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* add logs chart (#5352) * charts * chart data * log chart * delete * rename api * fix * move api * fix * fix * pro config * fix * feat: Repository interaction (#5356) * feat: 1好像功能没问题了,明天再测 * feat: 2 解决了昨天遗留的bug,但全选按钮又bug了 * feat: 3 第三版,解决了全选功能bug * feat: 4 第四版,下面改小细节 * feat: 5 我勒个痘 * feat: 6 * feat: 6 pr * feat: 7 * feat: 8 * feat: 9 * feat: 10 * feat: 11 * feat: 12 * perf: checkbox ui * refactor: tweak login loyout (#5357) Co-authored-by: Archer <545436317@qq.com> * login ui * app chat log chart pro display (#5392) * app chat log chart pro display * add canopen props * perf: pro tag tip * perf: pro tag tip * feat: openrouter provider (#5406) * perf: login ui * feat: openrouter provider * provider * perf: custom error throw * perf: emb batch (#5407) * perf: emb batch * perf: vector retry * doc * doc (#5411) * doc * fix: team folder will add to workflow * fix: generateToc shell * Tool price (#5376) * resolve conflicts for cherry-pick * fix i18n * Enhance system plugin template data structure and update ToolSelectModal to include CostTooltip component * refactor: update systemKeyCost type to support array of objects in plugin and workflow types * refactor: simplify systemKeyCost type across plugin and workflow types to a single number * refactor: streamline systemKeyCost handling in plugin and workflow components * fix * fix * perf: toolset price config;fix: workflow array selector ui (#5419) * fix: workflow array selector ui * update default model tip * perf: toolset price config * doc * fix: test * Refactor/chat (#5418) * refactor: add homepage configuration; add home chat page; add side bar animated collapse and layout * fix: fix lint rules * chore: improve logics and code * chore: more clearer logics * chore: adjust api --------- Co-authored-by: Archer <545436317@qq.com> * perf: chat setting code * del history * logo image * perf: home chat ui * feat: enhance chat response handling with external links and user info (#5427) * feat: enhance chat response handling with external links and user info * fix * cite code * perf: toolset add in workflow * fix: test * fix: search paraentId * Fix/chat (#5434) * wip: rebase了upstream * wip: adapt mobile UI * fix: fix chat page logic and UI * fix: fix UI and improve some logics * fix: model selector missing logo; vision model to retrieve file * perf: role selector * fix: chat ui * optimize export app chat log (#5436) * doc * chore: move components to proper directory; fix the api to get app list (#5437) * chore: improve team app panel display form (#5438) * feat: add home chat log tab * chore: improve team app panel display form * chore: improve log panel * fix: spec * doc * fix: log permission * fix: dataset schema required * add loading status * remove ui weight * manage log * fix: log detail per * doc * fix: log menu * rename permission * bg color * fix: app log per * fix: log key selector * fix: log * doc --------- Co-authored-by: heheer <zhiyu44@qq.com> Co-authored-by: colnii <1286949794@qq.com> Co-authored-by: 伍闲犬 <76519998+xqvvu@users.noreply.github.com> Co-authored-by: Ctrlz <143257420+ctrlz526@users.noreply.github.com> Co-authored-by: 伍闲犬 <whoeverimf5@gmail.com> Co-authored-by: heheer <heheer@sealos.io>
184 lines
4.5 KiB
TypeScript
184 lines
4.5 KiB
TypeScript
import { getEmbeddingModel } from '../../../../service/core/ai/model';
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import { type EmbeddingModelItemType, type LLMModelItemType } from '../../../core/ai/model.d';
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import {
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ChunkSettingModeEnum,
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DataChunkSplitModeEnum,
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DatasetCollectionDataProcessModeEnum,
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ParagraphChunkAIModeEnum
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} from '../constants';
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import type { ChunkSettingsType } from '../type';
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import { cloneDeep } from 'lodash';
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export const minChunkSize = 64; // min index and chunk size
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// Chunk size
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export const chunkAutoChunkSize = 1000;
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export const getMaxChunkSize = (model: LLMModelItemType) => {
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return Math.max(model.maxContext - model.maxResponse, 2000);
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};
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// QA
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export const defaultMaxChunkSize = 8000;
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export const getLLMDefaultChunkSize = (model?: LLMModelItemType) => {
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if (!model) return defaultMaxChunkSize;
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return Math.max(Math.min(model.maxContext - model.maxResponse, defaultMaxChunkSize), 2000);
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};
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export const getLLMMaxChunkSize = (model?: LLMModelItemType) => {
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if (!model) return 8000;
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return Math.max(model.maxContext, 4000);
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};
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// Index size
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export const getMaxIndexSize = (model?: EmbeddingModelItemType | string) => {
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if (!model) return 512;
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const modelData = typeof model === 'string' ? getEmbeddingModel(model) : model;
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return modelData?.maxToken || 512;
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};
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export const getAutoIndexSize = (model?: EmbeddingModelItemType | string) => {
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if (!model) return 512;
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const modelData = typeof model === 'string' ? getEmbeddingModel(model) : model;
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return modelData?.defaultToken || 512;
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};
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const indexSizeSelectList = [
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{
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label: '64',
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value: 64
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},
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{
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label: '128',
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value: 128
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},
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{
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label: '256',
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value: 256
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},
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{
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label: '512',
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value: 512
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},
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{
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label: '768',
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value: 768
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},
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{
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label: '1024',
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value: 1024
