18 +62,18 @@ export const ContextExtractModule: FlowNodeTemplateType = {
     {
       id: NodeOutputKeyEnum.success,
       key: NodeOutputKeyEnum.success,
-      label: '字段完全提取',
+      label: i18nT('workflow:full_field_extraction'),
       required: true,
-      description: '提取字段全部填充时返回 true （模型提取或使用默认值均属于成功）',
+      description: i18nT('workflow:full_field_extraction_description'),
       valueType: WorkflowIOValueTypeEnum.boolean,
       type: FlowNodeOutputTypeEnum.static
     },
     {
       id: NodeOutputKeyEnum.contextExtractFields,
       key: NodeOutputKeyEnum.contextExtractFields,
-      label: '完整提取结果',
+      label: i18nT('workflow:complete_extraction_result'),
       required: true,
-      description: '一个 JSON 字符串，例如：{"name:":"YY","Time":"2023/7/2 18:00"}',
+      description: i18nT('workflow:complete_extraction_result_description'),
       valueType: WorkflowIOValueTypeEnum.string,
       type: FlowNodeOutputTypeEnum.static
     }

packages/global/core/workflow/template/system/contextExtract/type.d.ts

@@ -1,5 +1,10 @@
+import { WorkflowIOValueTypeEnum } from '../../../constants';
+
 export type ContextExtractAgentItemType = {
-  valueType: 'string' | 'number' | 'boolean';
+  valueType:
+    | WorkflowIOValueTypeEnum.string
+    | WorkflowIOValueTypeEnum.number
+    | WorkflowIOValueTypeEnum.boolean;
   desc: string;
   key: string;
   required: boolean;

packages/global/core/workflow/template/system/customFeedback.ts

@@ -6,6 +6,7 @@ import {
   NodeInputKeyEnum
 } from '../../constants';
 import { getHandleConfig } from '../utils';
+import { i18nT } from '../../../../../web/i18n/utils';
 
 export const CustomFeedbackNode: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.customFeedback,
@@ -14,8 +15,8 @@ export const CustomFeedbackNode: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, true, true),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/customFeedback',
-  name: '自定义反馈',
-  intro: '该模块被触发时，会给当前的对话记录增加一条反馈。可用于自动记录对话效果等。',
+  name: i18nT('workflow:custom_feedback'),
+  intro: i18nT('workflow:intro_custom_feedback'),
   version: '486',
   inputs: [
     {
@@ -23,7 +24,7 @@ export const CustomFeedbackNode: FlowNodeTemplateType = {
       renderTypeList: [FlowNodeInputTypeEnum.textarea, FlowNodeInputTypeEnum.reference],
       valueType: WorkflowIOValueTypeEnum.string,
       required: true,
-      label: '反馈的文本'
+      label: i18nT('workflow:feedback_text')
     }
   ],
   outputs: []

packages/global/core/workflow/template/system/datasetConcat.ts

@@ -14,6 +14,7 @@ import {
 import { getNanoid } from '../../../../common/string/tools';
 import { getHandleConfig } from '../utils';
 import { FlowNodeInputItemType } from '../../type/io.d';
+import { i18nT } from '../../../../../web/i18n/utils';
 
 export const getOneQuoteInputTemplate = ({
   key = getNanoid(),
@@ -24,8 +25,8 @@ export const getOneQuoteInputTemplate = ({
 }): FlowNodeInputItemType => ({
   key,
   renderTypeList: [FlowNodeInputTypeEnum.reference],
-  label: `引用${index}`,
-  debugLabel: '知识库引用',
+  label: `${i18nT('workflow:quote_num')},{ num: ${index} }`,
+  debugLabel: i18nT('workflow:knowledge_base_reference'),
   canEdit: true,
   valueType: WorkflowIOValueTypeEnum.datasetQuote
 });
@@ -37,15 +38,17 @@ export const DatasetConcatModule: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, true, true),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/datasetConcat',
-  name: '知识库搜索引用合并',
-  intro: '可以将多个知识库搜索结果进行合并输出。使用 RRF 的合并方式进行最终排序输出。',
+  name: i18nT('workflow:knowledge_base_search_merge'),
+  intro: i18nT('workflow:intro_knowledge_base_search_merge'),
+
   showStatus: false,
   version: '486',
   inputs: [
     {
       key: NodeInputKeyEnum.datasetMaxTokens,
       renderTypeList: [FlowNodeInputTypeEnum.custom],
-      label: '最大 Tokens',
+      label: i18nT('workflow:max_tokens'),
+
       value: 3000,
       valueType: WorkflowIOValueTypeEnum.number
     },
@@ -60,7 +63,7 @@ export const DatasetConcatModule: FlowNodeTemplateType = {
     {
       id: NodeOutputKeyEnum.datasetQuoteQA,
       key: NodeOutputKeyEnum.datasetQuoteQA,
-      label: 'core.module.Dataset quote.label',
+      label: i18nT('common:core.module.Dataset quote.label'),
       type: FlowNodeOutputTypeEnum.static,
       valueType: WorkflowIOValueTypeEnum.datasetQuote,
       valueDesc: datasetQuoteValueDesc

packages/global/core/workflow/template/system/datasetSearch.ts

@@ -34,7 +34,7 @@ export const DatasetSearchModule: FlowNodeTemplateType = {
     {
       key: NodeInputKeyEnum.datasetSelectList,
       renderTypeList: [FlowNodeInputTypeEnum.selectDataset, FlowNodeInputTypeEnum.reference],
-      label: 'core.module.input.label.Select dataset',
+      label: i18nT('common:core.module.input.label.Select dataset'),
       value: [],
       valueType: WorkflowIOValueTypeEnum.selectDataset,
       required: true
@@ -90,34 +90,24 @@ export const DatasetSearchModule: FlowNodeTemplateType = {
     },
     {
       ...Input_Template_UserChatInput,
-      toolDescription: '需要检索的内容'
+      toolDescription: i18nT('workflow:content_to_search')
     },
     {
       key: NodeInputKeyEnum.collectionFilterMatch,
       renderTypeList: [FlowNodeInputTypeEnum.JSONEditor, FlowNodeInputTypeEnum.reference],
-      label: '集合元数据过滤',
+      label: i18nT('workflow:collection_metadata_filter'),
+
       valueType: WorkflowIOValueTypeEnum.object,
       isPro: true,
-      description: `目前支持标签和创建时间过滤，需按照以下格式填写：
-{
-  "tags": {
-    "$and": ["标签 1","标签 2"],
-    "$or": ["有 $and 标签时，and 生效，or 不生效"]
-  },
-  "createTime": {
-      "$gte": "YYYY-MM-DD HH:mm 格式即可，集合的创建时间大于该时间",
-      "$lte": "YYYY-MM-DD HH:mm 格式即可，集合的创建时间小于该时间,可和 $gte 共同使用"
-  }
-}
-      `
+      description: i18nT('workflow:filter_description')
     }
   ],
   outputs: [
     {
       id: NodeOutputKeyEnum.datasetQuoteQA,
       key: NodeOutputKeyEnum.datasetQuoteQA,
-      label: 'core.module.Dataset quote.label',
-      description: '特殊数组格式，搜索结果为空时，返回空数组。',
+      label: i18nT('common:core.module.Dataset quote.label'),
+      description: i18nT('workflow:special_array_format'),
       type: FlowNodeOutputTypeEnum.static,
       valueType: WorkflowIOValueTypeEnum.datasetQuote,
       valueDesc: datasetQuoteValueDesc

packages/global/core/workflow/template/system/http468.ts

@@ -13,6 +13,7 @@ import {
 import { Input_Template_DynamicInput } from '../input';
 import { Output_Template_AddOutput } from '../output';
 import { getHandleConfig } from '../utils';
+import { i18nT } from '../../../../../web/i18n/utils';
 
 export const HttpNode468: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.httpRequest468,
@@ -21,15 +22,15 @@ export const HttpNode468: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, true, true),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/httpRequest',
-  name: 'HTTP 请求',
-  intro: '可以发出一个 HTTP 请求，实现更为复杂的操作（联网搜索、数据库查询等）',
+  name: i18nT('workflow:http_request'),
+  intro: i18nT('workflow:intro_http_request'),
   showStatus: true,
   isTool: true,
   version: '481',
   inputs: [
     {
       ...Input_Template_DynamicInput,
-      description: 'core.module.input.description.HTTP Dynamic Input',
+      description: i18nT('common:core.module.input.description.HTTP Dynamic Input'),
       customInputConfig: {
         selectValueTypeList: Object.values(WorkflowIOValueTypeEnum),
         showDescription: false,
@@ -59,7 +60,7 @@ export const HttpNode468: FlowNodeTemplateType = {
       renderTypeList: [FlowNodeInputTypeEnum.hidden],
       valueType: WorkflowIOValueTypeEnum.string,
       label: '',
-      description: 'core.module.input.description.Http Request Url',
+      description: i18nT('common:core.module.input.description.Http Request Url'),
       placeholder: 'https://api.ai.com/getInventory',
       required: false
     },
@@ -69,8 +70,8 @@ export const HttpNode468: FlowNodeTemplateType = {
       valueType: WorkflowIOValueTypeEnum.any,
       value: [],
       label: '',
-      description: 'core.module.input.description.Http Request Header',
-      placeholder: 'core.module.input.description.Http Request Header',
+      description: i18nT('common:core.module.input.description.Http Request Header'),
+      placeholder: i18nT('common:core.module.input.description.Http Request Header'),
       required: false
     },
     {
@@ -97,17 +98,17 @@ export const HttpNode468: FlowNodeTemplateType = {
     {
       id: NodeOutputKeyEnum.error,
       key: NodeOutputKeyEnum.error,
-      label: '请求错误',
-      description: 'HTTP请求错误信息，成功时返回空',
+      label: i18nT('workflow:request_error'),
+      description: i18nT('workflow:http_request_error_info'),
       valueType: WorkflowIOValueTypeEnum.object,
       type: FlowNodeOutputTypeEnum.static
     },
     {
       id: NodeOutputKeyEnum.httpRawResponse,
       key: NodeOutputKeyEnum.httpRawResponse,
-      label: '原始响应',
       required: true,
-      description: 'HTTP请求的原始响应。只能接受字符串或JSON类型响应数据。',
+      label: i18nT('workflow:raw_response'),
+      description: i18nT('workflow:http_raw_response_description'),
       valueType: WorkflowIOValueTypeEnum.any,
       type: FlowNodeOutputTypeEnum.static
     }

packages/global/core/workflow/template/system/ifElse/constant.ts

@@ -1,3 +1,5 @@
+import { i18nT } from '../../../../../../web/i18n/utils';
+
 export enum VariableConditionEnum {
   equalTo = 'equalTo',
   notEqual = 'notEqual',
@@ -29,64 +31,85 @@ export enum IfElseResultEnum {
 }
 
 export const stringConditionList = [
-  { label: '为空', value: VariableConditionEnum.isEmpty },
-  { label: '不为空', value: VariableConditionEnum.isNotEmpty },
-  { label: '等于', value: VariableConditionEnum.equalTo },
-  { label: '不等于', value: VariableConditionEnum.notEqual },
-  { label: '正则', value: VariableConditionEnum.reg },
-  { label: '包含', value: VariableConditionEnum.include },
-  { label: '不包含', value: VariableConditionEnum.notInclude },
-  { label: '开始为', value: VariableConditionEnum.startWith },
-  { label: '结束为', value: VariableConditionEnum.endWith }
+  { label: i18nT('workflow:is_empty'), value: VariableConditionEnum.isEmpty },
+  { label: i18nT('workflow:is_not_empty'), value: VariableConditionEnum.isNotEmpty },
+  { label: i18nT('workflow:is_equal_to'), value: VariableConditionEnum.equalTo },
+  { label: i18nT('workflow:is_not_equal'), value: VariableConditionEnum.notEqual },
+  { label: i18nT('workflow:regex'), value: VariableConditionEnum.reg },
+  { label: i18nT('workflow:contains'), value: VariableConditionEnum.include },
+  { label: i18nT('workflow:not_contains'), value: VariableConditionEnum.notInclude },
+  { label: i18nT('workflow:start_with'), value: VariableConditionEnum.startWith },
+  { label: i18nT('workflow:end_with'), value: VariableConditionEnum.endWith }
 ];
 export const numberConditionList = [
-  { label: '为空', value: VariableConditionEnum.isEmpty },
-  { label: '不为空', value: VariableConditionEnum.isNotEmpty },
-  { label: '等于', value: VariableConditionEnum.equalTo },
-  { label: '不等于', value: VariableConditionEnum.notEqual },
-  { label: '大于', value: VariableConditionEnum.greaterThan },
-  { label: '大于等于', value: VariableConditionEnum.greaterThanOrEqualTo },
-  { label: '小于', value: VariableConditionEnum.lessThan },
-  { label: '小于等于', value: VariableConditionEnum.lessThanOrEqualTo }
+  { label: i18nT('workflow:is_empty'), value: VariableConditionEnum.isEmpty },
+  { label: i18nT('workflow:is_not_empty'), value: VariableConditionEnum.isNotEmpty },
+  { label: i18nT('workflow:is_equal_to'), value: VariableConditionEnum.equalTo },
+  { label: i18nT('workflow:is_not_equal'), value: VariableConditionEnum.notEqual },
+  { label: i18nT('workflow:greater_than'), value: VariableConditionEnum.greaterThan },
+  {
+    label: i18nT('workflow:greater_than_or_equal_to'),
+    value: VariableConditionEnum.greaterThanOrEqualTo
+  },
+  { label: i18nT('workflow:less_than'), value: VariableConditionEnum.lessThan },
+  { label: i18nT('workflow:less_than_or_equal_to'), value: VariableConditionEnum.lessThanOrEqualTo }
 ];
 export const booleanConditionList = [
-  { label: '为空', value: VariableConditionEnum.isEmpty },
-  { label: '不为空', value: VariableConditionEnum.isNotEmpty },
-  { label: '等于', value: VariableConditionEnum.equalTo }
+  { label: i18nT('workflow:is_empty'), value: VariableConditionEnum.isEmpty },
+  { label: i18nT('workflow:is_not_empty'), value: VariableConditionEnum.isNotEmpty },
+  { label: i18nT('workflow:is_equal_to'), value: VariableConditionEnum.equalTo }
 ];
 export const arrayConditionList = [
-  { label: '为空', value: VariableConditionEnum.isEmpty },
-  { label: '不为空', value: VariableConditionEnum.isNotEmpty },
-  { label: '包含', value: VariableConditionEnum.include },
-  { label: '不包含', value: VariableConditionEnum.notInclude },
-  { label: '长度等于', value: VariableConditionEnum.lengthEqualTo },
-  { label: '长度不等于', value: VariableConditionEnum.lengthNotEqualTo },
-  { label: '长度大于', value: VariableConditionEnum.lengthGreaterThan },
-  { label: '长度大于等于', value: VariableConditionEnum.lengthGreaterThanOrEqualTo },
-  { label: '长度小于', value: VariableConditionEnum.lengthLessThan },
-  { label: '长度小于等于', value: VariableConditionEnum.lengthLessThanOrEqualTo }
+  { label: i18nT('workflow:is_empty'), value: VariableConditionEnum.isEmpty },
+  { label: i18nT('workflow:is_not_empty'), value: VariableConditionEnum.isNotEmpty },
+  { label: i18nT('workflow:contains'), value: VariableConditionEnum.include },
+  { label: i18nT('workflow:not_contains'), value: VariableConditionEnum.notInclude },
+  { label: i18nT('workflow:length_equal_to'), value: VariableConditionEnum.lengthEqualTo },
+  { label: i18nT('workflow:length_not_equal_to'), value: VariableConditionEnum.lengthNotEqualTo },
+  { label: i18nT('workflow:length_greater_than'), value: VariableConditionEnum.lengthGreaterThan },
+  {
+    label: i18nT('workflow:length_greater_than_or_equal_to'),
+    value: VariableConditionEnum.lengthGreaterThanOrEqualTo
+  },
+  { label: i18nT('workflow:length_less_than'), value: VariableConditionEnum.lengthLessThan },
+  {
+    label: i18nT('workflow:length_less_than_or_equal_to'),
+    value: VariableConditionEnum.lengthLessThanOrEqualTo
+  }
 ];
 export const objectConditionList = [
-  { label: '为空', value: VariableConditionEnum.isEmpty },
-  { label: '不为空', value: VariableConditionEnum.isNotEmpty }
+  { label: i18nT('workflow:is_empty'), value: VariableConditionEnum.isEmpty },
+  { label: i18nT('workflow:is_not_empty'), value: VariableConditionEnum.isNotEmpty }
 ];
 export const allConditionList = [
-  { label: '为空', value: VariableConditionEnum.isEmpty },
-  { label: '不为空', value: VariableConditionEnum.isNotEmpty },
-  { label: '等于', value: VariableConditionEnum.equalTo },
-  { label: '不等于', value: VariableConditionEnum.notEqual },
-  { label: '包含', value: VariableConditionEnum.include },
-  { label: '不包含', value: VariableConditionEnum.notInclude },
-  { label: '开始为', value: VariableConditionEnum.startWith },
-  { label: '结束为', value: VariableConditionEnum.endWith },
-  { label: '大于', value: VariableConditionEnum.greaterThan },
-  { label: '大于等于', value: VariableConditionEnum.greaterThanOrEqualTo },
-  { label: '小于', value: VariableConditionEnum.lessThan },
-  { label: '小于等于', value: VariableConditionEnum.lessThanOrEqualTo },
-  { label: '长度等于', value: VariableConditionEnum.lengthEqualTo },
-  { label: '长度不等于', value: VariableConditionEnum.lengthNotEqualTo },
-  { label: '长度大于', value: VariableConditionEnum.lengthGreaterThan },
-  { label: '长度大于等于', value: VariableConditionEnum.lengthGreaterThanOrEqualTo },
-  { label: '长度小于', value: VariableConditionEnum.lengthLessThan },
-  { label: '长度小于等于', value: VariableConditionEnum.lengthLessThanOrEqualTo }
+  { label: i18nT('workflow:is_empty'), value: VariableConditionEnum.isEmpty },
+  { label: i18nT('workflow:is_not_empty'), value: VariableConditionEnum.isNotEmpty },
+  { label: i18nT('workflow:is_equal_to'), value: VariableConditionEnum.equalTo },
+  { label: i18nT('workflow:is_not_equal'), value: VariableConditionEnum.notEqual },
+  { label: i18nT('workflow:contains'), value: VariableConditionEnum.include },
+  { label: i18nT('workflow:not_contains'), value: VariableConditionEnum.notInclude },
+  { label: i18nT('workflow:start_with'), value: VariableConditionEnum.startWith },
+  { label: i18nT('workflow:end_with'), value: VariableConditionEnum.endWith },
+  { label: i18nT('workflow:greater_than'), value: VariableConditionEnum.greaterThan },
+  {
+    label: i18nT('workflow:greater_than_or_equal_to'),
+    value: VariableConditionEnum.greaterThanOrEqualTo
+  },
+  { label: i18nT('workflow:less_than'), value: VariableConditionEnum.lessThan },
+  {
+    label: i18nT('workflow:less_than_or_equal_to'),
+    value: VariableConditionEnum.lessThanOrEqualTo
+  },
+  { label: i18nT('workflow:length_equal_to'), value: VariableConditionEnum.lengthEqualTo },
+  { label: i18nT('workflow:length_not_equal_to'), value: VariableConditionEnum.lengthNotEqualTo },
+  { label: i18nT('workflow:length_greater_than'), value: VariableConditionEnum.lengthGreaterThan },
+  {
+    label: i18nT('workflow:length_greater_than_or_equal_to'),
+    value: VariableConditionEnum.lengthGreaterThanOrEqualTo
+  },
+  { label: i18nT('workflow:length_less_than'), value: VariableConditionEnum.lengthLessThan },
+  {
+    label: i18nT('workflow:length_less_than_or_equal_to'),
+    value: VariableConditionEnum.lengthLessThanOrEqualTo
+  }
 ];

packages/global/core/workflow/template/system/ifElse/index.ts

@@ -1,3 +1,4 @@
+import { i18nT } from '../../../../../../web/i18n/utils';
 import {
   FlowNodeTemplateTypeEnum,
   NodeInputKeyEnum,
@@ -19,8 +20,8 @@ export const IfElseNode: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(false, false, false, false),
   targetHandle: getHandleConfig(true, false, true, true),
   avatar: 'core/workflow/template/ifelse',
-  name: '判断器',
-  intro: '根据一定的条件，执行不同的分支。',
+  name: i18nT('workflow:condition_checker'),
+  intro: i18nT('workflow:execute_different_branches_based_on_conditions'),
   showStatus: true,
   version: '481',
   inputs: [
@@ -47,7 +48,7 @@ export const IfElseNode: FlowNodeTemplateType = {
     {
       id: NodeOutputKeyEnum.ifElseResult,
       key: NodeOutputKeyEnum.ifElseResult,
-      label: '判断结果',
+      label: i18nT('workflow:judgment_result'),
       valueType: WorkflowIOValueTypeEnum.string,
       type: FlowNodeOutputTypeEnum.static
     }

packages/global/core/workflow/template/system/laf.ts

@@ -13,6 +13,7 @@ import {
 import { Input_Template_DynamicInput } from '../input';
 import { Output_Template_AddOutput } from '../output';
 import { getHandleConfig } from '../utils';
+import { i18nT } from '../../../../../web/i18n/utils';
 
 export const nodeLafCustomInputConfig = {
   selectValueTypeList: Object.values(WorkflowIOValueTypeEnum),
@@ -27,15 +28,15 @@ export const LafModule: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, true, true),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/lafDispatch',
-  name: 'Laf 函数调用（测试）',
-  intro: '可以调用Laf账号下的云函数。',
+  name: i18nT('workflow:laf_function_call_test'),
+  intro: i18nT('workflow:intro_laf_function_call'),
   showStatus: true,
   isTool: true,
   version: '481',
   inputs: [
     {
       ...Input_Template_DynamicInput,
-      description: '接收前方节点的输出值作为变量，这些变量可以被 Laf 请求参数使用。',
+      description: i18nT('workflow:dynamic_input_description'),
       customInputConfig: nodeLafCustomInputConfig
     },
     {
@@ -52,8 +53,8 @@ export const LafModule: FlowNodeTemplateType = {
     {
       id: NodeOutputKeyEnum.httpRawResponse,
       key: NodeOutputKeyEnum.httpRawResponse,
-      label: '原始响应',
-      description: 'HTTP请求的原始响应。只能接受字符串或JSON类型响应数据。',
+      label: i18nT('workflow:raw_response'),
+      description: i18nT('workflow:http_raw_response_description'),
       valueType: WorkflowIOValueTypeEnum.any,
       type: FlowNodeOutputTypeEnum.static
     },

packages/global/core/workflow/template/system/pluginConfig.ts

@@ -0,0 +1,21 @@
+import { FlowNodeTypeEnum } from '../../node/constant';
+import { FlowNodeTemplateType } from '../../type/node.d';
+import { FlowNodeTemplateTypeEnum } from '../../constants';
+import { getHandleConfig } from '../utils';
+import { i18nT } from '../../../../../web/i18n/utils';
+
+export const PluginConfigNode: FlowNodeTemplateType = {
+  id: FlowNodeTypeEnum.pluginConfig,
+  templateType: FlowNodeTemplateTypeEnum.systemInput,
+  flowNodeType: FlowNodeTypeEnum.pluginConfig,
+  sourceHandle: getHandleConfig(false, false, false, false),
+  targetHandle: getHandleConfig(false, false, false, false),
+  avatar: 'core/workflow/template/systemConfig',
+  name: i18nT('workflow:template.system_config'),
+  intro: '',
+  unique: true,
+  forbidDelete: true,
+  version: '4811',
+  inputs: [],
+  outputs: []
+};

packages/global/core/workflow/template/system/pluginInput.ts

@@ -1,3 +1,4 @@
+import { i18nT } from '../../../../../web/i18n/utils';
 import { FlowNodeTemplateTypeEnum } from '../../constants';
 import { FlowNodeTypeEnum } from '../../node/constant';
 import { FlowNodeTemplateType } from '../../type/node';
@@ -12,8 +13,8 @@ export const PluginInputModule: FlowNodeTemplateType = {
   unique: true,
   forbidDelete: true,
   avatar: 'core/workflow/template/workflowStart',
-  name: '插件输入',
-  intro: '可以配置插件需要哪些输入，利用这些输入来运行插件',
+  name: i18nT('workflow:plugin_input'),
+  intro: i18nT('workflow:intro_plugin_input'),
   showStatus: false,
   version: '481',
   inputs: [],

packages/global/core/workflow/template/system/pluginOutput.ts

@@ -1,3 +1,4 @@
+import { i18nT } from '../../../../../web/i18n/utils';
 import { FlowNodeTemplateTypeEnum } from '../../constants';
 import { FlowNodeTypeEnum } from '../../node/constant';
 import { FlowNodeTemplateType } from '../../type/node';
@@ -12,8 +13,8 @@ export const PluginOutputModule: FlowNodeTemplateType = {
   unique: true,
   forbidDelete: true,
   avatar: 'core/workflow/template/pluginOutput',
-  name: '自定义插件输出',
-  intro: '自定义配置外部输出，使用插件时，仅暴露自定义配置的输出',
+  name: i18nT('workflow:custom_plugin_output'),
+  intro: i18nT('workflow:intro_custom_plugin_output'),
   showStatus: false,
   version: '481',
   inputs: [],

packages/global/core/workflow/template/system/queryExtension.ts

@@ -17,6 +17,7 @@ import {
 } from '../input';
 import { LLMModelTypeEnum } from '../../../ai/constants';
 import { getHandleConfig } from '../utils';
+import { i18nT } from '../../../../../web/i18n/utils';
 
 export const AiQueryExtension: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.queryExtension,
@@ -25,9 +26,8 @@ export const AiQueryExtension: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, true, true),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/queryExtension',
-  name: '问题优化',
-  intro:
-    '使用问题优化功能，可以提高知识库连续对话时搜索的精度。使用该功能后，会先利用 AI 根据上下文构建一个或多个新的检索词，这些检索词更利于进行知识库搜索。该模块已内置在知识库搜索模块中，如果您仅进行一次知识库搜索，可直接使用知识库内置的补全功能。',
+  name: i18nT('workflow:question_optimization'),
+  intro: i18nT('workflow:intro_question_optimization'),
   showStatus: true,
   version: '481',
   inputs: [
@@ -38,11 +38,11 @@ export const AiQueryExtension: FlowNodeTemplateType = {
     {
       key: NodeInputKeyEnum.aiSystemPrompt,
       renderTypeList: [FlowNodeInputTypeEnum.textarea, FlowNodeInputTypeEnum.reference],
-      label: 'core.app.edit.Query extension background prompt',
+      label: i18nT('common:core.app.edit.Query extension background prompt'),
       max: 300,
       valueType: WorkflowIOValueTypeEnum.string,
-      description: 'core.app.edit.Query extension background tip',
-      placeholder: 'core.module.QueryExtension.placeholder'
+      description: i18nT('common:core.app.edit.Query extension background tip'),
+      placeholder: i18nT('common:core.module.QueryExtension.placeholder')
     },
     Input_Template_History,
     Input_Template_UserChatInput
@@ -51,8 +51,8 @@ export const AiQueryExtension: FlowNodeTemplateType = {
     {
       id: NodeOutputKeyEnum.text,
       key: NodeOutputKeyEnum.text,
-      label: 'core.module.output.label.query extension result',
-      description: 'core.module.output.description.query extension result',
+      label: i18nT('common:core.module.output.label.query extension result'),
+      description: i18nT('common:core.module.output.description.query extension result'),
       valueType: WorkflowIOValueTypeEnum.string,
       type: FlowNodeOutputTypeEnum.static
     }

packages/global/core/workflow/template/system/runAppPlugin.ts

@@ -0,0 +1,19 @@
+import { FlowNodeTemplateTypeEnum } from '../../constants';
+import { FlowNodeTypeEnum } from '../../node/constant';
+import { FlowNodeTemplateType } from '../../type/node';
+import { getHandleConfig } from '../utils';
+
+export const RunAppPluginModule: FlowNodeTemplateType = {
+  id: FlowNodeTypeEnum.appModule,
+  templateType: FlowNodeTemplateTypeEnum.other,
+  flowNodeType: FlowNodeTypeEnum.appModule,
+  sourceHandle: getHandleConfig(true, true, true, true),
+  targetHandle: getHandleConfig(true, true, true, true),
+  intro: '',
+  name: '',
+  showStatus: false,
+  isTool: false,
+  version: '481',
+  inputs: [], // [{key:'pluginId'},...]
+  outputs: []
+};

packages/global/core/workflow/template/system/sandbox/index.ts

@@ -14,6 +14,7 @@ import { getHandleConfig } from '../../utils';
 import { Input_Template_DynamicInput } from '../../input';
 import { Output_Template_AddOutput } from '../../output';
 import { JS_TEMPLATE } from './constants';
+import { i18nT } from '../../../../../../web/i18n/utils';
 
 export const CodeNode: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.code,
@@ -22,14 +23,14 @@ export const CodeNode: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, true, true),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/codeRun',
-  name: '代码运行',
-  intro: '执行一段简单的脚本代码，通常用于进行复杂的数据处理。',
+  name: i18nT('workflow:code_execution'),
+  intro: i18nT('workflow:execute_a_simple_script_code_usually_for_complex_data_processing'),
   showStatus: true,
   version: '482',
   inputs: [
     {
       ...Input_Template_DynamicInput,
-      description: '这些变量会作为代码的运行的输入参数',
+      description: i18nT('workflow:these_variables_will_be_input_parameters_for_code_execution'),
       customInputConfig: {
         selectValueTypeList: Object.values(WorkflowIOValueTypeEnum),
         showDescription: false,
@@ -78,20 +79,20 @@ export const CodeNode: FlowNodeTemplateType = {
   outputs: [
     {
       ...Output_Template_AddOutput,
-      description: '将代码中 return 的对象作为输出，传递给后续的节点。变量名需要对应 return 的 key'
+      description: i18nT('workflow:pass_returned_object_as_output_to_next_nodes')
     },
     {
       id: NodeOutputKeyEnum.rawResponse,
       key: NodeOutputKeyEnum.rawResponse,
-      label: '完整响应数据',
+      label: i18nT('workflow:full_response_data'),
       valueType: WorkflowIOValueTypeEnum.object,
       type: FlowNodeOutputTypeEnum.static
     },
     {
       id: NodeOutputKeyEnum.error,
       key: NodeOutputKeyEnum.error,
-      label: '运行错误',
-      description: '代码运行错误信息，成功时返回空',
+      label: i18nT('workflow:execution_error'),
+      description: i18nT('workflow:error_info_returns_empty_on_success'),
       valueType: WorkflowIOValueTypeEnum.object,
       type: FlowNodeOutputTypeEnum.static
     },

packages/global/core/workflow/template/system/stopTool.ts

@@ -2,6 +2,7 @@ import { FlowNodeTypeEnum } from '../../node/constant';
 import { FlowNodeTemplateType } from '../../type/node';
 import { FlowNodeTemplateTypeEnum } from '../../constants';
 import { getHandleConfig } from '../utils';
+import { i18nT } from '../../../../../web/i18n/utils';
 
 export const StopToolNode: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.stopTool,
@@ -10,9 +11,8 @@ export const StopToolNode: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(false, false, false, false),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/stopTool',
-  name: '工具调用终止',
-  intro:
-    '该模块需配置工具调用使用。当该模块被执行时，本次工具调用将会强制结束，并且不再调用AI针对工具调用结果回答问题。',
+  name: i18nT('workflow:tool_call_termination'),
+  intro: i18nT('workflow:intro_tool_call_termination'),
   version: '481',
   inputs: [],
   outputs: []

