1382 lines
50 KiB
JavaScript
1382 lines
50 KiB
JavaScript
import {
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eventSource,
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event_types,
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extension_prompt_types,
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extension_prompt_roles,
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getCurrentChatId,
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getRequestHeaders,
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is_send_press,
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saveSettingsDebounced,
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setExtensionPrompt,
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substituteParams,
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generateRaw,
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} from '../../../script.js';
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import {
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ModuleWorkerWrapper,
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extension_settings,
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getContext,
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modules,
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renderExtensionTemplateAsync,
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doExtrasFetch, getApiUrl,
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} from '../../extensions.js';
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import { collapseNewlines } from '../../power-user.js';
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import { SECRET_KEYS, secret_state, writeSecret } from '../../secrets.js';
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import { getDataBankAttachments, getFileAttachment } from '../../chats.js';
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import { debounce, getStringHash as calculateHash, waitUntilCondition, onlyUnique, splitRecursive } from '../../utils.js';
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import { debounce_timeout } from '../../constants.js';
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import { getSortedEntries } from '../../world-info.js';
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const MODULE_NAME = 'vectors';
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export const EXTENSION_PROMPT_TAG = '3_vectors';
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export const EXTENSION_PROMPT_TAG_DB = '4_vectors_data_bank';
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const settings = {
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// For both
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source: 'transformers',
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include_wi: false,
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togetherai_model: 'togethercomputer/m2-bert-80M-32k-retrieval',
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openai_model: 'text-embedding-ada-002',
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cohere_model: 'embed-english-v3.0',
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summarize: false,
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summarize_sent: false,
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summary_source: 'main',
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summary_prompt: 'Pause your roleplay. Summarize the most important parts of the message. Limit yourself to 250 words or less. Your response should include nothing but the summary.',
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// For chats
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enabled_chats: false,
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template: 'Past events:\n{{text}}',
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depth: 2,
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position: extension_prompt_types.IN_PROMPT,
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protect: 5,
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insert: 3,
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query: 2,
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message_chunk_size: 400,
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// For files
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enabled_files: false,
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science_mode: false,
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translate_files: false,
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size_threshold: 10,
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chunk_size: 5000,
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chunk_count: 2,
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// For Data Bank
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size_threshold_db: 5,
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chunk_size_db: 2500,
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chunk_count_db: 5,
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file_template_db: 'Related information:\n{{text}}',
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file_position_db: extension_prompt_types.IN_PROMPT,
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file_depth_db: 4,
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file_depth_role_db: extension_prompt_roles.SYSTEM,
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// For World Info
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enabled_world_info: false,
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enabled_for_all: false,
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max_entries: 5,
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};
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const moduleWorker = new ModuleWorkerWrapper(synchronizeChat);
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/**
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* Gets the Collection ID for a file embedded in the chat.
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* @param {string} fileUrl URL of the file
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* @returns {string} Collection ID
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*/
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function getFileCollectionId(fileUrl) {
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return `file_${getStringHash(fileUrl)}`;
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}
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async function onVectorizeAllClick() {
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try {
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if (!settings.enabled_chats) {
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return;
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}
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const chatId = getCurrentChatId();
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if (!chatId) {
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toastr.info('No chat selected. Vectorization aborted.', 'Vector Storage');
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return;
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}
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const batchSize = 5;
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const elapsedLog = [];
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let finished = false;
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$('#vectorize_progress').show();
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$('#vectorize_progress_percent').text('0');
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$('#vectorize_progress_eta').text('...');
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while (!finished) {
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if (is_send_press) {
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toastr.info('Message generation is in progress. Vectorization aborted.', 'Vector Storage');
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throw new Error('Message generation is in progress.');
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}
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const startTime = Date.now();
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const remaining = await synchronizeChat(batchSize);
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const elapsed = Date.now() - startTime;
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elapsedLog.push(elapsed);
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finished = remaining <= 0;
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const total = getContext().chat.length;
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const processed = total - remaining;
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const processedPercent = Math.round((processed / total) * 100); // percentage of the work done
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const lastElapsed = elapsedLog.slice(-5); // last 5 elapsed times
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const averageElapsed = lastElapsed.reduce((a, b) => a + b, 0) / lastElapsed.length; // average time needed to process one item
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const pace = averageElapsed / batchSize; // time needed to process one item
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const remainingTime = Math.round(pace * remaining / 1000);
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$('#vectorize_progress_percent').text(processedPercent);
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$('#vectorize_progress_eta').text(remainingTime);
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if (chatId !== getCurrentChatId()) {
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throw new Error('Chat changed');
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}
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}
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} catch (error) {
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console.error('Vectors: Failed to vectorize all', error);
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toastr.error(`Vectorize all failed. ${new String(error)}`, 'Vector Storage')
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} finally {
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$('#vectorize_progress').hide();
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}
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}
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let syncBlocked = false;
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/**
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* Splits messages into chunks before inserting them into the vector index.