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},
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{
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label: '1536',
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value: 1536
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},
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{
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label: '2048',
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value: 2048
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},
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{
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label: '3072',
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value: 3072
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},
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{
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label: '4096',
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value: 4096
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},
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{
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label: '5120',
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value: 5120
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},
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{
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label: '6144',
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value: 6144
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},
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{
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label: '7168',
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value: 7168
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},
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{
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label: '8192',
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value: 8192
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}
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];
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export const getIndexSizeSelectList = (max = 512) => {
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return indexSizeSelectList.filter((item) => item.value <= max);
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};
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// Compute
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export const computedCollectionChunkSettings = <T extends ChunkSettingsType>({
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llmModel,
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vectorModel,
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...data
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}: {
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llmModel?: LLMModelItemType;
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vectorModel?: EmbeddingModelItemType;
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} & T) => {
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const {
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trainingType = DatasetCollectionDataProcessModeEnum.chunk,
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chunkSettingMode = ChunkSettingModeEnum.auto,
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chunkSplitMode,
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chunkSize,
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paragraphChunkDeep = 5,
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indexSize,
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autoIndexes
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} = data;
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const cloneChunkSettings = cloneDeep(data);
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if (trainingType !== DatasetCollectionDataProcessModeEnum.qa) {
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delete cloneChunkSettings.qaPrompt;
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}
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// Format training type indexSize/chunkSize
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const trainingModeSize: {
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autoChunkSize: number;
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autoIndexSize: number;
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chunkSize?: number;
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indexSize?: number;
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} = (() => {
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if (trainingType === DatasetCollectionDataProcessModeEnum.qa) {
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return {
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autoChunkSize: getLLMDefaultChunkSize(llmModel),
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autoIndexSize: getMaxIndexSize(vectorModel),
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chunkSize,
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indexSize: getMaxIndexSize(vectorModel)
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};
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} else if (autoIndexes) {
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return {
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autoChunkSize: chunkAutoChunkSize,
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autoIndexSize: getAutoIndexSize(vectorModel),
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chunkSize,
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indexSize
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};
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} else {
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return {
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autoChunkSize: chunkAutoChunkSize,
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autoIndexSize: getAutoIndexSize(vectorModel),
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chunkSize,
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indexSize
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};
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}
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})();
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if (chunkSettingMode === ChunkSettingModeEnum.auto) {
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cloneChunkSettings.chunkSplitMode = DataChunkSplitModeEnum.paragraph;
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cloneChunkSettings.paragraphChunkAIMode = ParagraphChunkAIModeEnum.forbid;
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cloneChunkSettings.paragraphChunkDeep = 5;
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cloneChunkSettings.paragraphChunkMinSize = 100;
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cloneChunkSettings.chunkSize = trainingModeSize.autoChunkSize;
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cloneChunkSettings.indexSize = trainingModeSize.autoIndexSize;
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cloneChunkSettings.chunkSplitter = undefined;
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} else {
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cloneChunkSettings.paragraphChunkDeep =
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chunkSplitMode === DataChunkSplitModeEnum.paragraph ? paragraphChunkDeep : 0;
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cloneChunkSettings.chunkSize = trainingModeSize.chunkSize
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? Math.min(trainingModeSize.chunkSize ?? chunkAutoChunkSize, getLLMMaxChunkSize(llmModel))
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: undefined;
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cloneChunkSettings.indexSize = trainingModeSize.indexSize;
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}
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return cloneChunkSettings;
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};
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