packages/global/core/workflow/template/system/textEditor.ts

@@ -12,6 +12,7 @@ import {
 } from '../../constants';
 import { getHandleConfig } from '../utils';
 import { Input_Template_DynamicInput } from '../input';
+import { i18nT } from '../../../../../web/i18n/utils';
 
 export const TextEditorNode: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.textEditor,
@@ -20,13 +21,13 @@ export const TextEditorNode: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, true, true),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/textConcat',
-  name: '文本拼接',
-  intro: '可对固定或传入的文本进行加工后输出，非字符串类型数据最终会转成字符串类型。',
+  name: i18nT('workflow:text_concatenation'),
+  intro: i18nT('workflow:intro_text_concatenation'),
   version: '486',
   inputs: [
     {
       ...Input_Template_DynamicInput,
-      description: '可以引用其他节点的输出，作为文本拼接的变量，输入 / 唤起变量列表',
+      description: i18nT('workflow:dynamic_input_description_concat'),
       customInputConfig: {
         selectValueTypeList: Object.values(WorkflowIOValueTypeEnum),
         showDescription: false,
@@ -38,15 +39,15 @@ export const TextEditorNode: FlowNodeTemplateType = {
       renderTypeList: [FlowNodeInputTypeEnum.textarea],
       valueType: WorkflowIOValueTypeEnum.string,
       required: true,
-      label: '拼接文本',
-      placeholder: '可输入 / 唤起变量列表'
+      label: i18nT('workflow:concatenation_text'),
+      placeholder: i18nT('workflow:input_variable_list')
     }
   ],
   outputs: [
     {
       id: NodeOutputKeyEnum.text,
       key: NodeOutputKeyEnum.text,
-      label: '拼接结果',
+      label: i18nT('workflow:concatenation_result'),
       type: FlowNodeOutputTypeEnum.static,
       valueType: WorkflowIOValueTypeEnum.string
     }

packages/global/core/workflow/template/system/tools.ts

@@ -61,7 +61,7 @@ export const ToolModule: FlowNodeTemplateType = {
 
     {
       ...Input_Template_System_Prompt,
-      label: 'core.ai.Prompt',
+      label: i18nT('common:core.ai.Prompt'),
       description: chatNodeSystemPromptTip,
       placeholder: chatNodeSystemPromptTip
     },
@@ -72,8 +72,8 @@ export const ToolModule: FlowNodeTemplateType = {
     {
       id: NodeOutputKeyEnum.answerText,
       key: NodeOutputKeyEnum.answerText,
-      label: 'core.module.output.label.Ai response content',
-      description: 'core.module.output.description.Ai response content',
+      label: i18nT('common:core.module.output.label.Ai response content'),
+      description: i18nT('common:core.module.output.description.Ai response content'),
       valueType: WorkflowIOValueTypeEnum.string,
       type: FlowNodeOutputTypeEnum.static
     }

packages/global/core/workflow/template/system/userSelect/type.d.ts

@@ -22,5 +22,3 @@ type UserSelectInteractive = {
 };
 
 export type InteractiveNodeResponseItemType = InteractiveBasicType & UserSelectInteractive;
-
-export type UserInteractiveType = UserSelectInteractive;

packages/global/core/workflow/template/system/variableUpdate/index.tsx

@@ -6,6 +6,7 @@ import {
   WorkflowIOValueTypeEnum
 } from '../../../constants';
 import { getHandleConfig } from '../../utils';
+import { i18nT } from '../../../../../../web/i18n/utils';
 
 export const VariableUpdateNode: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.variableUpdate,
@@ -14,8 +15,8 @@ export const VariableUpdateNode: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, true, true),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/variableUpdate',
-  name: '变量更新',
-  intro: '可以更新指定节点的输出值或更新全局变量',
+  name: i18nT('workflow:variable_update'),
+  intro: i18nT('workflow:update_specified_node_output_or_global_variable'),
   showStatus: false,
   isTool: false,
   version: '481',

packages/global/core/workflow/template/system/workflowStart.ts

@@ -31,7 +31,7 @@ export const WorkflowStart: FlowNodeTemplateType = {
   forbidDelete: true,
   unique: true,
   version: '481',
-  inputs: [{ ...Input_Template_UserChatInput, toolDescription: '用户问题' }],
+  inputs: [{ ...Input_Template_UserChatInput, toolDescription: i18nT('workflow:user_question') }],
   outputs: [
     {
       id: NodeOutputKeyEnum.userChatInput,

packages/global/core/workflow/type/index.d.ts

@@ -28,7 +28,7 @@ export type WorkflowTemplateBasicType = {
 };
 export type WorkflowTemplateType = {
   id: string;
-  parentId?: string;
+  parentId?: ParentIdType;
   isFolder?: boolean;
 
   name: string;
@@ -62,6 +62,8 @@ export type TemplateMarketListItemType = {
 
 // system plugin
 export type SystemPluginTemplateItemType = WorkflowTemplateType & {
+  customWorkflow?: string;
+
   templateType: FlowNodeTemplateTypeEnum;
   isTool?: boolean;
 
@@ -77,8 +79,6 @@ export type SystemPluginTemplateItemType = WorkflowTemplateType & {
     description: string;
     value?: any;
   }[];
-
-  workflow: WorkflowTemplateBasicType;
 };
 
 export type THelperLine = {

packages/global/core/workflow/utils.ts

@@ -1,10 +1,16 @@
-import { FlowNodeInputTypeEnum, FlowNodeOutputTypeEnum, FlowNodeTypeEnum } from './node/constant';
+import {
+  chatHistoryValueDesc,
+  FlowNodeInputTypeEnum,
+  FlowNodeOutputTypeEnum,
+  FlowNodeTypeEnum
+} from './node/constant';
 import {
   WorkflowIOValueTypeEnum,
   NodeInputKeyEnum,
   VariableInputEnum,
   variableMap,
-  VARIABLE_NODE_ID
+  VARIABLE_NODE_ID,
+  NodeOutputKeyEnum
 } from './constants';
 import { FlowNodeInputItemType, FlowNodeOutputItemType, ReferenceValueProps } from './type/io.d';
 import { StoreNodeItemType } from './type/node';
@@ -25,6 +31,8 @@ import {
 import { IfElseResultEnum } from './template/system/ifElse/constant';
 import { RuntimeNodeItemType } from './runtime/type';
 import { getReferenceVariableValue } from './runtime/utils';
+import { Input_Template_History, Input_Template_UserChatInput } from './template/input';
+import { i18nT } from '../../../web/i18n/utils';
 
 export const getHandleId = (nodeId: string, type: 'source' | 'target', key: string) => {
   return `${nodeId}-${type}-${key}`;
@@ -72,6 +80,10 @@ export const splitGuideModule = (guideModules?: StoreNodeItemType) => {
     guideModules?.inputs?.find((item) => item.key === NodeInputKeyEnum.chatInputGuide)?.value ||
     defaultChatInputGuideConfig;
 
+  // plugin
+  const instruction: string =
+    guideModules?.inputs?.find((item) => item.key === NodeInputKeyEnum.instruction)?.value || '';
+
   return {
     welcomeText,
     variables,
@@ -79,7 +91,8 @@ export const splitGuideModule = (guideModules?: StoreNodeItemType) => {
     ttsConfig,
     whisperConfig,
     scheduledTriggerConfig,
-    chatInputGuide
+    chatInputGuide,
+    instruction
   };
 };
 
@@ -104,7 +117,8 @@ export const getAppChatConfig = ({
     ttsConfig,
     whisperConfig,
     scheduledTriggerConfig,
-    chatInputGuide
+    chatInputGuide,
+    instruction
   } = splitGuideModule(systemConfigNode);
 
   const config: AppChatConfigType = {
@@ -113,6 +127,7 @@ export const getAppChatConfig = ({
     whisperConfig,
     scheduledTriggerConfig,
     chatInputGuide,
+    instruction,
     ...chatConfig,
     variables: storeVariables ?? chatConfig?.variables ?? variables,
     welcomeText: storeWelcomeText ?? chatConfig?.welcomeText ?? welcomeText
@@ -147,9 +162,11 @@ export const getModuleInputUiField = (input: FlowNodeInputItemType) => {
   return {};
 };
 
-export const pluginData2FlowNodeIO = (
-  nodes: StoreNodeItemType[]
-): {
+export const pluginData2FlowNodeIO = ({
+  nodes
+}: {
+  nodes: StoreNodeItemType[];
+}): {
   inputs: FlowNodeInputItemType[];
   outputs: FlowNodeOutputItemType[];
 } => {
@@ -157,14 +174,17 @@ export const pluginData2FlowNodeIO = (
   const pluginOutput = nodes.find((node) => node.flowNodeType === FlowNodeTypeEnum.pluginOutput);
 
   return {
-    inputs: pluginInput
-      ? pluginInput.inputs.map((item) => ({
-          ...item,
-          ...getModuleInputUiField(item),
-          value: getOrInitModuleInputValue(item),
-          canEdit: false
-        }))
-      : [],
+    inputs:
+      pluginInput?.inputs.map((item) => ({
+        ...item,
+        ...getModuleInputUiField(item),
+        value: getOrInitModuleInputValue(item),
+        canEdit: false,
+        renderTypeList:
+          item.renderTypeList[0] === FlowNodeInputTypeEnum.customVariable
+            ? [FlowNodeInputTypeEnum.reference, FlowNodeInputTypeEnum.input]
+            : item.renderTypeList
+      })) || [],
     outputs: pluginOutput
       ? [
           ...pluginOutput.inputs.map((item) => ({
@@ -180,6 +200,80 @@ export const pluginData2FlowNodeIO = (
   };
 };
 
+export const appData2FlowNodeIO = ({
+  chatConfig
+}: {
+  chatConfig?: AppChatConfigType;
+}): {
+  inputs: FlowNodeInputItemType[];
+  outputs: FlowNodeOutputItemType[];
+} => {
+  const variableInput = !chatConfig?.variables
+    ? []
+    : chatConfig.variables.map((item) => {
+        const renderTypeMap = {
+          [VariableInputEnum.input]: [FlowNodeInputTypeEnum.input, FlowNodeInputTypeEnum.reference],
+          [VariableInputEnum.textarea]: [
+            FlowNodeInputTypeEnum.textarea,
+            FlowNodeInputTypeEnum.reference
+          ],
+          [VariableInputEnum.select]: [FlowNodeInputTypeEnum.select],
+          [VariableInputEnum.custom]: [
+            FlowNodeInputTypeEnum.input,
+            FlowNodeInputTypeEnum.reference
+          ],
+          default: [FlowNodeInputTypeEnum.reference]
+        };
+
+        return {
+          key: item.key,
+          renderTypeList: renderTypeMap[item.type] || renderTypeMap.default,
+          label: item.label,
+          debugLabel: item.label,
+          description: '',
+          valueType: WorkflowIOValueTypeEnum.any,
+          required: item.required,
+          list: item.enums.map((enumItem) => ({
+            label: enumItem.value,
+            value: enumItem.value
+          }))
+        };
+      });
+
+  // const showFileLink =
+  //   chatConfig?.fileSelectConfig?.canSelectFile || chatConfig?.fileSelectConfig?.canSelectImg;
+
+  return {
+    inputs: [
+      Input_Template_History,
+      Input_Template_UserChatInput,
+      // ...(showFileLink ? [Input_Template_File_Link] : []),
+      ...variableInput
+    ],
+    outputs: [
+      {
+        id: NodeOutputKeyEnum.history,
+        key: NodeOutputKeyEnum.history,
+        required: true,
+        label: i18nT('common:core.module.output.label.New context'),
+        description: i18nT('common:core.module.output.description.New context'),
+        valueType: WorkflowIOValueTypeEnum.chatHistory,
+        valueDesc: chatHistoryValueDesc,
+        type: FlowNodeOutputTypeEnum.static
+      },
+      {
+        id: NodeOutputKeyEnum.answerText,
+        key: NodeOutputKeyEnum.answerText,
+        required: false,
+        label: i18nT('common:core.module.output.label.Ai response content'),
+        description: i18nT('common:core.module.output.description.Ai response content'),
+        valueType: WorkflowIOValueTypeEnum.string,
+        type: FlowNodeOutputTypeEnum.static
+      }
+    ]
+  };
+};
+
 export const formatEditorVariablePickerIcon = (
   variables: { key: string; label: string; type?: `${VariableInputEnum}`; required?: boolean }[]
 ): EditorVariablePickerType[] => {
@@ -232,6 +326,9 @@ export const updatePluginInputByVariables = (
   );
 };
 
+/* Remove pluginInput variables from global variables
+  (completions api: Plugin input get value from global variables) 
+*/
 export const removePluginInputVariables = (
   variables: Record<string, any>,
   nodes: RuntimeNodeItemType[]
@@ -250,7 +347,8 @@ export const removePluginInputVariables = (
   );
 };
 
-export function replaceVariableLabel({
+// replace {{$xx.xx$}} variables for text
+export function replaceEditorVariable({
   text,
   nodes,
   variables,
@@ -258,7 +356,7 @@ export function replaceVariableLabel({
 }: {
   text: any;
   nodes: RuntimeNodeItemType[];
-  variables: Record<string, string | number>;
+  variables: Record<string, any>; // global variables
   runningNode: RuntimeNodeItemType;
 }) {
   if (typeof text !== 'string') return text;

packages/global/support/wallet/bill/api.d.ts

@@ -1,16 +1,25 @@
+import { StandardSubLevelEnum, SubModeEnum } from '../sub/constants';
 import { BillTypeEnum } from './constants';
 
-export type CreateBillProps = {
-  type: BillTypeEnum;
-
-  // balance
-  balance?: number; // read
-
-  month?: number;
-  // extra dataset size
-  extraDatasetSize?: number; // 1k
-  extraPoints?: number; // 100w
+export type CreateStandPlanBill = {
+  type: BillTypeEnum.standSubPlan;
+  level: `${StandardSubLevelEnum}`;
+  subMode: `${SubModeEnum}`;
 };
+type CreateExtractPointsBill = {
+  type: BillTypeEnum.extraPoints;
+  extraPoints: number;
+};
+type CreateExtractDatasetBill = {
+  type: BillTypeEnum.extraDatasetSub;
+  extraDatasetSize: number;
+  month: number;
+};
+export type CreateBillProps =
+  | CreateStandPlanBill
+  | CreateExtractPointsBill
+  | CreateExtractDatasetBill;
+
 export type CreateBillResponse = {
   billId: string;
   codeUrl: string;

packages/global/support/wallet/bill/type.d.ts

@@ -19,7 +19,6 @@ export type BillSchemaType = {
     month?: number;
     datasetSize?: number;
     extraPoints?: number;
-    invoice: boolean;
   };
 };
 

packages/global/support/wallet/sub/constants.ts

@@ -1,3 +1,5 @@
+import { i18nT } from '../../../../web/i18n/utils';
+
 export enum SubTypeEnum {
   standard = 'standard',
   extraDatasetSize = 'extraDatasetSize',
@@ -19,19 +21,6 @@ export const subTypeMap = {
   }
 };
 
-export enum SubStatusEnum {
-  active = 'active',
-  expired = 'expired'
-}
-export const subStatusMap = {
-  [SubStatusEnum.active]: {
-    label: 'support.wallet.subscription.status.active'
-  },
-  [SubStatusEnum.expired]: {
-    label: 'support.wallet.subscription.status.canceled'
-  }
-};
-
 export enum SubModeEnum {
   month = 'month',
   year = 'year'
@@ -39,11 +28,13 @@ export enum SubModeEnum {
 export const subModeMap = {
   [SubModeEnum.month]: {
     label: 'support.wallet.subscription.mode.Month',
-    durationMonth: 1
+    durationMonth: 1,
+    payMonth: 1
   },
   [SubModeEnum.year]: {
     label: 'support.wallet.subscription.mode.Year',
-    durationMonth: 12
+    durationMonth: 12,
+    payMonth: 10
   }
 };
 
@@ -56,23 +47,28 @@ export enum StandardSubLevelEnum {
 }
 export const standardSubLevelMap = {
   [StandardSubLevelEnum.free]: {
-    label: 'support.wallet.subscription.standardSubLevel.free',
-    desc: 'support.wallet.subscription.standardSubLevel.free desc'
+    label: i18nT('common:support.wallet.subscription.standardSubLevel.free'),
+    desc: i18nT('common:support.wallet.subscription.standardSubLevel.free desc'),
+    weight: 1
   },
   [StandardSubLevelEnum.experience]: {
-    label: 'support.wallet.subscription.standardSubLevel.experience',
-    desc: ''
+    label: i18nT('common:support.wallet.subscription.standardSubLevel.experience'),
+    desc: i18nT('common:support.wallet.subscription.standardSubLevel.experience_desc'),
+    weight: 2
   },
   [StandardSubLevelEnum.team]: {
-    label: 'support.wallet.subscription.standardSubLevel.team',
-    desc: ''
+    label: i18nT('common:support.wallet.subscription.standardSubLevel.team'),
+    desc: i18nT('common:support.wallet.subscription.standardSubLevel.team_desc'),
+    weight: 3
   },
   [StandardSubLevelEnum.enterprise]: {
-    label: 'support.wallet.subscription.standardSubLevel.enterprise',
-    desc: ''
+    label: i18nT('common:support.wallet.subscription.standardSubLevel.enterprise'),
+    desc: i18nT('common:support.wallet.subscription.standardSubLevel.enterprise_desc'),
+    weight: 4
   },
   [StandardSubLevelEnum.custom]: {
-    label: 'support.wallet.subscription.standardSubLevel.custom',
-    desc: ''
+    label: i18nT('common:support.wallet.subscription.standardSubLevel.custom'),
+    desc: '',
+    weight: 5
   }
 };

packages/global/support/wallet/sub/type.d.ts

@@ -1,4 +1,4 @@
-import { StandardSubLevelEnum, SubModeEnum, SubStatusEnum, SubTypeEnum } from './constants';
+import { StandardSubLevelEnum, SubModeEnum, SubTypeEnum } from './constants';
 
 // Content of plan
 export type TeamStandardSubPlanItemType = {
@@ -36,17 +36,14 @@ export type TeamSubSchema = {
   _id: string;
   teamId: string;
   type: `${SubTypeEnum}`;
-  status: `${SubStatusEnum}`;
   startTime: Date;
   expiredTime: Date;
-  price: number;
 
   currentMode: `${SubModeEnum}`;
   nextMode: `${SubModeEnum}`;
-  currentSubLevel: `${StandardSubLevelEnum}`;
-  nextSubLevel: `${StandardSubLevelEnum}`;
+  currentSubLevel: StandardSubLevelEnum;
+  nextSubLevel: StandardSubLevelEnum;
 
-  pointPrice: number;
   totalPoints: number;
   surplusPoints: number;
 

packages/service/common/file/read/utils.ts

@@ -2,11 +2,14 @@ import { markdownProcess } from '@fastgpt/global/common/string/markdown';
 import { uploadMongoImg } from '../image/controller';
 import { MongoImageTypeEnum } from '@fastgpt/global/common/file/image/constants';
 import { addHours } from 'date-fns';
+import FormData from 'form-data';
 
 import { WorkerNameEnum, runWorker } from '../../../worker/utils';
 import fs from 'fs';
 import { detectFileEncoding } from '@fastgpt/global/common/file/tools';
 import type { ReadFileResponse } from '../../../worker/readFile/type';
+import axios from 'axios';
+import { addLog } from '../../system/log';
 
 export type readRawTextByLocalFileParams = {
   teamId: string;
@@ -51,15 +54,7 @@ export const readRawContentByFileBuffer = async ({
   metadata?: Record<string, any>;
 }) => {
   // Upload image in markdown
-  const matchMdImgTextAndUpload = ({
-    teamId,
-    md,
-    metadata
-  }: {
-    md: string;
-    teamId: string;
-    metadata?: Record<string, any>;
-  }) =>
+  const matchMdImgTextAndUpload = ({ teamId, md }: { md: string; teamId: string }) =>
     markdownProcess({
       rawText: md,
       uploadImgController: (base64Img) =>
@@ -72,18 +67,63 @@ export const readRawContentByFileBuffer = async ({
         })
     });
 
-  let { rawText, formatText } = await runWorker<ReadFileResponse>(WorkerNameEnum.readFile, {
-    extension,
-    encoding,
-    buffer
-  });
+  /* If */
+  const customReadfileUrl = process.env.CUSTOM_READ_FILE_URL;
+  const customReadFileExtension = process.env.CUSTOM_READ_FILE_EXTENSION || '';
+  const ocrParse = process.env.CUSTOM_READ_FILE_OCR || 'false';
+  const readFileFromCustomService = async (): Promise<ReadFileResponse | undefined> => {
+    if (
+      !customReadfileUrl ||
+      !customReadFileExtension ||
+      !customReadFileExtension.includes(extension)
+    )
+      return;
+
+    const start = Date.now();
+
+    const data = new FormData();
+    data.append('file', buffer, {
+      filename: `file.${extension}`
+    });
+    data.append('extension', extension);
+    data.append('ocr', ocrParse);
+    const { data: response } = await axios.post<{
+      success: boolean;
+      message: string;
+      data: {
+        page: number;
+        markdown: string;
+      };
+    }>(customReadfileUrl, data, {
+      timeout: 600000,
+      headers: {
+        ...data.getHeaders()
+      }
+    });
+
+    addLog.info(`Use custom read file service, time: ${Date.now() - start}ms`);
+
+    const rawText = response.data.markdown;
+
+    return {
+      rawText,
+      formatText: rawText
+    };
+  };
+
+  let { rawText, formatText } =
+    (await readFileFromCustomService()) ||
+    (await runWorker<ReadFileResponse>(WorkerNameEnum.readFile, {
+      extension,
+      encoding,
+      buffer
+    }));
 
   // markdown data format
-  if (['md', 'html', 'docx'].includes(extension)) {
+  if (['md', 'html', 'docx', ...customReadFileExtension.split(',')].includes(extension)) {
     rawText = await matchMdImgTextAndUpload({
       teamId: teamId,
-      md: rawText,
-      metadata: metadata
+      md: rawText
     });
   }
 

packages/service/core/app/controller.ts

@@ -50,9 +50,11 @@ export const getAppLatestVersion = async (appId: string, app?: AppSchema) => {
   const version = await MongoAppVersion.findOne({
     appId,
     isPublish: true
-  }).sort({
-    time: -1
-  });
+  })
+    .sort({
+      time: -1
+    })
+    .lean();
 
   if (version) {
     return {

packages/service/core/app/plugin/controller.ts

@@ -1,6 +1,6 @@
 import { FlowNodeTemplateType } from '@fastgpt/global/core/workflow/type/node.d';
 import { FlowNodeTypeEnum, defaultNodeVersion } from '@fastgpt/global/core/workflow/node/constant';
-import { pluginData2FlowNodeIO } from '@fastgpt/global/core/workflow/utils';
+import { appData2FlowNodeIO, pluginData2FlowNodeIO } from '@fastgpt/global/core/workflow/utils';
 import { PluginSourceEnum } from '@fastgpt/global/core/plugin/constants';
 import type { PluginRuntimeType } from '@fastgpt/global/core/workflow/runtime/type';
 import { FlowNodeTemplateTypeEnum } from '@fastgpt/global/core/workflow/constants';
@@ -52,10 +52,10 @@ const getPluginTemplateById = async (
       showStatus: true,
       workflow: {
         nodes: item.modules,
-        edges: item.edges
+        edges: item.edges,
+        chatConfig: item.chatConfig
       },
       templateType: FlowNodeTemplateTypeEnum.teamApp,
-      isTool: true,
       version: item?.pluginData?.nodeVersion || defaultNodeVersion,
       originCost: 0,
       currentCost: 0
@@ -71,22 +71,27 @@ const getPluginTemplateById = async (
 /* format plugin modules to plugin preview module */
 export async function getPluginPreviewNode({ id }: { id: string }): Promise<FlowNodeTemplateType> {
   const plugin = await getPluginTemplateById(id);
+  const isPlugin = !!plugin.workflow.nodes.find(
+    (node) => node.flowNodeType === FlowNodeTypeEnum.pluginInput
+  );
 
   return {
     id: getNanoid(),
     pluginId: plugin.id,
     templateType: plugin.templateType,
-    flowNodeType: FlowNodeTypeEnum.pluginModule,
+    flowNodeType: isPlugin ? FlowNodeTypeEnum.pluginModule : FlowNodeTypeEnum.appModule,
     avatar: plugin.avatar,
     name: plugin.name,
     intro: plugin.intro,
     inputExplanationUrl: plugin.inputExplanationUrl,
     showStatus: plugin.showStatus,
-    isTool: plugin.isTool,
+    isTool: isPlugin,
     version: plugin.version,
     sourceHandle: getHandleConfig(true, true, true, true),
     targetHandle: getHandleConfig(true, true, true, true),
-    ...pluginData2FlowNodeIO(plugin.workflow.nodes)
+    ...(isPlugin
+      ? pluginData2FlowNodeIO({ nodes: plugin.workflow.nodes })
+      : appData2FlowNodeIO({ chatConfig: plugin.workflow.chatConfig }))
   };
 }
 

packages/service/core/app/plugin/systemPluginSchema.ts

@@ -24,7 +24,8 @@ const SystemPluginSchema = new Schema({
   currentCost: {
     type: Number,
     default: 0
-  }
+  },
+  customConfig: Object
 });
 
 SystemPluginSchema.index({ pluginId: 1 });

packages/service/core/app/plugin/type.d.ts

@@ -1,4 +1,8 @@
-import { SystemPluginTemplateItemType } from '@fastgpt/global/core/workflow/type';
+import { FlowNodeTemplateTypeEnum } from '@fastgpt/global/core/workflow/constants';
+import {
+  SystemPluginTemplateItemType,
+  WorkflowTemplateBasicType
+} from '@fastgpt/global/core/workflow/type';
 
 export type SystemPluginConfigSchemaType = {
   pluginId: string;
@@ -7,4 +11,14 @@ export type SystemPluginConfigSchemaType = {
   currentCost: number;
   isActive: boolean;
   inputConfig: SystemPluginTemplateItemType['inputConfig'];
+
+  customConfig?: {
+    name: string;
+    avatar: string;
+    intro?: string;
+    version: string;
+    weight?: number;
+    workflow: WorkflowTemplateBasicType;
+    templateType: FlowNodeTemplateTypeEnum;
+  };
 };

packages/service/core/app/schema.ts

@@ -18,7 +18,8 @@ export const chatConfigType = {
   whisperConfig: Object,
   scheduledTriggerConfig: Object,
   chatInputGuide: Object,
-  fileSelectConfig: Object
+  fileSelectConfig: Object,
+  instruction: String
 };
 
 // schema

packages/service/core/chat/controller.ts

@@ -35,7 +35,8 @@ export async function getChatItems({
 
   return { histories };
 }
-/* 临时适配旧的对话记录 */
+
+/* Temporary adaptation for old conversation records */
 export const adaptStringValue = (value: any): ChatItemValueItemType[] => {
   if (typeof value === 'string') {
     return [

packages/service/core/dataset/collection/schema.ts

@@ -68,6 +68,7 @@ const DatasetCollectionSchema = new Schema({
   qaPrompt: {
     type: String
   },
+  ocrParse: Boolean,
 
   tags: {
     type: [String],

packages/service/core/dataset/collection/utils.ts

@@ -159,7 +159,8 @@ export const reloadCollectionChunks = async ({
   // split data
   const { chunks } = splitText2Chunks({
     text: newRawText,
-    chunkLen: col.chunkSize || 512
+    chunkLen: col.chunkSize || 512,
+    customReg: col.chunkSplitter ? [col.chunkSplitter] : [],
   });
 
   // insert to training queue

packages/service/core/workflow/dispatch/abandoned/runApp.ts

@@ -1,8 +1,8 @@
+/* Abandoned */
 import type { ChatItemType } from '@fastgpt/global/core/chat/type.d';
 import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { SelectAppItemType } from '@fastgpt/global/core/workflow/template/system/runApp/type';
+import { SelectAppItemType } from '@fastgpt/global/core/workflow/template/system/abandoned/runApp/type';
 import { dispatchWorkFlow } from '../index';
-import { responseWrite } from '../../../../common/response';
 import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
 import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
 import {
@@ -31,10 +31,8 @@ type Response = DispatchNodeResultType<{
 
 export const dispatchAppRequest = async (props: Props): Promise<Response> => {
   const {
-    res,
     app: workflowApp,
-    stream,
-    detail,
+    workflowStreamResponse,
     histories,
     query,
     params: { userChatInput, history, app }
@@ -51,15 +49,12 @@ export const dispatchAppRequest = async (props: Props): Promise<Response> => {
     per: ReadPermissionVal
   });
 
-  if (res && stream) {
-    responseWrite({
-      res,
-      event: detail ? SseResponseEventEnum.answer : undefined,
-      data: textAdaptGptResponse({
-        text: '\n'
-      })
-    });
-  }
+  workflowStreamResponse?.({
+    event: SseResponseEventEnum.fastAnswer,
+    data: textAdaptGptResponse({
+      text: '\n'
+    })
+  });
 
   const chatHistories = getHistories(history, histories);
   const { files } = chatValue2RuntimePrompt(query);

packages/service/core/workflow/dispatch/agent/runApp.ts

@@ -1,85 +1,96 @@
-// @ts-nocheck
 import type { ChatItemType } from '@fastgpt/global/core/chat/type.d';
 import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { SelectAppItemType } from '@fastgpt/global/core/workflow/template/system/runApp/type';
-import { dispatchWorkFlowV1 } from '../index';
-import { MongoApp } from '../../../../core/app/schema';
-import { responseWrite } from '../../../../common/response';
+import { dispatchWorkFlow } from '../index';
 import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
 import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
+import {
+  getWorkflowEntryNodeIds,
+  initWorkflowEdgeStatus,
+  storeNodes2RuntimeNodes,
+  textAdaptGptResponse
+} from '@fastgpt/global/core/workflow/runtime/utils';
 import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
 import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import { getHistories, setEntryEntries } from '../utils';
+import { filterSystemVariables, getHistories } from '../utils';
 import { chatValue2RuntimePrompt, runtimePrompt2ChatsValue } from '@fastgpt/global/core/chat/adapt';
 import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
+import { authAppByTmbId } from '../../../../support/permission/app/auth';
+import { ReadPermissionVal } from '@fastgpt/global/support/permission/constant';
 
 type Props = ModuleDispatchProps<{
   [NodeInputKeyEnum.userChatInput]: string;
   [NodeInputKeyEnum.history]?: ChatItemType[] | number;
-  app: SelectAppItemType;
+  [NodeInputKeyEnum.fileUrlList]?: string[];
 }>;
 type Response = DispatchNodeResultType<{
   [NodeOutputKeyEnum.answerText]: string;
   [NodeOutputKeyEnum.history]: ChatItemType[];
 }>;
 
-export const dispatchAppRequest = async (props: Props): Promise<Response> => {
+export const dispatchRunAppNode = async (props: Props): Promise<Response> => {
   const {
-    res,
-    teamId,
-    stream,
-    detail,
+    app: workflowApp,
     histories,
-    inputFiles,
-    params: { userChatInput, history, app }
+    query,
+    node: { pluginId },
+    workflowStreamResponse,
+    params,
+    variables
   } = props;
-  let start = Date.now();
 
+  const { userChatInput, history, ...childrenAppVariables } = params;
   if (!userChatInput) {
     return Promise.reject('Input is empty');
   }
+  if (!pluginId) {
+    return Promise.reject('pluginId is empty');
+  }
 
-  const appData = await MongoApp.findOne({
-    _id: app.id,
-    teamId
+  // Auth the app by tmbId(Not the user, but the workflow user)
+  const { app: appData } = await authAppByTmbId({
+    appId: pluginId,
+    tmbId: workflowApp.tmbId,
+    per: ReadPermissionVal
   });
 