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* @param {object[]} items Array of vector items
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* @returns {object[]} Array of vector items (possibly chunked)
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*/
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function splitByChunks(items) {
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if (settings.message_chunk_size <= 0) {
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return items;
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}
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const chunkedItems = [];
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for (const item of items) {
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const chunks = splitRecursive(item.text, settings.message_chunk_size);
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for (const chunk of chunks) {
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const chunkedItem = { ...item, text: chunk };
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chunkedItems.push(chunkedItem);
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}
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}
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return chunkedItems;
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}
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async function summarizeExtra(hashedMessages) {
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for (const element of hashedMessages) {
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try {
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const url = new URL(getApiUrl());
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url.pathname = '/api/summarize';
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const apiResult = await doExtrasFetch(url, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'Bypass-Tunnel-Reminder': 'bypass',
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},
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body: JSON.stringify({
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text: element.text,
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params: {},
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}),
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});
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if (apiResult.ok) {
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const data = await apiResult.json();
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element.text = data.summary;
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}
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}
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catch (error) {
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console.log(error);
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}
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}
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return hashedMessages;
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}
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async function summarizeMain(hashedMessages) {
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for (const element of hashedMessages) {
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element.text = await generateRaw(element.text, '', false, false, settings.summary_prompt);
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}
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return hashedMessages;
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}
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async function summarize(hashedMessages, endpoint = 'main') {
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switch (endpoint) {
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case 'main':
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return await summarizeMain(hashedMessages);
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case 'extras':
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return await summarizeExtra(hashedMessages);
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default:
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console.error('Unsupported endpoint', endpoint);
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}
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}
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async function synchronizeChat(batchSize = 5) {
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if (!settings.enabled_chats) {
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return -1;
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}
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try {
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await waitUntilCondition(() => !syncBlocked && !is_send_press, 1000);
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} catch {
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console.log('Vectors: Synchronization blocked by another process');
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return -1;
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}
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try {
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syncBlocked = true;
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const context = getContext();
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const chatId = getCurrentChatId();
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if (!chatId || !Array.isArray(context.chat)) {
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console.debug('Vectors: No chat selected');
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return -1;
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}
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let hashedMessages = context.chat.filter(x => !x.is_system).map(x => ({ text: String(substituteParams(x.mes)), hash: getStringHash(substituteParams(x.mes)), index: context.chat.indexOf(x) }));
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const hashesInCollection = await getSavedHashes(chatId);
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if (settings.summarize) {
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hashedMessages = await summarize(hashedMessages, settings.summary_source);
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}
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const newVectorItems = hashedMessages.filter(x => !hashesInCollection.includes(x.hash));
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const deletedHashes = hashesInCollection.filter(x => !hashedMessages.some(y => y.hash === x));
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if (newVectorItems.length > 0) {
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const chunkedBatch = splitByChunks(newVectorItems.slice(0, batchSize));
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console.log(`Vectors: Found ${newVectorItems.length} new items. Processing ${batchSize}...`);
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await insertVectorItems(chatId, chunkedBatch);
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}
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if (deletedHashes.length > 0) {
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await deleteVectorItems(chatId, deletedHashes);
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console.log(`Vectors: Deleted ${deletedHashes.length} old hashes`);
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}
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return newVectorItems.length - batchSize;
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} catch (error) {
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/**
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* Gets the error message for a given cause
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* @param {string} cause Error cause key
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* @returns {string} Error message
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*/
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function getErrorMessage(cause) {
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switch (cause) {
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case 'api_key_missing':
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return 'API key missing. Save it in the "API Connections" panel.';
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case 'extras_module_missing':
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return 'Extras API must provide an "embeddings" module.';
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default:
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return 'Check server console for more details.';
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}
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}
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console.error('Vectors: Failed to synchronize chat', error);
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const message = getErrorMessage(error.cause);
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toastr.error(`Vectorization failed. ${message}`, 'Vector Storage', { preventDuplicates: true });
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return -1;
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} finally {
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syncBlocked = false;
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}
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}
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/**
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* @type {Map<string, number>} Cache object for storing hash values
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*/
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const hashCache = new Map();
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/**
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* Gets the hash value for a given string
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* @param {string} str Input string
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* @returns {number} Hash value
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*/
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function getStringHash(str) {
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// Check if the hash is already in the cache
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if (hashCache.has(str)) {
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return hashCache.get(str);
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}
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// Calculate the hash value
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const hash = calculateHash(str);
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// Store the hash in the cache
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hashCache.set(str, hash);
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return hash;
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}
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/**
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* Retrieves files from the chat and inserts them into the vector index.
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* @param {object[]} chat Array of chat messages
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* @returns {Promise<void>}
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*/
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async function processFiles(chat) {
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try {
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if (!settings.enabled_files) {
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return;
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}
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const dataBank = getDataBankAttachments();
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const dataBankCollectionIds = [];
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for (const file of dataBank) {
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const collectionId = getFileCollectionId(file.url);
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const hashesInCollection = await getSavedHashes(collectionId);
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dataBankCollectionIds.push(collectionId);
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// File is already in the collection
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if (hashesInCollection.length) {
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continue;
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}
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// Download and process the file
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file.text = await getFileAttachment(file.url);
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console.log(`Vectors: Retrieved file ${file.name} from Data Bank`);
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// Convert kilobytes to string length
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const thresholdLength = settings.size_threshold_db * 1024;
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// Use chunk size from settings if file is larger than threshold
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const chunkSize = file.size > thresholdLength ? settings.chunk_size_db : -1;
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await vectorizeFile(file.text, file.name, collectionId, chunkSize);
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}
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if (dataBankCollectionIds.length) {
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const queryText = await getQueryText(chat);
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await injectDataBankChunks(queryText, dataBankCollectionIds);
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}
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for (const message of chat) {
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// Message has no file
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if (!message?.extra?.file) {
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continue;
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}
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console.debug(`Vectors: processFiles: message ${message.index}: has a file attachment, processing.`)
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// Trim file inserted by the script
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const fileText = String(message.mes)
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.substring(0, message.extra.fileLength).trim();
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// Convert kilobytes to string length
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const thresholdLength = settings.size_threshold * 1024;
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// File is too small
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if (fileText.length < thresholdLength) {
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console.debug(`Vectors: processFiles: message ${message.index}: text of file "${message.extra.file.name}" shorter than vectorization threshold (${fileText.length} < ${thresholdLength} chars), keeping inlined.`)
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continue;
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}
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message.mes = message.mes.substring(message.extra.fileLength);
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const fileName = message.extra.file.name;
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const fileUrl = message.extra.file.url;
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const collectionId = getFileCollectionId(fileUrl);
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const hashesInCollection = await getSavedHashes(collectionId);
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// File is already in the collection
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if (!hashesInCollection.length) {
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console.debug(`Vectors: processFiles: message ${message.index}: file "${fileName}" not yet in collection, vectorizing.`)
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await vectorizeFile(fileText, fileName, collectionId, settings.chunk_size);
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} else {
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console.debug(`Vectors: processFiles: message ${message.index}: file "${fileName}" found in collection.`)
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}
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console.debug(`Vectors: processFiles: message ${message.index}: querying vector DB.`)
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const queryText = await getQueryText(chat);
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const fileChunks = await retrieveFileChunks(queryText, collectionId);
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console.debug(`Vectors: processFiles: message ${message.index}: retrieved ${fileChunks.length} chars.`);
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message.mes = `${fileChunks}\n\n${message.mes}`;
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}
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} catch (error) {
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console.error('Vectors: Failed to retrieve files', error);
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}
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}
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/**
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* Inserts file chunks from the Data Bank into the prompt.
