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112 lines
3.3 KiB
TypeScript
112 lines
3.3 KiB
TypeScript
import { adaptChat2GptMessages } from '@/utils/common/adapt/message';
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import { ChatContextFilter } from '@/service/common/tiktoken';
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import type { ChatHistoryItemResType, ChatItemType } from '@/types/chat';
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import { ChatRoleEnum, TaskResponseKeyEnum } from '@/constants/chat';
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import { getAIChatApi, axiosConfig } from '@/service/lib/openai';
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import type { ClassifyQuestionAgentItemType } from '@/types/app';
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import { countModelPrice } from '@/service/events/pushBill';
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import { getModel } from '@/service/utils/data';
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import { SystemInputEnum } from '@/constants/app';
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import { SpecialInputKeyEnum } from '@/constants/flow';
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import { FlowModuleTypeEnum } from '@/constants/flow';
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import { ModuleDispatchProps } from '@/types/core/modules';
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export type CQProps = ModuleDispatchProps<{
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systemPrompt?: string;
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history?: ChatItemType[];
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[SystemInputEnum.userChatInput]: string;
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[SpecialInputKeyEnum.agents]: ClassifyQuestionAgentItemType[];
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}>;
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export type CQResponse = {
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[TaskResponseKeyEnum.responseData]: ChatHistoryItemResType;
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[key: string]: any;
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};
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const agentModel = 'gpt-3.5-turbo';
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const agentFunName = 'agent_user_question';
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const maxTokens = 3000;
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/* request openai chat */
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export const dispatchClassifyQuestion = async (props: Record<string, any>): Promise<CQResponse> => {
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const {
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moduleName,
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userOpenaiAccount,
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inputs: { agents, systemPrompt, history = [], userChatInput }
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} = props as CQProps;
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if (!userChatInput) {
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return Promise.reject('Input is empty');
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}
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const messages: ChatItemType[] = [
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...(systemPrompt
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? [
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{
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obj: ChatRoleEnum.System,
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value: systemPrompt
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}
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]
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: []),
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...history,
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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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messages,
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maxTokens
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});
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const adaptMessages = adaptChat2GptMessages({ messages: filterMessages, reserveId: false });
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// function body
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const agentFunction = {
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name: agentFunName,
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description: '判断用户问题的类型属于哪方面，返回对应的枚举字段',
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parameters: {
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type: 'object',
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properties: {
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type: {
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type: 'string',
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description: agents.map((item) => `${item.value}，返回：'${item.key}'`).join('；'),
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enum: agents.map((item) => item.key)
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}
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},
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required: ['type']
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}
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};
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const chatAPI = getAIChatApi(userOpenaiAccount);
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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(userOpenaiAccount)
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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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const tokens = response.data.usage?.total_tokens || 0;
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const result = agents.find((item) => item.key === arg?.type) || agents[0];
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return {
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[result.key]: 1,
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[TaskResponseKeyEnum.responseData]: {
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moduleType: FlowModuleTypeEnum.classifyQuestion,
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moduleName,
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price: userOpenaiAccount?.key ? 0 : countModelPrice({ model: agentModel, tokens }),
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model: getModel(agentModel)?.name || agentModel,
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tokens,
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cqList: agents,
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cqResult: result.value
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}
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};
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};
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