-  if (!appData) {
-    return Promise.reject('App not found');
-  }
-
-  if (stream) {
-    responseWrite({
-      res,
-      event: detail ? SseResponseEventEnum.answer : undefined,
-      data: textAdaptGptResponse({
-        text: '\n'
-      })
-    });
-  }
+  // Auto line
+  workflowStreamResponse?.({
+    event: SseResponseEventEnum.answer,
+    data: textAdaptGptResponse({
+      text: '\n'
+    })
+  });
 
   const chatHistories = getHistories(history, histories);
+  const { files } = chatValue2RuntimePrompt(query);
 
-  const { flowResponses, flowUsages, assistantResponses } = await dispatchWorkFlowV1({
+  // Concat variables
+  const systemVariables = filterSystemVariables(variables);
+  const childrenRunVariables = {
+    ...systemVariables,
+    ...childrenAppVariables
+  };
+
+  const { flowResponses, flowUsages, assistantResponses } = await dispatchWorkFlow({
     ...props,
-    appId: app.id,
-    modules: setEntryEntries(appData.modules),
-    runtimeModules: undefined, // must reset
+    app: appData,
+    runtimeNodes: storeNodes2RuntimeNodes(
+      appData.modules,
+      getWorkflowEntryNodeIds(appData.modules)
+    ),
+    runtimeEdges: initWorkflowEdgeStatus(appData.edges),
     histories: chatHistories,
-    inputFiles,
-    startParams: {
-      userChatInput
-    }
+    query: runtimePrompt2ChatsValue({
+      files,
+      text: userChatInput
+    }),
+    variables: childrenRunVariables
   });
 
   const completeMessages = chatHistories.concat([
     {
       obj: ChatRoleEnum.Human,
-      value: runtimePrompt2ChatsValue({
-        files: inputFiles,
-        text: userChatInput
-      })
+      value: query
     },
     {
       obj: ChatRoleEnum.AI,

packages/service/core/workflow/dispatch/agent/runTool/functionCall.ts

@@ -11,18 +11,14 @@ import {
   ChatCompletionAssistantMessageParam
 } from '@fastgpt/global/core/ai/type.d';
 import { NextApiResponse } from 'next';
-import {
-  responseWrite,
-  responseWriteController,
-  responseWriteNodeStatus
-} from '../../../../../common/response';
+import { responseWriteController } from '../../../../../common/response';
 import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
 import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
 import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
 import { dispatchWorkFlow } from '../../index';
 import { DispatchToolModuleProps, RunToolResponse, ToolNodeItemType } from './type.d';
 import json5 from 'json5';
-import { DispatchFlowResponse } from '../../type';
+import { DispatchFlowResponse, WorkflowResponseType } from '../../type';
 import { countGptMessagesTokens } from '../../../../../common/string/tiktoken/index';
 import { getNanoid, sliceStrStartEnd } from '@fastgpt/global/common/string/tools';
 import { AIChatItemType } from '@fastgpt/global/core/chat/type';
@@ -50,9 +46,9 @@ export const runToolWithFunctionCall = async (
     res,
     requestOrigin,
     runtimeNodes,
-    detail = false,
     node,
     stream,
+    workflowStreamResponse,
     params: { temperature = 0, maxToken = 4000, aiChatVision }
   } = props;
   const assistantResponses = response?.assistantResponses || [];
@@ -143,9 +139,9 @@ export const runToolWithFunctionCall = async (
     if (res && stream) {
       return streamResponse({
         res,
-        detail,
         toolNodes,
-        stream: aiResponse
+        stream: aiResponse,
+        workflowStreamResponse
       });
     } else {
       const result = aiResponse as ChatCompletion;
@@ -216,21 +212,18 @@ export const runToolWithFunctionCall = async (
           content: stringToolResponse
         };
 
-        if (stream && detail) {
-          responseWrite({
-            res,
-            event: SseResponseEventEnum.toolResponse,
-            data: JSON.stringify({
-              tool: {
-                id: tool.id,
-                toolName: '',
-                toolAvatar: '',
-                params: '',
-                response: sliceStrStartEnd(stringToolResponse, 500, 500)
-              }
-            })
-          });
-        }
+        workflowStreamResponse?.({
+          event: SseResponseEventEnum.toolResponse,
+          data: {
+            tool: {
+              id: tool.id,
+              toolName: '',
+              toolAvatar: '',
+              params: '',
+              response: sliceStrStartEnd(stringToolResponse, 500, 500)
+            }
+          }
+        });
 
         return {
           toolRunResponse,
@@ -260,10 +253,14 @@ export const runToolWithFunctionCall = async (
     ];
     // console.log(tokens, 'tool');
 
-    if (stream && detail) {
-      responseWriteNodeStatus({
-        res,
-        name: node.name
+    // Run tool status
+    if (node.showStatus) {
+      workflowStreamResponse?.({
+        event: SseResponseEventEnum.flowNodeStatus,
+        data: {
+          status: 'running',
+          name: node.name
+        }
       });
     }
 
@@ -337,14 +334,14 @@ export const runToolWithFunctionCall = async (
 
 async function streamResponse({
   res,
-  detail,
   toolNodes,
-  stream
+  stream,
+  workflowStreamResponse
 }: {
   res: NextApiResponse;
-  detail: boolean;
   toolNodes: ToolNodeItemType[];
   stream: StreamChatType;
+  workflowStreamResponse?: WorkflowResponseType;
 }) {
   const write = responseWriteController({
     res,
@@ -367,9 +364,9 @@ async function streamResponse({
       const content = responseChoice?.content || '';
       textAnswer += content;
 
-      responseWrite({
+      workflowStreamResponse?.({
         write,
-        event: detail ? SseResponseEventEnum.answer : undefined,
+        event: SseResponseEventEnum.answer,
         data: textAdaptGptResponse({
           text: content
         })
@@ -397,22 +394,20 @@ async function streamResponse({
             toolAvatar: toolNode.avatar
           });
 
-          if (detail) {
-            responseWrite({
-              write,
-              event: SseResponseEventEnum.toolCall,
-              data: JSON.stringify({
-                tool: {
-                  id: functionId,
-                  toolName: toolNode.name,
-                  toolAvatar: toolNode.avatar,
-                  functionName: functionCall.name,
-                  params: functionCall.arguments,
-                  response: ''
-                }
-              })
-            });
-          }
+          workflowStreamResponse?.({
+            write,
+            event: SseResponseEventEnum.toolCall,
+            data: {
+              tool: {
+                id: functionId,
+                toolName: toolNode.name,
+                toolAvatar: toolNode.avatar,
+                functionName: functionCall.name,
+                params: functionCall.arguments,
+                response: ''
+              }
+            }
+          });
         }
 
         continue;
@@ -424,21 +419,19 @@ async function streamResponse({
       if (currentTool) {
         currentTool.arguments += arg;
 
-        if (detail) {
-          responseWrite({
-            write,
-            event: SseResponseEventEnum.toolParams,
-            data: JSON.stringify({
-              tool: {
-                id: functionId,
-                toolName: '',
-                toolAvatar: '',
-                params: arg,
-                response: ''
-              }
-            })
-          });
-        }
+        workflowStreamResponse?.({
+          write,
+          event: SseResponseEventEnum.toolParams,
+          data: {
+            tool: {
+              id: functionId,
+              toolName: '',
+              toolAvatar: '',
+              params: arg,
+              response: ''
+            }
+          }
+        });
       }
     }
   }

packages/service/core/workflow/dispatch/agent/runTool/promptCall.ts

@@ -8,11 +8,7 @@ import {
   ChatCompletionAssistantMessageParam
 } from '@fastgpt/global/core/ai/type';
 import { NextApiResponse } from 'next';
-import {
-  responseWrite,
-  responseWriteController,
-  responseWriteNodeStatus
-} from '../../../../../common/response';
+import { responseWriteController } from '../../../../../common/response';
 import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
 import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
 import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
@@ -30,6 +26,7 @@ import { AIChatItemType } from '@fastgpt/global/core/chat/type';
 import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
 import { updateToolInputValue } from './utils';
 import { computedMaxToken, computedTemperature } from '../../../../ai/utils';
+import { WorkflowResponseType } from '../../type';
 
 type FunctionCallCompletion = {
   id: string;
@@ -56,9 +53,9 @@ export const runToolWithPromptCall = async (
     res,
     requestOrigin,
     runtimeNodes,
-    detail = false,
     node,
     stream,
+    workflowStreamResponse,
     params: { temperature = 0, maxToken = 4000, aiChatVision }
   } = props;
   const assistantResponses = response?.assistantResponses || [];
@@ -143,9 +140,9 @@ export const runToolWithPromptCall = async (
     if (res && stream) {
       const { answer } = await streamResponse({
         res,
-        detail,
         toolNodes,
-        stream: aiResponse
+        stream: aiResponse,
+        workflowStreamResponse
       });
 
       return answer;
@@ -159,9 +156,8 @@ export const runToolWithPromptCall = async (
   const { answer: replaceAnswer, toolJson } = parseAnswer(answer);
   // No tools
   if (!toolJson) {
-    if (replaceAnswer === ERROR_TEXT && stream && detail) {
-      responseWrite({
-        res,
+    if (replaceAnswer === ERROR_TEXT) {
+      workflowStreamResponse?.({
         event: SseResponseEventEnum.answer,
         data: textAdaptGptResponse({
           text: replaceAnswer
@@ -206,22 +202,19 @@ export const runToolWithPromptCall = async (
     })();
 
     // SSE response to client
-    if (stream && detail) {
-      responseWrite({
-        res,
-        event: SseResponseEventEnum.toolCall,
-        data: JSON.stringify({
-          tool: {
-            id: toolJson.id,
-            toolName: toolNode.name,
-            toolAvatar: toolNode.avatar,
-            functionName: toolJson.name,
-            params: toolJson.arguments,
-            response: ''
-          }
-        })
-      });
-    }
+    workflowStreamResponse?.({
+      event: SseResponseEventEnum.toolCall,
+      data: {
+        tool: {
+          id: toolJson.id,
+          toolName: toolNode.name,
+          toolAvatar: toolNode.avatar,
+          functionName: toolJson.name,
+          params: toolJson.arguments,
+          response: ''
+        }
+      }
+    });
 
     const moduleRunResponse = await dispatchWorkFlow({
       ...props,
@@ -245,21 +238,18 @@ export const runToolWithPromptCall = async (
       return moduleRunResponse.toolResponses ? String(moduleRunResponse.toolResponses) : 'none';
     })();
 
-    if (stream && detail) {
-      responseWrite({
-        res,
-        event: SseResponseEventEnum.toolResponse,
-        data: JSON.stringify({
-          tool: {
-            id: toolJson.id,
-            toolName: '',
-            toolAvatar: '',
-            params: '',
-            response: sliceStrStartEnd(stringToolResponse, 500, 500)
-          }
-        })
-      });
-    }
+    workflowStreamResponse?.({
+      event: SseResponseEventEnum.toolResponse,
+      data: {
+        tool: {
+          id: toolJson.id,
+          toolName: '',
+          toolAvatar: '',
+          params: '',
+          response: sliceStrStartEnd(stringToolResponse, 500, 500)
+        }
+      }
+    });
 
     return {
       moduleRunResponse,
@@ -267,10 +257,14 @@ export const runToolWithPromptCall = async (
     };
   })();
 
-  if (stream && detail) {
-    responseWriteNodeStatus({
-      res,
-      name: node.name
+  // Run tool status
+  if (node.showStatus) {
+    workflowStreamResponse?.({
+      event: SseResponseEventEnum.flowNodeStatus,
+      data: {
+        status: 'running',
+        name: node.name
+      }
     });
   }
 
@@ -340,13 +334,13 @@ ANSWER: `;
 
 async function streamResponse({
   res,
-  detail,
-  stream
+  stream,
+  workflowStreamResponse
 }: {
   res: NextApiResponse;
-  detail: boolean;
   toolNodes: ToolNodeItemType[];
   stream: StreamChatType;
+  workflowStreamResponse?: WorkflowResponseType;
 }) {
   const write = responseWriteController({
     res,
@@ -365,14 +359,14 @@ async function streamResponse({
     const responseChoice = part.choices?.[0]?.delta;
     // console.log(responseChoice, '---===');
 
-    if (responseChoice.content) {
+    if (responseChoice?.content) {
       const content = responseChoice?.content || '';
       textAnswer += content;
 
       if (startResponseWrite) {
-        responseWrite({
+        workflowStreamResponse?.({
           write,
-          event: detail ? SseResponseEventEnum.answer : undefined,
+          event: SseResponseEventEnum.answer,
           data: textAdaptGptResponse({
             text: content
           })
@@ -384,9 +378,9 @@ async function streamResponse({
           // find first : index
           const firstIndex = textAnswer.indexOf(':');
           textAnswer = textAnswer.substring(firstIndex + 1).trim();
-          responseWrite({
+          workflowStreamResponse?.({
             write,
-            event: detail ? SseResponseEventEnum.answer : undefined,
+            event: SseResponseEventEnum.answer,
             data: textAdaptGptResponse({
               text: textAnswer
             })

packages/service/core/workflow/dispatch/agent/runTool/toolChoice.ts

@@ -12,24 +12,21 @@ import {
   ChatCompletionAssistantMessageParam
 } from '@fastgpt/global/core/ai/type';
 import { NextApiResponse } from 'next';
-import {
-  responseWrite,
-  responseWriteController,
-  responseWriteNodeStatus
-} from '../../../../../common/response';
+import { responseWriteController } from '../../../../../common/response';
 import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
 import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
 import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
 import { dispatchWorkFlow } from '../../index';
 import { DispatchToolModuleProps, RunToolResponse, ToolNodeItemType } from './type.d';
 import json5 from 'json5';
-import { DispatchFlowResponse } from '../../type';
+import { DispatchFlowResponse, WorkflowResponseType } from '../../type';
 import { countGptMessagesTokens } from '../../../../../common/string/tiktoken/index';
 import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
 import { AIChatItemType } from '@fastgpt/global/core/chat/type';
 import { updateToolInputValue } from './utils';
 import { computedMaxToken, computedTemperature } from '../../../../ai/utils';
 import { sliceStrStartEnd } from '@fastgpt/global/common/string/tools';
+import { addLog } from '../../../../../common/system/log';
 
 type ToolRunResponseType = {
   toolRunResponse: DispatchFlowResponse;
@@ -58,9 +55,9 @@ export const runToolWithToolChoice = async (
     res,
     requestOrigin,
     runtimeNodes,
-    detail = false,
     node,
     stream,
+    workflowStreamResponse,
     params: { temperature = 0, maxToken = 4000, aiChatVision }
   } = props;
   const assistantResponses = response?.assistantResponses || [];
@@ -145,91 +142,91 @@ export const runToolWithToolChoice = async (
   const ai = getAIApi({
     timeout: 480000
   });
-  const aiResponse = await ai.chat.completions.create(requestBody, {
-    headers: {
-      Accept: 'application/json, text/plain, */*'
-    }
-  });
 
-  const { answer, toolCalls } = await (async () => {
-    if (res && stream) {
-      return streamResponse({
-        res,
-        detail,
-        toolNodes,
-        stream: aiResponse
-      });
-    } else {
-      const result = aiResponse as ChatCompletion;
-      const calls = result.choices?.[0]?.message?.tool_calls || [];
+  try {
+    const aiResponse = await ai.chat.completions.create(requestBody, {
+      headers: {
+        Accept: 'application/json, text/plain, */*'
+      }
+    });
 
-      // 加上name和avatar
-      const toolCalls = calls.map((tool) => {
-        const toolNode = toolNodes.find((item) => item.nodeId === tool.function?.name);
-        return {
-          ...tool,
-          toolName: toolNode?.name || '',
-          toolAvatar: toolNode?.avatar || ''
-        };
-      });
+    const { answer, toolCalls } = await (async () => {
+      if (res && stream) {
+        return streamResponse({
+          res,
+          workflowStreamResponse,
+          toolNodes,
+          stream: aiResponse
+        });
+      } else {
+        const result = aiResponse as ChatCompletion;
+        const calls = result.choices?.[0]?.message?.tool_calls || [];
 
-      return {
-        answer: result.choices?.[0]?.message?.content || '',
-        toolCalls: toolCalls
-      };
-    }
-  })();
-
-  // Run the selected tool by LLM.
-  const toolsRunResponse = (
-    await Promise.all(
-      toolCalls.map(async (tool) => {
-        const toolNode = toolNodes.find((item) => item.nodeId === tool.function?.name);
-
-        if (!toolNode) return;
-
-        const startParams = (() => {
-          try {
-            return json5.parse(tool.function.arguments);
-          } catch (error) {
-            return {};
-          }
-        })();
-
-        const toolRunResponse = await dispatchWorkFlow({
-          ...props,
-          isToolCall: true,
-          runtimeNodes: runtimeNodes.map((item) =>
-            item.nodeId === toolNode.nodeId
-              ? {
-                  ...item,
-                  isEntry: true,
-                  inputs: updateToolInputValue({ params: startParams, inputs: item.inputs })
-                }
-              : item
-          )
+        // 加上name和avatar
+        const toolCalls = calls.map((tool) => {
+          const toolNode = toolNodes.find((item) => item.nodeId === tool.function?.name);
+          return {
+            ...tool,
+            toolName: toolNode?.name || '',
+            toolAvatar: toolNode?.avatar || ''
+          };
         });
 
-        const stringToolResponse = (() => {
-          if (typeof toolRunResponse.toolResponses === 'object') {
-            return JSON.stringify(toolRunResponse.toolResponses, null, 2);
-          }
-
-          return toolRunResponse.toolResponses ? String(toolRunResponse.toolResponses) : 'none';
-        })();
-
-        const toolMsgParams: ChatCompletionToolMessageParam = {
-          tool_call_id: tool.id,
-          role: ChatCompletionRequestMessageRoleEnum.Tool,
-          name: tool.function.name,
-          content: stringToolResponse
+        return {
+          answer: result.choices?.[0]?.message?.content || '',
+          toolCalls: toolCalls
         };
+      }
+    })();
 
-        if (stream && detail) {
-          responseWrite({
-            res,
+    // Run the selected tool by LLM.
+    const toolsRunResponse = (
+      await Promise.all(
+        toolCalls.map(async (tool) => {
+          const toolNode = toolNodes.find((item) => item.nodeId === tool.function?.name);
+
+          if (!toolNode) return;
+
+          const startParams = (() => {
+            try {
+              return json5.parse(tool.function.arguments);
+            } catch (error) {
+              return {};
+            }
+          })();
+
+          const toolRunResponse = await dispatchWorkFlow({
+            ...props,
+            isToolCall: true,
+            runtimeNodes: runtimeNodes.map((item) =>
+              item.nodeId === toolNode.nodeId
+                ? {
+                    ...item,
+                    isEntry: true,
+                    inputs: updateToolInputValue({ params: startParams, inputs: item.inputs })
+                  }
+                : item
+            )
+          });
+
+          const stringToolResponse = (() => {
+            if (typeof toolRunResponse.toolResponses === 'object') {
+              return JSON.stringify(toolRunResponse.toolResponses, null, 2);
+            }
+
+            return toolRunResponse.toolResponses ? String(toolRunResponse.toolResponses) : 'none';
+          })();
+
+          const toolMsgParams: ChatCompletionToolMessageParam = {
+            tool_call_id: tool.id,
+            role: ChatCompletionRequestMessageRoleEnum.Tool,
+            name: tool.function.name,
+            content: stringToolResponse
+          };
+
+          workflowStreamResponse?.({
             event: SseResponseEventEnum.toolResponse,
-            data: JSON.stringify({
+            data: {
               tool: {
                 id: tool.id,
                 toolName: '',
@@ -237,123 +234,132 @@ export const runToolWithToolChoice = async (
                 params: '',
                 response: sliceStrStartEnd(stringToolResponse, 500, 500)
               }
-            })
+            }
           });
-        }
 
+          return {
+            toolRunResponse,
+            toolMsgParams
+          };
+        })
+      )
+    ).filter(Boolean) as ToolRunResponseType;
+
+    const flatToolsResponseData = toolsRunResponse.map((item) => item.toolRunResponse).flat();
+    if (toolCalls.length > 0 && !res?.closed) {
+      // Run the tool, combine its results, and perform another round of AI calls
+      const assistantToolMsgParams: ChatCompletionAssistantToolParam = {
+        role: ChatCompletionRequestMessageRoleEnum.Assistant,
+        tool_calls: toolCalls
+      };
+      const concatToolMessages = [
+        ...requestMessages,
+        assistantToolMsgParams
+      ] as ChatCompletionMessageParam[];
+      const tokens = await countGptMessagesTokens(concatToolMessages, tools);
+      const completeMessages = [
+        ...concatToolMessages,
+        ...toolsRunResponse.map((item) => item?.toolMsgParams)
+      ];
+
+      // console.log(tokens, 'tool');
+
+      // Run tool status
+      if (node.showStatus) {
+        workflowStreamResponse?.({
+          event: SseResponseEventEnum.flowNodeStatus,
+          data: {
+            status: 'running',
+            name: node.name
+          }
+        });
+      }
+
+      // tool assistant
+      const toolAssistants = toolsRunResponse
+        .map((item) => {
+          const assistantResponses = item.toolRunResponse.assistantResponses || [];
+          return assistantResponses;
+        })
+        .flat();
+
+      // tool node assistant
+      const adaptChatMessages = GPTMessages2Chats(completeMessages);
+      const toolNodeAssistant = adaptChatMessages.pop() as AIChatItemType;
+
+      const toolNodeAssistants = [
+        ...assistantResponses,
+        ...toolAssistants,
+        ...toolNodeAssistant.value
+      ];
+
+      // concat tool responses
+      const dispatchFlowResponse = response
+        ? response.dispatchFlowResponse.concat(flatToolsResponseData)
+        : flatToolsResponseData;
+
+      /* check stop signal */
+      const hasStopSignal = flatToolsResponseData.some(
+        (item) => !!item.flowResponses?.find((item) => item.toolStop)
+      );
+      if (hasStopSignal) {
         return {
-          toolRunResponse,
-          toolMsgParams
+          dispatchFlowResponse,
+          totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
+          completeMessages,
+          assistantResponses: toolNodeAssistants
         };
-      })
-    )
-  ).filter(Boolean) as ToolRunResponseType;
+      }
 
-  const flatToolsResponseData = toolsRunResponse.map((item) => item.toolRunResponse).flat();
-  if (toolCalls.length > 0 && !res?.closed) {
-    // Run the tool, combine its results, and perform another round of AI calls
-    const assistantToolMsgParams: ChatCompletionAssistantToolParam = {
-      role: ChatCompletionRequestMessageRoleEnum.Assistant,
-      tool_calls: toolCalls
-    };
-    const concatToolMessages = [
-      ...requestMessages,
-      assistantToolMsgParams
-    ] as ChatCompletionMessageParam[];
-    const tokens = await countGptMessagesTokens(concatToolMessages, tools);
-    const completeMessages = [
-      ...concatToolMessages,
-      ...toolsRunResponse.map((item) => item?.toolMsgParams)
-    ];
+      return runToolWithToolChoice(
+        {
+          ...props,
+          messages: completeMessages
+        },
+        {
+          dispatchFlowResponse,
+          totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
+          assistantResponses: toolNodeAssistants
+        }
+      );
+    } else {
+      // No tool is invoked, indicating that the process is over
+      const gptAssistantResponse: ChatCompletionAssistantMessageParam = {
+        role: ChatCompletionRequestMessageRoleEnum.Assistant,
+        content: answer
+      };
+      const completeMessages = filterMessages.concat(gptAssistantResponse);
+      const tokens = await countGptMessagesTokens(completeMessages, tools);
+      // console.log(tokens, 'response token');
 
-    // console.log(tokens, 'tool');
+      // concat tool assistant
+      const toolNodeAssistant = GPTMessages2Chats([gptAssistantResponse])[0] as AIChatItemType;
 
-    if (stream && detail) {
-      responseWriteNodeStatus({
-        res,
-        name: node.name
-      });
-    }
-
-    // tool assistant
-    const toolAssistants = toolsRunResponse
-      .map((item) => {
-        const assistantResponses = item.toolRunResponse.assistantResponses || [];
-        return assistantResponses;
-      })
-      .flat();
-
-    // tool node assistant
-    const adaptChatMessages = GPTMessages2Chats(completeMessages);
-    const toolNodeAssistant = adaptChatMessages.pop() as AIChatItemType;
-
-    const toolNodeAssistants = [
-      ...assistantResponses,
-      ...toolAssistants,
-      ...toolNodeAssistant.value
-    ];
-
-    // concat tool responses
-    const dispatchFlowResponse = response
-      ? response.dispatchFlowResponse.concat(flatToolsResponseData)
-      : flatToolsResponseData;
-
-    /* check stop signal */
-    const hasStopSignal = flatToolsResponseData.some(
-      (item) => !!item.flowResponses?.find((item) => item.toolStop)
-    );
-    if (hasStopSignal) {
       return {
-        dispatchFlowResponse,
+        dispatchFlowResponse: response?.dispatchFlowResponse || [],
         totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
         completeMessages,
-        assistantResponses: toolNodeAssistants
+        assistantResponses: [...assistantResponses, ...toolNodeAssistant.value]
       };
     }
-
-    return runToolWithToolChoice(
-      {
-        ...props,
-        messages: completeMessages
-      },
-      {
-        dispatchFlowResponse,
-        totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
-        assistantResponses: toolNodeAssistants
-      }
-    );
-  } else {
-    // No tool is invoked, indicating that the process is over
-    const gptAssistantResponse: ChatCompletionAssistantMessageParam = {
-      role: ChatCompletionRequestMessageRoleEnum.Assistant,
-      content: answer
-    };
-    const completeMessages = filterMessages.concat(gptAssistantResponse);
-    const tokens = await countGptMessagesTokens(completeMessages, tools);
-    // console.log(tokens, 'response token');
-
-    // concat tool assistant
-    const toolNodeAssistant = GPTMessages2Chats([gptAssistantResponse])[0] as AIChatItemType;
-
-    return {
-      dispatchFlowResponse: response?.dispatchFlowResponse || [],
-      totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
-      completeMessages,
-      assistantResponses: [...assistantResponses, ...toolNodeAssistant.value]
-    };
+  } catch (error) {
+    addLog.warn(`LLM response error`, {
+      requestBody
+    });
+    return Promise.reject(error);
   }
 };
 
 async function streamResponse({
   res,
-  detail,
   toolNodes,
-  stream
+  stream,
+  workflowStreamResponse
 }: {
   res: NextApiResponse;
-  detail: boolean;
   toolNodes: ToolNodeItemType[];
   stream: StreamChatType;
+  workflowStreamResponse?: WorkflowResponseType;
 }) {
   const write = responseWriteController({
     res,
@@ -375,9 +381,9 @@ async function streamResponse({
       const content = responseChoice.content || '';
       textAnswer += content;
 
-      responseWrite({
+      workflowStreamResponse?.({
         write,
-        event: detail ? SseResponseEventEnum.answer : undefined,
+        event: SseResponseEventEnum.answer,
         data: textAdaptGptResponse({
           text: content
         })
@@ -405,22 +411,19 @@ async function streamResponse({
               toolAvatar: toolNode.avatar
             });
 
-            if (detail) {
-              responseWrite({
-                write,
-                event: SseResponseEventEnum.toolCall,
-                data: JSON.stringify({
-                  tool: {
-                    id: toolCall.id,
-                    toolName: toolNode.name,
-                    toolAvatar: toolNode.avatar,
-                    functionName: toolCall.function.name,
-                    params: toolCall.function.arguments,
-                    response: ''
-                  }
-                })
-              });
-            }
+            workflowStreamResponse?.({
+              event: SseResponseEventEnum.toolCall,
+              data: {
+                tool: {
+                  id: toolCall.id,
+                  toolName: toolNode.name,
+                  toolAvatar: toolNode.avatar,
+                  functionName: toolCall.function.name,
+                  params: toolCall.function.arguments,
+                  response: ''
+                }
+              }
+            });
 
             continue;
           }
@@ -431,27 +434,25 @@ async function streamResponse({
       }
 
       /* arg 插入最后一个工具的参数里 */
-      const arg: string = toolCall?.function?.arguments;
+      const arg: string = toolCall?.function?.arguments ?? '';
       const currentTool = toolCalls[toolCalls.length - 1];
 
       if (currentTool) {
         currentTool.function.arguments += arg;
 
-        if (detail) {
-          responseWrite({
-            write,
-            event: SseResponseEventEnum.toolParams,
-            data: JSON.stringify({
-              tool: {
-                id: currentTool.id,
-                toolName: '',
-                toolAvatar: '',
-                params: arg,
-                response: ''
-              }
-            })
-          });
-        }
+        workflowStreamResponse?.({
+          write,
+          event: SseResponseEventEnum.toolParams,
+          data: {
+            tool: {
+              id: currentTool.id,
+              toolName: '',
+              toolAvatar: '',
+              params: arg,
+              response: ''
+            }
+          }
+        });
       }
     }
   }

packages/service/core/workflow/dispatch/chat/oneapi.ts

@@ -31,7 +31,7 @@ import {
 import type { AIChatNodeProps } from '@fastgpt/global/core/workflow/runtime/type.d';
 import { replaceVariable } from '@fastgpt/global/common/string/tools';
 import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { responseWrite, responseWriteController } from '../../../../common/response';
+import { responseWriteController } from '../../../../common/response';
 import { getLLMModel, ModelTypeEnum } from '../../../ai/model';
 import type { SearchDataResponseItemType } from '@fastgpt/global/core/dataset/type';
 import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
@@ -41,6 +41,7 @@ import { filterSearchResultsByMaxChars } from '../../utils';
 import { getHistoryPreview } from '@fastgpt/global/core/chat/utils';
 import { addLog } from '../../../../common/system/log';
 import { computedMaxToken, computedTemperature } from '../../../ai/utils';
+import { WorkflowResponseType } from '../type';
 
 export type ChatProps = ModuleDispatchProps<
   AIChatNodeProps & {
@@ -60,11 +61,11 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
     res,
     requestOrigin,
     stream = false,
-    detail = false,
     user,
     histories,
     node: { name },
     query,
+    workflowStreamResponse,
     params: {
       model,
       temperature = 0,
@@ -179,8 +180,8 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
         // sse response
         const { answer } = await streamResponse({
           res,
-          detail,
-          stream: response
+          stream: response,
+          workflowStreamResponse
         });
 
         if (!answer) {
@@ -340,12 +341,12 @@ async function getChatMessages({
 
 async function streamResponse({
   res,
-  detail,
-  stream
+  stream,
+  workflowStreamResponse
 }: {
   res: NextApiResponse;
-  detail: boolean;
   stream: StreamChatType;
+  workflowStreamResponse?: WorkflowResponseType;
 }) {
   const write = responseWriteController({
     res,
@@ -360,9 +361,9 @@ async function streamResponse({
     const content = part.choices?.[0]?.delta?.content || '';
     answer += content;
 
