fix: sse headers and extract module

client/src/components/MyModal/index.tsx

@@ -42,7 +42,7 @@ const MyModal = ({
         {...props}
       >
         {!!title && <ModalHeader>{title}</ModalHeader>}
-        <Box overflow={'overlay'}>
+        <Box overflow={'overlay'} h={'100%'}>
           {showCloseBtn && <ModalCloseButton />}
           {children}
         </Box>

client/src/components/MyTooltip/index.tsx

@@ -20,7 +20,7 @@ const MyTooltip = ({ children, forceShow = false, ...props }: Props) => {
       py={2}
       borderRadius={'8px'}
       whiteSpace={'pre-wrap'}
-      shouldWrapChildren
+      boxShadow={'1px 1px 10px rgba(0,0,0,0.2)'}
       {...props}
     >
       {children}

client/src/constants/flow/ModuleTemplate.ts

@@ -60,7 +60,6 @@ export const UserInputModule: FlowModuleTemplateType = {
   name: '用户问题(对话入口)',
   intro: '用户输入的内容。该模块通常作为应用的入口，用户在发送消息后会首先执行该模块。',
   flowType: FlowModuleTypeEnum.questionInput,
-  url: '/app/modules/init/userChatInput',
   inputs: [
     {
       key: SystemInputEnum.userChatInput,
@@ -83,7 +82,6 @@ export const HistoryModule: FlowModuleTemplateType = {
   name: '聊天记录',
   intro: '用户输入的内容。该模块通常作为应用的入口，用户在发送消息后会首先执行该模块。',
   flowType: FlowModuleTypeEnum.historyNode,
-  url: '/app/modules/init/history',
   inputs: [
     {
       key: 'maxContext',
@@ -116,7 +114,6 @@ export const ChatModule: FlowModuleTemplateType = {
   name: 'AI 对话',
   intro: 'AI 大模型对话',
   flowType: FlowModuleTypeEnum.chatNode,
-  url: '/app/modules/chat/gpt',
   inputs: [
     {
       key: 'model',
@@ -206,7 +203,6 @@ export const KBSearchModule: FlowModuleTemplateType = {
   name: '知识库搜索',
   intro: '去知识库中搜索对应的答案。可作为 AI 对话引用参考。',
   flowType: FlowModuleTypeEnum.kbSearchNode,
-  url: '/app/modules/kb/search',
   inputs: [
     {
       key: 'kbList',
@@ -322,7 +318,6 @@ export const ClassifyQuestionModule: FlowModuleTemplateType = {
   intro: '可以判断用户问题属于哪方面问题，从而执行不同的操作。',
   description:
     '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 laf 通用问题\n类型3: 关于 laf 代码问题\n类型4: 其他问题',
-  url: '/app/modules/agent/classifyQuestion',
   flowType: FlowModuleTypeEnum.classifyQuestion,
   inputs: [
     {
@@ -378,6 +373,65 @@ export const ClassifyQuestionModule: FlowModuleTemplateType = {
     }
   ]
 };
+export const ContextExtractModule: FlowModuleTemplateType = {
+  logo: '/imgs/module/extract.png',
+  name: '内容提取',
+  intro: '从文本中提取出指定格式的数据',
+  description: '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',
+  flowType: FlowModuleTypeEnum.contentExtract,
+  inputs: [
+    {
+      key: 'systemPrompt',
+      type: FlowInputItemTypeEnum.textarea,
+      valueType: FlowValueTypeEnum.string,
+      label: '提取内容描述',
+      description: '写一段提取要求，告诉 AI 需要提取哪些内容',
+      placeholder: '例如: \n1. 根据用户的\n2. Sealos 是一个集群操作系统',
+      value: ''
+    },
+    Input_Template_History,
+    Input_Template_UserChatInput,
+    {
+      key: 'agents',
+      type: FlowInputItemTypeEnum.custom,
+      label: '',
+      value: [
+        {
+          value: '打招呼',
+          key: 'fasw'
+        },
+        {
+          value: '关于 xxx 的问题',
+          key: 'fqsw'
+        },
+        {
+          value: '其他问题',
+          key: 'fesw'
+        }
+      ]
+    }
+  ],
+  outputs: [
+    {
+      key: 'fasw',
+      label: '',
+      type: FlowOutputItemTypeEnum.hidden,
+      targets: []
+    },
+    {
+      key: 'fqsw',
+      label: '',
+      type: FlowOutputItemTypeEnum.hidden,
+      targets: []
+    },
+    {
+      key: 'fesw',
+      label: '',
+      type: FlowOutputItemTypeEnum.hidden,
+      targets: []
+    }
+  ]
+};
 export const EmptyModule: FlowModuleTemplateType = {
   logo: '/imgs/module/cq.png',
   name: '该模块已被移除',

client/src/constants/flow/index.ts

@@ -30,7 +30,8 @@ export enum FlowModuleTypeEnum {
   kbSearchNode = 'kbSearchNode',
   tfSwitchNode = 'tfSwitchNode',
   answerNode = 'answerNode',
-  classifyQuestion = 'classifyQuestion'
+  classifyQuestion = 'classifyQuestion',
+  contentExtract = 'contentExtract'
 }
 
 export enum SpecialInputKeyEnum {

client/src/constants/theme.ts

@@ -199,16 +199,6 @@ const Select = selectMultiStyle({
   }
 });
 