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* @param {string} queryText Text to query
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* @param {string[]} collectionIds File collection IDs
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* @returns {Promise<void>}
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*/
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async function injectDataBankChunks(queryText, collectionIds) {
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try {
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const queryResults = await queryMultipleCollections(collectionIds, queryText, settings.chunk_count_db);
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console.debug(`Vectors: Retrieved ${collectionIds.length} Data Bank collections`, queryResults);
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let textResult = '';
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for (const collectionId in queryResults) {
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console.debug(`Vectors: Processing Data Bank collection ${collectionId}`, queryResults[collectionId]);
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const metadata = queryResults[collectionId].metadata?.filter(x => x.text)?.sort((a, b) => a.index - b.index)?.map(x => x.text)?.filter(onlyUnique) || [];
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textResult += metadata.join('\n') + '\n\n';
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}
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if (!textResult) {
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console.debug('Vectors: No Data Bank chunks found');
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return;
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}
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const insertedText = substituteParams(settings.file_template_db.replace(/{{text}}/i, textResult));
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setExtensionPrompt(EXTENSION_PROMPT_TAG_DB, insertedText, settings.file_position_db, settings.file_depth_db, settings.include_wi, settings.file_depth_role_db);
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} catch (error) {
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console.error('Vectors: Failed to insert Data Bank chunks', error);
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}
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}
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|
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/**
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* Retrieves file chunks from the vector index and inserts them into the chat.
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* @param {string} queryText Text to query
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* @param {string} collectionId File collection ID
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* @returns {Promise<string>} Retrieved file text
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*/
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async function retrieveFileChunks(queryText, collectionId) {
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console.debug(`Vectors: Retrieving file chunks for collection ${collectionId}`, queryText);
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const queryResults = await queryCollection(collectionId, queryText, settings.chunk_count);
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console.debug(`Vectors: Retrieved ${queryResults.hashes.length} file chunks for collection ${collectionId}`, queryResults);
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const metadata = queryResults.metadata.filter(x => x.text).sort((a, b) => a.index - b.index).map(x => x.text).filter(onlyUnique);
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const fileText = metadata.join('\n');
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return fileText;
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}
|
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|
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/**
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* Sanitizes the text content of a scientific paper to obtain higher-quality text for vectorization.
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*
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* This is a really simplistic, classical regex-based algorithm. An LLM could likely do better, but that would be slow.
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* We hope to get a result that's not horribly broken and that won't include irrelevant RAG query poisoning stuff.
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*
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* Currently, we:
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*
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* - Strip the reference list.
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*
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* The reference list contains the highest concentration of keywords of any kind (in the titles of the cited studies),
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* so it usually poisons RAG queries so that no matter what you search for, you'll only get chunks of the reference list.
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* Omitting the reference list from the text to be vectorized, RAG will look for matches in the paper content only.
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*
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* - F IX H EADINGS T HAT L OOK L IKE T HIS.
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*
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* This is a rather common issue in text extraction from a PDF.
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*
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* @param {string} fileText The text to sanitize
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* @returns {string} The sanitized text
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*/
|
|
function sanitizeScientificInput(fileText) {
|
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// Fix section headings
|
|
//
|
|
const brokenUppercaseWordsFinder = new RegExp(/(?<!\b[A-Z]\s+)\b([A-Z])\s+([A-Z]+)\b/, 'g'); // "H EADING", but not "C H EADING" (appendix section)
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fileText = fileText.replaceAll(brokenUppercaseWordsFinder, '$1$2');
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const brokenAppendixHeadingFinder = new RegExp(/([A-Z])\s+([A-Z])\s+([A-Z]+)\b/, 'g'); // "C H EADING"
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fileText = fileText.replaceAll(brokenAppendixHeadingFinder, '$1 $2$3'); // -> "C HEADING"
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|
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const brokenHeadingsFinder = new RegExp(/^\s*([A-Z])\s+([a-z]+)\s*$/, 'mg'); // "H eading", on its own line
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fileText = fileText.replaceAll(brokenHeadingsFinder, '$1$2');
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|
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// Strip reference list (easier now that the headings are already fixed).
|
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//
|
|
// Linefeeds are sometimes lost, so the references may begin in the middle of a line.
|
|
// Since we can't trigger on any random mention of the word "References", we trigger in the middle of a line
|
|
// only for an all-uppercase "REFERENCES".
|
|
//
|
|
const referencesFinder = new RegExp(/(^\s*References\s*$|^\s*REFERENCES\s*$|\bREFERENCES\s*)/, 'mg');
|
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const referencesMatches = [...fileText.matchAll(referencesFinder)];
|
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if (referencesMatches.length > 0) { // Detected a reference list
|
|
const appendixFinder = new RegExp(/(^\s*Appendi(x|ces)\s*$|^\s*A\s*PPENDI(X|CES)\s*$|\bAPPENDI(X|CES)\s*)/, 'mg');
|
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// Some documents just start appendices like "A Some stuff..." without a heading, but there's not much we can do about that.
|
|
// In those cases, we will simply ignore the appendices.
|
|
const appendixMatches = [...fileText.matchAll(appendixFinder)];
|
|
if (appendixMatches.length > 0) { // Detected both a reference list and appendices
|
|
fileText = fileText.substring(0, referencesMatches[0].index).trim() + fileText.substring(appendixMatches[0].index);
|
|
} else { // Detected only a reference list, no appendices
|
|
fileText = fileText.substring(0, referencesMatches[0].index).trim();
|
|
}
|
|
}
|
|
|
|
console.debug(fileText);
|
|
return fileText;
|
|
}
|
|
|
|
/**
|
|
* Vectorizes a file and inserts it into the vector index.