-    responseWrite({
+    workflowStreamResponse?.({
       write,
-      event: detail ? SseResponseEventEnum.answer : undefined,
+      event: SseResponseEventEnum.answer,
       data: textAdaptGptResponse({
         text: content
       })

packages/service/core/workflow/dispatch/index.ts

@@ -1,4 +1,3 @@
-import { NextApiResponse } from 'next';
 import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
 import {
   DispatchNodeResponseKeyEnum,
@@ -20,10 +19,9 @@ import {
   FlowNodeInputTypeEnum,
   FlowNodeTypeEnum
 } from '@fastgpt/global/core/workflow/node/constant';
-import { replaceVariable } from '@fastgpt/global/common/string/tools';
-import { responseWrite, responseWriteNodeStatus } from '../../../common/response';
+import { getNanoid, replaceVariable } from '@fastgpt/global/common/string/tools';
 import { getSystemTime } from '@fastgpt/global/common/time/timezone';
-import { replaceVariableLabel } from '@fastgpt/global/core/workflow/utils';
+import { replaceEditorVariable } from '@fastgpt/global/core/workflow/utils';
 
 import { dispatchWorkflowStart } from './init/workflowStart';
 import { dispatchChatCompletion } from './chat/oneapi';
@@ -33,7 +31,7 @@ import { dispatchAnswer } from './tools/answer';
 import { dispatchClassifyQuestion } from './agent/classifyQuestion';
 import { dispatchContentExtract } from './agent/extract';
 import { dispatchHttp468Request } from './tools/http468';
-import { dispatchAppRequest } from './tools/runApp';
+import { dispatchAppRequest } from './abandoned/runApp';
 import { dispatchQueryExtension } from './tools/queryExternsion';
 import { dispatchRunPlugin } from './plugin/run';
 import { dispatchPluginInput } from './plugin/runInput';
@@ -41,8 +39,7 @@ import { dispatchPluginOutput } from './plugin/runOutput';
 import { removeSystemVariable, valueTypeFormat } from './utils';
 import {
   filterWorkflowEdges,
-  checkNodeRunStatus,
-  getLastInteractiveValue
+  checkNodeRunStatus
 } from '@fastgpt/global/core/workflow/runtime/utils';
 import { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
 import { dispatchRunTools } from './agent/runTool/index';
@@ -62,12 +59,11 @@ import { dispatchTextEditor } from './tools/textEditor';
 import { dispatchCustomFeedback } from './tools/customFeedback';
 import { dispatchReadFiles } from './tools/readFiles';
 import { dispatchUserSelect } from './interactive/userSelect';
-import { FlowNodeOutputItemType } from '@fastgpt/global/core/workflow/type/io';
 import {
   InteractiveNodeResponseItemType,
-  UserInteractiveType,
   UserSelectInteractive
 } from '@fastgpt/global/core/workflow/template/system/userSelect/type';
+import { dispatchRunAppNode } from './agent/runApp';
 
 const callbackMap: Record<FlowNodeTypeEnum, Function> = {
   [FlowNodeTypeEnum.workflowStart]: dispatchWorkflowStart,
@@ -78,7 +74,7 @@ const callbackMap: Record<FlowNodeTypeEnum, Function> = {
   [FlowNodeTypeEnum.classifyQuestion]: dispatchClassifyQuestion,
   [FlowNodeTypeEnum.contentExtract]: dispatchContentExtract,
   [FlowNodeTypeEnum.httpRequest468]: dispatchHttp468Request,
-  [FlowNodeTypeEnum.runApp]: dispatchAppRequest,
+  [FlowNodeTypeEnum.appModule]: dispatchRunAppNode,
   [FlowNodeTypeEnum.pluginModule]: dispatchRunPlugin,
   [FlowNodeTypeEnum.pluginInput]: dispatchPluginInput,
   [FlowNodeTypeEnum.pluginOutput]: dispatchPluginOutput,
@@ -96,8 +92,11 @@ const callbackMap: Record<FlowNodeTypeEnum, Function> = {
 
   // none
   [FlowNodeTypeEnum.systemConfig]: dispatchSystemConfig,
+  [FlowNodeTypeEnum.pluginConfig]: () => Promise.resolve(),
   [FlowNodeTypeEnum.emptyNode]: () => Promise.resolve(),
-  [FlowNodeTypeEnum.globalVariable]: () => Promise.resolve()
+  [FlowNodeTypeEnum.globalVariable]: () => Promise.resolve(),
+
+  [FlowNodeTypeEnum.runApp]: dispatchAppRequest // abandoned
 };
 
 type Props = ChatDispatchProps & {
@@ -115,7 +114,6 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
     variables = {},
     user,
     stream = false,
-    detail = false,
     ...props
   } = data;
 
@@ -261,13 +259,10 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
       nodeOutputs
     };
 
-    if (stream && res) {
-      responseWrite({
-        res,
-        event: SseResponseEventEnum.interactive,
-        data: JSON.stringify({ interactive: interactiveResult })
-      });
-    }
+    props.workflowStreamResponse?.({
+      event: SseResponseEventEnum.interactive,
+      data: { interactive: interactiveResult }
+    });
 
     return {
       type: ChatItemValueTypeEnum.interactive,
@@ -317,8 +312,10 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
 
       const flat = result.flat().filter(Boolean) as unknown as {
         node: RuntimeNodeItemType;
+        runStatus: 'run' | 'skip';
         result: Record<string, any>;
       }[];
+      // If there are no running nodes, the workflow is complete
       if (flat.length === 0) return;
 
       // Update the node output at the end of the run and get the next nodes
@@ -375,7 +372,7 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
       let value = replaceVariable(input.value, variables);
 
       // replace {{$xx.xx$}} variables
-      value = replaceVariableLabel({
+      value = replaceEditorVariable({
         text: value,
         nodes: runtimeNodes,
         variables,
@@ -401,11 +398,13 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
   }
   async function nodeRunWithActive(node: RuntimeNodeItemType) {
     // push run status messages
-    if (res && stream && detail && node.showStatus) {
-      responseStatus({
-        res,
-        name: node.name,
-        status: 'running'
+    if (node.showStatus) {
+      props.workflowStreamResponse?.({
+        event: SseResponseEventEnum.flowNodeStatus,
+        data: {
+          status: 'running',
+          name: node.name
+        }
       });
     }
     const startTime = Date.now();
@@ -420,7 +419,6 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
       histories,
       user,
       stream,
-      detail,
       node,
       runtimeNodes,
       runtimeEdges,
@@ -440,6 +438,7 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
     const formatResponseData: ChatHistoryItemResType = (() => {
       if (!dispatchRes[DispatchNodeResponseKeyEnum.nodeResponse]) return undefined;
       return {
+        id: getNanoid(),
         nodeId: node.nodeId,
         moduleName: node.name,
         moduleType: node.flowNodeType,
@@ -459,6 +458,7 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
 
     return {
       node,
+      runStatus: 'run',
       result: {
         ...dispatchRes,
         [DispatchNodeResponseKeyEnum.nodeResponse]: formatResponseData
@@ -472,6 +472,7 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
 
     return {
       node,
+      runStatus: 'skip',
       result: {
         [DispatchNodeResponseKeyEnum.skipHandleId]: targetEdges.map((item) => item.sourceHandle)
       }
@@ -510,23 +511,6 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
   };
 }
 
-/* sse response modules staus */
-export function responseStatus({
-  res,
-  status,
-  name
-}: {
-  res: NextApiResponse;
-  status?: 'running' | 'finish';
-  name?: string;
-}) {
-  if (!name) return;
-  responseWriteNodeStatus({
-    res,
-    name
-  });
-}
-
 /* get system variable */
 export function getSystemVariable({
   user,

packages/service/core/workflow/dispatch/init/workflowStart.tsx

@@ -1,14 +1,18 @@
 import { chatValue2RuntimePrompt } from '@fastgpt/global/core/chat/adapt';
 import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
+import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
+import type {
+  DispatchNodeResultType,
+  ModuleDispatchProps
+} from '@fastgpt/global/core/workflow/runtime/type';
 
 export type UserChatInputProps = ModuleDispatchProps<{
   [NodeInputKeyEnum.userChatInput]: string;
 }>;
-type Response = {
+type Response = DispatchNodeResultType<{
   [NodeOutputKeyEnum.userChatInput]: string;
   [NodeOutputKeyEnum.userFiles]: string[];
-};
+}>;
 
 export const dispatchWorkflowStart = (props: Record<string, any>): Response => {
   const {
@@ -19,6 +23,7 @@ export const dispatchWorkflowStart = (props: Record<string, any>): Response => {
   const { text, files } = chatValue2RuntimePrompt(query);
 
   return {
+    [DispatchNodeResponseKeyEnum.nodeResponse]: {},
     [NodeInputKeyEnum.userChatInput]: text || userChatInput,
     [NodeOutputKeyEnum.userFiles]: files
       .map((item) => {

packages/service/core/workflow/dispatch/interactive/userSelect.ts

@@ -14,7 +14,6 @@ import type {
 } from '@fastgpt/global/core/workflow/template/system/userSelect/type';
 import { updateUserSelectedResult } from '../../../chat/controller';
 import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
-import { responseWrite } from '../../../../common/response';
 import { chatValue2RuntimePrompt } from '@fastgpt/global/core/chat/adapt';
 
 type Props = ModuleDispatchProps<{
@@ -29,10 +28,7 @@ type UserSelectResponse = DispatchNodeResultType<{
 
 export const dispatchUserSelect = async (props: Props): Promise<UserSelectResponse> => {
   const {
-    res,
-    detail,
-    histories,
-    stream,
+    workflowStreamResponse,
     app: { _id: appId },
     chatId,
     node: { nodeId, isEntry },
@@ -43,10 +39,9 @@ export const dispatchUserSelect = async (props: Props): Promise<UserSelectRespon
   // Interactive node is not the entry node, return interactive result
   if (!isEntry) {
     const answerText = description ? `\n${description}` : undefined;
-    if (res && stream && answerText) {
-      responseWrite({
-        res,
-        event: detail ? SseResponseEventEnum.fastAnswer : undefined,
+    if (answerText) {
+      workflowStreamResponse?.({
+        event: SseResponseEventEnum.fastAnswer,
         data: textAdaptGptResponse({
           text: answerText
         })

packages/service/core/workflow/dispatch/plugin/run.ts

@@ -9,10 +9,10 @@ import {
   storeNodes2RuntimeNodes
 } from '@fastgpt/global/core/workflow/runtime/utils';
 import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
-import { updateToolInputValue } from '../agent/runTool/utils';
 import { authPluginByTmbId } from '../../../../support/permission/app/auth';
 import { ReadPermissionVal } from '@fastgpt/global/support/permission/constant';
 import { computedPluginUsage } from '../../../app/plugin/utils';
+import { filterSystemVariables } from '../utils';
 
 type RunPluginProps = ModuleDispatchProps<{
   [key: string]: any;
@@ -25,7 +25,7 @@ export const dispatchRunPlugin = async (props: RunPluginProps): Promise<RunPlugi
     app: workflowApp,
     mode,
     teamId,
-    params: data
+    params: data // Plugin input
   } = props;
 
   if (!pluginId) {
@@ -41,32 +41,31 @@ export const dispatchRunPlugin = async (props: RunPluginProps): Promise<RunPlugi
 
   const plugin = await getPluginRuntimeById(pluginId);
 
-  // concat dynamic inputs
-  const inputModule = plugin.nodes.find(
-    (item) => item.flowNodeType === FlowNodeTypeEnum.pluginInput
-  );
-  if (!inputModule) return Promise.reject('Plugin error, It has no set input.');
+  const runtimeNodes = storeNodes2RuntimeNodes(
+    plugin.nodes,
+    getWorkflowEntryNodeIds(plugin.nodes)
+  ).map((node) => {
+    // Update plugin input value
+    if (node.flowNodeType === FlowNodeTypeEnum.pluginInput) {
+      return {
+        ...node,
+        showStatus: false,
+        inputs: node.inputs.map((input) => ({
+          ...input,
+          value: data[input.key] ?? input.value
+        }))
+      };
+    }
+    return {
+      ...node,
+      showStatus: false
+    };
+  });
 
   const { flowResponses, flowUsages, assistantResponses } = await dispatchWorkFlow({
     ...props,
-    runtimeNodes: storeNodes2RuntimeNodes(plugin.nodes, getWorkflowEntryNodeIds(plugin.nodes)).map(
-      (node) => {
-        if (node.flowNodeType === FlowNodeTypeEnum.pluginInput) {
-          return {
-            ...node,
-            showStatus: false,
-            inputs: updateToolInputValue({
-              inputs: node.inputs,
-              params: data
-            })
-          };
-        }
-        return {
-          ...node,
-          showStatus: false
-        };
-      }
-    ),
+    variables: filterSystemVariables(props.variables),
+    runtimeNodes,
     runtimeEdges: initWorkflowEdgeStatus(plugin.edges)
   });
 

packages/service/core/workflow/dispatch/tools/answer.ts

@@ -2,7 +2,6 @@ import {
   DispatchNodeResponseKeyEnum,
   SseResponseEventEnum
 } from '@fastgpt/global/core/workflow/runtime/constants';
-import { responseWrite } from '../../../../common/response';
 import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
 import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
 import { NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
@@ -16,24 +15,19 @@ export type AnswerResponse = DispatchNodeResultType<{
 
 export const dispatchAnswer = (props: Record<string, any>): AnswerResponse => {
   const {
-    res,
-    detail,
-    stream,
+    workflowStreamResponse,
     params: { text = '' }
   } = props as AnswerProps;
 
   const formatText = typeof text === 'string' ? text : JSON.stringify(text, null, 2);
   const responseText = `\n${formatText}`;
 
-  if (res && stream) {
-    responseWrite({
-      res,
-      event: detail ? SseResponseEventEnum.fastAnswer : undefined,
-      data: textAdaptGptResponse({
-        text: responseText
-      })
-    });
-  }
+  workflowStreamResponse?.({
+    event: SseResponseEventEnum.fastAnswer,
+    data: textAdaptGptResponse({
+      text: responseText
+    })
+  });
 
   return {
     [NodeOutputKeyEnum.answerText]: responseText,

packages/service/core/workflow/dispatch/tools/customFeedback.ts

@@ -6,7 +6,6 @@ import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/
 import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
 import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
 import { addCustomFeedbacks } from '../../../chat/controller';
-import { responseWrite } from '../../../../common/response';
 import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
 
 type Props = ModuleDispatchProps<{
@@ -16,12 +15,11 @@ type Response = DispatchNodeResultType<{}>;
 
 export const dispatchCustomFeedback = (props: Record<string, any>): Response => {
   const {
-    res,
     app: { _id: appId },
     chatId,
     responseChatItemId: chatItemId,
     stream,
-    detail,
+    workflowStreamResponse,
     params: { system_textareaInput: feedbackText = '' }
   } = props as Props;
 
@@ -36,9 +34,8 @@ export const dispatchCustomFeedback = (props: Record<string, any>): Response =>
 
   if (stream) {
     if (!chatId || !chatItemId) {
-      responseWrite({
-        res,
-        event: detail ? SseResponseEventEnum.fastAnswer : undefined,
+      workflowStreamResponse?.({
+        event: SseResponseEventEnum.fastAnswer,
         data: textAdaptGptResponse({
           text: `\n\n**自定义反馈成功: (仅调试模式下展示该内容)**: "${feedbackText}"\n\n`
         })

packages/service/core/workflow/dispatch/tools/http468.ts

@@ -14,7 +14,6 @@ import { SERVICE_LOCAL_HOST } from '../../../../common/system/tools';
 import { addLog } from '../../../../common/system/log';
 import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
 import { getErrText } from '@fastgpt/global/common/error/utils';
-import { responseWrite } from '../../../../common/response';
 import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
 import { getSystemPluginCb } from '../../../../../plugins/register';
 
@@ -43,15 +42,13 @@ const UNDEFINED_SIGN = 'UNDEFINED_SIGN';
 
 export const dispatchHttp468Request = async (props: HttpRequestProps): Promise<HttpResponse> => {
   let {
-    res,
-    detail,
     app: { _id: appId },
     chatId,
-    stream,
     responseChatItemId,
     variables,
     node: { outputs },
     histories,
+    workflowStreamResponse,
     params: {
       system_httpMethod: httpMethod = 'POST',
       system_httpReqUrl: httpReqUrl,
@@ -158,10 +155,9 @@ export const dispatchHttp468Request = async (props: HttpRequestProps): Promise<H
       results[key] = valueTypeFormat(formatResponse[key], output.valueType);
     }
 
-    if (stream && typeof formatResponse[NodeOutputKeyEnum.answerText] === 'string') {
-      responseWrite({
-        res,
-        event: detail ? SseResponseEventEnum.fastAnswer : undefined,
+    if (typeof formatResponse[NodeOutputKeyEnum.answerText] === 'string') {
+      workflowStreamResponse?.({
+        event: SseResponseEventEnum.fastAnswer,
         data: textAdaptGptResponse({
           text: formatResponse[NodeOutputKeyEnum.answerText]
         })

packages/service/core/workflow/dispatch/tools/readFiles.ts

@@ -73,25 +73,35 @@ export const dispatchReadFiles = async (props: Props): Promise<Response> => {
   // Concat fileUrlList and filesFromHistories; remove not supported files
   const parseUrlList = [...fileUrlList, ...filesFromHistories]
     .map((url) => {
-      // System file
-      if (url.startsWith('/') || (requestOrigin && url.startsWith(requestOrigin))) {
-        // Parse url, get filename query. Keep only documents that can be parsed
-        const parseUrl = new URL(url);
-        const filenameQuery = parseUrl.searchParams.get('filename');
-        if (filenameQuery) {
-          const extensionQuery = filenameQuery.split('.').pop()?.toLowerCase() || '';
-          if (!documentFileType.includes(extensionQuery)) {
-            return '';
+      try {
+        // Avoid "/api/xxx" file error.
+        const origin = requestOrigin ?? 'http://localhost:3000';
+
+        // Check is system upload file
+        if (url.startsWith('/') || (requestOrigin && url.startsWith(requestOrigin))) {
+          // Parse url, get filename query. Keep only documents that can be parsed
+          const parseUrl = new URL(url, origin);
+          const filenameQuery = parseUrl.searchParams.get('filename');
+
+          // Not document
+          if (filenameQuery) {
+            const extensionQuery = filenameQuery.split('.').pop()?.toLowerCase() || '';
+            if (!documentFileType.includes(extensionQuery)) {
+              return '';
+            }
+          }
+
+          //  Remove the origin(Make intranet requests directly)
+          if (requestOrigin && url.startsWith(requestOrigin)) {
+            url = url.replace(requestOrigin, '');
           }
         }
 
-        //  Remove the origin(Make intranet requests directly)
-        if (requestOrigin && url.startsWith(requestOrigin)) {
-          url = url.replace(requestOrigin, '');
-        }
+        return url;
+      } catch (error) {
+        console.log(error);
+        return '';
       }
-
-      return url;
     })
     .filter(Boolean)
     .slice(0, maxFiles);

packages/service/core/workflow/dispatch/tools/runIfElse.ts

@@ -13,7 +13,6 @@ import {
 import { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
 import { getElseIFLabel, getHandleId } from '@fastgpt/global/core/workflow/utils';
 import { getReferenceVariableValue } from '@fastgpt/global/core/workflow/runtime/utils';
-import { replaceRegChars } from '@fastgpt/global/common/string/tools';
 
 type Props = ModuleDispatchProps<{
   [NodeInputKeyEnum.condition]: IfElseConditionType;

packages/service/core/workflow/dispatch/tools/runUpdateVar.ts

@@ -8,7 +8,7 @@ import { getReferenceVariableValue } from '@fastgpt/global/core/workflow/runtime
 import { TUpdateListItem } from '@fastgpt/global/core/workflow/template/system/variableUpdate/type';
 import { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
 import { removeSystemVariable, valueTypeFormat } from '../utils';
-import { responseWrite } from '../../../../common/response';
+import { replaceEditorVariable } from '@fastgpt/global/core/workflow/utils';
 
 type Props = ModuleDispatchProps<{
   [NodeInputKeyEnum.updateList]: TUpdateListItem[];
@@ -16,20 +16,29 @@ type Props = ModuleDispatchProps<{
 type Response = DispatchNodeResultType<{}>;
 
 export const dispatchUpdateVariable = async (props: Props): Promise<Response> => {
-  const { res, detail, stream, params, variables, runtimeNodes } = props;
+  const { params, variables, runtimeNodes, workflowStreamResponse, node } = props;
 
   const { updateList } = params;
-  updateList.forEach((item) => {
+  const result = updateList.map((item) => {
     const varNodeId = item.variable?.[0];
     const varKey = item.variable?.[1];
 
     if (!varNodeId || !varKey) {
-      return;
+      return null;
     }
 
     const value = (() => {
       if (!item.value?.[0]) {
-        return valueTypeFormat(item.value?.[1], item.valueType);
+        const formatValue = valueTypeFormat(item.value?.[1], item.valueType);
+
+        return typeof formatValue === 'string'
+          ? replaceEditorVariable({
+              text: formatValue,
+              nodes: runtimeNodes,
+              variables,
+              runningNode: node
+            })
+          : formatValue;
       } else {
         return getReferenceVariableValue({
           value: item.value,
@@ -39,10 +48,11 @@ export const dispatchUpdateVariable = async (props: Props): Promise<Response> =>
       }
     })();
 
+    // Global variable
     if (varNodeId === VARIABLE_NODE_ID) {
-      // update global variable
       variables[varKey] = value;
     } else {
+      // Other nodes
       runtimeNodes
         .find((node) => node.nodeId === varNodeId)
         ?.outputs?.find((output) => {
@@ -52,19 +62,18 @@ export const dispatchUpdateVariable = async (props: Props): Promise<Response> =>
           }
         });
     }
+
+    return value;
   });
 
-  if (detail && stream) {
-    responseWrite({
-      res,
-      event: SseResponseEventEnum.updateVariables,
-      data: JSON.stringify(removeSystemVariable(variables))
-    });
-  }
+  workflowStreamResponse?.({
+    event: SseResponseEventEnum.updateVariables,
+    data: removeSystemVariable(variables)
+  });
 
   return {
     [DispatchNodeResponseKeyEnum.nodeResponse]: {
-      totalPoints: 0
+      updateVarResult: result
     }
   };
 };

packages/service/core/workflow/dispatch/type.d.ts

@@ -4,7 +4,10 @@ import {
   ChatItemValueItemType,
   ToolRunResponseItemType
 } from '@fastgpt/global/core/chat/type';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
+import {
+  DispatchNodeResponseKeyEnum,
+  SseResponseEventEnum
+} from '@fastgpt/global/core/workflow/runtime/constants';
 import { RuntimeNodeItemType } from '@fastgpt/global/core/workflow/runtime/type';
 import { RuntimeEdgeItemType } from '@fastgpt/global/core/workflow/type/edge';
 import { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
@@ -21,3 +24,15 @@ export type DispatchFlowResponse = {
   [DispatchNodeResponseKeyEnum.assistantResponses]: AIChatItemValueItemType[];
   newVariables: Record<string, string>;
 };
+
+export type WorkflowResponseType = ({
+  write,
+  event,
+  data,
+  stream
+}: {
+  write?: ((text: string) => void) | undefined;
+  event: SseResponseEventEnum;
+  data: Record<string, any>;
+  stream?: boolean | undefined;
+}) => void;

packages/service/core/workflow/dispatch/utils.ts

@@ -6,6 +6,56 @@ import {
   NodeOutputKeyEnum
 } from '@fastgpt/global/core/workflow/constants';
 import { RuntimeEdgeItemType } from '@fastgpt/global/core/workflow/runtime/type';
+import { responseWrite } from '../../../common/response';
+import { NextApiResponse } from 'next';
+import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
+
+export const getWorkflowResponseWrite = ({
+  res,
+  detail,
+  streamResponse,
+  id
+}: {
+  res?: NextApiResponse;
+  detail: boolean;
+  streamResponse: boolean;
+  id: string;
+}) => {
+  return ({
+    write,
+    event,
+    data,
+    stream
+  }: {
+    write?: (text: string) => void;
+    event: SseResponseEventEnum;
+    data: Record<string, any>;
+    stream?: boolean; // Focus set stream response
+  }) => {
+    const useStreamResponse = stream ?? streamResponse;
+
+    if (!res || res.closed || !useStreamResponse) return;
+
+    const detailEvent = [
+      SseResponseEventEnum.error,
+      SseResponseEventEnum.flowNodeStatus,
+      SseResponseEventEnum.flowResponses,
+      SseResponseEventEnum.interactive,
+      SseResponseEventEnum.toolCall,
+      SseResponseEventEnum.toolParams,
+      SseResponseEventEnum.toolResponse,
+      SseResponseEventEnum.updateVariables
+    ];
+    if (!detail && detailEvent.includes(event)) return;
+
+    responseWrite({
+      res,
+      write,
+      event: detail ? event : undefined,
+      data: JSON.stringify(data)
+    });
+  };
+};
 
 export const filterToolNodeIdByEdges = ({
   nodeId,
@@ -94,6 +144,15 @@ export const removeSystemVariable = (variables: Record<string, any>) => {
 
   return copyVariables;
 };
+export const filterSystemVariables = (variables: Record<string, any>) => {
+  return {
+    appId: variables.appId,
+    chatId: variables.chatId,
+    responseChatItemId: variables.responseChatItemId,
+    histories: variables.histories,
+    cTime: variables.cTime
+  };
+};
 
 export const formatHttpError = (error: any) => {
   return {

packages/service/core/workflow/dispatchV1/agent/classifyQuestion.ts

@@ -1,321 +0,0 @@
-// @ts-nocheck
-import { chats2GPTMessages } from '@fastgpt/global/core/chat/adapt';
-import { filterGPTMessageByMaxTokens } from '../../../chat/utils';
-
-import type { ChatItemType } from '@fastgpt/global/core/chat/type.d';
-import { ChatItemValueTypeEnum, ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
-import { getAIApi } from '../../../ai/config';
-import type { ClassifyQuestionAgentItemType } from '@fastgpt/global/core/workflow/template/system/classifyQuestion/type';
-import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { replaceVariable } from '@fastgpt/global/common/string/tools';
-import { Prompt_CQJson } from '@fastgpt/global/core/ai/prompt/agent';
-import { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
-import { ModelTypeEnum, getLLMModel } from '../../../ai/model';
-import { getHistories } from '../utils';
-import { formatModelChars2Points } from '../../../../support/wallet/usage/utils';
-import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
-import {
-  ChatCompletionCreateParams,
-  ChatCompletionMessageParam,
-  ChatCompletionTool
-} from '@fastgpt/global/core/ai/type';
-import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
-import { chatValue2RuntimePrompt } from '@fastgpt/global/core/chat/adapt';
-import {
-  countMessagesTokens,
-  countGptMessagesTokens
-} from '../../../../common/string/tiktoken/index';
-
-type Props = ModuleDispatchProps<{
-  [NodeInputKeyEnum.aiModel]: string;
-  [NodeInputKeyEnum.aiSystemPrompt]?: string;
-  [NodeInputKeyEnum.history]?: ChatItemType[] | number;
-  [NodeInputKeyEnum.userChatInput]: string;
-  [NodeInputKeyEnum.agents]: ClassifyQuestionAgentItemType[];
-}>;
-type CQResponse = DispatchNodeResultType<{
-  [key: string]: any;
-}>;
-type ActionProps = Props & { cqModel: LLMModelItemType };
-
-const agentFunName = 'classify_question';
-
-/* request openai chat */
-export const dispatchClassifyQuestion = async (props: Props): Promise<CQResponse> => {
-  const {
-    user,
-    module: { name },
-    histories,
-    params: { model, history = 6, agents, userChatInput }
-  } = props as Props;
-
-  if (!userChatInput) {
-    return Promise.reject('Input is empty');
-  }
-
-  const cqModel = getLLMModel(model);
-
-  const chatHistories = getHistories(history, histories);
-
-  const { arg, tokens } = await (async () => {
-    if (cqModel.toolChoice) {
-      return toolChoice({
-        ...props,
-        histories: chatHistories,
-        cqModel
-      });
-    }
-    if (cqModel.functionCall) {
-      return functionCall({
-        ...props,
-        histories: chatHistories,
-        cqModel
-      });
-    }
-    return completions({
-      ...props,
-      histories: chatHistories,
-      cqModel
-    });
-  })();
-
-  const result = agents.find((item) => item.key === arg?.type) || agents[agents.length - 1];
-
-  const { totalPoints, modelName } = formatModelChars2Points({
-    model: cqModel.model,
-    tokens,
-    modelType: ModelTypeEnum.llm
-  });
-
-  return {
-    [result.key]: true,
-    [DispatchNodeResponseKeyEnum.nodeResponse]: {
-      totalPoints: user.openaiAccount?.key ? 0 : totalPoints,
-      model: modelName,
-      query: userChatInput,
-      tokens,
-      cqList: agents,
-      cqResult: result.value,
-      contextTotalLen: chatHistories.length + 2
-    },
-    [DispatchNodeResponseKeyEnum.nodeDispatchUsages]: [
-      {
-        moduleName: name,
-        totalPoints: user.openaiAccount?.key ? 0 : totalPoints,
-        model: modelName,
-        tokens
-      }
-    ]
-  };
-};
-
-const getFunctionCallSchema = async ({
-  cqModel,
-  histories,
-  params: { agents, systemPrompt, userChatInput }
-}: ActionProps) => {
-  const messages: ChatItemType[] = [
-    ...histories,
-    {
-      obj: ChatRoleEnum.Human,
-      value: [
-        {
-          type: ChatItemValueTypeEnum.text,
-          text: {
-            content: systemPrompt
-              ? `<背景知识>
-    ${systemPrompt}
-    </背景知识>
-
-    问题: "${userChatInput}"
-          `
-              : userChatInput
-          }
-        }
-      ]
-    }
-  ];
-
-  const adaptMessages = chats2GPTMessages({ messages, reserveId: false });
-  const filterMessages = await filterGPTMessageByMaxTokens({
-    messages: adaptMessages,
-    maxTokens: cqModel.maxContext
-  });
-
-  // function body
-  const agentFunction = {
-    name: agentFunName,
-    description: '结合对话记录及背景知识，对问题进行分类，并返回对应的类型字段',
-    parameters: {
-      type: 'object',
-      properties: {
-        type: {
-          type: 'string',
-          description: `问题类型。下面是几种可选的问题类型: ${agents
-            .map((item) => `${item.value}，返回：'${item.key}'`)
-            .join('；')}`,
-          enum: agents.map((item) => item.key)
-        }
-      },
-      required: ['type']
-    }
-  };
-
-  return {
-    agentFunction,
-    filterMessages
-  };
-};
-
-const toolChoice = async (props: ActionProps) => {
-  const { user, cqModel } = props;
-
-  const { agentFunction, filterMessages } = await getFunctionCallSchema(props);
-  // function body
-  const tools: ChatCompletionTool[] = [
-    {
-      type: 'function',
-      function: agentFunction
-    }
-  ];
-
-  const ai = getAIApi({
-    userKey: user.openaiAccount,
-    timeout: 480000
-  });
-
-  const response = await ai.chat.completions.create({
-    model: cqModel.model,
-    temperature: 0.01,
-    messages: filterMessages,
-    tools,
-    tool_choice: { type: 'function', function: { name: agentFunName } }
-  });
-
-  try {
-    const arg = JSON.parse(
-      response?.choices?.[0]?.message?.tool_calls?.[0]?.function?.arguments || ''
-    );
-    const completeMessages: ChatCompletionMessageParam[] = [
-      ...filterMessages,
-      {
-        role: ChatCompletionRequestMessageRoleEnum.Assistant,
-        tool_calls: response.choices?.[0]?.message?.tool_calls
-      }
-    ];
-
-    return {
-      arg,
-      tokens: await countGptMessagesTokens(completeMessages, tools)
-    };
-  } catch (error) {
-    console.log(response.choices?.[0]?.message);
-
-    console.log('Your model may not support toll_call', error);
-
-    return {
-      arg: {},
-      tokens: 0
-    };
-  }
-};
-
-const functionCall = async (props: ActionProps) => {
-  const { user, cqModel } = props;
-
-  const { agentFunction, filterMessages } = await getFunctionCallSchema(props);
-  const functions: ChatCompletionCreateParams.Function[] = [agentFunction];
-
-  const ai = getAIApi({
-    userKey: user.openaiAccount,
-    timeout: 480000
-  });
-
-  const response = await ai.chat.completions.create({
-    model: cqModel.model,
-    temperature: 0.01,
-    messages: filterMessages,
-    function_call: {
-      name: agentFunName
-    },
-    functions
-  });
-
-  try {
-    const arg = JSON.parse(response?.choices?.[0]?.message?.function_call?.arguments || '');
-    const completeMessages: ChatCompletionMessageParam[] = [
-      ...filterMessages,
-      {
-        role: ChatCompletionRequestMessageRoleEnum.Assistant,
-        function_call: response.choices?.[0]?.message?.function_call
-      }
-    ];
-
-    return {
-      arg,
-      tokens: await countGptMessagesTokens(completeMessages, undefined, functions)
-    };
-  } catch (error) {
-    console.log(response.choices?.[0]?.message);
-
-    console.log('Your model may not support toll_call', error);
-
-    return {
-      arg: {},
-      tokens: 0
-    };
-  }
-};
-
-const completions = async ({
-  cqModel,
-  user,
-  histories,
-  params: { agents, systemPrompt = '', userChatInput }
-}: ActionProps) => {
-  const messages: ChatItemType[] = [
-    {
-      obj: ChatRoleEnum.Human,
-      value: [
-        {
-          type: ChatItemValueTypeEnum.text,
-          text: {
-            content: replaceVariable(cqModel.customCQPrompt || Prompt_CQJson, {
-              systemPrompt: systemPrompt || 'null',
-              typeList: agents
-                .map((item) => `{"questionType": "${item.value}", "typeId": "${item.key}"}`)
-                .join('\n'),
-              history: histories
-                .map((item) => `${item.obj}:${chatValue2RuntimePrompt(item.value).text}`)
-                .join('\n'),
-              question: userChatInput
-            })
-          }
-        }
-      ]
-    }
-  ];
-
-  const ai = getAIApi({
-    userKey: user.openaiAccount,
-    timeout: 480000
-  });
-
-  const data = await ai.chat.completions.create({
-    model: cqModel.model,
-    temperature: 0.01,
-    messages: chats2GPTMessages({ messages, reserveId: false }),
-    stream: false
-  });
-  const answer = data.choices?.[0].message?.content || '';
-
-  const id =
-    agents.find((item) => answer.includes(item.key) || answer.includes(item.value))?.key || '';
-
-  return {
-    tokens: await countMessagesTokens(messages),
-    arg: { type: id }
-  };
-};