-const Tooltip = defineStyleConfig({
-  baseStyle: {
-    p: 3,
-    bg: 'white',
-    color: 'blackAlpha.800',
-    borderRadius: '8px',
-    boxShadow: '1px 1px 10px rgba(0,0,0,0.2)'
-  }
-});
-
 // 全局主题
 export const theme = extendTheme({
   styles: {
@@ -309,7 +299,6 @@ export const theme = extendTheme({
     Textarea,
     Switch,
     Select,
-    Tooltip,
     NumberInput
   }
 });

client/src/pages/api/openapi/v1/chat/completions.ts

@@ -113,6 +113,12 @@ export default withNextCors(async function handler(req: NextApiRequest, res: Nex
       throw new Error('Question is empty');
     }
 
+    // 创建响应流
+    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');
+
     /* start process */
     const { responseData, answerText } = await dispatchModules({
       res,

client/src/service/moduleDispatch/agent/classifyQuestion.ts

@@ -7,13 +7,14 @@ import type { ClassifyQuestionAgentItemType } from '@/types/app';
 import { countModelPrice } from '@/service/events/pushBill';
 import { UserModelSchema } from '@/types/mongoSchema';
 import { getModel } from '@/service/utils/data';
+import { SystemInputEnum } from '@/constants/app';
 
 export type CQProps = {
   systemPrompt?: string;
   history?: ChatItemType[];
-  userChatInput: string;
+  [SystemInputEnum.userChatInput]: string;
-  agents: ClassifyQuestionAgentItemType[];
   userOpenaiAccount: UserModelSchema['openaiAccount'];
+  agents: ClassifyQuestionAgentItemType[];
 };
 export type CQResponse = {
   [TaskResponseKeyEnum.responseData]: ChatHistoryItemResType;
@@ -22,7 +23,7 @@ export type CQResponse = {
 
 const agentModel = 'gpt-3.5-turbo';
 const agentFunName = 'agent_user_question';
-const maxTokens = 2000;
+const maxTokens = 3000;
 
 /* request openai chat */
 export const dispatchClassifyQuestion = async (props: Record<string, any>): Promise<CQResponse> => {

client/src/service/moduleDispatch/agent/extract.ts

@@ -7,40 +7,41 @@ import type { ChatItemType } from '@/types/chat';
 import { ChatRoleEnum } from '@/constants/chat';
 import { getAIChatApi, axiosConfig } from '@/service/ai/openai';
 import type { ClassifyQuestionAgentItemType } from '@/types/app';
-import { authUser } from '@/service/utils/auth';
+import { SystemInputEnum } from '@/constants/app';
 
 export type Props = {
+  systemPrompt?: string;
   history?: ChatItemType[];
-  userChatInput: string;
+  [SystemInputEnum.userChatInput]: string;
-  agents: ClassifyQuestionAgentItemType[];
   description: string;
+  agents: ClassifyQuestionAgentItemType[];
+};
+export type Response = {
+  arguments: Record<string, any>;
+  deficiency: boolean;
 };
-export type Response = { history: ChatItemType[] };
 
-const agentModel = 'gpt-3.5-turbo-16k';
+const agentModel = 'gpt-3.5-turbo';
 const agentFunName = 'agent_extract_data';
+const maxTokens = 3000;
 
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
+export async function extract({
-  try {
+  systemPrompt,
-    await authUser({ req, authRoot: true });
+  agents,
-
+  history = [],
-    const response = await extract(req.body);
+  userChatInput,
-
+  description
-    jsonRes(res, {
+}: Props): Promise<Response> {
-      data: response
-    });
-  } catch (err) {
-    jsonRes(res, {
-      code: 500,
-      error: err
-    });
-  }
-}
-
-/* request openai chat */
-export async function extract({ agents, history = [], userChatInput, description }: Props) {
   const messages: ChatItemType[] = [
-    ...history.slice(-4),
+    ...(systemPrompt
+      ? [
+          {
+            obj: ChatRoleEnum.System,
+            value: systemPrompt
+          }
+        ]
+      : []),
+    ...history,
     {
       obj: ChatRoleEnum.Human,
       value: userChatInput
@@ -50,7 +51,7 @@ export async function extract({ agents, history = [], userChatInput, description
     // @ts-ignore
     model: agentModel,
     prompts: messages,
-    maxTokens: 3000
+    maxTokens
   });
   const adaptMessages = adaptChatItem_openAI({ messages: filterMessages, reserveId: false });
 
@@ -94,7 +95,17 @@ export async function extract({ agents, history = [], userChatInput, description
     }
   );
 
-  const arg = JSON.parse(response.data.choices?.[0]?.message?.function_call?.arguments || '');
+  const arg = JSON.parse(response.data.choices?.[0]?.message?.function_call?.arguments || '{}');
+  let deficiency = false;
+  for (const key in arg) {
+    if (arg[key] === '') {
+      deficiency = true;
+      break;
+    }
+  }
 
-  return arg;
+  return {
+    arguments: arg,
+    deficiency
+  };
 }

client/src/types/flow.d.ts

@@ -52,7 +52,6 @@ export type FlowModuleTemplateType = {
   description?: string;
   intro: string;
   flowType: `${FlowModuleTypeEnum}`;
-  url?: string;
   inputs: FlowInputItemType[];
   outputs: FlowOutputItemType[];
 };