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V4.12.0 features (#5435)
* 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>
This commit is contained in:
Archer
@@ -3,6 +3,7 @@ import { getAxiosConfig } from '../config';
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import axios from 'axios';
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import FormData from 'form-data';
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import { type STTModelType } from '@fastgpt/global/core/ai/model.d';
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import { UserError } from '@fastgpt/global/common/error/utils';
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export const aiTranscriptions = async ({
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model: modelData,
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@@ -14,7 +15,7 @@ export const aiTranscriptions = async ({
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headers?: Record<string, string>;
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}) => {
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if (!modelData) {
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return Promise.reject('no model');
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return Promise.reject(new UserError('no model'));
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}
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const data = new FormData();
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@@ -6,7 +6,7 @@ import { addLog } from '../../../common/system/log';
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type GetVectorProps = {
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model: EmbeddingModelItemType;
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input: string;
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input: string[] | string;
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type?: `${EmbeddingTypeEnm}`;
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headers?: Record<string, string>;
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};
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@@ -19,60 +19,85 @@ export async function getVectorsByText({ model, input, type, headers }: GetVecto
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message: 'input is empty'
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});
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}
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const ai = getAIApi();
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const formatInput = Array.isArray(input) ? input : [input];
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// 20 size every request
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const chunkSize = 20;
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const chunks = [];
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for (let i = 0; i < formatInput.length; i += chunkSize) {
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chunks.push(formatInput.slice(i, i + chunkSize));
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}
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try {
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const ai = getAIApi();
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// Process chunks sequentially
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let totalTokens = 0;
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const allVectors: number[][] = [];
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// input text to vector
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const result = await ai.embeddings
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.create(
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{
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...model.defaultConfig,
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...(type === EmbeddingTypeEnm.db && model.dbConfig),
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...(type === EmbeddingTypeEnm.query && model.queryConfig),
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model: model.model,
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input: [input]
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},
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model.requestUrl
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? {
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path: model.requestUrl,
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headers: {
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...(model.requestAuth ? { Authorization: `Bearer ${model.requestAuth}` } : {}),
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...headers
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for (const chunk of chunks) {
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// input text to vector
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const result = await ai.embeddings
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.create(
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{
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...model.defaultConfig,
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...(type === EmbeddingTypeEnm.db && model.dbConfig),
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...(type === EmbeddingTypeEnm.query && model.queryConfig),
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model: model.model,
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input: chunk
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},
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model.requestUrl
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? {
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path: model.requestUrl,
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headers: {
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...(model.requestAuth ? { Authorization: `Bearer ${model.requestAuth}` } : {}),
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...headers
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}
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}
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}
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: { headers }
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)
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.then(async (res) => {
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if (!res.data) {
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addLog.error('Embedding API is not responding', res);
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return Promise.reject('Embedding API is not responding');
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}
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if (!res?.data?.[0]?.embedding) {
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console.log(res);
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// @ts-ignore
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return Promise.reject(res.data?.err?.message || 'Embedding API Error');
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}
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: { headers }
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)
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.then(async (res) => {
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if (!res.data) {
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addLog.error('Embedding API is not responding', res);
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return Promise.reject('Embedding API is not responding');
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}
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if (!res?.data?.[0]?.embedding) {
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console.log(res);
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// @ts-ignore
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return Promise.reject(res.data?.err?.message || 'Embedding API Error');
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}
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const [tokens, vectors] = await Promise.all([
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countPromptTokens(input),
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Promise.all(
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res.data
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.map((item) => unityDimensional(item.embedding))
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.map((item) => {
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if (model.normalization) return normalization(item);
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return item;
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})
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)
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]);
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const [tokens, vectors] = await Promise.all([
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(async () => {
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if (res.usage) return res.usage.total_tokens;
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return {
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tokens,
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vectors
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};
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});
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const tokens = await Promise.all(chunk.map((item) => countPromptTokens(item)));
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return tokens.reduce((sum, item) => sum + item, 0);
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})(),
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Promise.all(
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res.data
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.map((item) => unityDimensional(item.embedding))
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.map((item) => {
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if (model.normalization) return normalization(item);
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return item;
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})
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)
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]);
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return result;
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return {
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tokens,
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vectors
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};
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});
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totalTokens += result.tokens;
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allVectors.push(...result.vectors);
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}
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return {
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tokens: totalTokens,
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vectors: allVectors
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};
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} catch (error) {
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addLog.error(`Embedding Error`, error);
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