packages/service/core/workflow/dispatchV1/agent/extract.ts

@@ -1,384 +0,0 @@
-// @ts-nocheck
-import { chats2GPTMessages } from '@fastgpt/global/core/chat/adapt';
-import { filterGPTMessageByMaxTokens } from '../../../chat/utils';
-import type { ChatItemType } from '@fastgpt/global/core/chat/type.d';
-import {
-  countMessagesTokens,
-  countGptMessagesTokens
-} from '../../../../common/string/tiktoken/index';
-
-import { ChatItemValueTypeEnum, ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
-import { getAIApi } from '../../../ai/config';
-import type { ContextExtractAgentItemType } from '@fastgpt/global/core/workflow/template/system/contextExtract/type';
-import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { Prompt_ExtractJson } from '@fastgpt/global/core/ai/prompt/agent';
-import { replaceVariable } from '@fastgpt/global/common/string/tools';
-import { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
-import { getHistories } from '../utils';
-import { ModelTypeEnum, getLLMModel } from '../../../ai/model';
-import { formatModelChars2Points } from '../../../../support/wallet/usage/utils';
-import json5 from 'json5';
-import {
-  ChatCompletionCreateParams,
-  ChatCompletionMessageParam,
-  ChatCompletionTool
-} from '@fastgpt/global/core/ai/type';
-import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
-import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
-import { chatValue2RuntimePrompt } from '@fastgpt/global/core/chat/adapt';
-
-type Props = ModuleDispatchProps<{
-  [NodeInputKeyEnum.history]?: ChatItemType[];
-  [NodeInputKeyEnum.contextExtractInput]: string;
-  [NodeInputKeyEnum.extractKeys]: ContextExtractAgentItemType[];
-  [NodeInputKeyEnum.description]: string;
-  [NodeInputKeyEnum.aiModel]: string;
-}>;
-type Response = DispatchNodeResultType<{
-  [NodeOutputKeyEnum.success]?: boolean;
-  [NodeOutputKeyEnum.failed]?: boolean;
-  [NodeOutputKeyEnum.contextExtractFields]: string;
-}>;
-
-type ActionProps = Props & { extractModel: LLMModelItemType };
-
-const agentFunName = 'request_function';
-
-export async function dispatchContentExtract(props: Props): Promise<Response> {
-  const {
-    user,
-    module: { name },
-    histories,
-    params: { content, history = 6, model, description, extractKeys }
-  } = props;
-
-  if (!content) {
-    return Promise.reject('Input is empty');
-  }
-
-  const extractModel = getLLMModel(model);
-  const chatHistories = getHistories(history, histories);
-
-  const { arg, tokens } = await (async () => {
-    if (extractModel.toolChoice) {
-      return toolChoice({
-        ...props,
-        histories: chatHistories,
-        extractModel
-      });
-    }
-    if (extractModel.functionCall) {
-      return functionCall({
-        ...props,
-        histories: chatHistories,
-        extractModel
-      });
-    }
-    return completions({
-      ...props,
-      histories: chatHistories,
-      extractModel
-    });
-  })();
-
-  // remove invalid key
-  for (let key in arg) {
-    const item = extractKeys.find((item) => item.key === key);
-    if (!item) {
-      delete arg[key];
-    }
-    if (arg[key] === '') {
-      delete arg[key];
-    }
-  }
-
-  // auto fill required fields
-  extractKeys.forEach((item) => {
-    if (item.required && !arg[item.key]) {
-      arg[item.key] = item.defaultValue || '';
-    }
-  });
-
-  // auth fields
-  let success = !extractKeys.find((item) => !(item.key in arg));
-  // auth empty value
-  if (success) {
-    for (const key in arg) {
-      const item = extractKeys.find((item) => item.key === key);
-      if (!item) {
-        success = false;
-        break;
-      }
-    }
-  }
-
-  const { totalPoints, modelName } = formatModelChars2Points({
-    model: extractModel.model,
-    tokens,
-    modelType: ModelTypeEnum.llm
-  });
-
-  return {
-    [NodeOutputKeyEnum.success]: success ? true : undefined,
-    [NodeOutputKeyEnum.failed]: success ? undefined : true,
-    [NodeOutputKeyEnum.contextExtractFields]: JSON.stringify(arg),
-    ...arg,
-    [DispatchNodeResponseKeyEnum.nodeResponse]: {
-      totalPoints: user.openaiAccount?.key ? 0 : totalPoints,
-      model: modelName,
-      query: content,
-      tokens,
-      extractDescription: description,
-      extractResult: arg,
-      contextTotalLen: chatHistories.length + 2
-    },
-    [DispatchNodeResponseKeyEnum.nodeDispatchUsages]: [
-      {
-        moduleName: name,
-        totalPoints: user.openaiAccount?.key ? 0 : totalPoints,
-        model: modelName,
-        tokens
-      }
-    ]
-  };
-}
-
-const getFunctionCallSchema = async ({
-  extractModel,
-  histories,
-  params: { content, extractKeys, description }
-}: ActionProps) => {
-  const messages: ChatItemType[] = [
-    ...histories,
-    {
-      obj: ChatRoleEnum.Human,
-      value: [
-        {
-          type: ChatItemValueTypeEnum.text,
-          text: {
-            content: `我正在执行一个函数，需要你提供一些参数，请以 JSON 字符串格式返回这些参数，要求：
-"""
-${description ? `- ${description}` : ''}
-- 不是每个参数都是必须生成的，如果没有合适的参数值，不要生成该参数，或返回空字符串。
-- 需要结合前面的对话内容，一起生成合适的参数。
-"""
-
-本次输入内容: ${content}
-            `
-          }
-        }
-      ]
-    }
-  ];
-  const adaptMessages = chats2GPTMessages({ messages, reserveId: false });
-  const filterMessages = await filterGPTMessageByMaxTokens({
-    messages: adaptMessages,
-    maxTokens: extractModel.maxContext
-  });
-
-  const properties: Record<
-    string,
-    {
-      type: string;
-      description: string;
-    }
-  > = {};
-  extractKeys.forEach((item) => {
-    properties[item.key] = {
-      type: 'string',
-      description: item.desc,
-      ...(item.enum ? { enum: item.enum.split('\n') } : {})
-    };
-  });
-  // function body
-  const agentFunction = {
-    name: agentFunName,
-    description: '需要执行的函数',
-    parameters: {
-      type: 'object',
-      properties
-    }
-  };
-
-  return {
-    filterMessages,
-    agentFunction
-  };
-};
-
-const toolChoice = async (props: ActionProps) => {
-  const { user, extractModel } = props;
-
-  const { filterMessages, agentFunction } = await getFunctionCallSchema(props);
-
-  const tools: ChatCompletionTool[] = [
-    {
-      type: 'function',
-      function: agentFunction
-    }
-  ];
-
-  const ai = getAIApi({
-    userKey: user.openaiAccount,
-    timeout: 480000
-  });
-
-  const response = await ai.chat.completions.create({
-    model: extractModel.model,
-    temperature: 0,
-    messages: filterMessages,
-    tools,
-    tool_choice: { type: 'function', function: { name: agentFunName } }
-  });
-
-  const arg: Record<string, any> = (() => {
-    try {
-      return json5.parse(
-        response?.choices?.[0]?.message?.tool_calls?.[0]?.function?.arguments || '{}'
-      );
-    } catch (error) {
-      console.log(agentFunction.parameters);
-      console.log(response.choices?.[0]?.message?.tool_calls?.[0]?.function);
-      console.log('Your model may not support tool_call', error);
-      return {};
-    }
-  })();
-
-  const completeMessages: ChatCompletionMessageParam[] = [
-    ...filterMessages,
-    {
-      role: ChatCompletionRequestMessageRoleEnum.Assistant,
-      tool_calls: response.choices?.[0]?.message?.tool_calls
-    }
-  ];
-  return {
-    tokens: await countGptMessagesTokens(completeMessages, tools),
-    arg
-  };
-};
-
-const functionCall = async (props: ActionProps) => {
-  const { user, extractModel } = props;
-
-  const { agentFunction, filterMessages } = await getFunctionCallSchema(props);
-  const functions: ChatCompletionCreateParams.Function[] = [agentFunction];
-
-  const ai = getAIApi({
-    userKey: user.openaiAccount,
-    timeout: 480000
-  });
-
-  const response = await ai.chat.completions.create({
-    model: extractModel.model,
-    temperature: 0,
-    messages: filterMessages,
-    function_call: {
-      name: agentFunName
-    },
-    functions
-  });
-
-  try {
-    const arg = JSON.parse(response?.choices?.[0]?.message?.function_call?.arguments || '');
-    const completeMessages: ChatCompletionMessageParam[] = [
-      ...filterMessages,
-      {
-        role: ChatCompletionRequestMessageRoleEnum.Assistant,
-        function_call: response.choices?.[0]?.message?.function_call
-      }
-    ];
-
-    return {
-      arg,
-      tokens: await countGptMessagesTokens(completeMessages, undefined, functions)
-    };
-  } catch (error) {
-    console.log(response.choices?.[0]?.message);
-
-    console.log('Your model may not support toll_call', error);
-
-    return {
-      arg: {},
-      tokens: 0
-    };
-  }
-};
-
-const completions = async ({
-  extractModel,
-  user,
-  histories,
-  params: { content, extractKeys, description }
-}: ActionProps) => {
-  const messages: ChatItemType[] = [
-    {
-      obj: ChatRoleEnum.Human,
-      value: [
-        {
-          type: ChatItemValueTypeEnum.text,
-          text: {
-            content: replaceVariable(extractModel.customExtractPrompt || Prompt_ExtractJson, {
-              description,
-              json: extractKeys
-                .map(
-                  (item) =>
-                    `{"key":"${item.key}", "description":"${item.desc}"${
-                      item.enum ? `, "enum":"[${item.enum.split('\n')}]"` : ''
-                    }}`
-                )
-                .join('\n'),
-              text: `${histories.map((item) => `${item.obj}:${chatValue2RuntimePrompt(item.value).text}`).join('\n')}
-Human: ${content}`
-            })
-          }
-        }
-      ]
-    }
-  ];
-
-  const ai = getAIApi({
-    userKey: user.openaiAccount,
-    timeout: 480000
-  });
-  const data = await ai.chat.completions.create({
-    model: extractModel.model,
-    temperature: 0.01,
-    messages: chats2GPTMessages({ messages, reserveId: false }),
-    stream: false
-  });
-  const answer = data.choices?.[0].message?.content || '';
-
-  // parse response
-  const start = answer.indexOf('{');
-  const end = answer.lastIndexOf('}');
-
-  if (start === -1 || end === -1) {
-    return {
-      rawResponse: answer,
-      tokens: await countMessagesTokens(messages),
-      arg: {}
-    };
-  }
-
-  const jsonStr = answer
-    .substring(start, end + 1)
-    .replace(/(\\n|\\)/g, '')
-    .replace(/  /g, '');
-
-  try {
-    return {
-      rawResponse: answer,
-      tokens: await countMessagesTokens(messages),
-      arg: json5.parse(jsonStr) as Record<string, any>
-    };
-  } catch (error) {
-    console.log(error);
-    return {
-      rawResponse: answer,
-      tokens: await countMessagesTokens(messages),
-      arg: {}
-    };
-  }
-};

packages/service/core/workflow/dispatchV1/agent/runTool/constants.ts

@@ -1,39 +0,0 @@
-export const Prompt_Tool_Call = `<Instruction>
-你是一个智能机器人，除了可以回答用户问题外，你还掌握工具的使用能力。有时候，你可以依赖工具的运行结果，来更准确的回答用户。
-
-工具使用了 JSON Schema 的格式声明，其中 toolId 是工具的 description 是工具的描述，parameters 是工具的参数，包括参数的类型和描述，required 是必填参数的列表。
-
-请你根据工具描述，决定回答问题或是使用工具。在完成任务过程中，USER代表用户的输入，TOOL_RESPONSE代表工具运行结果。ASSISTANT 代表你的输出。
-你的每次输出都必须以0,1开头，代表是否需要调用工具：
-0: 不使用工具，直接回答内容。
-1: 使用工具，返回工具调用的参数。
-
-例如：
-
-USER: 你好呀
-ANSWER: 0: 你好，有什么可以帮助你的么？
-USER: 今天杭州的天气如何
-ANSWER: 1: {"toolId":"testToolId",arguments:{"city": "杭州"}}
-TOOL_RESPONSE: """
-晴天......
-"""
-ANSWER: 0: 今天杭州是晴天。
-USER: 今天杭州的天气适合去哪里玩？
-ANSWER: 1: {"toolId":"testToolId2",arguments:{"query": "杭州 天气 去哪里玩"}}
-TOOL_RESPONSE: """
-晴天. 西湖、灵隐寺、千岛湖……
-"""
-ANSWER: 0: 今天杭州是晴天，适合去西湖、灵隐寺、千岛湖等地玩。
-</Instruction>
-
-现在，我们开始吧！下面是你本次可以使用的工具：
-
-"""
-{{toolsPrompt}}
-"""
-
-下面是正式的对话内容：
-
-USER: {{question}}
-ANSWER: 
-`;

packages/service/core/workflow/dispatchV1/agent/runTool/functionCall.ts

@@ -1,410 +0,0 @@
-// @ts-nocheck
-import { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
-import { getAIApi } from '../../../../ai/config';
-import { filterGPTMessageByMaxTokens } from '../../../../chat/utils';
-import {
-  ChatCompletion,
-  StreamChatType,
-  ChatCompletionMessageParam,
-  ChatCompletionCreateParams,
-  ChatCompletionMessageFunctionCall,
-  ChatCompletionFunctionMessageParam,
-  ChatCompletionAssistantMessageParam
-} from '@fastgpt/global/core/ai/type';
-import { NextApiResponse } from 'next';
-import {
-  responseWrite,
-  responseWriteController,
-  responseWriteNodeStatus
-} from '../../../../../common/response';
-import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
-import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
-import { dispatchWorkFlowV1 } from '../../index';
-import { DispatchToolModuleProps, RunToolResponse, ToolModuleItemType } from './type.d';
-import json5 from 'json5';
-import { DispatchFlowResponse } from '../../type';
-import { countGptMessagesTokens } from '../../../../../common/string/tiktoken';
-import { getNanoid } from '@fastgpt/global/common/string/tools';
-import { AIChatItemType, AIChatItemValueItemType } from '@fastgpt/global/core/chat/type';
-import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
-
-type FunctionRunResponseType = {
-  moduleRunResponse: DispatchFlowResponse;
-  functionCallMsg: ChatCompletionFunctionMessageParam;
-}[];
-
-export const runToolWithFunctionCall = async (
-  props: DispatchToolModuleProps & {
-    messages: ChatCompletionMessageParam[];
-    toolModules: ToolModuleItemType[];
-    toolModel: LLMModelItemType;
-  },
-  response?: RunToolResponse
-): Promise<RunToolResponse> => {
-  const {
-    toolModel,
-    toolModules,
-    messages,
-    res,
-    runtimeModules,
-    detail = false,
-    module,
-    stream
-  } = props;
-  const assistantResponses = response?.assistantResponses || [];
-
-  const functions: ChatCompletionCreateParams.Function[] = toolModules.map((module) => {
-    const properties: Record<
-      string,
-      {
-        type: string;
-        description: string;
-        required?: boolean;
-      }
-    > = {};
-    module.toolParams.forEach((item) => {
-      properties[item.key] = {
-        type: 'string',
-        description: item.toolDescription || ''
-      };
-    });
-
-    return {
-      name: module.moduleId,
-      description: module.intro,
-      parameters: {
-        type: 'object',
-        properties,
-        required: module.toolParams.filter((item) => item.required).map((item) => item.key)
-      }
-    };
-  });
-
-  const filterMessages = await filterGPTMessageByMaxTokens({
-    messages,
-    maxTokens: toolModel.maxContext - 500 // filter token. not response maxToken
-  });
-
-  /* Run llm */
-  const ai = getAIApi({
-    timeout: 480000
-  });
-  const aiResponse = await ai.chat.completions.create(
-    {
-      ...toolModel?.defaultConfig,
-      model: toolModel.model,
-      temperature: 0,
-      stream,
-      messages: filterMessages,
-      functions,
-      function_call: 'auto'
-    },
-    {
-      headers: {
-        Accept: 'application/json, text/plain, */*'
-      }
-    }
-  );
-
-  const { answer, functionCalls } = await (async () => {
-    if (stream) {
-      return streamResponse({
-        res,
-        detail,
-        toolModules,
-        stream: aiResponse
-      });
-    } else {
-      const result = aiResponse as ChatCompletion;
-      const function_call = result.choices?.[0]?.message?.function_call;
-      const toolModule = toolModules.find((module) => module.moduleId === function_call?.name);
-
-      const toolCalls = function_call
-        ? [
-            {
-              ...function_call,
-              id: getNanoid(),
-              toolName: toolModule?.name,
-              toolAvatar: toolModule?.avatar
-            }
-          ]
-        : [];
-
-      return {
-        answer: result.choices?.[0]?.message?.content || '',
-        functionCalls: toolCalls
-      };
-    }
-  })();
-
-  // Run the selected tool.
-  const toolsRunResponse = (
-    await Promise.all(
-      functionCalls.map(async (tool) => {
-        if (!tool) return;
-
-        const toolModule = toolModules.find((module) => module.moduleId === tool.name);
-
-        if (!toolModule) return;
-
-        const startParams = (() => {
-          try {
-            return json5.parse(tool.arguments);
-          } catch (error) {
-            return {};
-          }
-        })();
-
-        const moduleRunResponse = await dispatchWorkFlowV1({
-          ...props,
-          runtimeModules: runtimeModules.map((module) => ({
-            ...module,
-            isEntry: module.moduleId === toolModule.moduleId
-          })),
-          startParams
-        });
-
-        const stringToolResponse = (() => {
-          if (typeof moduleRunResponse.toolResponses === 'object') {
-            return JSON.stringify(moduleRunResponse.toolResponses, null, 2);
-          }
-
-          return moduleRunResponse.toolResponses ? String(moduleRunResponse.toolResponses) : 'none';
-        })();
-
-        const functionCallMsg: ChatCompletionFunctionMessageParam = {
-          role: ChatCompletionRequestMessageRoleEnum.Function,
-          name: tool.name,
-          content: stringToolResponse
-        };
-
-        if (stream && detail) {
-          responseWrite({
-            res,
-            event: SseResponseEventEnum.toolResponse,
-            data: JSON.stringify({
-              tool: {
-                id: tool.id,
-                toolName: '',
-                toolAvatar: '',
-                params: '',
-                response: stringToolResponse
-              }
-            })
-          });
-        }
-
-        return {
-          moduleRunResponse,
-          functionCallMsg
-        };
-      })
-    )
-  ).filter(Boolean) as FunctionRunResponseType;
-
-  const flatToolsResponseData = toolsRunResponse.map((item) => item.moduleRunResponse).flat();
-
-  const functionCall = functionCalls[0];
-  if (functionCall && !res.closed) {
-    // Run the tool, combine its results, and perform another round of AI calls
-    const assistantToolMsgParams: ChatCompletionAssistantMessageParam = {
-      role: ChatCompletionRequestMessageRoleEnum.Assistant,
-      function_call: functionCall
-    };
-    const concatToolMessages = [
-      ...filterMessages,
-      assistantToolMsgParams
-    ] as ChatCompletionMessageParam[];
-    const tokens = await countGptMessagesTokens(concatToolMessages, undefined, functions);
-    const completeMessages = [
-      ...concatToolMessages,
-      ...toolsRunResponse.map((item) => item?.functionCallMsg)
-    ];
-    // console.log(tokens, 'tool');
-
-    if (stream && detail) {
-      responseWriteNodeStatus({
-        res,
-        name: module.name
-      });
-    }
-
-    // tool assistant
-    const toolAssistants = toolsRunResponse
-      .map((item) => {
-        const assistantResponses = item.moduleRunResponse.assistantResponses || [];
-        return assistantResponses;
-      })
-      .flat();
-    // tool node assistant
-    const adaptChatMessages = GPTMessages2Chats(completeMessages);
-    const toolNodeAssistant = adaptChatMessages.pop() as AIChatItemType;
-
-    const toolNodeAssistants = [
-      ...assistantResponses,
-      ...toolAssistants,
-      ...toolNodeAssistant.value
-    ];
-
-    // concat tool responses
-    const dispatchFlowResponse = response
-      ? response.dispatchFlowResponse.concat(flatToolsResponseData)
-      : flatToolsResponseData;
-
-    /* check stop signal */
-    const hasStopSignal = flatToolsResponseData.some(
-      (item) => !!item.flowResponses?.find((item) => item.toolStop)
-    );
-    if (hasStopSignal) {
-      return {
-        dispatchFlowResponse,
-        totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
-        completeMessages: filterMessages,
-        assistantResponses: toolNodeAssistants
-      };
-    }
-
-    return runToolWithFunctionCall(
-      {
-        ...props,
-        messages: completeMessages
-      },
-      {
-        dispatchFlowResponse,
-        totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
-        assistantResponses: toolNodeAssistants
-      }
-    );
-  } else {
-    // No tool is invoked, indicating that the process is over
-    const gptAssistantResponse: ChatCompletionAssistantMessageParam = {
-      role: ChatCompletionRequestMessageRoleEnum.Assistant,
-      content: answer
-    };
-    const completeMessages = filterMessages.concat(gptAssistantResponse);
-    const tokens = await countGptMessagesTokens(completeMessages, undefined, functions);
-    // console.log(tokens, 'response token');
-
-    // concat tool assistant
-    const toolNodeAssistant = GPTMessages2Chats([gptAssistantResponse])[0] as AIChatItemType;
-
-    return {
-      dispatchFlowResponse: response?.dispatchFlowResponse || [],
-      totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
-      completeMessages,
-      assistantResponses: [...assistantResponses, ...toolNodeAssistant.value]
-    };
-  }
-};
-
-async function streamResponse({
-  res,
-  detail,
-  toolModules,
-  stream
-}: {
-  res: NextApiResponse;
-  detail: boolean;
-  toolModules: ToolModuleItemType[];
-  stream: StreamChatType;
-}) {
-  const write = responseWriteController({
-    res,
-    readStream: stream
-  });
-
-  let textAnswer = '';
-  let functionCalls: ChatCompletionMessageFunctionCall[] = [];
-  let functionId = getNanoid();
-
-  for await (const part of stream) {
-    if (res.closed) {
-      stream.controller?.abort();
-      break;
-    }
-
-    const responseChoice = part.choices?.[0]?.delta;
-    if (responseChoice.content) {
-      const content = responseChoice?.content || '';
-      textAnswer += content;
-
-      responseWrite({
-        write,
-        event: detail ? SseResponseEventEnum.answer : undefined,
-        data: textAdaptGptResponse({
-          text: content
-        })
-      });
-    } else if (responseChoice.function_call) {
-      const functionCall: {
-        arguments: string;
-        name?: string;
-      } = responseChoice.function_call;
-
-      // 流响应中,每次只会返回一个函数，如果带了name，说明触发某个函数
-      if (functionCall?.name) {
-        functionId = getNanoid();
-        const toolModule = toolModules.find((module) => module.moduleId === functionCall?.name);
-
-        if (toolModule) {
-          if (functionCall?.arguments === undefined) {
-            functionCall.arguments = '';
-          }
-          functionCalls.push({
-            ...functionCall,
-            id: functionId,
-            name: functionCall.name,
-            toolName: toolModule.name,
-            toolAvatar: toolModule.avatar
-          });
-
-          if (detail) {
-            responseWrite({
-              write,
-              event: SseResponseEventEnum.toolCall,
-              data: JSON.stringify({
-                tool: {
-                  id: functionId,
-                  toolName: toolModule.name,
-                  toolAvatar: toolModule.avatar,
-                  functionName: functionCall.name,
-                  params: functionCall.arguments,
-                  response: ''
-                }
-              })
-            });
-          }
-        }
-      }
-      /* arg 插入最后一个工具的参数里 */
-      const arg: string = functionCall?.arguments || '';
-      const currentTool = functionCalls[functionCalls.length - 1];
-      if (currentTool) {
-        currentTool.arguments += arg;
-
-        if (detail) {
-          responseWrite({
-            write,
-            event: SseResponseEventEnum.toolParams,
-            data: JSON.stringify({
-              tool: {
-                id: functionId,
-                toolName: '',
-                toolAvatar: '',
-                params: arg,
-                response: ''
-              }
-            })
-          });
-        }
-      }
-    }
-  }
-
-  if (!textAnswer && functionCalls.length === 0) {
-    return Promise.reject('LLM api response empty');
-  }
-
-  return { answer: textAnswer, functionCalls };
-}

packages/service/core/workflow/dispatchV1/agent/runTool/index.ts

@@ -1,158 +0,0 @@
-// @ts-nocheck
-import { NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import type {
-  DispatchNodeResultType,
-  RuntimeNodeItemType
-} from '@fastgpt/global/core/workflow/runtime/type';
-import { ModelTypeEnum, getLLMModel } from '../../../../ai/model';
-import { getHistories } from '../../utils';
-import { runToolWithToolChoice } from './toolChoice';
-import { DispatchToolModuleProps, ToolModuleItemType } from './type.d';
-import { ChatItemType } from '@fastgpt/global/core/chat/type';
-import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
-import {
-  GPTMessages2Chats,
-  chats2GPTMessages,
-  getSystemPrompt,
-  runtimePrompt2ChatsValue
-} from '@fastgpt/global/core/chat/adapt';
-import { formatModelChars2Points } from '../../../../../support/wallet/usage/utils';
-import { getHistoryPreview } from '@fastgpt/global/core/chat/utils';
-import { runToolWithFunctionCall } from './functionCall';
-import { runToolWithPromptCall } from './promptCall';
-import { replaceVariable } from '@fastgpt/global/common/string/tools';
-import { Prompt_Tool_Call } from './constants';
-
-type Response = DispatchNodeResultType<{}>;
-
-export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<Response> => {
-  const {
-    module: { name, outputs },
-    runtimeModules,
-    histories,
-    params: { model, systemPrompt, userChatInput, history = 6 }
-  } = props;
-
-  const toolModel = getLLMModel(model);
-  const chatHistories = getHistories(history, histories);
-
-  /* get tool params */
-
-  // get tool output targets
-  const toolOutput = outputs.find((output) => output.key === NodeOutputKeyEnum.selectedTools);
-
-  if (!toolOutput) {
-    return Promise.reject('No tool output found');
-  }
-
-  const targets = toolOutput.targets;
-
-  // Gets the module to which the tool is connected
-  const toolModules = targets
-    .map((item) => {
-      const tool = runtimeModules.find((module) => module.moduleId === item.moduleId);
-      return tool;
-    })
-    .filter(Boolean)
-    .map<ToolModuleItemType>((tool) => {
-      const toolParams = tool?.inputs.filter((input) => !!input.toolDescription) || [];
-      return {
-        ...(tool as RuntimeNodeItemType),
-        toolParams
-      };
-    });
-
-  const messages: ChatItemType[] = [
-    ...getSystemPrompt(systemPrompt),
-    ...chatHistories,
-    {
-      obj: ChatRoleEnum.Human,
-      value: runtimePrompt2ChatsValue({
-        text: userChatInput,
-        files: []
-      })
-    }
-  ];
-
-  const {
-    dispatchFlowResponse, // tool flow response
-    totalTokens,
-    completeMessages = [], // The actual message sent to AI(just save text)
-    assistantResponses = [] // FastGPT system store assistant.value response
-  } = await (async () => {
-    const adaptMessages = chats2GPTMessages({ messages, reserveId: false });
-
-    if (toolModel.toolChoice) {
-      return runToolWithToolChoice({
-        ...props,
-        toolModules,
-        toolModel,
-        messages: adaptMessages
-      });
-    }
-    if (toolModel.functionCall) {
-      return runToolWithFunctionCall({
-        ...props,
-        toolModules,
-        toolModel,
-        messages: adaptMessages
-      });
-    }
-
-    const lastMessage = adaptMessages[adaptMessages.length - 1];
-    if (typeof lastMessage.content !== 'string') {
-      return Promise.reject('暂时只支持纯文本');
-    }
-
-    lastMessage.content = replaceVariable(Prompt_Tool_Call, {
-      question: userChatInput
-    });
-
-    return runToolWithPromptCall({
-      ...props,
-      toolModules,
-      toolModel,
-      messages: adaptMessages
-    });
-  })();
-
-  const { totalPoints, modelName } = formatModelChars2Points({
-    model,
-    tokens: totalTokens,
-    modelType: ModelTypeEnum.llm
-  });
-
-  // flat child tool response
-  const childToolResponse = dispatchFlowResponse.map((item) => item.flowResponses).flat();
-
-  // concat tool usage
-  const totalPointsUsage =
-    totalPoints +
-    dispatchFlowResponse.reduce((sum, item) => {
-      const childrenTotal = item.flowUsages.reduce((sum, item) => sum + item.totalPoints, 0);
-      return sum + childrenTotal;
-    }, 0);
-  const flatUsages = dispatchFlowResponse.map((item) => item.flowUsages).flat();
-
-  return {
-    [DispatchNodeResponseKeyEnum.assistantResponses]: assistantResponses,
-    [DispatchNodeResponseKeyEnum.nodeResponse]: {
-      totalPoints: totalPointsUsage,
-      toolCallTokens: totalTokens,
-      model: modelName,
-      query: userChatInput,
-      historyPreview: getHistoryPreview(GPTMessages2Chats(completeMessages, false)),
-      toolDetail: childToolResponse
-    },
-    [DispatchNodeResponseKeyEnum.nodeDispatchUsages]: [
-      {
-        moduleName: name,
-        totalPoints,
-        model: modelName,
-        tokens: totalTokens
-      },
-      ...flatUsages
-    ]
-  };
-};

