perf: response store
This commit is contained in:
archer
@@ -6,12 +6,13 @@ import { modelToolMap } from '@/utils/plugin';
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import { ChatContextFilter } from '@/service/utils/chat/index';
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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 { ChatModuleEnum, ChatRoleEnum, sseResponseEventEnum } from '@/constants/chat';
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import { parseStreamChunk, textAdaptGptResponse } from '@/utils/adapt';
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import { getOpenAIApi, axiosConfig } from '@/service/ai/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/events/pushBill';
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import { ChatModelItemType } from '@/types/model';
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export type ChatProps = {
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res: NextApiResponse;
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@@ -30,8 +31,6 @@ export type ChatResponse = {
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[TaskResponseKeyEnum.responseData]: ChatHistoryItemResType;
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};
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const moduleName = 'AI Chat';
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/* request openai chat */
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export const dispatchChatCompletion = async (props: Record<string, any>): Promise<ChatResponse> => {
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let {
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@@ -54,24 +53,153 @@ export const dispatchChatCompletion = async (props: Record<string, any>): Promis
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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, quotePrompt } = filterQuote({
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quoteQA,
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model: modelConstantsData
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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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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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const chatAPI = getOpenAIApi();
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const response = await chatAPI.createChatCompletion(
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{
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model,
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temperature: Number(temperature || 0),
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max_tokens,
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messages,
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// frequency_penalty: 0.5, // 越大，重复内容越少
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// presence_penalty: -0.5, // 越大，越容易出现新内容
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stream
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},
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{
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timeout: stream ? 60000 : 480000,
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responseType: stream ? 'stream' : 'json',
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...axiosConfig()
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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({ res, response });
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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 = countOpenAIToken({
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messages: completeMessages
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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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} else {
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const answer = stream ? '' : response.data.choices?.[0].message?.content || '';
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const totalTokens = stream ? 0 : 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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moduleName: ChatModuleEnum.AIChat,
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price: 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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answer: answerText,
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maxToken,
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quoteList: filterQuoteQA,
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completeMessages
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}
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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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}: {
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quoteQA: ChatProps['quoteQA'];
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model: ChatModelItemType;
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}) {
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const sliceResult = modelToolMap.tokenSlice({
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model: model.model,
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maxToken: model.quoteMaxToken,
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messages: quoteQA.map((item, i) => ({
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obj: ChatRoleEnum.System,
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value: `${i + 1}. [${item.q}\n${item.a}]`
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}))
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});
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// slice filterSearch
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const filterQuoteQA = quoteQA.slice(0, sliceResult.length);
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const quotePrompt =
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filterQuoteQA.length > 0
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? `下面是知识库内容:
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${filterQuoteQA.map((item, i) => `${i + 1}. [${item.q}\n${item.a}]`).join('\n')}
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`
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: '';
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return {
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filterQuoteQA,
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quotePrompt
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};
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}
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function getChatMessages({
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quotePrompt,
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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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history: ChatProps['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 limitText = (() => {
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if (limitPrompt) return limitPrompt;
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if (quoteQA.length > 0 && !limitPrompt) {
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if (quotePrompt && !limitPrompt) {
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return '根据知识库内容回答问题，仅回复知识库提供的内容，不要对知识库内容做补充说明。';
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}
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return '';
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})();
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const quotePrompt =
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quoteQA.length > 0
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? `下面是知识库内容:
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${quoteQA.map((item, i) => `${i + 1}. [${item.q}\n${item.a}]`).join('\n')}
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`
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: '';
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const messages: ChatItemType[] = [
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...(quotePrompt
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? [
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@@ -103,92 +231,41 @@ ${quoteQA.map((item, i) => `${i + 1}. [${item.q}\n${item.a}]`).join('\n')}
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value: userChatInput
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}
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];
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const modelTokenLimit = getChatModel(model)?.contextMaxToken || 4000;
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const filterMessages = ChatContextFilter({
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model,
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model: model.model,
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prompts: messages,
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maxTokens: Math.ceil(modelTokenLimit - 300) // filter token. not response maxToken
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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 = adaptChatItem_openAI({ messages: filterMessages, reserveId: false });
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const chatAPI = getOpenAIApi();
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console.log(adaptMessages);
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/* count response max token */
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const promptsToken = modelToolMap.countTokens({
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model,
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messages: filterMessages
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});
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maxToken = maxToken + promptsToken > modelTokenLimit ? modelTokenLimit - promptsToken : maxToken;
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const response = await chatAPI.createChatCompletion(
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{
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model,
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temperature: Number(temperature || 0),
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max_tokens: maxToken,
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messages: adaptMessages,
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// frequency_penalty: 0.5, // 越大，重复内容越少
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// presence_penalty: -0.5, // 越大，越容易出现新内容
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stream
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},
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{
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timeout: stream ? 60000 : 480000,
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responseType: stream ? 'stream' : 'json',
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...axiosConfig()
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}
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);
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const { answerText, totalTokens, finishMessages } = await (async () => {
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if (stream) {
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// sse response
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const { answer } = await streamResponse({ res, response });
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// count tokens
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const finishMessages = 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 = countOpenAIToken({
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messages: finishMessages
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});
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return {
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answerText: answer,
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totalTokens,
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finishMessages
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};
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} else {
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const answer = stream ? '' : response.data.choices?.[0].message?.content || '';
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const totalTokens = stream ? 0 : response.data.usage?.total_tokens || 0;
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const finishMessages = 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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finishMessages
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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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moduleName,
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price: 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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answer: answerText,
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maxToken,
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finishMessages
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}
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messages: adaptMessages,
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filterMessages
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};
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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['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 = modelToolMap.countTokens({
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model: model.model,
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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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async function streamResponse({ res, response }: { res: NextApiResponse; response: any }) {
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let answer = '';
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