|
|
* @param {string} fileText File text
|
|
* @param {string} fileName File name
|
|
* @param {string} collectionId File collection ID
|
|
* @param {number} chunkSize Chunk size
|
|
* @returns {Promise<boolean>} True if successful, false if not
|
|
*/
|
|
async function vectorizeFile(fileText, fileName, collectionId, chunkSize) {
|
|
try {
|
|
if (settings.translate_files && typeof window['translate'] === 'function') {
|
|
console.log(`Vectors: Translating file ${fileName} to English...`);
|
|
const translatedText = await window['translate'](fileText, 'en');
|
|
fileText = translatedText;
|
|
}
|
|
|
|
const toast = toastr.info(`Ingesting file ${fileName}. Vectorization may take some time, please wait...`, 'Vector Storage');
|
|
|
|
if (settings.science_mode) {
|
|
console.debug(`Vectors: Science mode is enabled. Sanitizing input ${fileName}.`);
|
|
fileText = sanitizeScientificInput(fileText);
|
|
}
|
|
|
|
const chunks = splitRecursive(fileText, settings.chunk_size);
|
|
console.debug(`Vectors: Split file ${fileName} into ${chunks.length} chunks`, chunks);
|
|
|
|
const items = chunks.map((chunk, index) => ({ hash: getStringHash(chunk), text: chunk, index: index }));
|
|
await insertVectorItems(collectionId, items);
|
|
toastr.info(`Vectorization complete for ${fileName}.`, `Vector Storage`);
|
|
|
|
toastr.clear(toast);
|
|
console.log(`Vectors: Inserted ${chunks.length} vector items for file ${fileName} into ${collectionId}`);
|
|
return true;
|
|
} catch (error) {
|
|
toastr.error(String(error), 'Failed to vectorize file', { preventDuplicates: true });
|
|
console.error('Vectors: Failed to vectorize file', error);
|
|
toastr.error(`Vectorization failed for ${fileName}. ${new String(error)}`, 'Vector Storage');
|
|
return false;
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Removes the most relevant messages from the chat and displays them in the extension prompt
|
|
* @param {object[]} chat Array of chat messages
|
|
*/
|
|
async function rearrangeChat(chat) {
|
|
try {
|
|
// Clear the extension prompt
|
|
setExtensionPrompt(EXTENSION_PROMPT_TAG, '', settings.position, settings.depth, settings.include_wi);
|
|
setExtensionPrompt(EXTENSION_PROMPT_TAG_DB, '', settings.file_position_db, settings.file_depth_db, settings.include_wi, settings.file_depth_role_db);
|
|
|
|
if (settings.enabled_files) {
|
|
await processFiles(chat);
|
|
}
|
|
|
|
if (settings.enabled_world_info) {
|
|
await activateWorldInfo(chat);
|
|
}
|
|
|
|
if (!settings.enabled_chats) {
|
|
return;
|
|
}
|
|
|
|
const chatId = getCurrentChatId();
|
|
|
|
if (!chatId || !Array.isArray(chat)) {
|
|
console.debug('Vectors: No chat selected');
|
|
return;
|
|
}
|
|
|
|
if (chat.length < settings.protect) {
|
|
console.debug(`Vectors: Not enough messages to rearrange (less than ${settings.protect})`);
|
|
return;
|
|
}
|
|
|
|
const queryText = await getQueryText(chat);
|
|
|
|
if (queryText.length === 0) {
|
|
console.debug('Vectors: No text to query');
|
|
return;
|
|
}
|
|
|
|
// Get the most relevant messages, excluding the last few
|
|
const queryResults = await queryCollection(chatId, queryText, settings.insert);
|
|
const queryHashes = queryResults.hashes.filter(onlyUnique);
|
|
const queriedMessages = [];
|
|
const insertedHashes = new Set();
|
|
const retainMessages = chat.slice(-settings.protect);
|
|
|
|
for (const message of chat) {
|
|
if (retainMessages.includes(message) || !message.mes) {
|
|
continue;
|
|
}
|
|
const hash = getStringHash(substituteParams(message.mes));
|
|
if (queryHashes.includes(hash) && !insertedHashes.has(hash)) {
|
|
queriedMessages.push(message);
|
|
insertedHashes.add(hash);
|
|
}
|
|
}
|
|
|
|
// Rearrange queried messages to match query order
|
|
// Order is reversed because more relevant are at the lower indices
|
|
queriedMessages.sort((a, b) => queryHashes.indexOf(getStringHash(substituteParams(b.mes))) - queryHashes.indexOf(getStringHash(substituteParams(a.mes))));
|
|
|
|
// Remove queried messages from the original chat array
|
|
for (const message of chat) {
|
|
if (queriedMessages.includes(message)) {
|
|
chat.splice(chat.indexOf(message), 1);
|
|
}
|
|
}
|
|
|
|
if (queriedMessages.length === 0) {
|
|
console.debug('Vectors: No relevant messages found');
|
|
return;
|
|
}
|
|
|
|
// Format queried messages into a single string
|
|
const insertedText = getPromptText(queriedMessages);
|
|
setExtensionPrompt(EXTENSION_PROMPT_TAG, insertedText, settings.position, settings.depth, settings.include_wi);
|
|
} catch (error) {
|
|
toastr.error('Generation interceptor aborted. Check browser console for more details.', 'Vector Storage');
|
|
console.error('Vectors: Failed to rearrange chat', error);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* @param {any[]} queriedMessages
|
|
* @returns {string}
|
|
*/
|
|
function getPromptText(queriedMessages) {
|
|
const queriedText = queriedMessages.map(x => collapseNewlines(`${x.name}: ${x.mes}`).trim()).join('\n\n');
|
|
console.log('Vectors: relevant past messages found.\n', queriedText);
|
|
return substituteParams(settings.template.replace(/{{text}}/i, queriedText));
|
|
}
|
|
|
|
window['vectors_rearrangeChat'] = rearrangeChat;
|
|
|
|
const onChatEvent = debounce(async () => await moduleWorker.update(), debounce_timeout.relaxed);
|
|
|
|
/**
|
|
* Gets the text to query from the chat
|
|
* @param {object[]} chat Chat messages
|
|
* @returns {Promise<string>} Text to query
|
|
*/
|
|
async function getQueryText(chat) {
|
|
let queryText = '';
|
|
let i = 0;
|
|
|
|
let hashedMessages = chat.map(x => ({ text: String(substituteParams(x.mes)) }));