packages/service/core/workflow/dispatchV1/agent/runTool/promptCall.ts

@@ -1,388 +0,0 @@
-// @ts-nocheck
-import { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
-import { getAIApi } from '../../../../ai/config';
-import { filterGPTMessageByMaxTokens } from '../../../../chat/utils';
-import {
-  ChatCompletion,
-  StreamChatType,
-  ChatCompletionMessageParam,
-  ChatCompletionAssistantMessageParam
-} from '@fastgpt/global/core/ai/type';
-import { NextApiResponse } from 'next';
-import {
-  responseWrite,
-  responseWriteController,
-  responseWriteNodeStatus
-} from '../../../../../common/response';
-import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
-import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
-import { dispatchWorkFlowV1 } from '../../index';
-import { DispatchToolModuleProps, RunToolResponse, ToolModuleItemType } from './type.d';
-import json5 from 'json5';
-import { countGptMessagesTokens } from '../../../../../common/string/tiktoken';
-import { getNanoid, replaceVariable } from '@fastgpt/global/common/string/tools';
-import { AIChatItemType } from '@fastgpt/global/core/chat/type';
-import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
-
-type FunctionCallCompletion = {
-  id: string;
-  name: string;
-  arguments: string;
-  toolName?: string;
-  toolAvatar?: string;
-};
-
-export const runToolWithPromptCall = async (
-  props: DispatchToolModuleProps & {
-    messages: ChatCompletionMessageParam[];
-    toolModules: ToolModuleItemType[];
-    toolModel: LLMModelItemType;
-  },
-  response?: RunToolResponse
-): Promise<RunToolResponse> => {
-  const {
-    toolModel,
-    toolModules,
-    messages,
-    res,
-    runtimeModules,
-    detail = false,
-    module,
-    stream
-  } = props;
-  const assistantResponses = response?.assistantResponses || [];
-
-  const toolsPrompt = JSON.stringify(
-    toolModules.map((module) => {
-      const properties: Record<
-        string,
-        {
-          type: string;
-          description: string;
-          required?: boolean;
-        }
-      > = {};
-      module.toolParams.forEach((item) => {
-        properties[item.key] = {
-          type: 'string',
-          description: item.toolDescription || ''
-        };
-      });
-
-      return {
-        toolId: module.moduleId,
-        description: module.intro,
-        parameters: {
-          type: 'object',
-          properties,
-          required: module.toolParams.filter((item) => item.required).map((item) => item.key)
-        }
-      };
-    })
-  );
-
-  const lastMessage = messages[messages.length - 1];
-  if (typeof lastMessage.content !== 'string') {
-    return Promise.reject('暂时只支持纯文本');
-  }
-  lastMessage.content = replaceVariable(lastMessage.content, {
-    toolsPrompt
-  });
-
-  const filterMessages = await filterGPTMessageByMaxTokens({
-    messages,
-    maxTokens: toolModel.maxContext - 500 // filter token. not response maxToken
-  });
-  // console.log(JSON.stringify(filterMessages, null, 2));
-  /* Run llm */
-  const ai = getAIApi({
-    timeout: 480000
-  });
-  const aiResponse = await ai.chat.completions.create(
-    {
-      ...toolModel?.defaultConfig,
-      model: toolModel.model,
-      temperature: 0,
-      stream,
-      messages: filterMessages
-    },
-    {
-      headers: {
-        Accept: 'application/json, text/plain, */*'
-      }
-    }
-  );
-
-  const answer = await (async () => {
-    if (stream) {
-      const { answer } = await streamResponse({
-        res,
-        detail,
-        toolModules,
-        stream: aiResponse
-      });
-
-      return answer;
-    } else {
-      const result = aiResponse as ChatCompletion;
-
-      return result.choices?.[0]?.message?.content || '';
-    }
-  })();
-
-  const parseAnswerResult = parseAnswer(answer);
-  // console.log(parseAnswer, '==11==');
-  // No tools
-  if (typeof parseAnswerResult === 'string') {
-    // No tool is invoked, indicating that the process is over
-    const gptAssistantResponse: ChatCompletionAssistantMessageParam = {
-      role: ChatCompletionRequestMessageRoleEnum.Assistant,
-      content: parseAnswerResult
-    };
-    const completeMessages = filterMessages.concat(gptAssistantResponse);
-    const tokens = await countGptMessagesTokens(completeMessages, undefined);
-    // console.log(tokens, 'response token');
-
-    // concat tool assistant
-    const toolNodeAssistant = GPTMessages2Chats([gptAssistantResponse])[0] as AIChatItemType;
-
-    return {
-      dispatchFlowResponse: response?.dispatchFlowResponse || [],
-      totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
-      completeMessages,
-      assistantResponses: [...assistantResponses, ...toolNodeAssistant.value]
-    };
-  }
-
-  // Run the selected tool.
-  const toolsRunResponse = await (async () => {
-    if (!parseAnswerResult) return Promise.reject('tool run error');
-
-    const toolModule = toolModules.find((module) => module.moduleId === parseAnswerResult.name);
-    if (!toolModule) return Promise.reject('tool not found');
-
-    parseAnswerResult.toolName = toolModule.name;
-    parseAnswerResult.toolAvatar = toolModule.avatar;
-
-    // run tool flow
-    const startParams = (() => {
-      try {
-        return json5.parse(parseAnswerResult.arguments);
-      } catch (error) {
-        return {};
-      }
-    })();
-
-    // SSE response to client
-    if (stream && detail) {
-      responseWrite({
-        res,
-        event: SseResponseEventEnum.toolCall,
-        data: JSON.stringify({
-          tool: {
-            id: parseAnswerResult.id,
-            toolName: toolModule.name,
-            toolAvatar: toolModule.avatar,
-            functionName: parseAnswerResult.name,
-            params: parseAnswerResult.arguments,
-            response: ''
-          }
-        })
-      });
-    }
-
-    const moduleRunResponse = await dispatchWorkFlowV1({
-      ...props,
-      runtimeModules: runtimeModules.map((module) => ({
-        ...module,
-        isEntry: module.moduleId === toolModule.moduleId
-      })),
-      startParams
-    });
-
-    const stringToolResponse = (() => {
-      if (typeof moduleRunResponse.toolResponses === 'object') {
-        return JSON.stringify(moduleRunResponse.toolResponses, null, 2);
-      }
-
-      return moduleRunResponse.toolResponses ? String(moduleRunResponse.toolResponses) : 'none';
-    })();
-
-    if (stream && detail) {
-      responseWrite({
-        res,
-        event: SseResponseEventEnum.toolResponse,
-        data: JSON.stringify({
-          tool: {
-            id: parseAnswerResult.id,
-            toolName: '',
-            toolAvatar: '',
-            params: '',
-            response: stringToolResponse
-          }
-        })
-      });
-    }
-
-    return {
-      moduleRunResponse,
-      toolResponsePrompt: stringToolResponse
-    };
-  })();
-
-  if (stream && detail) {
-    responseWriteNodeStatus({
-      res,
-      name: module.name
-    });
-  }
-
-  // 合并工具调用的结果，使用 functionCall 格式存储。
-  const assistantToolMsgParams: ChatCompletionAssistantMessageParam = {
-    role: ChatCompletionRequestMessageRoleEnum.Assistant,
-    function_call: parseAnswerResult
-  };
-  const concatToolMessages = [
-    ...filterMessages,
-    assistantToolMsgParams
-  ] as ChatCompletionMessageParam[];
-  const tokens = await countGptMessagesTokens(concatToolMessages, undefined);
-  const completeMessages: ChatCompletionMessageParam[] = [
-    ...concatToolMessages,
-    {
-      role: ChatCompletionRequestMessageRoleEnum.Function,
-      name: parseAnswerResult.name,
-      content: toolsRunResponse.toolResponsePrompt
-    }
-  ];
-
-  // tool assistant
-  const toolAssistants = toolsRunResponse.moduleRunResponse.assistantResponses || [];
-  // tool node assistant
-  const adaptChatMessages = GPTMessages2Chats(completeMessages);
-  const toolNodeAssistant = adaptChatMessages.pop() as AIChatItemType;
-
-  const toolNodeAssistants = [...assistantResponses, ...toolAssistants, ...toolNodeAssistant.value];
-
-  const dispatchFlowResponse = response
-    ? response.dispatchFlowResponse.concat(toolsRunResponse.moduleRunResponse)
-    : [toolsRunResponse.moduleRunResponse];
-
-  // get the next user prompt
-  lastMessage.content += `${answer}
-TOOL_RESPONSE: """
-${toolsRunResponse.toolResponsePrompt}
-"""
-ANSWER: `;
-
-  /* check stop signal */
-  const hasStopSignal = toolsRunResponse.moduleRunResponse.flowResponses.some(
-    (item) => !!item.toolStop
-  );
-  if (hasStopSignal) {
-    return {
-      dispatchFlowResponse,
-      totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
-      completeMessages: filterMessages,
-      assistantResponses: toolNodeAssistants
-    };
-  }
-
-  return runToolWithPromptCall(
-    {
-      ...props,
-      messages
-    },
-    {
-      dispatchFlowResponse,
-      totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
-      assistantResponses: toolNodeAssistants
-    }
-  );
-};
-
-async function streamResponse({
-  res,
-  detail,
-  stream
-}: {
-  res: NextApiResponse;
-  detail: boolean;
-  toolModules: ToolModuleItemType[];
-  stream: StreamChatType;
-}) {
-  const write = responseWriteController({
-    res,
-    readStream: stream
-  });
-
-  let startResponseWrite = false;
-  let textAnswer = '';
-
-  for await (const part of stream) {
-    if (res.closed) {
-      stream.controller?.abort();
-      break;
-    }
-
-    const responseChoice = part.choices?.[0]?.delta;
-    if (responseChoice.content) {
-      const content = responseChoice?.content || '';
-      textAnswer += content;
-
-      if (startResponseWrite) {
-        responseWrite({
-          write,
-          event: detail ? SseResponseEventEnum.answer : undefined,
-          data: textAdaptGptResponse({
-            text: content
-          })
-        });
-      } else if (textAnswer.length >= 3) {
-        textAnswer = textAnswer.trim();
-        if (textAnswer.startsWith('0')) {
-          startResponseWrite = true;
-          // find first : index
-          const firstIndex = textAnswer.indexOf(':');
-          textAnswer = textAnswer.substring(firstIndex + 1).trim();
-          responseWrite({
-            write,
-            event: detail ? SseResponseEventEnum.answer : undefined,
-            data: textAdaptGptResponse({
-              text: textAnswer
-            })
-          });
-        }
-      }
-    }
-  }
-
-  if (!textAnswer) {
-    return Promise.reject('LLM api response empty');
-  }
-  // console.log(textAnswer, '---===');
-  return { answer: textAnswer.trim() };
-}
-
-const parseAnswer = (str: string): FunctionCallCompletion | string => {
-  // 首先，使用正则表达式提取TOOL_ID和TOOL_ARGUMENTS
-  const prefix = '1:';
-  str = str.trim();
-  if (str.startsWith(prefix)) {
-    const toolString = str.substring(prefix.length).trim();
-
-    try {
-      const toolCall = json5.parse(toolString);
-      return {
-        id: getNanoid(),
-        name: toolCall.toolId,
-        arguments: JSON.stringify(toolCall.arguments || toolCall.parameters)
-      };
-    } catch (error) {
-      return str;
-    }
-  } else {
-    return str;
-  }
-};

packages/service/core/workflow/dispatchV1/agent/runTool/stopTool.ts

@@ -1,14 +0,0 @@
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
-
-export type AnswerProps = ModuleDispatchProps<{}>;
-export type AnswerResponse = DispatchNodeResultType<{}>;
-
-export const dispatchStopToolCall = (props: Record<string, any>): AnswerResponse => {
-  return {
-    [DispatchNodeResponseKeyEnum.nodeResponse]: {
-      toolStop: true
-    }
-  };
-};

packages/service/core/workflow/dispatchV1/agent/runTool/toolChoice.ts

@@ -1,413 +0,0 @@
-// @ts-nocheck
-import { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
-import { getAIApi } from '../../../../ai/config';
-import { filterGPTMessageByMaxTokens } from '../../../../chat/utils';
-import {
-  ChatCompletion,
-  ChatCompletionMessageToolCall,
-  StreamChatType,
-  ChatCompletionToolMessageParam,
-  ChatCompletionAssistantToolParam,
-  ChatCompletionMessageParam,
-  ChatCompletionTool,
-  ChatCompletionAssistantMessageParam
-} from '@fastgpt/global/core/ai/type';
-import { NextApiResponse } from 'next';
-import {
-  responseWrite,
-  responseWriteController,
-  responseWriteNodeStatus
-} from '../../../../../common/response';
-import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
-import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
-import { dispatchWorkFlowV1 } from '../../index';
-import { DispatchToolModuleProps, RunToolResponse, ToolModuleItemType } from './type.d';
-import json5 from 'json5';
-import { DispatchFlowResponse } from '../../type';
-import { countGptMessagesTokens } from '../../../../../common/string/tiktoken';
-import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
-import { AIChatItemType } from '@fastgpt/global/core/chat/type';
-
-type ToolRunResponseType = {
-  moduleRunResponse: DispatchFlowResponse;
-  toolMsgParams: ChatCompletionToolMessageParam;
-}[];
-
-/* 
-  调用思路
-  1. messages 接收发送给AI的消息
-  2. response 记录递归运行结果(累计计算 dispatchFlowResponse, totalTokens和assistantResponses)
-  3. 如果运行工具的话，则需要把工具中的结果累计加到dispatchFlowResponse中。 本次消耗的 token 加到 totalTokens, assistantResponses 记录当前工具运行的内容。
-*/
-
-export const runToolWithToolChoice = async (
-  props: DispatchToolModuleProps & {
-    messages: ChatCompletionMessageParam[];
-    toolModules: ToolModuleItemType[];
-    toolModel: LLMModelItemType;
-  },
-  response?: RunToolResponse
-): Promise<RunToolResponse> => {
-  const {
-    toolModel,
-    toolModules,
-    messages,
-    res,
-    runtimeModules,
-    detail = false,
-    module,
-    stream
-  } = props;
-  const assistantResponses = response?.assistantResponses || [];
-
-  const tools: ChatCompletionTool[] = toolModules.map((module) => {
-    const properties: Record<
-      string,
-      {
-        type: string;
-        description: string;
-        required?: boolean;
-      }
-    > = {};
-    module.toolParams.forEach((item) => {
-      properties[item.key] = {
-        type: 'string',
-        description: item.toolDescription || ''
-      };
-    });
-
-    return {
-      type: 'function',
-      function: {
-        name: module.moduleId,
-        description: module.intro,
-        parameters: {
-          type: 'object',
-          properties,
-          required: module.toolParams.filter((item) => item.required).map((item) => item.key)
-        }
-      }
-    };
-  });
-
-  const filterMessages = await filterGPTMessageByMaxTokens({
-    messages,
-    maxTokens: toolModel.maxContext - 300 // filter token. not response maxToken
-  });
-
-  /* Run llm */
-  const ai = getAIApi({
-    timeout: 480000
-  });
-  const aiResponse = await ai.chat.completions.create(
-    {
-      ...toolModel?.defaultConfig,
-      model: toolModel.model,
-      temperature: 0,
-      stream,
-      messages: filterMessages,
-      tools,
-      tool_choice: 'auto'
-    },
-    {
-      headers: {
-        Accept: 'application/json, text/plain, */*'
-      }
-    }
-  );
-
-  const { answer, toolCalls } = await (async () => {
-    if (stream) {
-      return streamResponse({
-        res,
-        detail,
-        toolModules,
-        stream: aiResponse
-      });
-    } else {
-      const result = aiResponse as ChatCompletion;
-      const calls = result.choices?.[0]?.message?.tool_calls || [];
-
-      // 加上name和avatar
-      const toolCalls = calls.map((tool) => {
-        const toolModule = toolModules.find((module) => module.moduleId === tool.function?.name);
-        return {
-          ...tool,
-          toolName: toolModule?.name || '',
-          toolAvatar: toolModule?.avatar || ''
-        };
-      });
-
-      return {
-        answer: result.choices?.[0]?.message?.content || '',
-        toolCalls: toolCalls
-      };
-    }
-  })();
-
-  // Run the selected tool.
-  const toolsRunResponse = (
-    await Promise.all(
-      toolCalls.map(async (tool) => {
-        const toolModule = toolModules.find((module) => module.moduleId === tool.function?.name);
-
-        if (!toolModule) return;
-
-        const startParams = (() => {
-          try {
-            return json5.parse(tool.function.arguments);
-          } catch (error) {
-            return {};
-          }
-        })();
-
-        const moduleRunResponse = await dispatchWorkFlowV1({
-          ...props,
-          runtimeModules: runtimeModules.map((module) => ({
-            ...module,
-            isEntry: module.moduleId === toolModule.moduleId
-          })),
-          startParams
-        });
-
-        const stringToolResponse = (() => {
-          if (typeof moduleRunResponse.toolResponses === 'object') {
-            return JSON.stringify(moduleRunResponse.toolResponses, null, 2);
-          }
-
-          return moduleRunResponse.toolResponses ? String(moduleRunResponse.toolResponses) : 'none';
-        })();
-
-        const toolMsgParams: ChatCompletionToolMessageParam = {
-          tool_call_id: tool.id,
-          role: ChatCompletionRequestMessageRoleEnum.Tool,
-          name: tool.function.name,
-          content: stringToolResponse
-        };
-
-        if (stream && detail) {
-          responseWrite({
-            res,
-            event: SseResponseEventEnum.toolResponse,
-            data: JSON.stringify({
-              tool: {
-                id: tool.id,
-                toolName: '',
-                toolAvatar: '',
-                params: '',
-                response: stringToolResponse
-              }
-            })
-          });
-        }
-
-        return {
-          moduleRunResponse,
-          toolMsgParams
-        };
-      })
-    )
-  ).filter(Boolean) as ToolRunResponseType;
-
-  const flatToolsResponseData = toolsRunResponse.map((item) => item.moduleRunResponse).flat();
-  if (toolCalls.length > 0 && !res.closed) {
-    // Run the tool, combine its results, and perform another round of AI calls
-    const assistantToolMsgParams: ChatCompletionAssistantToolParam = {
-      role: ChatCompletionRequestMessageRoleEnum.Assistant,
-      tool_calls: toolCalls
-    };
-    const concatToolMessages = [
-      ...filterMessages,
-      assistantToolMsgParams
-    ] as ChatCompletionMessageParam[];
-    const tokens = await countGptMessagesTokens(concatToolMessages, tools);
-    const completeMessages = [
-      ...concatToolMessages,
-      ...toolsRunResponse.map((item) => item?.toolMsgParams)
-    ];
-
-    // console.log(tokens, 'tool');
-
-    if (stream && detail) {
-      responseWriteNodeStatus({
-        res,
-        name: module.name
-      });
-    }
-
-    // tool assistant
-    const toolAssistants = toolsRunResponse
-      .map((item) => {
-        const assistantResponses = item.moduleRunResponse.assistantResponses || [];
-        return assistantResponses;
-      })
-      .flat();
-
-    // tool node assistant
-    const adaptChatMessages = GPTMessages2Chats(completeMessages);
-    const toolNodeAssistant = adaptChatMessages.pop() as AIChatItemType;
-
-    const toolNodeAssistants = [
-      ...assistantResponses,
-      ...toolAssistants,
-      ...toolNodeAssistant.value
-    ];
-
-    // concat tool responses
-    const dispatchFlowResponse = response
-      ? response.dispatchFlowResponse.concat(flatToolsResponseData)
-      : flatToolsResponseData;
-
-    /* check stop signal */
-    const hasStopSignal = flatToolsResponseData.some(
-      (item) => !!item.flowResponses?.find((item) => item.toolStop)
-    );
-    if (hasStopSignal) {
-      return {
-        dispatchFlowResponse,
-        totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
-        completeMessages,
-        assistantResponses: toolNodeAssistants
-      };
-    }
-
-    return runToolWithToolChoice(
-      {
-        ...props,
-        messages: completeMessages
-      },
-      {
-        dispatchFlowResponse,
-        totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
-        assistantResponses: toolNodeAssistants
-      }
-    );
-  } else {
-    // No tool is invoked, indicating that the process is over
-    const gptAssistantResponse: ChatCompletionAssistantMessageParam = {
-      role: ChatCompletionRequestMessageRoleEnum.Assistant,
-      content: answer
-    };
-    const completeMessages = filterMessages.concat(gptAssistantResponse);
-    const tokens = await countGptMessagesTokens(completeMessages, tools);
-    // console.log(tokens, 'response token');
-
-    // concat tool assistant
-    const toolNodeAssistant = GPTMessages2Chats([gptAssistantResponse])[0] as AIChatItemType;
-
-    return {
-      dispatchFlowResponse: response?.dispatchFlowResponse || [],
-      totalTokens: response?.totalTokens ? response.totalTokens + tokens : tokens,
-      completeMessages,
-      assistantResponses: [...assistantResponses, ...toolNodeAssistant.value]
-    };
-  }
-};
-
-async function streamResponse({
-  res,
-  detail,
-  toolModules,
-  stream
-}: {
-  res: NextApiResponse;
-  detail: boolean;
-  toolModules: ToolModuleItemType[];
-  stream: StreamChatType;
-}) {
-  const write = responseWriteController({
-    res,
-    readStream: stream
-  });
-
-  let textAnswer = '';
-  let toolCalls: ChatCompletionMessageToolCall[] = [];
-
-  for await (const part of stream) {
-    if (res.closed) {
-      stream.controller?.abort();
-      break;
-    }
-
-    const responseChoice = part.choices?.[0]?.delta;
-    // console.log(JSON.stringify(responseChoice, null, 2));
-    if (responseChoice?.content) {
-      const content = responseChoice.content || '';
-      textAnswer += content;
-
-      responseWrite({
-        write,
-        event: detail ? SseResponseEventEnum.answer : undefined,
-        data: textAdaptGptResponse({
-          text: content
-        })
-      });
-    } else if (responseChoice?.tool_calls?.[0]) {
-      const toolCall: ChatCompletionMessageToolCall = responseChoice.tool_calls[0];
-
-      // 流响应中,每次只会返回一个工具. 如果带了 id，说明是执行一个工具
-      if (toolCall.id) {
-        const toolModule = toolModules.find(
-          (module) => module.moduleId === toolCall.function?.name
-        );
-
-        if (toolModule) {
-          if (toolCall.function?.arguments === undefined) {
-            toolCall.function.arguments = '';
-          }
-          toolCalls.push({
-            ...toolCall,
-            toolName: toolModule.name,
-            toolAvatar: toolModule.avatar
-          });
-
-          if (detail) {
-            responseWrite({
-              write,
-              event: SseResponseEventEnum.toolCall,
-              data: JSON.stringify({
-                tool: {
-                  id: toolCall.id,
-                  toolName: toolModule.name,
-                  toolAvatar: toolModule.avatar,
-                  functionName: toolCall.function.name,
-                  params: toolCall.function.arguments,
-                  response: ''
-                }
-              })
-            });
-          }
-        }
-      }
-      /* arg 插入最后一个工具的参数里 */
-      const arg: string = responseChoice.tool_calls?.[0]?.function?.arguments;
-      const currentTool = toolCalls[toolCalls.length - 1];
-      if (currentTool) {
-        currentTool.function.arguments += arg;
-
-        if (detail) {
-          responseWrite({
-            write,
-            event: SseResponseEventEnum.toolParams,
-            data: JSON.stringify({
-              tool: {
-                id: currentTool.id,
-                toolName: '',
-                toolAvatar: '',
-                params: arg,
-                response: ''
-              }
-            })
-          });
-        }
-      }
-    }
-  }
-
-  if (!textAnswer && toolCalls.length === 0) {
-    return Promise.reject('LLM api response empty');
-  }
-
-  return { answer: textAnswer, toolCalls };
-}

packages/service/core/workflow/dispatchV1/agent/runTool/type.d.ts

@@ -1,28 +0,0 @@
-import { ChatCompletionMessageParam } from '@fastgpt/global/core/ai/type';
-import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import { FlowNodeInputItemType } from '@fastgpt/global/core/workflow/node/type';
-import type {
-  ModuleDispatchProps,
-  DispatchNodeResponseType
-} from '@fastgpt/global/core/workflow/runtime/type';
-import type { RuntimeNodeItemType } from '@fastgpt/global/core/workflow/runtime/type';
-import { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
-import type { DispatchFlowResponse } from '../../type.d';
-import { AIChatItemValueItemType, ChatItemValueItemType } from '@fastgpt/global/core/chat/type';
-
-export type DispatchToolModuleProps = ModuleDispatchProps<{
-  [NodeInputKeyEnum.history]?: ChatItemType[];
-  [NodeInputKeyEnum.aiModel]: string;
-  [NodeInputKeyEnum.aiSystemPrompt]: string;
-  [NodeInputKeyEnum.userChatInput]: string;
-}>;
-
-export type RunToolResponse = {
-  dispatchFlowResponse: DispatchFlowResponse[];
-  totalTokens: number;
-  completeMessages?: ChatCompletionMessageParam[];
-  assistantResponses?: AIChatItemValueItemType[];
-};
-export type ToolModuleItemType = RuntimeNodeItemType & {
-  toolParams: RuntimeNodeItemType['inputs'];
-};

packages/service/core/workflow/dispatchV1/chat/oneapi.ts

@@ -1,396 +0,0 @@
-// @ts-nocheck
-import type { NextApiResponse } from 'next';
-import { filterGPTMessageByMaxTokens, loadRequestMessages } from '../../../chat/utils';
-import type { ChatItemType, UserChatItemValueItemType } from '@fastgpt/global/core/chat/type.d';
-import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
-import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
-import { getAIApi } from '../../../ai/config';
-import type {
-  ChatCompletion,
-  ChatCompletionMessageParam,
-  StreamChatType
-} from '@fastgpt/global/core/ai/type.d';
-import { formatModelChars2Points } from '../../../../support/wallet/usage/utils';
-import type { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
-import { postTextCensor } from '../../../../common/api/requestPlusApi';
-import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
-import type { FlowNodeItemType } from '@fastgpt/global/core/workflow/type/node';
-import type { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
-import {
-  countMessagesTokens,
-  countGptMessagesTokens
-} from '../../../../common/string/tiktoken/index';
-import {
-  chats2GPTMessages,
-  getSystemPrompt,
-  GPTMessages2Chats,
-  runtimePrompt2ChatsValue
-} from '@fastgpt/global/core/chat/adapt';
-import {
-  Prompt_QuotePromptList,
-  Prompt_QuoteTemplateList
-} from '@fastgpt/global/core/ai/prompt/AIChat';
-import type { AIChatNodeProps } from '@fastgpt/global/core/workflow/runtime/type.d';
-import { replaceVariable } from '@fastgpt/global/common/string/tools';
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { responseWrite, responseWriteController } from '../../../../common/response';
-import { getLLMModel, ModelTypeEnum } from '../../../ai/model';
-import type { SearchDataResponseItemType } from '@fastgpt/global/core/dataset/type';
-import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import { getHistories } from '../utils';
-import { filterSearchResultsByMaxChars } from '../../utils';
-import { getHistoryPreview } from '@fastgpt/global/core/chat/utils';
-
-export type ChatProps = ModuleDispatchProps<
-  AIChatNodeProps & {
-    [NodeInputKeyEnum.userChatInput]: string;
-    [NodeInputKeyEnum.history]?: ChatItemType[] | number;
-    [NodeInputKeyEnum.aiChatDatasetQuote]?: SearchDataResponseItemType[];
-  }
->;
-export type ChatResponse = DispatchNodeResultType<{
-  [NodeOutputKeyEnum.answerText]: string;
-  [NodeOutputKeyEnum.history]: ChatItemType[];
-}>;
-
-/* request openai chat */
-export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResponse> => {
-  let {
-    res,
-    stream = false,
-    detail = false,
-    user,
-    histories,
-    module: { name, outputs },
-    inputFiles = [],
-    params: {
-      model,
-      temperature = 0,
-      maxToken = 4000,
-      history = 6,
-      quoteQA,
-      userChatInput,
-      isResponseAnswerText = true,
-      systemPrompt = '',
-      quoteTemplate,
-      quotePrompt
-    }
-  } = props;
-  if (!userChatInput && inputFiles.length === 0) {
-    return Promise.reject('Question is empty');
-  }
-  stream = stream && isResponseAnswerText;
-
-  const chatHistories = getHistories(history, histories);
-
-  // temperature adapt
-  const modelConstantsData = getLLMModel(model);
-
-  if (!modelConstantsData) {
-    return Promise.reject('The chat model is undefined, you need to select a chat model.');
-  }
-
-  const { quoteText } = await filterQuote({
-    quoteQA,
-    model: modelConstantsData,
-    quoteTemplate
-  });
-
-  // censor model and system key
-  if (modelConstantsData.censor && !user.openaiAccount?.key) {
-    await postTextCensor({
-      text: `${systemPrompt}
-      ${quoteText}
-      ${userChatInput}
-      `
-    });
-  }
-
-  const { filterMessages } = await getChatMessages({
-    model: modelConstantsData,
-    histories: chatHistories,
-    quoteQA,
-    quoteText,
-    quotePrompt,
-    userChatInput,
-    inputFiles,
-    systemPrompt
-  });
-
-  const { max_tokens } = await getMaxTokens({
-    model: modelConstantsData,
-    maxToken,
-    filterMessages
-  });
-
-  // FastGPT temperature range: 1~10
-  temperature = +(modelConstantsData.maxTemperature * (temperature / 10)).toFixed(2);
-  temperature = Math.max(temperature, 0.01);
-  const ai = getAIApi({
-    userKey: user.openaiAccount,
-    timeout: 480000
-  });
-
-  const concatMessages = [
-    ...(modelConstantsData.defaultSystemChatPrompt
-      ? [
-          {
-            role: ChatCompletionRequestMessageRoleEnum.System,
-            content: modelConstantsData.defaultSystemChatPrompt
-          }
-        ]
-      : []),
-    ...filterMessages
-  ] as ChatCompletionMessageParam[];
-
-  if (concatMessages.length === 0) {
-    return Promise.reject('core.chat.error.Messages empty');
-  }
-
-  const loadMessages = await loadRequestMessages({
-    messages: concatMessages,
-    useVision: false
-  });
-
-  const response = await ai.chat.completions.create(
-    {
-      ...modelConstantsData?.defaultConfig,
-      model: modelConstantsData.model,
-      temperature,
-      max_tokens,
-      stream,
-      messages: loadMessages
-    },
-    {
-      headers: {
-        Accept: 'application/json, text/plain, */*'
-      }
-    }
-  );
-
-  const { answerText } = await (async () => {
-    if (res && stream) {
-      // sse response
-      const { answer } = await streamResponse({
-        res,
-        detail,
-        stream: response
-      });
-
-      targetResponse({ res, detail, outputs });
-
-      return {
-        answerText: answer
-      };
-    } else {
-      const unStreamResponse = response as ChatCompletion;
-      const answer = unStreamResponse.choices?.[0]?.message?.content || '';
-
-      return {
-        answerText: answer
-      };
-    }
-  })();
-
-  const completeMessages = filterMessages.concat({
-    role: ChatCompletionRequestMessageRoleEnum.Assistant,
-    content: answerText
-  });
-  const chatCompleteMessages = GPTMessages2Chats(completeMessages);
-
-  const tokens = await countMessagesTokens(chatCompleteMessages);
-  const { totalPoints, modelName } = formatModelChars2Points({
-    model,
-    tokens,
-    modelType: ModelTypeEnum.llm
-  });
-
-  return {
-    answerText,
-    [DispatchNodeResponseKeyEnum.nodeResponse]: {
-      totalPoints: user.openaiAccount?.key ? 0 : totalPoints,
-      model: modelName,
-      tokens,
-      query: `${userChatInput}`,
-      maxToken: max_tokens,
-      historyPreview: getHistoryPreview(chatCompleteMessages),
-      contextTotalLen: completeMessages.length
-    },
-    [DispatchNodeResponseKeyEnum.nodeDispatchUsages]: [
-      {
-        moduleName: name,
-        totalPoints: user.openaiAccount?.key ? 0 : totalPoints,
-        model: modelName,
-        tokens
-      }
-    ],
-    [DispatchNodeResponseKeyEnum.toolResponses]: answerText,
-    history: chatCompleteMessages
-  };
-};
-
-async function filterQuote({
-  quoteQA = [],
-  model,
-  quoteTemplate
-}: {
-  quoteQA: ChatProps['params']['quoteQA'];
-  model: LLMModelItemType;
-  quoteTemplate?: string;
-}) {
-  function getValue(item: SearchDataResponseItemType, index: number) {
-    return replaceVariable(quoteTemplate || Prompt_QuoteTemplateList[0].value, {
-      q: item.q,
-      a: item.a,
-      source: item.sourceName,
-      sourceId: String(item.sourceId || 'UnKnow'),
-      index: index + 1
-    });
-  }
-
-  // slice filterSearch
-  const filterQuoteQA = await filterSearchResultsByMaxChars(quoteQA, model.quoteMaxToken);
-
-  const quoteText =
-    filterQuoteQA.length > 0
-      ? `${filterQuoteQA.map((item, index) => getValue(item, index).trim()).join('\n------\n')}`
-      : '';
-
-  return {
-    quoteText
-  };
-}
-async function getChatMessages({
-  quotePrompt,
-  quoteText,
-  quoteQA,
-  histories = [],
-  systemPrompt,
-  userChatInput,
-  inputFiles,
-  model
-}: {
-  quotePrompt?: string;
-  quoteText: string;
-  quoteQA: ChatProps['params']['quoteQA'];
-  histories: ChatItemType[];
-  systemPrompt: string;
-  userChatInput: string;
-  inputFiles: UserChatItemValueItemType['file'][];
-  model: LLMModelItemType;
-}) {
-  const replaceInputValue =
-    quoteQA !== undefined
-      ? replaceVariable(quotePrompt || Prompt_QuotePromptList[0].value, {
-          quote: quoteText,
-          question: userChatInput
-        })
-      : userChatInput;
-
-  const messages: ChatItemType[] = [
-    ...getSystemPrompt(systemPrompt),
-    ...histories,
-    {
-      obj: ChatRoleEnum.Human,
-      value: runtimePrompt2ChatsValue({
-        files: inputFiles,
-        text: replaceInputValue
-      })
-    }
-  ];
-  const adaptMessages = chats2GPTMessages({ messages, reserveId: false });
-
-  const filterMessages = await filterGPTMessageByMaxTokens({
-    messages: adaptMessages,
-    maxTokens: model.maxContext - 300 // filter token. not response maxToken
-  });
-
-  return {
-    filterMessages
-  };
-}
-async function getMaxTokens({
-  maxToken,
-  model,
-  filterMessages = []
-}: {
-  maxToken: number;
-  model: LLMModelItemType;
-  filterMessages: ChatCompletionMessageParam[];
-}) {
-  maxToken = Math.min(maxToken, model.maxResponse);
-  const tokensLimit = model.maxContext;
-
-  /* count response max token */
-  const promptsToken = await countGptMessagesTokens(filterMessages);
-  maxToken = promptsToken + maxToken > tokensLimit ? tokensLimit - promptsToken : maxToken;
-
-  if (maxToken <= 0) {
-    maxToken = 200;
-  }
-  return {
-    max_tokens: maxToken
-  };
-}
-
-function targetResponse({
-  res,
-  outputs,
-  detail
-}: {
-  res: NextApiResponse;
-  outputs: FlowNodeItemType['outputs'];
-  detail: boolean;
-}) {
-  const targets =
-    outputs.find((output) => output.key === NodeOutputKeyEnum.answerText)?.targets || [];
-
-  if (targets.length === 0) return;
-  responseWrite({
-    res,
-    event: detail ? SseResponseEventEnum.answer : undefined,
-    data: textAdaptGptResponse({
-      text: '\n'
-    })
-  });
-}
-
-async function streamResponse({
-  res,
-  detail,
-  stream
-}: {
-  res: NextApiResponse;
-  detail: boolean;
-  stream: StreamChatType;
-}) {
-  const write = responseWriteController({
-    res,
-    readStream: stream
-  });
-  let answer = '';
-  for await (const part of stream) {
-    if (res.closed) {
-      stream.controller?.abort();
-      break;
-    }
-    const content = part.choices?.[0]?.delta?.content || '';
-    answer += content;
-
-    responseWrite({
-      write,
-      event: detail ? SseResponseEventEnum.answer : undefined,
-      data: textAdaptGptResponse({
-        text: content
-      })
-    });
-  }
-
-  if (!answer) {
-    return Promise.reject('core.chat.Chat API is error or undefined');
-  }
-
-  return { answer };
-}

