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monorepo packages (#344)
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Archer
405
projects/app/src/service/moduleDispatch/chat/oneapi.ts
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405
projects/app/src/service/moduleDispatch/chat/oneapi.ts
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import type { NextApiResponse } from 'next';
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import { ChatContextFilter } from '@/service/common/tiktoken';
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import type { ChatItemType, QuoteItemType } from '@/types/chat';
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import type { ChatHistoryItemResType } from '@/types/chat';
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import { ChatRoleEnum, sseResponseEventEnum } from '@/constants/chat';
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import { SSEParseData, parseStreamChunk } from '@/utils/sse';
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import { textAdaptGptResponse } from '@/utils/adapt';
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import { getAIChatApi, axiosConfig } from '@/service/lib/openai';
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import { TaskResponseKeyEnum } from '@/constants/chat';
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import { getChatModel } from '@/service/utils/data';
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import { countModelPrice } from '@/service/common/bill/push';
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import { ChatModelItemType } from '@/types/model';
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import { textCensor } from '@/api/service/plugins';
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import { ChatCompletionRequestMessageRoleEnum } from 'openai';
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import { AppModuleItemType } from '@/types/app';
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import { countMessagesTokens, sliceMessagesTB } from '@/utils/common/tiktoken';
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import { adaptChat2GptMessages } from '@/utils/common/adapt/message';
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import { defaultQuotePrompt, defaultQuoteTemplate } from '@/prompts/core/AIChat';
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import type { AIChatProps } from '@/types/core/aiChat';
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import { replaceVariable } from '@/utils/common/tools/text';
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import { FlowModuleTypeEnum } from '@/constants/flow';
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import { ModuleDispatchProps } from '@/types/core/modules';
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import { Readable } from 'stream';
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import { responseWrite, responseWriteController } from '@/service/common/stream';
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import { addLog } from '@/service/utils/tools';
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export type ChatProps = ModuleDispatchProps<
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AIChatProps & {
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userChatInput: string;
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history?: ChatItemType[];
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quoteQA?: QuoteItemType[];
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limitPrompt?: string;
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}
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>;
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export type ChatResponse = {
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[TaskResponseKeyEnum.answerText]: string;
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[TaskResponseKeyEnum.responseData]: ChatHistoryItemResType;
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finish: boolean;
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};
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/* request openai chat */
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export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResponse> => {
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let {
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res,
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moduleName,
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stream = false,
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detail = false,
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userOpenaiAccount,
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outputs,
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inputs: {
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model = global.chatModels[0]?.model,
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temperature = 0,
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maxToken = 4000,
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history = [],
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quoteQA = [],
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userChatInput,
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systemPrompt = '',
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limitPrompt,
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quoteTemplate,
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quotePrompt
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}
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} = props;
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if (!userChatInput) {
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return Promise.reject('Question is empty');
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}
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// temperature adapt
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const modelConstantsData = getChatModel(model);
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if (!modelConstantsData) {
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return Promise.reject('The chat model is undefined, you need to select a chat model.');
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}
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const { filterQuoteQA, quoteText } = filterQuote({
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quoteQA,
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model: modelConstantsData,
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quoteTemplate
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});
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if (modelConstantsData.censor) {
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await textCensor({
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text: `${systemPrompt}
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${quoteText}
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${userChatInput}
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`
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});
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}
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const { messages, filterMessages } = getChatMessages({
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model: modelConstantsData,
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history,
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quoteText,
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quotePrompt,
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userChatInput,
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systemPrompt,
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limitPrompt
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});
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const { max_tokens } = getMaxTokens({
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model: modelConstantsData,
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maxToken,
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filterMessages
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});
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// console.log(messages);
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// FastGPT temperature range: 1~10
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temperature = +(modelConstantsData.maxTemperature * (temperature / 10)).toFixed(2);
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temperature = Math.max(temperature, 0.01);
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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,
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temperature,
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max_tokens,
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messages: [
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...(modelConstantsData.defaultSystem
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? [
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{
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role: ChatCompletionRequestMessageRoleEnum.System,
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content: modelConstantsData.defaultSystem
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}
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]
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: []),
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...messages
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],
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stream
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},
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{
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timeout: 480000,
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responseType: stream ? 'stream' : 'json',
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...axiosConfig(userOpenaiAccount)
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}
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);
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const { answerText, totalTokens, completeMessages } = await (async () => {
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if (stream) {
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// sse response
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const { answer } = await streamResponse({
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res,
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detail,
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response
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});
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// count tokens
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const completeMessages = filterMessages.concat({
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obj: ChatRoleEnum.AI,
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value: answer
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});
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const totalTokens = countMessagesTokens({
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messages: completeMessages
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});
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targetResponse({ res, detail, outputs });
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return {
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answerText: answer,
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totalTokens,
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completeMessages
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};
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} else {
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const answer = response.data.choices?.[0].message?.content || '';
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const totalTokens = response.data.usage?.total_tokens || 0;
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const completeMessages = filterMessages.concat({
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obj: ChatRoleEnum.AI,
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value: answer
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});
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return {
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answerText: answer,
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totalTokens,
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completeMessages
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};
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}
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})();
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return {
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[TaskResponseKeyEnum.answerText]: answerText,
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[TaskResponseKeyEnum.responseData]: {
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moduleType: FlowModuleTypeEnum.chatNode,
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moduleName,
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price: userOpenaiAccount?.key ? 0 : countModelPrice({ model, tokens: totalTokens }),
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model: modelConstantsData.name,
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tokens: totalTokens,
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question: userChatInput,
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maxToken: max_tokens,
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quoteList: filterQuoteQA,