|
|
|
|
if (settings.summarize && settings.summarize_sent) {
|
|
hashedMessages = await summarize(hashedMessages, settings.summary_source);
|
|
}
|
|
|
|
for (const message of hashedMessages.slice().reverse()) {
|
|
if (message.text) {
|
|
queryText += message.text + '\n';
|
|
i++;
|
|
}
|
|
|
|
if (i === settings.query) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
return collapseNewlines(queryText).trim();
|
|
}
|
|
|
|
/**
|
|
* Gets the saved hashes for a collection
|
|
* @param {string} collectionId
|
|
* @returns {Promise<number[]>} Saved hashes
|
|
*/
|
|
async function getSavedHashes(collectionId) {
|
|
const response = await fetch('/api/vector/list', {
|
|
method: 'POST',
|
|
headers: getRequestHeaders(),
|
|
body: JSON.stringify({
|
|
collectionId: collectionId,
|
|
source: settings.source,
|
|
}),
|
|
});
|
|
|
|
if (!response.ok) {
|
|
throw new Error(`Failed to get saved hashes for collection ${collectionId}`);
|
|
}
|
|
|
|
const hashes = await response.json();
|
|
return hashes;
|
|
}
|
|
|
|
function getVectorHeaders() {
|
|
const headers = getRequestHeaders();
|
|
switch (settings.source) {
|
|
case 'extras':
|
|
addExtrasHeaders(headers);
|
|
break;
|
|
case 'togetherai':
|
|
addTogetherAiHeaders(headers);
|
|
break;
|
|
case 'openai':
|
|
addOpenAiHeaders(headers);
|
|
break;
|
|
case 'cohere':
|
|
addCohereHeaders(headers);
|
|
break;
|
|
default:
|
|
break;
|
|
}
|
|
return headers;
|
|
}
|
|
|
|
/**
|
|
* Add headers for the Extras API source.
|
|
* @param {object} headers Headers object
|
|
*/
|
|
function addExtrasHeaders(headers) {
|
|
console.log(`Vector source is extras, populating API URL: ${extension_settings.apiUrl}`);
|
|
Object.assign(headers, {
|
|
'X-Extras-Url': extension_settings.apiUrl,
|
|
'X-Extras-Key': extension_settings.apiKey,
|
|
});
|
|
}
|
|
|
|
/**
|
|
* Add headers for the TogetherAI API source.
|
|
* @param {object} headers Headers object
|
|
*/
|
|
function addTogetherAiHeaders(headers) {
|
|
Object.assign(headers, {
|
|
'X-Togetherai-Model': extension_settings.vectors.togetherai_model,
|
|
});
|
|
}
|
|
|
|
/**
|
|
* Add headers for the OpenAI API source.
|
|
* @param {object} headers Header object
|
|
*/
|
|
function addOpenAiHeaders(headers) {
|
|
Object.assign(headers, {
|
|
'X-OpenAI-Model': extension_settings.vectors.openai_model,
|
|
});
|
|
}
|
|
|
|
/**
|
|
* Add headers for the Cohere API source.
|
|
* @param {object} headers Header object
|
|
*/
|
|
function addCohereHeaders(headers) {
|
|
Object.assign(headers, {
|
|
'X-Cohere-Model': extension_settings.vectors.cohere_model,
|
|
});
|
|
}
|
|
|
|
/**
|
|
* Inserts vector items into a collection
|
|
* @param {string} collectionId - The collection to insert into
|
|
* @param {{ hash: number, text: string }[]} items - The items to insert
|
|
* @returns {Promise<void>}
|
|
*/
|
|
async function insertVectorItems(collectionId, items) {
|
|
if (settings.source === 'openai' && !secret_state[SECRET_KEYS.OPENAI] ||
|
|
settings.source === 'palm' && !secret_state[SECRET_KEYS.MAKERSUITE] ||
|
|
settings.source === 'mistral' && !secret_state[SECRET_KEYS.MISTRALAI] ||
|
|
settings.source === 'togetherai' && !secret_state[SECRET_KEYS.TOGETHERAI] ||
|
|
settings.source === 'nomicai' && !secret_state[SECRET_KEYS.NOMICAI] ||
|
|
settings.source === 'cohere' && !secret_state[SECRET_KEYS.COHERE]) {
|
|
throw new Error('Vectors: API key missing', { cause: 'api_key_missing' });
|
|
}
|
|
|
|
if (settings.source === 'extras' && !modules.includes('embeddings')) {
|
|
throw new Error('Vectors: Embeddings module missing', { cause: 'extras_module_missing' });
|
|
}
|
|
|
|
const headers = getVectorHeaders();
|
|
|
|
const response = await fetch('/api/vector/insert', {
|
|
method: 'POST',
|
|
headers: headers,
|
|
body: JSON.stringify({
|
|
collectionId: collectionId,
|
|
items: items,
|
|
source: settings.source,
|
|
}),
|
|
});
|
|
|
|
if (!response.ok) {
|
|
throw new Error(`Failed to insert vector items for collection ${collectionId}`);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Deletes vector items from a collection
|
|
* @param {string} collectionId - The collection to delete from
|
|
* @param {number[]} hashes - The hashes of the items to delete
|
|
* @returns {Promise<void>}
|
|
*/
|
|
async function deleteVectorItems(collectionId, hashes) {
|
|
const response = await fetch('/api/vector/delete', {
|
|
method: 'POST',
|
|
headers: getRequestHeaders(),
|
|
body: JSON.stringify({
|
|
collectionId: collectionId,
|
|
hashes: hashes,
|
|
source: settings.source,
|
|
}),
|
|
});
|
|
|
|
if (!response.ok) {
|
|
throw new Error(`Failed to delete vector items for collection ${collectionId}`);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* @param {string} collectionId - The collection to query
|
|
* @param {string} searchText - The text to query
|
|
* @param {number} topK - The number of results to return
|
|
* @returns {Promise<{ hashes: number[], metadata: object[]}>} - Hashes of the results
|
|
*/
|
|
async function queryCollection(collectionId, searchText, topK) {
|
|
const headers = getVectorHeaders();
|
|
|
|
const response = await fetch('/api/vector/query', {
|
|
method: 'POST',
|
|
headers: headers,
|
|
body: JSON.stringify({
|
|
collectionId: collectionId,
|
|
searchText: searchText,
|
|
topK: topK,
|
|
source: settings.source,
|
|
}),
|
|
});
|
|
|
|
if (!response.ok) {
|
|
throw new Error(`Failed to query collection ${collectionId}`);
|
|
}
|
|
|
|
return await response.json();
|
|
}
|
|
|
|
/**
|
|
* Queries multiple collections for a given text.