packages/service/core/workflow/dispatchV1/dataset/concat.ts

@@ -1,35 +0,0 @@
-import type { SearchDataResponseItemType } from '@fastgpt/global/core/dataset/type';
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import { datasetSearchResultConcat } from '@fastgpt/global/core/dataset/search/utils';
-import { filterSearchResultsByMaxChars } from '../../utils';
-
-type DatasetConcatProps = ModuleDispatchProps<
-  {
-    [NodeInputKeyEnum.datasetMaxTokens]: number;
-  } & { [key: string]: SearchDataResponseItemType[] }
->;
-type DatasetConcatResponse = {
-  [NodeOutputKeyEnum.datasetQuoteQA]: SearchDataResponseItemType[];
-};
-
-export async function dispatchDatasetConcat(
-  props: DatasetConcatProps
-): Promise<DatasetConcatResponse> {
-  const {
-    params: { limit = 1500, ...quoteMap }
-  } = props as DatasetConcatProps;
-
-  const quoteList = Object.values(quoteMap).filter((list) => Array.isArray(list));
-
-  const rrfConcatResults = datasetSearchResultConcat(
-    quoteList.map((list) => ({
-      k: 60,
-      list
-    }))
-  );
-
-  return {
-    [NodeOutputKeyEnum.datasetQuoteQA]: await filterSearchResultsByMaxChars(rrfConcatResults, limit)
-  };
-}

packages/service/core/workflow/dispatchV1/dataset/search.ts

@@ -1,165 +0,0 @@
-// @ts-nocheck
-import {
-  DispatchNodeResponseType,
-  DispatchNodeResultType
-} from '@fastgpt/global/core/workflow/runtime/type.d';
-import { formatModelChars2Points } from '../../../../support/wallet/usage/utils';
-import type { SelectedDatasetType } from '@fastgpt/global/core/workflow/api.d';
-import type { SearchDataResponseItemType } from '@fastgpt/global/core/dataset/type';
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { ModelTypeEnum, getLLMModel, getVectorModel } from '../../../ai/model';
-import { searchDatasetData } from '../../../dataset/search/controller';
-import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import { DatasetSearchModeEnum } from '@fastgpt/global/core/dataset/constants';
-import { getHistories } from '../utils';
-import { datasetSearchQueryExtension } from '../../../dataset/search/utils';
-import { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
-import { checkTeamReRankPermission } from '../../../../support/permission/teamLimit';
-
-type DatasetSearchProps = ModuleDispatchProps<{
-  [NodeInputKeyEnum.datasetSelectList]: SelectedDatasetType;
-  [NodeInputKeyEnum.datasetSimilarity]: number;
-  [NodeInputKeyEnum.datasetMaxTokens]: number;
-  [NodeInputKeyEnum.datasetSearchMode]: `${DatasetSearchModeEnum}`;
-  [NodeInputKeyEnum.userChatInput]: string;
-  [NodeInputKeyEnum.datasetSearchUsingReRank]: boolean;
-  [NodeInputKeyEnum.datasetSearchUsingExtensionQuery]: boolean;
-  [NodeInputKeyEnum.datasetSearchExtensionModel]: string;
-  [NodeInputKeyEnum.datasetSearchExtensionBg]: string;
-}>;
-export type DatasetSearchResponse = DispatchNodeResultType<{
-  isEmpty?: boolean;
-  unEmpty?: boolean;
-  [NodeOutputKeyEnum.datasetQuoteQA]: SearchDataResponseItemType[];
-}>;
-
-export async function dispatchDatasetSearch(
-  props: DatasetSearchProps
-): Promise<DatasetSearchResponse> {
-  const {
-    teamId,
-    histories,
-    module,
-    params: {
-      datasets = [],
-      similarity,
-      limit = 1500,
-      usingReRank,
-      searchMode,
-      userChatInput,
-
-      datasetSearchUsingExtensionQuery,
-      datasetSearchExtensionModel,
-      datasetSearchExtensionBg
-    }
-  } = props as DatasetSearchProps;
-
-  if (!Array.isArray(datasets)) {
-    return Promise.reject('Quote type error');
-  }
-
-  if (datasets.length === 0) {
-    return Promise.reject('core.chat.error.Select dataset empty');
-  }
-
-  if (!userChatInput) {
-    return Promise.reject('core.chat.error.User input empty');
-  }
-
-  // query extension
-  const extensionModel =
-    datasetSearchUsingExtensionQuery && datasetSearchExtensionModel
-      ? getLLMModel(datasetSearchExtensionModel)
-      : undefined;
-  const { concatQueries, rewriteQuery, aiExtensionResult } = await datasetSearchQueryExtension({
-    query: userChatInput,
-    extensionModel,
-    extensionBg: datasetSearchExtensionBg,
-    histories: getHistories(6, histories)
-  });
-
-  // console.log(concatQueries, rewriteQuery, aiExtensionResult);
-
-  // get vector
-  const vectorModel = getVectorModel(datasets[0]?.vectorModel?.model);
-
-  // start search
-  const {
-    searchRes,
-    tokens,
-    usingSimilarityFilter,
-    usingReRank: searchUsingReRank
-  } = await searchDatasetData({
-    teamId,
-    reRankQuery: `${rewriteQuery}`,
-    queries: concatQueries,
-    model: vectorModel.model,
-    similarity,
-    limit,
-    datasetIds: datasets.map((item) => item.datasetId),
-    searchMode,
-    usingReRank: usingReRank && (await checkTeamReRankPermission(teamId))
-  });
-
-  // count bill results
-  // vector
-  const { totalPoints, modelName } = formatModelChars2Points({
-    model: vectorModel.model,
-    tokens,
-    modelType: ModelTypeEnum.vector
-  });
-  const responseData: DispatchNodeResponseType & { totalPoints: number } = {
-    totalPoints,
-    query: concatQueries.join('\n'),
-    model: modelName,
-    tokens,
-    similarity: usingSimilarityFilter ? similarity : undefined,
-    limit,
-    searchMode,
-    searchUsingReRank: searchUsingReRank,
-    quoteList: searchRes
-  };
-  const nodeDispatchUsages: ChatNodeUsageType[] = [
-    {
-      totalPoints,
-      moduleName: module.name,
-      model: modelName,
-      tokens
-    }
-  ];
-
-  if (aiExtensionResult) {
-    const { totalPoints, modelName } = formatModelChars2Points({
-      model: aiExtensionResult.model,
-      tokens: aiExtensionResult.tokens,
-      modelType: ModelTypeEnum.llm
-    });
-
-    responseData.totalPoints += totalPoints;
-    responseData.tokens = aiExtensionResult.tokens;
-    responseData.extensionModel = modelName;
-    responseData.extensionResult =
-      aiExtensionResult.extensionQueries?.join('\n') ||
-      JSON.stringify(aiExtensionResult.extensionQueries);
-
-    nodeDispatchUsages.push({
-      totalPoints,
-      moduleName: 'core.module.template.Query extension',
-      model: modelName,
-      tokens: aiExtensionResult.tokens
-    });
-  }
-
-  return {
-    isEmpty: searchRes.length === 0 ? true : undefined,
-    unEmpty: searchRes.length > 0 ? true : undefined,
-    quoteQA: searchRes,
-    [DispatchNodeResponseKeyEnum.nodeResponse]: responseData,
-    nodeDispatchUsages,
-    [DispatchNodeResponseKeyEnum.toolResponses]: searchRes.map((item) => ({
-      id: item.id,
-      text: `${item.q}\n${item.a}`.trim()
-    }))
-  };
-}

packages/service/core/workflow/dispatchV1/index.ts

@@ -1,430 +0,0 @@
-// @ts-nocheck
-import { NextApiResponse } from 'next';
-import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import { NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import type { ChatDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import type { RuntimeNodeItemType } from '@fastgpt/global/core/workflow/runtime/type.d';
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import type {
-  AIChatItemValueItemType,
-  ChatHistoryItemResType,
-  ToolRunResponseItemType
-} from '@fastgpt/global/core/chat/type.d';
-import { FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant';
-import { FlowNodeItemType } from '@fastgpt/global/core/workflow/type/node';
-import { replaceVariable } from '@fastgpt/global/common/string/tools';
-import { responseWriteNodeStatus } from '../../../common/response';
-import { getSystemTime } from '@fastgpt/global/common/time/timezone';
-
-import { dispatchChatInput } from './init/userChatInput';
-import { dispatchChatCompletion } from './chat/oneapi';
-import { dispatchDatasetSearch } from './dataset/search';
-import { dispatchDatasetConcat } from './dataset/concat';
-import { dispatchAnswer } from './tools/answer';
-import { dispatchClassifyQuestion } from './agent/classifyQuestion';
-import { dispatchContentExtract } from './agent/extract';
-import { dispatchHttpRequest } from './tools/http';
-import { dispatchHttp468Request } from './tools/http468';
-import { dispatchAppRequest } from './tools/runApp';
-import { dispatchQueryExtension } from './tools/queryExternsion';
-import { dispatchRunPlugin } from './plugin/run';
-import { dispatchPluginInput } from './plugin/runInput';
-import { dispatchPluginOutput } from './plugin/runOutput';
-import { checkTheModuleConnectedByTool, valueTypeFormat } from './utils';
-import { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
-import { dispatchRunTools } from './agent/runTool/index';
-import { ChatItemValueTypeEnum } from '@fastgpt/global/core/chat/constants';
-import { DispatchFlowResponse } from './type';
-import { dispatchStopToolCall } from './agent/runTool/stopTool';
-import { dispatchLafRequest } from './tools/runLaf';
-
-const callbackMap: Record<string, Function> = {
-  questionInput: dispatchChatInput,
-  [FlowNodeTypeEnum.answerNode]: dispatchAnswer,
-  [FlowNodeTypeEnum.chatNode]: dispatchChatCompletion,
-  [FlowNodeTypeEnum.datasetSearchNode]: dispatchDatasetSearch,
-  [FlowNodeTypeEnum.datasetConcatNode]: dispatchDatasetConcat,
-  [FlowNodeTypeEnum.classifyQuestion]: dispatchClassifyQuestion,
-  [FlowNodeTypeEnum.contentExtract]: dispatchContentExtract,
-  [FlowNodeTypeEnum.httpRequest468]: dispatchHttp468Request,
-  [FlowNodeTypeEnum.runApp]: dispatchAppRequest,
-  [FlowNodeTypeEnum.pluginModule]: dispatchRunPlugin,
-  [FlowNodeTypeEnum.pluginInput]: dispatchPluginInput,
-  [FlowNodeTypeEnum.pluginOutput]: dispatchPluginOutput,
-  [FlowNodeTypeEnum.queryExtension]: dispatchQueryExtension,
-  [FlowNodeTypeEnum.tools]: dispatchRunTools,
-  [FlowNodeTypeEnum.stopTool]: dispatchStopToolCall,
-  [FlowNodeTypeEnum.lafModule]: dispatchLafRequest
-};
-
-/* running */
-export async function dispatchWorkFlowV1({
-  res,
-  modules = [],
-  runtimeModules,
-  startParams = {},
-  histories = [],
-  variables = {},
-  user,
-  stream = false,
-  detail = false,
-  ...props
-}: ChatDispatchProps & {
-  modules?: FlowNodeItemType[]; // app modules
-  runtimeModules?: RuntimeNodeItemType[];
-  startParams?: Record<string, any>; // entry module params
-}): Promise<DispatchFlowResponse> {
-  // set sse response headers
-  if (res && stream) {
-    res.setHeader('Content-Type', 'text/event-stream;charset=utf-8');
-    res.setHeader('Access-Control-Allow-Origin', '*');
-    res.setHeader('X-Accel-Buffering', 'no');
-    res.setHeader('Cache-Control', 'no-cache, no-transform');
-  }
-
-  variables = {
-    ...getSystemVariable({ timezone: user.timezone }),
-    ...variables
-  };
-  const runningModules = runtimeModules ? runtimeModules : loadModules(modules, variables);
-
-  let chatResponses: ChatHistoryItemResType[] = []; // response request and save to database
-  let chatAssistantResponse: AIChatItemValueItemType[] = []; // The value will be returned to the user
-  let chatNodeUsages: ChatNodeUsageType[] = [];
-  let toolRunResponse: ToolRunResponseItemType;
-  let runningTime = Date.now();
-
-  /* Store special response field  */
-  function pushStore(
-    { inputs = [] }: RuntimeNodeItemType,
-    {
-      answerText = '',
-      responseData,
-      nodeDispatchUsages,
-      toolResponses,
-      assistantResponses
-    }: {
-      [NodeOutputKeyEnum.answerText]?: string;
-      [DispatchNodeResponseKeyEnum.nodeResponse]?: ChatHistoryItemResType;
-      [DispatchNodeResponseKeyEnum.nodeDispatchUsages]?: ChatNodeUsageType[];
-      [DispatchNodeResponseKeyEnum.toolResponses]?: ToolRunResponseItemType;
-      [DispatchNodeResponseKeyEnum.assistantResponses]?: AIChatItemValueItemType[]; // tool module, save the response value
-    }
-  ) {
-    const time = Date.now();
-
-    if (responseData) {
-      chatResponses.push({
-        ...responseData,
-        runningTime: +((time - runningTime) / 1000).toFixed(2)
-      });
-    }
-    if (nodeDispatchUsages) {
-      chatNodeUsages = chatNodeUsages.concat(nodeDispatchUsages);
-      props.maxRunTimes -= nodeDispatchUsages.length;
-    }
-    if (toolResponses !== undefined) {
-      if (Array.isArray(toolResponses) && toolResponses.length === 0) return;
-      if (typeof toolResponses === 'object' && Object.keys(toolResponses).length === 0) {
-        return;
-      }
-      toolRunResponse = toolResponses;
-    }
-    if (assistantResponses) {
-      chatAssistantResponse = chatAssistantResponse.concat(assistantResponses);
-    }
-
-    // save assistant text response
-    if (answerText) {
-      const isResponseAnswerText =
-        inputs.find((item) => item.key === NodeInputKeyEnum.aiChatIsResponseText)?.value ?? true;
-      if (isResponseAnswerText) {
-        chatAssistantResponse.push({
-          type: ChatItemValueTypeEnum.text,
-          text: {
-            content: answerText
-          }
-        });
-      }
-    }
-
-    runningTime = time;
-  }
-  /* Inject data into module input */
-  function moduleInput(module: RuntimeNodeItemType, data: Record<string, any> = {}) {
-    const updateInputValue = (key: string, value: any) => {
-      const index = module.inputs.findIndex((item: any) => item.key === key);
-      if (index === -1) return;
-      module.inputs[index].value = value;
-    };
-    Object.entries(data).map(([key, val]: any) => {
-      updateInputValue(key, val);
-    });
-
-    return;
-  }
-  /* Pass the output of the module to the next stage */
-  function moduleOutput(
-    module: RuntimeNodeItemType,
-    result: Record<string, any> = {}
-  ): Promise<any> {
-    pushStore(module, result);
-
-    const nextRunModules: RuntimeNodeItemType[] = [];
-
-    // Assign the output value to the next module
-    module.outputs.map((outputItem) => {
-      if (result[outputItem.key] === undefined) return;
-      /* update output value */
-      outputItem.value = result[outputItem.key];
-
-      /* update target */
-      outputItem.targets.map((target: any) => {
-        // find module
-        const targetModule = runningModules.find((item) => item.moduleId === target.moduleId);
-        if (!targetModule) return;
-
-        // push to running queue
-        nextRunModules.push(targetModule);
-
-        // update input
-        moduleInput(targetModule, { [target.key]: outputItem.value });
-      });
-    });
-
-    // Ensure the uniqueness of running modules
-    const set = new Set<string>();
-    const filterModules = nextRunModules.filter((module) => {
-      if (set.has(module.moduleId)) return false;
-      set.add(module.moduleId);
-      return true;
-    });
-
-    return checkModulesCanRun(filterModules);
-  }
-  function checkModulesCanRun(modules: RuntimeNodeItemType[] = []) {
-    return Promise.all(
-      modules.map((module) => {
-        if (!module.inputs.find((item: any) => item.value === undefined)) {
-          // remove switch
-          moduleInput(module, { [NodeInputKeyEnum.switch]: undefined });
-          return moduleRun(module);
-        }
-      })
-    );
-  }
-  async function moduleRun(module: RuntimeNodeItemType): Promise<any> {
-    if (res?.closed || props.maxRunTimes <= 0) return Promise.resolve();
-
-    if (res && stream && detail && module.showStatus) {
-      responseStatus({
-        res,
-        name: module.name,
-        status: 'running'
-      });
-    }
-
-    // get module running params
-    const params: Record<string, any> = {};
-    module.inputs.forEach((item) => {
-      params[item.key] = valueTypeFormat(item.value, item.valueType);
-    });
-
-    const dispatchData: ModuleDispatchProps<Record<string, any>> = {
-      ...props,
-      res,
-      variables,
-      histories,
-      user,
-      stream,
-      detail,
-      module,
-      runtimeModules: runningModules,
-      params
-    };
-
-    // run module
-    const dispatchRes: Record<string, any> = await (async () => {
-      if (callbackMap[module.flowType]) {
-        return callbackMap[module.flowType](dispatchData);
-      }
-      return {};
-    })();
-
-    // format response data. Add modulename and module type
-    const formatResponseData: ChatHistoryItemResType = (() => {
-      if (!dispatchRes[DispatchNodeResponseKeyEnum.nodeResponse]) return undefined;
-      return {
-        moduleName: module.name,
-        moduleType: module.flowType,
-        ...dispatchRes[DispatchNodeResponseKeyEnum.nodeResponse]
-      };
-    })();
-
-    // Add output default value
-    module.outputs.forEach((item) => {
-      if (!item.required) return;
-      if (dispatchRes[item.key] !== undefined) return;
-      dispatchRes[item.key] = valueTypeFormat(item.defaultValue, item.valueType);
-    });
-
-    // Pass userChatInput
-    const hasUserChatInputTarget = !!module.outputs.find(
-      (item) => item.key === NodeOutputKeyEnum.userChatInput
-    )?.targets?.length;
-
-    return moduleOutput(module, {
-      finish: true,
-      [NodeOutputKeyEnum.userChatInput]: hasUserChatInputTarget
-        ? params[NodeOutputKeyEnum.userChatInput]
-        : undefined,
-      ...dispatchRes,
-      [DispatchNodeResponseKeyEnum.nodeResponse]: formatResponseData,
-      [DispatchNodeResponseKeyEnum.nodeDispatchUsages]:
-        dispatchRes[DispatchNodeResponseKeyEnum.nodeDispatchUsages]
-    });
-  }
-  // start process width initInput
-  const initModules = runningModules.filter((item) => item.isEntry);
-  // reset entry
-  modules.forEach((item) => {
-    item.isEntry = false;
-  });
-  // console.log(JSON.stringify(runningModules, null, 2));
-  initModules.map((module) =>
-    moduleInput(module, {
-      ...startParams,
-      history: [] // abandon history field. History module will get histories from other fields.
-    })
-  );
-
-  await checkModulesCanRun(initModules);
-
-  // focus try to run pluginOutput
-  const pluginOutputModule = runningModules.find(
-    (item) => item.flowType === FlowNodeTypeEnum.pluginOutput
-  );
-  if (pluginOutputModule) {
-    await moduleRun(pluginOutputModule);
-  }
-
-  return {
-    flowResponses: chatResponses,
-    flowUsages: chatNodeUsages,
-    [DispatchNodeResponseKeyEnum.assistantResponses]:
-      concatAssistantResponseAnswerText(chatAssistantResponse),
-    [DispatchNodeResponseKeyEnum.toolResponses]: toolRunResponse
-  };
-}
-
-/* init store modules to running modules */
-function loadModules(
-  modules: FlowNodeItemType[],
-  variables: Record<string, any>
-): RuntimeNodeItemType[] {
-  return modules
-    .filter((item) => {
-      return ![FlowNodeTypeEnum.userGuide].includes(item.moduleId as any);
-    })
-    .map<RuntimeNodeItemType>((module) => {
-      return {
-        moduleId: module.moduleId,
-        name: module.name,
-        avatar: module.avatar,
-        intro: module.intro,
-        flowType: module.flowType,
-        showStatus: module.showStatus,
-        isEntry: module.isEntry,
-        inputs: module.inputs
-          .filter(
-            /* 
-              1. system input must be save
-              2. connected by source handle
-              3. manual input value or have default value
-              4. For the module connected by the tool, leave the toolDescription input
-            */
-            (item) => {
-              const isTool = checkTheModuleConnectedByTool(modules, module);
-
-              if (isTool && item.toolDescription) {
-                return true;
-              }
-
-              return item.type === 'systemInput' || item.connected || item.value !== undefined;
-            }
-          ) // filter unconnected target input
-          .map((item) => {
-            const replace = ['string'].includes(typeof item.value);
-
-            return {
-              key: item.key,
-              // variables replace
-              value: replace ? replaceVariable(item.value, variables) : item.value,
-              valueType: item.valueType,
-              required: item.required,
-              toolDescription: item.toolDescription
-            };
-          }),
-        outputs: module.outputs
-          .map((item) => ({
-            key: item.key,
-            required: item.required,
-            defaultValue: item.defaultValue,
-            answer: item.key === NodeOutputKeyEnum.answerText,
-            value: undefined,
-            valueType: item.valueType,
-            targets: item.targets
-          }))
-          .sort((a, b) => {
-            // finish output always at last
-            if (a.key === NodeOutputKeyEnum.finish) return 1;
-            if (b.key === NodeOutputKeyEnum.finish) return -1;
-            return 0;
-          })
-      };
-    });
-}
-
-/* sse response modules staus */
-export function responseStatus({
-  res,
-  status,
-  name
-}: {
-  res: NextApiResponse;
-  status?: 'running' | 'finish';
-  name?: string;
-}) {
-  if (!name) return;
-  responseWriteNodeStatus({
-    res,
-    name
-  });
-}
-
-/* get system variable */
-export function getSystemVariable({ timezone }: { timezone: string }) {
-  return {
-    cTime: getSystemTime(timezone)
-  };
-}
-
-export const concatAssistantResponseAnswerText = (response: AIChatItemValueItemType[]) => {
-  const result: AIChatItemValueItemType[] = [];
-  // 合并连续的text
-  for (let i = 0; i < response.length; i++) {
-    const item = response[i];
-    if (item.type === ChatItemValueTypeEnum.text) {
-      let text = item.text?.content || '';
-      const lastItem = result[result.length - 1];
-      if (lastItem && lastItem.type === ChatItemValueTypeEnum.text && lastItem.text?.content) {
-        lastItem.text.content += text;
-        continue;
-      }
-    }
-    result.push(item);
-  }
-
-  return result;
-};

packages/service/core/workflow/dispatchV1/init/history.tsx

@@ -1,19 +0,0 @@
-import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import type { ChatItemType } from '@fastgpt/global/core/chat/type.d';
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { getHistories } from '../utils';
-export type HistoryProps = ModuleDispatchProps<{
-  maxContext?: number;
-  [NodeInputKeyEnum.history]: ChatItemType[];
-}>;
-
-export const dispatchHistory = (props: Record<string, any>) => {
-  const {
-    histories,
-    params: { maxContext }
-  } = props as HistoryProps;
-
-  return {
-    history: getHistories(maxContext, histories)
-  };
-};

packages/service/core/workflow/dispatchV1/init/userChatInput.tsx

@@ -1,14 +0,0 @@
-import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-export type UserChatInputProps = ModuleDispatchProps<{
-  [NodeInputKeyEnum.userChatInput]: string;
-}>;
-
-export const dispatchChatInput = (props: Record<string, any>) => {
-  const {
-    params: { userChatInput }
-  } = props as UserChatInputProps;
-  return {
-    userChatInput
-  };
-};

packages/service/core/workflow/dispatchV1/plugin/run.ts

@@ -1,146 +0,0 @@
-// @ts-nocheck
-
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { dispatchWorkFlowV1 } from '../index';
-import { FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant';
-import {
-  FlowNodeTemplateTypeEnum,
-  NodeInputKeyEnum
-} from '@fastgpt/global/core/workflow/constants';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import { splitCombinePluginId } from '../../../app/plugin/controller';
-import { setEntryEntries, DYNAMIC_INPUT_KEY } from '../utils';
-import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
-import { PluginRuntimeType, PluginTemplateType } from '@fastgpt/global/core/plugin/type';
-import { PluginSourceEnum } from '@fastgpt/global/core/plugin/constants';
-import { MongoPlugin } from '../../../plugin/schema';
-
-type RunPluginProps = ModuleDispatchProps<{
-  [NodeInputKeyEnum.pluginId]: string;
-  [key: string]: any;
-}>;
-type RunPluginResponse = DispatchNodeResultType<{}>;
-
-const getPluginTemplateById = async (id: string): Promise<PluginTemplateType> => {
-  const { source, pluginId } = await splitCombinePluginId(id);
-  if (source === PluginSourceEnum.community) {
-    const item = global.communityPluginsV1?.find((plugin) => plugin.id === pluginId);
-
-    if (!item) return Promise.reject('plugin not found');
-
-    return item;
-  }
-  if (source === PluginSourceEnum.personal) {
-    const item = await MongoPlugin.findById(id).lean();
-    if (!item) return Promise.reject('plugin not found');
-    return {
-      id: String(item._id),
-      teamId: String(item.teamId),
-      name: item.name,
-      avatar: item.avatar,
-      intro: item.intro,
-      showStatus: true,
-      source: PluginSourceEnum.personal,
-      modules: item.modules,
-      templateType: FlowNodeTemplateTypeEnum.teamApp
-    };
-  }
-  return Promise.reject('plugin not found');
-};
-
-const getPluginRuntimeById = async (id: string): Promise<PluginRuntimeType> => {
-  const plugin = await getPluginTemplateById(id);
-
-  return {
-    teamId: plugin.teamId,
-    name: plugin.name,
-    avatar: plugin.avatar,
-    showStatus: plugin.showStatus,
-    modules: plugin.modules
-  };
-};
-
-export const dispatchRunPlugin = async (props: RunPluginProps): Promise<RunPluginResponse> => {
-  const {
-    mode,
-    teamId,
-    tmbId,
-    params: { pluginId, ...data }
-  } = props;
-
-  if (!pluginId) {
-    return Promise.reject('pluginId can not find');
-  }
-
-  const plugin = await getPluginRuntimeById(pluginId);
-  if (plugin.teamId && plugin.teamId !== teamId) {
-    return Promise.reject('plugin not found');
-  }
-
-  // concat dynamic inputs
-  const inputModule = plugin.modules.find((item) => item.flowType === FlowNodeTypeEnum.pluginInput);
-  if (!inputModule) return Promise.reject('Plugin error, It has no set input.');
-  const hasDynamicInput = inputModule.inputs.find((input) => input.key === DYNAMIC_INPUT_KEY);
-
-  const startParams: Record<string, any> = (() => {
-    if (!hasDynamicInput) return data;
-
-    const params: Record<string, any> = {
-      [DYNAMIC_INPUT_KEY]: {}
-    };
-
-    for (const key in data) {
-      const input = inputModule.inputs.find((input) => input.key === key);
-      if (input) {
-        params[key] = data[key];
-      } else {
-        params[DYNAMIC_INPUT_KEY][key] = data[key];
-      }
-    }
-
-    return params;
-  })();
-
-  const { flowResponses, flowUsages, assistantResponses } = await dispatchWorkFlowV1({
-    ...props,
-    modules: setEntryEntries(plugin.modules).map((module) => ({
-      ...module,
-      showStatus: false
-    })),
-    runtimeModules: undefined, // must reset
-    startParams
-  });
-
-  const output = flowResponses.find((item) => item.moduleType === FlowNodeTypeEnum.pluginOutput);
-
-  if (output) {
-    output.moduleLogo = plugin.avatar;
-  }
-
-  return {
-    assistantResponses,
-    // responseData, // debug
-    [DispatchNodeResponseKeyEnum.nodeResponse]: {
-      moduleLogo: plugin.avatar,
-      totalPoints: flowResponses.reduce((sum, item) => sum + (item.totalPoints || 0), 0),
-      pluginOutput: output?.pluginOutput,
-      pluginDetail:
-        mode === 'test' && plugin.teamId === teamId
-          ? flowResponses.filter((item) => {
-              const filterArr = [FlowNodeTypeEnum.pluginOutput];
-              return !filterArr.includes(item.moduleType as any);
-            })
-          : undefined
-    },
-    [DispatchNodeResponseKeyEnum.nodeDispatchUsages]: [
-      {
-        moduleName: plugin.name,
-        totalPoints: flowUsages.reduce((sum, item) => sum + (item.totalPoints || 0), 0),
-        model: plugin.name,
-        tokens: 0
-      }
-    ],
-    [DispatchNodeResponseKeyEnum.toolResponses]: output?.pluginOutput ? output.pluginOutput : {},
-    ...(output ? output.pluginOutput : {})
-  };
-};

