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* feat: workflow catch error (#5220) * feat: error catch * feat: workflow catch error * perf: add catch error to node * feat: system tool error catch * catch error * fix: ts * update doc * perf: training queue code (#5232) * doc * perf: training queue code * Feat: 优化错误提示与重试逻辑 (#5192) * feat: 批量重试异常数据 & 报错信息国际化 - 新增“全部重试”按钮,支持批量重试所有训练异常数据 - 报错信息支持国际化,常见错误自动映射为 i18n key - 相关文档和 i18n 资源已同步更新 * feat: enhance error message and retry mechanism * feat: enhance error message and retry mechanism * feat: add retry_failed i18n key * feat: enhance error message and retry mechanism * feat: enhance error message and retry mechanism * feat: enhance error message and retry mechanism : 5 * feat: enhance error message and retry mechanism : 6 * feat: enhance error message and retry mechanism : 7 * feat: enhance error message and retry mechanism : 8 * perf: catch chat error * perf: copy hook (#5246) * perf: copy hook * doc * doc * add app evaluation (#5083) * add app evaluation * fix * usage * variables * editing condition * var ui * isplus filter * migrate code * remove utils * name * update type * build * fix * fix * fix * delete comment * fix * perf: eval code * eval code * eval code * feat: ttfb time in model log * Refactor chat page (#5253) * feat: update side bar layout; add login and logout logic at chat page * refactor: encapsulate login logic and reuse it in `LoginModal` and `Login` page * chore: improve some logics and comments * chore: improve some logics * chore: remove redundant side effect; add translations --------- Co-authored-by: Archer <545436317@qq.com> * perf: chat page code * doc * perf: provider redirect * chore: ui improvement (#5266) * Fix: SSE * Fix: SSE * eval pagination (#5264) * eval scroll pagination * change eval list to manual pagination * number * fix build * fix * version doc (#5267) * version doc * version doc * doc * feat: eval model select * config eval model * perf: eval detail modal ui * doc * doc * fix: chat store reload * doc --------- Co-authored-by: colnii <1286949794@qq.com> Co-authored-by: heheer <heheer@sealos.io> Co-authored-by: 酒川户 <76519998+chuanhu9@users.noreply.github.com>
281 lines
6.1 KiB
TypeScript
281 lines
6.1 KiB
TypeScript
import { UsageSourceEnum } from '@fastgpt/global/support/wallet/usage/constants';
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import { MongoUsage } from './schema';
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import { type ClientSession } from '../../../common/mongo';
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import { addLog } from '../../../common/system/log';
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import { type ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
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import {
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type ConcatUsageProps,
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type CreateUsageProps
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} from '@fastgpt/global/support/wallet/usage/api';
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import { i18nT } from '../../../../web/i18n/utils';
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import { formatModelChars2Points } from './utils';
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import { ModelTypeEnum } from '@fastgpt/global/core/ai/model';
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export async function createUsage(data: CreateUsageProps) {
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try {
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await global.createUsageHandler(data);
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} catch (error) {
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addLog.error('createUsage error', error);
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}
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}
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export async function concatUsage(data: ConcatUsageProps) {
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try {
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await global.concatUsageHandler(data);
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} catch (error) {
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addLog.error('concatUsage error', error);
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}
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}
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export const createChatUsage = ({
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appName,
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appId,
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pluginId,
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teamId,
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tmbId,
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source,
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flowUsages
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}: {
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appName: string;
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appId?: string;
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pluginId?: string;
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teamId: string;
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tmbId: string;
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source: UsageSourceEnum;
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flowUsages: ChatNodeUsageType[];
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}) => {
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const totalPoints = flowUsages.reduce((sum, item) => sum + (item.totalPoints || 0), 0);
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createUsage({
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teamId,
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tmbId,
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appName,
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appId,
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pluginId,
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totalPoints,
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source,
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list: flowUsages.map((item) => ({
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moduleName: item.moduleName,
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amount: item.totalPoints || 0,
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model: item.model,
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inputTokens: item.inputTokens,
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outputTokens: item.outputTokens
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}))
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});
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addLog.debug(`Create chat usage`, {
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source,
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teamId,
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totalPoints
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});
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return { totalPoints };
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};
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export type DatasetTrainingMode = 'paragraph' | 'qa' | 'autoIndex' | 'imageIndex' | 'imageParse';
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export const datasetTrainingUsageIndexMap: Record<DatasetTrainingMode, number> = {
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paragraph: 1,
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qa: 2,
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autoIndex: 3,
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imageIndex: 4,
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imageParse: 5
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};
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export const createTrainingUsage = async ({
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teamId,
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tmbId,
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appName,
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billSource,
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vectorModel,
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agentModel,
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vllmModel,
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session
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}: {
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teamId: string;
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tmbId: string;
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appName: string;
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billSource: UsageSourceEnum;
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vectorModel?: string;
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agentModel?: string;
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vllmModel?: string;
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session?: ClientSession;
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}) => {
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const [{ _id }] = await MongoUsage.create(
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[
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{
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teamId,
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tmbId,
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appName,
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source: billSource,
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totalPoints: 0,
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list: [
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...(vectorModel
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? [
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{
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moduleName: i18nT('account_usage:embedding_index'),
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model: vectorModel,
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amount: 0,
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inputTokens: 0,
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outputTokens: 0
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}