|
|
* @param {string[]} collectionIds - Collection IDs to query
|
|
* @param {string} searchText - Text to query
|
|
* @param {number} topK - Number of results to return
|
|
* @returns {Promise<Record<string, { hashes: number[], metadata: object[] }>>} - Results mapped to collection IDs
|
|
*/
|
|
async function queryMultipleCollections(collectionIds, searchText, topK) {
|
|
const headers = getVectorHeaders();
|
|
|
|
const response = await fetch('/api/vector/query-multi', {
|
|
method: 'POST',
|
|
headers: headers,
|
|
body: JSON.stringify({
|
|
collectionIds: collectionIds,
|
|
searchText: searchText,
|
|
topK: topK,
|
|
source: settings.source,
|
|
}),
|
|
});
|
|
|
|
if (!response.ok) {
|
|
throw new Error('Failed to query multiple collections');
|
|
}
|
|
|
|
return await response.json();
|
|
}
|
|
|
|
/**
|
|
* Purges the vector index for a file.
|
|
* @param {string} fileUrl File URL to purge
|
|
*/
|
|
async function purgeFileVectorIndex(fileUrl) {
|
|
try {
|
|
if (!settings.enabled_files) {
|
|
return;
|
|
}
|
|
|
|
console.log(`Vectors: Purging file vector index for ${fileUrl}`);
|
|
const collectionId = getFileCollectionId(fileUrl);
|
|
|
|
const response = await fetch('/api/vector/purge', {
|
|
method: 'POST',
|
|
headers: getRequestHeaders(),
|
|
body: JSON.stringify({
|
|
collectionId: collectionId,
|
|
}),
|
|
});
|
|
|
|
if (!response.ok) {
|
|
throw new Error(`Could not delete vector index for collection ${collectionId}`);
|
|
}
|
|
|
|
console.log(`Vectors: Purged vector index for collection ${collectionId}`);
|
|
} catch (error) {
|
|
console.error('Vectors: Failed to purge file', error);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Purges the vector index for a collection.
|
|
* @param {string} collectionId Collection ID to purge
|
|
* @returns <Promise<boolean>> True if deleted, false if not
|
|
*/
|
|
async function purgeVectorIndex(collectionId) {
|
|
try {
|
|
if (!settings.enabled_chats) {
|
|
return true;
|
|
}
|
|
|
|
const response = await fetch('/api/vector/purge', {
|
|
method: 'POST',
|
|
headers: getRequestHeaders(),
|
|
body: JSON.stringify({
|
|
collectionId: collectionId,
|
|
}),
|
|
});
|
|
|
|
if (!response.ok) {
|
|
throw new Error(`Could not delete vector index for collection ${collectionId}`);
|
|
}
|
|
|
|
console.log(`Vectors: Purged vector index for collection ${collectionId}`);
|
|
return true;
|
|
} catch (error) {
|
|
console.error('Vectors: Failed to purge', error);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
function toggleSettings() {
|
|
$('#vectors_files_settings').toggle(!!settings.enabled_files);
|
|
$('#vectors_chats_settings').toggle(!!settings.enabled_chats);
|
|
$('#vectors_world_info_settings').toggle(!!settings.enabled_world_info);
|
|
$('#together_vectorsModel').toggle(settings.source === 'togetherai');
|
|
$('#openai_vectorsModel').toggle(settings.source === 'openai');
|
|
$('#cohere_vectorsModel').toggle(settings.source === 'cohere');
|
|
$('#nomicai_apiKey').toggle(settings.source === 'nomicai');
|
|
}
|
|
|
|
async function onPurgeClick() {
|
|
const chatId = getCurrentChatId();
|
|
if (!chatId) {
|
|
toastr.info('No chat selected. Purge aborted.', 'Vector Storage');
|
|
return;
|
|
}
|
|
if (await purgeVectorIndex(chatId)) {
|
|
toastr.success('Vector index purged successfully.', 'Vector Storage');
|
|
} else {
|
|
toastr.error('Failed to purge vector index', 'Vector Storage');
|
|
}
|
|
}
|
|
|
|
async function onViewStatsClick() {
|
|
const chatId = getCurrentChatId();
|
|
if (!chatId) {
|
|
toastr.info('No chat selected', 'Vector Storage');
|
|
return;
|
|
}
|
|
|
|
const hashesInCollection = await getSavedHashes(chatId);
|
|
const totalHashes = hashesInCollection.length;
|
|
const uniqueHashes = hashesInCollection.filter(onlyUnique).length;
|
|
|
|
toastr.info(`Total hashes: <b>${totalHashes}</b><br>
|
|
Unique hashes: <b>${uniqueHashes}</b><br><br>
|
|
I'll mark collected messages with a green circle.`,
|
|
`Stats for chat ${chatId}`,
|
|
{ timeOut: 10000, escapeHtml: false });
|
|
|
|
const chat = getContext().chat;
|
|
for (const message of chat) {
|
|
if (hashesInCollection.includes(getStringHash(substituteParams(message.mes)))) {
|
|
const messageElement = $(`.mes[mesid="${chat.indexOf(message)}"]`);
|
|
messageElement.addClass('vectorized');
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
async function onVectorizeAllFilesClick() {
|
|
try {
|
|
const dataBank = getDataBankAttachments();
|
|
const chatAttachments = getContext().chat.filter(x => x.extra?.file).map(x => x.extra.file);
|
|
const allFiles = [...dataBank, ...chatAttachments];
|
|
|
|
let allSuccess = true;
|
|
|
|
for (const file of allFiles) {
|
|
const text = await getFileAttachment(file.url);
|
|
const collectionId = getFileCollectionId(file.url);
|
|
const hashes = await getSavedHashes(collectionId);
|
|
|
|
if (hashes.length) {
|
|
console.log(`Vectors: File ${file.name} is already vectorized`);
|
|
continue;
|
|
}
|
|
|
|
const result = await vectorizeFile(text, file.name, collectionId, settings.chunk_size);
|
|
|
|
if (!result) {
|
|
allSuccess = false;
|
|
}
|
|
}
|
|
|
|
if (allSuccess) {
|
|
toastr.success('All files vectorized', 'Vectorization successful');
|
|
} else {
|
|
toastr.warning('Some files failed to vectorize. Check browser console for more details.', 'Vector Storage');