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historyPreview: getHistoryPreview(completeMessages)
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},
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finish: true
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};
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};
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function filterQuote({
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quoteQA = [],
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model,
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quoteTemplate
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}: {
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quoteQA: ChatProps['inputs']['quoteQA'];
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model: ChatModelItemType;
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quoteTemplate?: string;
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}) {
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const sliceResult = sliceMessagesTB({
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maxTokens: model.quoteMaxToken,
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messages: quoteQA.map((item, index) => ({
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obj: ChatRoleEnum.System,
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value: replaceVariable(quoteTemplate || defaultQuoteTemplate, {
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...item,
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index: `${index + 1}`
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})
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}))
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});
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// slice filterSearch
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const filterQuoteQA = quoteQA.slice(0, sliceResult.length);
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const quoteText =
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filterQuoteQA.length > 0
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? `${filterQuoteQA
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.map((item, index) =>
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replaceVariable(quoteTemplate || defaultQuoteTemplate, {
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...item,
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index: `${index + 1}`
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})
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)
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.join('\n')}`
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: '';
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return {
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filterQuoteQA,
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quoteText
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};
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}
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function getChatMessages({
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quotePrompt,
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quoteText,
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history = [],
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systemPrompt,
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limitPrompt,
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userChatInput,
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model
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}: {
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quotePrompt?: string;
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quoteText: string;
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history: ChatProps['inputs']['history'];
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systemPrompt: string;
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limitPrompt?: string;
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userChatInput: string;
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model: ChatModelItemType;
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}) {
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const question = quoteText
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? replaceVariable(quotePrompt || defaultQuotePrompt, {
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quote: quoteText,
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question: userChatInput
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})
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: userChatInput;
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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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...(limitPrompt
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? [
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{
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obj: ChatRoleEnum.System,
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value: limitPrompt
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}
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]
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: []),
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{
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obj: ChatRoleEnum.Human,
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value: question
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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: Math.ceil(model.contextMaxToken - 300) // filter token. not response maxToken
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});
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const adaptMessages = adaptChat2GptMessages({ messages: filterMessages, reserveId: false });
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return {
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messages: adaptMessages,
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filterMessages
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};
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}
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function getMaxTokens({
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maxToken,
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model,
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filterMessages = []
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}: {
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maxToken: number;
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model: ChatModelItemType;
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filterMessages: ChatProps['inputs']['history'];
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}) {
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const tokensLimit = model.contextMaxToken;
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/* count response max token */
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const promptsToken = countMessagesTokens({
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messages: filterMessages
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});
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maxToken = maxToken + promptsToken > tokensLimit ? tokensLimit - promptsToken : maxToken;
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return {
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max_tokens: maxToken
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};
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}
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function targetResponse({
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res,
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outputs,
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detail
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}: {
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res: NextApiResponse;
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outputs: AppModuleItemType['outputs'];
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detail: boolean;
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}) {
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const targets =
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outputs.find((output) => output.key === TaskResponseKeyEnum.answerText)?.targets || [];
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if (targets.length === 0) return;
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responseWrite({
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res,
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event: detail ? sseResponseEventEnum.answer : undefined,
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data: textAdaptGptResponse({
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text: '\n'
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})
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});
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}
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async function streamResponse({
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res,
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detail,
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response
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}: {
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res: NextApiResponse;
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detail: boolean;
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response: any;
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}) {
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return new Promise<{ answer: string }>((resolve, reject) => {
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const stream = response.data as Readable;
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let answer = '';
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const parseData = new SSEParseData();
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const write = responseWriteController({
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res,
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readStream: stream
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});
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stream.on('data', (data) => {
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if (res.closed) {
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stream.destroy();
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return resolve({ answer });
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}
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const parse = parseStreamChunk(data);
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parse.forEach((item) => {
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const { data } = parseData.parse(item);
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if (!data || data === '[DONE]') return;
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const content: string = data?.choices?.[0]?.delta?.content || '';
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if (data.error) {
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addLog.error(`SSE response`, data.error);
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} else {
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answer += content;
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responseWrite({
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write,
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event: detail ? sseResponseEventEnum.answer : undefined,
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data: textAdaptGptResponse({
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text: content
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})
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});
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}
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});
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});
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stream.on('end', () => {
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resolve({ answer });
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});
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stream.on('close', () => {
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resolve({ answer });
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});
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stream.on('error', (err) => {
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reject(err);
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});
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});
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}
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function getHistoryPreview(completeMessages: ChatItemType[]) {
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return completeMessages.map((item, i) => {
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if (item.obj === ChatRoleEnum.System) return item;
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if (i >= completeMessages.length - 2) return item;
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return {
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...item,
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value: item.value.length > 15 ? `${item.value.slice(0, 15)}...` : item.value
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};
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});
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}
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