packages/service/core/workflow/dispatchV1/plugin/runInput.ts

@@ -1,11 +0,0 @@
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-
-export type PluginInputProps = ModuleDispatchProps<{
-  [key: string]: any;
-}>;
-
-export const dispatchPluginInput = (props: PluginInputProps) => {
-  const { params } = props;
-
-  return params;
-};

packages/service/core/workflow/dispatchV1/plugin/runOutput.ts

@@ -1,19 +0,0 @@
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type.d';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-
-export type PluginOutputProps = ModuleDispatchProps<{
-  [key: string]: any;
-}>;
-export type PluginOutputResponse = DispatchNodeResultType<{}>;
-
-export const dispatchPluginOutput = (props: PluginOutputProps): PluginOutputResponse => {
-  const { params } = props;
-
-  return {
-    [DispatchNodeResponseKeyEnum.nodeResponse]: {
-      totalPoints: 0,
-      pluginOutput: params
-    }
-  };
-};

packages/service/core/workflow/dispatchV1/tools/answer.ts

@@ -1,37 +0,0 @@
-import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import { responseWrite } from '../../../../common/response';
-import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
-export type AnswerProps = ModuleDispatchProps<{
-  text: string;
-}>;
-export type AnswerResponse = DispatchNodeResultType<{
-  [NodeOutputKeyEnum.answerText]: string;
-}>;
-
-export const dispatchAnswer = (props: Record<string, any>): AnswerResponse => {
-  const {
-    res,
-    detail,
-    stream,
-    params: { text = '' }
-  } = props as AnswerProps;
-
-  const formatText = typeof text === 'string' ? text : JSON.stringify(text, null, 2);
-
-  if (stream) {
-    responseWrite({
-      res,
-      event: detail ? SseResponseEventEnum.fastAnswer : undefined,
-      data: textAdaptGptResponse({
-        text: `\n${formatText}`
-      })
-    });
-  }
-
-  return {
-    [NodeOutputKeyEnum.answerText]: `\n${formatText}`
-  };
-};

packages/service/core/workflow/dispatchV1/tools/http.ts

@@ -1,249 +0,0 @@
-// @ts-nocheck
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import axios from 'axios';
-import { valueTypeFormat } from '../utils';
-import { SERVICE_LOCAL_HOST } from '../../../../common/system/tools';
-import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
-import { DYNAMIC_INPUT_KEY } from '../utils';
-
-type HttpRequestProps = ModuleDispatchProps<{
-  [NodeInputKeyEnum.abandon_httpUrl]: string;
-  [NodeInputKeyEnum.httpMethod]: string;
-  [NodeInputKeyEnum.httpReqUrl]: string;
-  [NodeInputKeyEnum.httpHeaders]: string;
-  [key: string]: any;
-}>;
-type HttpResponse = DispatchNodeResultType<{
-  [NodeOutputKeyEnum.failed]?: boolean;
-  [key: string]: any;
-}>;
-
-const flatDynamicParams = (params: Record<string, any>) => {
-  const dynamicParams = params[DYNAMIC_INPUT_KEY];
-  if (!dynamicParams) return params;
-  return {
-    ...params,
-    ...dynamicParams,
-    [DYNAMIC_INPUT_KEY]: undefined
-  };
-};
-
-export const dispatchHttpRequest = async (props: HttpRequestProps): Promise<HttpResponse> => {
-  let {
-    appId,
-    chatId,
-    responseChatItemId,
-    variables,
-    module: { outputs },
-    params: {
-      system_httpMethod: httpMethod = 'POST',
-      system_httpReqUrl: httpReqUrl,
-      system_httpHeader: httpHeader,
-      ...body
-    }
-  } = props;
-
-  if (!httpReqUrl) {
-    return Promise.reject('Http url is empty');
-  }
-
-  body = flatDynamicParams(body);
-
-  const requestBody = {
-    appId,
-    chatId,
-    responseChatItemId,
-    variables,
-    data: body
-  };
-  const requestQuery = {
-    appId,
-    chatId,
-    ...variables,
-    ...body
-  };
-
-  const formatBody = transformFlatJson({ ...requestBody });
-
-  // parse header
-  const headers = await (() => {
-    try {
-      if (!httpHeader) return {};
-      return JSON.parse(httpHeader);
-    } catch (error) {
-      return Promise.reject('Header 为非法 JSON 格式');
-    }
-  })();
-
-  try {
-    const response = await fetchData({
-      method: httpMethod,
-      url: httpReqUrl,
-      headers,
-      body: formatBody,
-      query: requestQuery
-    });
-
-    // format output value type
-    const results: Record<string, any> = {};
-    for (const key in response) {
-      const output = outputs.find((item) => item.key === key);
-      if (!output) continue;
-      results[key] = valueTypeFormat(response[key], output.valueType);
-    }
-
-    return {
-      [DispatchNodeResponseKeyEnum.nodeResponse]: {
-        totalPoints: 0,
-        body: formatBody,
-        httpResult: response
-      },
-      ...results
-    };
-  } catch (error) {
-    console.log(error);
-
-    return {
-      [NodeOutputKeyEnum.failed]: true,
-      [DispatchNodeResponseKeyEnum.nodeResponse]: {
-        totalPoints: 0,
-        body: formatBody,
-        httpResult: { error }
-      }
-    };
-  }
-};
-
-async function fetchData({
-  method,
-  url,
-  headers,
-  body,
-  query
-}: {
-  method: string;
-  url: string;
-  headers: Record<string, any>;
-  body: Record<string, any>;
-  query: Record<string, any>;
-}): Promise<Record<string, any>> {
-  const { data: response } = await axios<Record<string, any>>({
-    method,
-    baseURL: `http://${SERVICE_LOCAL_HOST}`,
-    url,
-    headers: {
-      'Content-Type': 'application/json',
-      ...headers
-    },
-    timeout: 360000,
-    params: method === 'GET' ? query : {},
-    data: method === 'POST' ? body : {}
-  });
-
-  /*
-    parse the json:
-    {
-      user: {
-        name: 'xxx',
-        age: 12
-      },
-      list: [
-        {
-          name: 'xxx',
-          age: 50
-        },
-        [{ test: 22 }]
-      ],
-      psw: 'xxx'
-    }
-
-    result: {
-      'user': { name: 'xxx', age: 12 },
-      'user.name': 'xxx',
-      'user.age': 12,
-      'list': [ { name: 'xxx', age: 50 }, [ [Object] ] ],
-      'list[0]': { name: 'xxx', age: 50 },
-      'list[0].name': 'xxx',
-      'list[0].age': 50,
-      'list[1]': [ { test: 22 } ],
-      'list[1][0]': { test: 22 },
-      'list[1][0].test': 22,
-      'psw': 'xxx'
-    }
-  */
-  const parseJson = (obj: Record<string, any>, prefix = '') => {
-    let result: Record<string, any> = {};
-
-    if (Array.isArray(obj)) {
-      for (let i = 0; i < obj.length; i++) {
-        result[`${prefix}[${i}]`] = obj[i];
-
-        if (Array.isArray(obj[i])) {
-          result = {
-            ...result,
-            ...parseJson(obj[i], `${prefix}[${i}]`)
-          };
-        } else if (typeof obj[i] === 'object') {
-          result = {
-            ...result,
-            ...parseJson(obj[i], `${prefix}[${i}].`)
-          };
-        }
-      }
-    } else if (typeof obj == 'object') {
-      for (const key in obj) {
-        result[`${prefix}${key}`] = obj[key];
-
-        if (Array.isArray(obj[key])) {
-          result = {
-            ...result,
-            ...parseJson(obj[key], `${prefix}${key}`)
-          };
-        } else if (typeof obj[key] === 'object') {
-          result = {
-            ...result,
-            ...parseJson(obj[key], `${prefix}${key}.`)
-          };
-        }
-      }
-    }
-
-    return result;
-  };
-
-  return parseJson(response);
-}
-
-function transformFlatJson(obj: Record<string, any>) {
-  for (let key in obj) {
-    if (typeof obj[key] === 'object') {
-      transformFlatJson(obj[key]);
-    }
-    if (key.includes('.')) {
-      let parts = key.split('.');
-      if (parts.length <= 1) continue;
-
-      const firstKey = parts.shift();
-
-      if (!firstKey) continue;
-
-      const lastKey = parts.join('.');
-
-      if (obj[firstKey]) {
-        obj[firstKey] = {
-          ...obj[firstKey],
-          [lastKey]: obj[key]
-        };
-      } else {
-        obj[firstKey] = { [lastKey]: obj[key] };
-      }
-
-      transformFlatJson(obj[firstKey]);
-
-      delete obj[key];
-    }
-  }
-  return obj;
-}

packages/service/core/workflow/dispatchV1/tools/http468.ts

@@ -1,294 +0,0 @@
-// @ts-nocheck
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import axios from 'axios';
-import { DYNAMIC_INPUT_KEY, valueTypeFormat } from '../utils';
-import { SERVICE_LOCAL_HOST } from '../../../../common/system/tools';
-import { addLog } from '../../../../common/system/log';
-import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
-import { getErrText } from '@fastgpt/global/common/error/utils';
-
-type PropsArrType = {
-  key: string;
-  type: string;
-  value: string;
-};
-type HttpRequestProps = ModuleDispatchProps<{
-  [NodeInputKeyEnum.abandon_httpUrl]: string;
-  [NodeInputKeyEnum.httpMethod]: string;
-  [NodeInputKeyEnum.httpReqUrl]: string;
-  [NodeInputKeyEnum.httpHeaders]: PropsArrType[];
-  [NodeInputKeyEnum.httpParams]: PropsArrType[];
-  [NodeInputKeyEnum.httpJsonBody]: string;
-  [DYNAMIC_INPUT_KEY]: Record<string, any>;
-  [key: string]: any;
-}>;
-type HttpResponse = DispatchNodeResultType<{
-  [NodeOutputKeyEnum.failed]?: boolean;
-  [key: string]: any;
-}>;
-
-const UNDEFINED_SIGN = 'UNDEFINED_SIGN';
-
-export const dispatchHttp468Request = async (props: HttpRequestProps): Promise<HttpResponse> => {
-  let {
-    appId,
-    chatId,
-    responseChatItemId,
-    variables,
-    module: { outputs },
-    histories,
-    params: {
-      system_httpMethod: httpMethod = 'POST',
-      system_httpReqUrl: httpReqUrl,
-      system_httpHeader: httpHeader,
-      system_httpParams: httpParams = [],
-      system_httpJsonBody: httpJsonBody,
-      [DYNAMIC_INPUT_KEY]: dynamicInput,
-      ...body
-    }
-  } = props;
-
-  if (!httpReqUrl) {
-    return Promise.reject('Http url is empty');
-  }
-
-  const concatVariables = {
-    appId,
-    chatId,
-    responseChatItemId,
-    ...variables,
-    histories: histories.slice(-10),
-    ...body
-  };
-
-  httpReqUrl = replaceVariable(httpReqUrl, concatVariables);
-  // parse header
-  const headers = await (() => {
-    try {
-      if (!httpHeader || httpHeader.length === 0) return {};
-      // array
-      return httpHeader.reduce((acc: Record<string, string>, item) => {
-        const key = replaceVariable(item.key, concatVariables);
-        const value = replaceVariable(item.value, concatVariables);
-        acc[key] = valueTypeFormat(value, 'string');
-        return acc;
-      }, {});
-    } catch (error) {
-      return Promise.reject('Header 为非法 JSON 格式');
-    }
-  })();
-  const params = httpParams.reduce((acc: Record<string, string>, item) => {
-    const key = replaceVariable(item.key, concatVariables);
-    const value = replaceVariable(item.value, concatVariables);
-    acc[key] = valueTypeFormat(value, 'string');
-    return acc;
-  }, {});
-  const requestBody = await (() => {
-    if (!httpJsonBody) return { [DYNAMIC_INPUT_KEY]: dynamicInput };
-    httpJsonBody = replaceVariable(httpJsonBody, concatVariables);
-    try {
-      const jsonParse = JSON.parse(httpJsonBody);
-      const removeSignJson = removeUndefinedSign(jsonParse);
-      return { [DYNAMIC_INPUT_KEY]: dynamicInput, ...removeSignJson };
-    } catch (error) {
-      console.log(error);
-      return Promise.reject(`Invalid JSON body: ${httpJsonBody}`);
-    }
-  })();
-
-  try {
-    const { formatResponse, rawResponse } = await fetchData({
-      method: httpMethod,
-      url: httpReqUrl,
-      headers,
-      body: requestBody,
-      params
-    });
-
-    // format output value type
-    const results: Record<string, any> = {};
-    for (const key in formatResponse) {
-      const output = outputs.find((item) => item.key === key);
-      if (!output) continue;
-      results[key] = valueTypeFormat(formatResponse[key], output.valueType);
-    }
-
-    return {
-      [DispatchNodeResponseKeyEnum.nodeResponse]: {
-        totalPoints: 0,
-        params: Object.keys(params).length > 0 ? params : undefined,
-        body: Object.keys(requestBody).length > 0 ? requestBody : undefined,
-        headers: Object.keys(headers).length > 0 ? headers : undefined,
-        httpResult: rawResponse
-      },
-      [DispatchNodeResponseKeyEnum.toolResponses]: results,
-      [NodeOutputKeyEnum.httpRawResponse]: rawResponse,
-      ...results
-    };
-  } catch (error) {
-    addLog.error('Http request error', error);
-    return {
-      [NodeOutputKeyEnum.failed]: true,
-      [DispatchNodeResponseKeyEnum.nodeResponse]: {
-        totalPoints: 0,
-        params: Object.keys(params).length > 0 ? params : undefined,
-        body: Object.keys(requestBody).length > 0 ? requestBody : undefined,
-        headers: Object.keys(headers).length > 0 ? headers : undefined,
-        httpResult: { error: formatHttpError(error) }
-      },
-      [NodeOutputKeyEnum.httpRawResponse]: getErrText(error)
-    };
-  }
-};
-
-async function fetchData({
-  method,
-  url,
-  headers,
-  body,
-  params
-}: {
-  method: string;
-  url: string;
-  headers: Record<string, any>;
-  body: Record<string, any>;
-  params: Record<string, any>;
-}): Promise<Record<string, any>> {
-  const { data: response } = await axios({
-    method,
-    baseURL: `http://${SERVICE_LOCAL_HOST}`,
-    url,
-    headers: {
-      'Content-Type': 'application/json',
-      ...headers
-    },
-    timeout: 120000,
-    params: params,
-    data: ['POST', 'PUT', 'PATCH'].includes(method) ? body : undefined
-  });
-
-  /*
-    parse the json:
-    {
-      user: {
-        name: 'xxx',
-        age: 12
-      },
-      list: [
-        {
-          name: 'xxx',
-          age: 50
-        },
-        [{ test: 22 }]
-      ],
-      psw: 'xxx'
-    }
-
-    result: {
-      'user': { name: 'xxx', age: 12 },
-      'user.name': 'xxx',
-      'user.age': 12,
-      'list': [ { name: 'xxx', age: 50 }, [ [Object] ] ],
-      'list[0]': { name: 'xxx', age: 50 },
-      'list[0].name': 'xxx',
-      'list[0].age': 50,
-      'list[1]': [ { test: 22 } ],
-      'list[1][0]': { test: 22 },
-      'list[1][0].test': 22,
-      'psw': 'xxx'
-    }
-  */
-  const parseJson = (obj: Record<string, any>, prefix = '') => {
-    let result: Record<string, any> = {};
-
-    if (Array.isArray(obj)) {
-      for (let i = 0; i < obj.length; i++) {
-        result[`${prefix}[${i}]`] = obj[i];
-
-        if (Array.isArray(obj[i])) {
-          result = {
-            ...result,
-            ...parseJson(obj[i], `${prefix}[${i}]`)
-          };
-        } else if (typeof obj[i] === 'object') {
-          result = {
-            ...result,
-            ...parseJson(obj[i], `${prefix}[${i}].`)
-          };
-        }
-      }
-    } else if (typeof obj == 'object') {
-      for (const key in obj) {
-        result[`${prefix}${key}`] = obj[key];
-
-        if (Array.isArray(obj[key])) {
-          result = {
-            ...result,
-            ...parseJson(obj[key], `${prefix}${key}`)
-          };
-        } else if (typeof obj[key] === 'object') {
-          result = {
-            ...result,
-            ...parseJson(obj[key], `${prefix}${key}.`)
-          };
-        }
-      }
-    }
-
-    return result;
-  };
-
-  return {
-    formatResponse:
-      typeof response === 'object' && !Array.isArray(response) ? parseJson(response) : {},
-    rawResponse: response
-  };
-}
-
-function replaceVariable(text: string, obj: Record<string, any>) {
-  for (const [key, value] of Object.entries(obj)) {
-    if (value === undefined) {
-      text = text.replace(new RegExp(`{{${key}}}`, 'g'), UNDEFINED_SIGN);
-    } else {
-      const replacement = JSON.stringify(value);
-      const unquotedReplacement =
-        replacement.startsWith('"') && replacement.endsWith('"')
-          ? replacement.slice(1, -1)
-          : replacement;
-      text = text.replace(new RegExp(`{{${key}}}`, 'g'), unquotedReplacement);
-    }
-  }
-  return text || '';
-}
-function removeUndefinedSign(obj: Record<string, any>) {
-  for (const key in obj) {
-    if (obj[key] === UNDEFINED_SIGN) {
-      obj[key] = undefined;
-    } else if (Array.isArray(obj[key])) {
-      obj[key] = obj[key].map((item: any) => {
-        if (item === UNDEFINED_SIGN) {
-          return undefined;
-        } else if (typeof item === 'object') {
-          removeUndefinedSign(item);
-        }
-        return item;
-      });
-    } else if (typeof obj[key] === 'object') {
-      removeUndefinedSign(obj[key]);
-    }
-  }
-  return obj;
-}
-function formatHttpError(error: any) {
-  return {
-    message: error?.message,
-    name: error?.name,
-    method: error?.config?.method,
-    baseURL: error?.config?.baseURL,
-    url: error?.config?.url,
-    code: error?.code,
-    status: error?.status
-  };
-}

packages/service/core/workflow/dispatchV1/tools/queryExternsion.ts

@@ -1,77 +0,0 @@
-// @ts-nocheck
-import type { ChatItemType } from '@fastgpt/global/core/chat/type.d';
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import { ModelTypeEnum, getLLMModel } from '../../../../core/ai/model';
-import { formatModelChars2Points } from '../../../../support/wallet/usage/utils';
-import { queryExtension } from '../../../../core/ai/functions/queryExtension';
-import { getHistories } from '../utils';
-import { hashStr } from '@fastgpt/global/common/string/tools';
-import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
-
-type Props = ModuleDispatchProps<{
-  [NodeInputKeyEnum.aiModel]: string;
-  [NodeInputKeyEnum.aiSystemPrompt]?: string;
-  [NodeInputKeyEnum.history]?: ChatItemType[] | number;
-  [NodeInputKeyEnum.userChatInput]: string;
-}>;
-type Response = DispatchNodeResultType<{
-  [NodeOutputKeyEnum.text]: string;
-}>;
-
-export const dispatchQueryExtension = async ({
-  histories,
-  module,
-  params: { model, systemPrompt, history, userChatInput }
-}: Props): Promise<Response> => {
-  if (!userChatInput) {
-    return Promise.reject('Question is empty');
-  }
-
-  const queryExtensionModel = getLLMModel(model);
-  const chatHistories = getHistories(history, histories);
-
-  const { extensionQueries, tokens } = await queryExtension({
-    chatBg: systemPrompt,
-    query: userChatInput,
-    histories: chatHistories,
-    model: queryExtensionModel.model
-  });
-
-  extensionQueries.unshift(userChatInput);
-
-  const { totalPoints, modelName } = formatModelChars2Points({
-    model: queryExtensionModel.model,
-    tokens,
-    modelType: ModelTypeEnum.llm
-  });
-
-  const set = new Set<string>();
-  const filterSameQueries = extensionQueries.filter((item) => {
-    // 删除所有的标点符号与空格等，只对文本进行比较
-    const str = hashStr(item.replace(/[^\p{L}\p{N}]/gu, ''));
-    if (set.has(str)) return false;
-    set.add(str);
-    return true;
-  });
-
-  return {
-    [DispatchNodeResponseKeyEnum.nodeResponse]: {
-      totalPoints,
-      model: modelName,
-      tokens,
-      query: userChatInput,
-      textOutput: JSON.stringify(filterSameQueries)
-    },
-    [DispatchNodeResponseKeyEnum.nodeDispatchUsages]: [
-      {
-        moduleName: module.name,
-        totalPoints,
-        model: modelName,
-        tokens
-      }
-    ],
-    [NodeOutputKeyEnum.text]: JSON.stringify(filterSameQueries)
-  };
-};

packages/service/core/workflow/dispatchV1/tools/runLaf.ts

@@ -1,209 +0,0 @@
-// @ts-nocheck
-import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
-import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import axios from 'axios';
-import { DYNAMIC_INPUT_KEY, valueTypeFormat } from '../utils';
-import { SERVICE_LOCAL_HOST } from '../../../../common/system/tools';
-import { addLog } from '../../../../common/system/log';
-import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
-
-type LafRequestProps = ModuleDispatchProps<{
-  [NodeInputKeyEnum.httpReqUrl]: string;
-  [DYNAMIC_INPUT_KEY]: Record<string, any>;
-  [key: string]: any;
-}>;
-type LafResponse = DispatchNodeResultType<{
-  [NodeOutputKeyEnum.failed]?: boolean;
-  [key: string]: any;
-}>;
-
-const UNDEFINED_SIGN = 'UNDEFINED_SIGN';
-
-export const dispatchLafRequest = async (props: LafRequestProps): Promise<LafResponse> => {
-  let {
-    appId,
-    chatId,
-    responseChatItemId,
-    variables,
-    module: { outputs },
-    histories,
-    params: { system_httpReqUrl: httpReqUrl, [DYNAMIC_INPUT_KEY]: dynamicInput, ...body }
-  } = props;
-
-  if (!httpReqUrl) {
-    return Promise.reject('Http url is empty');
-  }
-
-  const concatVariables = {
-    appId,
-    chatId,
-    responseChatItemId,
-    ...variables,
-    ...body
-  };
-
-  httpReqUrl = replaceVariable(httpReqUrl, concatVariables);
-
-  const requestBody = {
-    systemParams: {
-      appId,
-      chatId,
-      responseChatItemId,
-      histories: histories.slice(0, 10)
-    },
-    variables,
-    ...dynamicInput,
-    ...body
-  };
-
-  try {
-    const { formatResponse, rawResponse } = await fetchData({
-      method: 'POST',
-      url: httpReqUrl,
-      body: requestBody
-    });
-
-    // format output value type
-    const results: Record<string, any> = {};
-    for (const key in formatResponse) {
-      const output = outputs.find((item) => item.key === key);
-      if (!output) continue;
-      results[key] = valueTypeFormat(formatResponse[key], output.valueType);
-    }
-
-    return {
-      assistantResponses: [],
-      [DispatchNodeResponseKeyEnum.nodeResponse]: {
-        totalPoints: 0,
-        body: Object.keys(requestBody).length > 0 ? requestBody : undefined,
-        httpResult: rawResponse
-      },
-      [DispatchNodeResponseKeyEnum.toolResponses]: rawResponse,
-      [NodeOutputKeyEnum.httpRawResponse]: rawResponse,
-      ...results
-    };
-  } catch (error) {
-    addLog.error('Http request error', error);
-    return {
-      [NodeOutputKeyEnum.failed]: true,
-      [DispatchNodeResponseKeyEnum.nodeResponse]: {
-        totalPoints: 0,
-        body: Object.keys(requestBody).length > 0 ? requestBody : undefined,
-        httpResult: { error: formatHttpError(error) }
-      }
-    };
-  }
-};
-
-async function fetchData({
-  method,
-  url,
-  body
-}: {
-  method: string;
-  url: string;
-  body: Record<string, any>;
-}): Promise<Record<string, any>> {
-  const { data: response } = await axios({
-    method,
-    baseURL: `http://${SERVICE_LOCAL_HOST}`,
-    url,
-    headers: {
-      'Content-Type': 'application/json'
-    },
-    data: body
-  });
-
-  const parseJson = (obj: Record<string, any>, prefix = '') => {
-    let result: Record<string, any> = {};
-
-    if (Array.isArray(obj)) {
-      for (let i = 0; i < obj.length; i++) {
-        result[`${prefix}[${i}]`] = obj[i];
-
-        if (Array.isArray(obj[i])) {
-          result = {
-            ...result,
-            ...parseJson(obj[i], `${prefix}[${i}]`)
-          };
-        } else if (typeof obj[i] === 'object') {
-          result = {
-            ...result,
-            ...parseJson(obj[i], `${prefix}[${i}].`)
-          };
-        }
-      }
-    } else if (typeof obj == 'object') {
-      for (const key in obj) {
-        result[`${prefix}${key}`] = obj[key];
-
-        if (Array.isArray(obj[key])) {
-          result = {
-            ...result,
-            ...parseJson(obj[key], `${prefix}${key}`)
-          };
-        } else if (typeof obj[key] === 'object') {
-          result = {
-            ...result,
-            ...parseJson(obj[key], `${prefix}${key}.`)
-          };
-        }
-      }
-    }
-
-    return result;
-  };
-
-  return {
-    formatResponse:
-      typeof response === 'object' && !Array.isArray(response) ? parseJson(response) : {},
-    rawResponse: response
-  };
-}
-
-function replaceVariable(text: string, obj: Record<string, any>) {
-  for (const [key, value] of Object.entries(obj)) {
-    if (value === undefined) {
-      text = text.replace(new RegExp(`{{${key}}}`, 'g'), UNDEFINED_SIGN);
-    } else {
-      const replacement = JSON.stringify(value);
-      const unquotedReplacement =
-        replacement.startsWith('"') && replacement.endsWith('"')
-          ? replacement.slice(1, -1)
-          : replacement;
-      text = text.replace(new RegExp(`{{${key}}}`, 'g'), unquotedReplacement);
-    }
-  }
-  return text || '';
-}
-function removeUndefinedSign(obj: Record<string, any>) {
-  for (const key in obj) {
-    if (obj[key] === UNDEFINED_SIGN) {
-      obj[key] = undefined;
-    } else if (Array.isArray(obj[key])) {
-      obj[key] = obj[key].map((item: any) => {
-        if (item === UNDEFINED_SIGN) {
-          return undefined;
-        } else if (typeof item === 'object') {
-          removeUndefinedSign(item);
-        }
-        return item;
-      });
-    } else if (typeof obj[key] === 'object') {
-      removeUndefinedSign(obj[key]);
-    }
-  }
-  return obj;
-}
-function formatHttpError(error: any) {
-  return {
-    message: error?.message,
-    name: error?.name,
-    method: error?.config?.method,
-    baseURL: error?.config?.baseURL,
-    url: error?.config?.url,
-    code: error?.code,
-    status: error?.status
-  };
-}

packages/service/core/workflow/dispatchV1/type.d.ts

@@ -1,16 +0,0 @@
-import {
-  AIChatItemValueItemType,
-  ChatHistoryItemResType,
-  ChatItemValueItemType,
-  ToolRunResponseItemType
-} from '@fastgpt/global/core/chat/type';
-import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
-
-export type DispatchFlowResponse = {
-  flowResponses: ChatHistoryItemResType[];
-  flowUsages: ChatNodeUsageType[];
-  [DispatchNodeResponseKeyEnum.toolResponses]: ToolRunResponseItemType;
-  [DispatchNodeResponseKeyEnum.assistantResponses]: AIChatItemValueItemType[];
-  newVariables: Record<string, string>;
-};

packages/service/core/workflow/dispatchV1/utils.ts

@@ -1,66 +0,0 @@
-// @ts-nocheck
-import type { ChatItemType } from '@fastgpt/global/core/chat/type.d';
-import {
-  WorkflowIOValueTypeEnum,
-  NodeOutputKeyEnum
-} from '@fastgpt/global/core/workflow/constants';
-import { FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant';
-import { FlowNodeItemType, StoreNodeItemType } from '@fastgpt/global/core/workflow/type/node';
-export const DYNAMIC_INPUT_KEY = 'DYNAMIC_INPUT_KEY';
-
-export const setEntryEntries = (modules: StoreNodeItemType[]) => {
-  const initRunningModuleType: Record<string, boolean> = {
-    questionInput: true,
-    [FlowNodeTypeEnum.pluginInput]: true
-  };
-
-  modules.forEach((item) => {
-    if (initRunningModuleType[item.flowType]) {
-      item.isEntry = true;
-    }
-  });
-  return modules;
-};
-
-export const checkTheModuleConnectedByTool = (
-  modules: FlowNodeItemType[],
-  module: FlowNodeItemType
-) => {
-  let sign = false;
-  const toolModules = modules.filter((item) => item.flowType === FlowNodeTypeEnum.tools);
-
-  toolModules.forEach((item) => {
-    const toolOutput = item.outputs.find(
-      (output) => output.key === NodeOutputKeyEnum.selectedTools
-    );
-    toolOutput?.targets.forEach((target) => {
-      if (target.moduleId === module.moduleId) {
-        sign = true;
-      }
-    });
-  });
-
-  return sign;
-};
-
-export const getHistories = (history?: ChatItemType[] | number, histories: ChatItemType[] = []) => {
-  if (!history) return [];
-  if (typeof history === 'number') return histories.slice(-history);
-  if (Array.isArray(history)) return history;
-
-  return [];
-};
-
-/* value type format */
-export const valueTypeFormat = (value: any, type?: `${WorkflowIOValueTypeEnum}`) => {
-  if (value === undefined) return;
-
-  if (type === 'string') {
-    if (typeof value !== 'object') return String(value);
-    return JSON.stringify(value);
-  }
-  if (type === 'number') return Number(value);
-  if (type === 'boolean') return Boolean(value);
-
-  return value;
-};