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]
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: []),
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...(agentModel
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? [
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{
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moduleName: i18nT('account_usage:llm_paragraph'),
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model: agentModel,
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amount: 0,
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inputTokens: 0,
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outputTokens: 0
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},
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{
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moduleName: i18nT('account_usage:qa'),
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model: agentModel,
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amount: 0,
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inputTokens: 0,
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outputTokens: 0
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},
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{
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moduleName: i18nT('account_usage:auto_index'),
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model: agentModel,
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amount: 0,
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inputTokens: 0,
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outputTokens: 0
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}
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]
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: []),
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...(vllmModel
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? [
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{
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moduleName: i18nT('account_usage:image_index'),
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model: vllmModel,
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amount: 0,
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inputTokens: 0,
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outputTokens: 0
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},
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{
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moduleName: i18nT('account_usage:image_parse'),
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model: vllmModel,
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amount: 0,
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inputTokens: 0,
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outputTokens: 0
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}
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]
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: [])
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]
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}
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],
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{ session, ordered: true }
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);
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return { billId: String(_id) };
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};
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export const createPdfParseUsage = async ({
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teamId,
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tmbId,
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pages
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}: {
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teamId: string;
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tmbId: string;
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pages: number;
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}) => {
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const unitPrice = global.systemEnv?.customPdfParse?.price || 0;
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const totalPoints = pages * unitPrice;
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createUsage({
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teamId,
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tmbId,
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appName: i18nT('account_usage:pdf_enhanced_parse'),
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totalPoints,
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source: UsageSourceEnum.pdfParse,
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list: [
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{
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moduleName: i18nT('account_usage:pdf_enhanced_parse'),
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amount: totalPoints,
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pages
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}
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]
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});
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};
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export const pushLLMTrainingUsage = async ({
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teamId,
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tmbId,
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model,
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inputTokens,
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outputTokens,
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billId,
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mode
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}: {
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teamId: string;
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tmbId: string;
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model: string;
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inputTokens: number;
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outputTokens: number;
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billId: string;
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mode: DatasetTrainingMode;
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}) => {
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const index = datasetTrainingUsageIndexMap[mode];
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// Compute points
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const { totalPoints } = formatModelChars2Points({
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model,
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modelType: ModelTypeEnum.llm,
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inputTokens,
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outputTokens
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});
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concatUsage({
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billId,
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teamId,
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tmbId,
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totalPoints,
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inputTokens,
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outputTokens,
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listIndex: index
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});
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return { totalPoints };
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};
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export const createEvaluationUsage = async ({
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teamId,
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tmbId,
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appName,
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model,
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session
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}: {
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teamId: string;
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tmbId: string;
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appName: string;
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model: string;
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session?: ClientSession;
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}) => {
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const [{ _id: usageId }] = await MongoUsage.create(
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[
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{
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teamId,
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tmbId,
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appName,
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source: UsageSourceEnum.evaluation,
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totalPoints: 0,
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list: [
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{
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moduleName: i18nT('account_usage:generate_answer'),
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amount: 0,
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count: 0
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},
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{
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moduleName: i18nT('account_usage:answer_accuracy'),
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amount: 0,
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inputTokens: 0,
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outputTokens: 0,
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model
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}
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]
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}
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],
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{ session, ordered: true }
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);
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return { usageId };
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};
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