101 lines
2.5 KiB
TypeScript
101 lines
2.5 KiB
TypeScript
// Next.js API route support: https://nextjs.org/docs/api-routes/introduction
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import type { NextApiRequest, NextApiResponse } from 'next';
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import { jsonRes } from '@/service/response';
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import { adaptChatItem_openAI } from '@/utils/plugin/openai';
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import { ChatContextFilter } from '@/service/utils/chat/index';
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import type { ChatItemType } from '@/types/chat';
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import { ChatRoleEnum } from '@/constants/chat';
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import { getAIChatApi, axiosConfig } from '@/service/ai/openai';
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import type { ClassifyQuestionAgentItemType } from '@/types/app';
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import { authUser } from '@/service/utils/auth';
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export type Props = {
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history?: ChatItemType[];
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userChatInput: string;
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agents: ClassifyQuestionAgentItemType[];
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description: string;
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};
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export type Response = { history: ChatItemType[] };
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const agentModel = 'gpt-3.5-turbo-16k';
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const agentFunName = 'agent_extract_data';
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export default async function handler(req: NextApiRequest, res: NextApiResponse) {
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try {
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await authUser({ req, authRoot: true });
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const response = await extract(req.body);
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jsonRes(res, {
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data: response
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});
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} catch (err) {
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jsonRes(res, {
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code: 500,
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error: err
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});
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}
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}
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/* request openai chat */
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export async function extract({ agents, history = [], userChatInput, description }: Props) {
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const messages: ChatItemType[] = [
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...history.slice(-4),
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{
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obj: ChatRoleEnum.Human,
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value: userChatInput
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}
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];
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const filterMessages = ChatContextFilter({
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// @ts-ignore
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model: agentModel,
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prompts: messages,
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maxTokens: 3000
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});
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const adaptMessages = adaptChatItem_openAI({ messages: filterMessages, reserveId: false });
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const properties: Record<
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string,
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{
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type: string;
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description: string;
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}
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> = {};
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agents.forEach((item) => {
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properties[item.key] = {
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type: 'string',
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description: item.value
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};
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});
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// function body
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const agentFunction = {
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name: agentFunName,
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description,
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parameters: {
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type: 'object',
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properties,
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required: agents.map((item) => item.key)
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}
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};
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const chatAPI = getAIChatApi();
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const response = await chatAPI.createChatCompletion(
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{
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model: agentModel,
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temperature: 0,
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messages: [...adaptMessages],
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function_call: { name: agentFunName },
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functions: [agentFunction]
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},
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{
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...axiosConfig()
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}
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);
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const arg = JSON.parse(response.data.choices?.[0]?.message?.function_call?.arguments || '');
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return arg;
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}
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