|
|
}
|
|
} catch (error) {
|
|
console.error('Vectors: Failed to vectorize all files', error);
|
|
toastr.error('Failed to vectorize all files', 'Vectorization failed');
|
|
}
|
|
}
|
|
|
|
async function onPurgeFilesClick() {
|
|
try {
|
|
const dataBank = getDataBankAttachments();
|
|
const chatAttachments = getContext().chat.filter(x => x.extra?.file).map(x => x.extra.file);
|
|
const allFiles = [...dataBank, ...chatAttachments];
|
|
|
|
for (const file of allFiles) {
|
|
await purgeFileVectorIndex(file.url);
|
|
}
|
|
|
|
toastr.success('All files purged', 'Purge successful');
|
|
} catch (error) {
|
|
console.error('Vectors: Failed to purge all files', error);
|
|
toastr.error('Failed to purge all files', 'Purge failed');
|
|
}
|
|
}
|
|
|
|
async function activateWorldInfo(chat) {
|
|
if (!settings.enabled_world_info) {
|
|
console.debug('Vectors: Disabled for World Info');
|
|
return;
|
|
}
|
|
|
|
const entries = await getSortedEntries();
|
|
|
|
if (!Array.isArray(entries) || entries.length === 0) {
|
|
console.debug('Vectors: No WI entries found');
|
|
return;
|
|
}
|
|
|
|
// Group entries by "world" field
|
|
const groupedEntries = {};
|
|
|
|
for (const entry of entries) {
|
|
// Skip orphaned entries. Is it even possible?
|
|
if (!entry.world) {
|
|
console.debug('Vectors: Skipped orphaned WI entry', entry);
|
|
continue;
|
|
}
|
|
|
|
// Skip disabled entries
|
|
if (entry.disable) {
|
|
console.debug('Vectors: Skipped disabled WI entry', entry);
|
|
continue;
|
|
}
|
|
|
|
// Skip entries without content
|
|
if (!entry.content) {
|
|
console.debug('Vectors: Skipped WI entry without content', entry);
|
|
continue;
|
|
}
|
|
|
|
// Skip non-vectorized entries
|
|
if (!entry.vectorized && !settings.enabled_for_all) {
|
|
console.debug('Vectors: Skipped non-vectorized WI entry', entry);
|
|
continue;
|
|
}
|
|
|
|
if (!Object.hasOwn(groupedEntries, entry.world)) {
|
|
groupedEntries[entry.world] = [];
|
|
}
|
|
|
|
groupedEntries[entry.world].push(entry);
|
|
}
|
|
|
|
const collectionIds = [];
|
|
|
|
if (Object.keys(groupedEntries).length === 0) {
|
|
console.debug('Vectors: No WI entries to synchronize');
|
|
return;
|
|
}
|
|
|
|
// Synchronize collections
|
|
for (const world in groupedEntries) {
|
|
const collectionId = `world_${getStringHash(world)}`;
|
|
const hashesInCollection = await getSavedHashes(collectionId);
|
|
const newEntries = groupedEntries[world].filter(x => !hashesInCollection.includes(getStringHash(x.content)));
|
|
const deletedHashes = hashesInCollection.filter(x => !groupedEntries[world].some(y => getStringHash(y.content) === x));
|
|
|
|
if (newEntries.length > 0) {
|
|
console.log(`Vectors: Found ${newEntries.length} new WI entries for world ${world}`);
|
|
await insertVectorItems(collectionId, newEntries.map(x => ({ hash: getStringHash(x.content), text: x.content, index: x.uid })));
|
|
}
|
|
|
|
if (deletedHashes.length > 0) {
|
|
console.log(`Vectors: Deleted ${deletedHashes.length} old hashes for world ${world}`);
|
|
await deleteVectorItems(collectionId, deletedHashes);
|
|
}
|
|
|
|
collectionIds.push(collectionId);
|
|
}
|
|
|
|
// Perform a multi-query
|
|
const queryText = await getQueryText(chat);
|
|
|
|
if (queryText.length === 0) {
|
|
console.debug('Vectors: No text to query for WI');
|
|
return;
|
|
}
|
|
|
|
const queryResults = await queryMultipleCollections(collectionIds, queryText, settings.max_entries);
|
|
const activatedHashes = Object.values(queryResults).flatMap(x => x.hashes).filter(onlyUnique);
|
|
const activatedEntries = [];
|
|
|
|
// Activate entries found in the query results
|
|
for (const entry of entries) {
|
|
const hash = getStringHash(entry.content);
|
|
|
|
if (activatedHashes.includes(hash)) {
|
|
activatedEntries.push(entry);
|
|
}
|
|
}
|
|
|
|
if (activatedEntries.length === 0) {
|
|
console.debug('Vectors: No activated WI entries found');
|
|
return;
|
|
}
|
|
|
|
console.log(`Vectors: Activated ${activatedEntries.length} WI entries`, activatedEntries);
|
|
await eventSource.emit(event_types.WORLDINFO_FORCE_ACTIVATE, activatedEntries);
|
|
}
|
|
|
|
jQuery(async () => {
|
|
if (!extension_settings.vectors) {
|
|
extension_settings.vectors = settings;
|
|
}
|
|
|
|
// Migrate from old settings
|
|
if (settings['enabled']) {
|
|
settings.enabled_chats = true;
|
|
}
|
|
|
|
Object.assign(settings, extension_settings.vectors);
|
|
|
|
// Migrate from TensorFlow to Transformers
|
|
settings.source = settings.source !== 'local' ? settings.source : 'transformers';
|
|
const template = await renderExtensionTemplateAsync(MODULE_NAME, 'settings');
|
|
$('#extensions_settings2').append(template);
|
|
$('#vectors_enabled_chats').prop('checked', settings.enabled_chats).on('input', () => {
|
|
settings.enabled_chats = $('#vectors_enabled_chats').prop('checked');
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
toggleSettings();
|
|
});
|
|
$('#vectors_modelWarning').hide();
|
|
$('#vectors_enabled_files').prop('checked', settings.enabled_files).on('input', () => {
|
|
settings.enabled_files = $('#vectors_enabled_files').prop('checked');
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
toggleSettings();
|
|
});
|
|
$('#vectors_science_mode').prop('checked', settings.science_mode).on('input', () => {
|
|
settings.science_mode = $('#vectors_science_mode').prop('checked');
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
$('#vectors_source').val(settings.source).on('change', () => {
|
|
settings.source = String($('#vectors_source').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
toggleSettings();
|
|
});
|
|
$('#api_key_nomicai').on('change', () => {
|
|
const nomicKey = String($('#api_key_nomicai').val()).trim();
|
|
if (nomicKey.length) {
|
|
writeSecret(SECRET_KEYS.NOMICAI, nomicKey);
|
|
}
|
|
saveSettingsDebounced();
|
|
});
|
|
$('#vectors_togetherai_model').val(settings.togetherai_model).on('change', () => {
|
|
$('#vectors_modelWarning').show();
|
|
settings.togetherai_model = String($('#vectors_togetherai_model').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
$('#vectors_openai_model').val(settings.openai_model).on('change', () => {
|
|
$('#vectors_modelWarning').show();
|
|
settings.openai_model = String($('#vectors_openai_model').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
$('#vectors_cohere_model').val(settings.cohere_model).on('change', () => {
|
|
$('#vectors_modelWarning').show();
|
|
settings.cohere_model = String($('#vectors_cohere_model').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
$('#vectors_template').val(settings.template).on('input', () => {
|
|
settings.template = String($('#vectors_template').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
$('#vectors_depth').val(settings.depth).on('input', () => {
|
|
settings.depth = Number($('#vectors_depth').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
$('#vectors_protect').val(settings.protect).on('input', () => {
|
|
settings.protect = Number($('#vectors_protect').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
$('#vectors_insert').val(settings.insert).on('input', () => {
|
|
settings.insert = Number($('#vectors_insert').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
$('#vectors_query').val(settings.query).on('input', () => {
|
|
settings.query = Number($('#vectors_query').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
$(`input[name="vectors_position"][value="${settings.position}"]`).prop('checked', true);
|
|
$('input[name="vectors_position"]').on('change', () => {
|
|
settings.position = Number($('input[name="vectors_position"]:checked').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
$('#vectors_vectorize_all').on('click', onVectorizeAllClick);
|
|
$('#vectors_purge').on('click', onPurgeClick);
|
|
$('#vectors_view_stats').on('click', onViewStatsClick);
|
|
$('#vectors_files_vectorize_all').on('click', onVectorizeAllFilesClick);
|
|
$('#vectors_files_purge').on('click', onPurgeFilesClick);
|
|
|
|
$('#vectors_size_threshold').val(settings.size_threshold).on('input', () => {
|
|
settings.size_threshold = Number($('#vectors_size_threshold').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_chunk_size').val(settings.chunk_size).on('input', () => {
|
|
settings.chunk_size = Number($('#vectors_chunk_size').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_chunk_count').val(settings.chunk_count).on('input', () => {
|
|
settings.chunk_count = Number($('#vectors_chunk_count').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_include_wi').prop('checked', settings.include_wi).on('input', () => {
|
|
settings.include_wi = !!$('#vectors_include_wi').prop('checked');
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_summarize').prop('checked', settings.summarize).on('input', () => {
|
|
settings.summarize = !!$('#vectors_summarize').prop('checked');
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_summarize_user').prop('checked', settings.summarize_sent).on('input', () => {
|
|
settings.summarize_sent = !!$('#vectors_summarize_user').prop('checked');
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_summary_source').val(settings.summary_source).on('change', () => {
|
|
settings.summary_source = String($('#vectors_summary_source').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_summary_prompt').val(settings.summary_prompt).on('input', () => {
|
|
settings.summary_prompt = String($('#vectors_summary_prompt').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_message_chunk_size').val(settings.message_chunk_size).on('input', () => {
|
|
settings.message_chunk_size = Number($('#vectors_message_chunk_size').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_size_threshold_db').val(settings.size_threshold_db).on('input', () => {
|
|
settings.size_threshold_db = Number($('#vectors_size_threshold_db').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_chunk_size_db').val(settings.chunk_size_db).on('input', () => {
|
|
settings.chunk_size_db = Number($('#vectors_chunk_size_db').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_chunk_count_db').val(settings.chunk_count_db).on('input', () => {
|
|
settings.chunk_count_db = Number($('#vectors_chunk_count_db').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_file_template_db').val(settings.file_template_db).on('input', () => {
|
|
settings.file_template_db = String($('#vectors_file_template_db').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$(`input[name="vectors_file_position_db"][value="${settings.file_position_db}"]`).prop('checked', true);
|
|
$('input[name="vectors_file_position_db"]').on('change', () => {
|
|
settings.file_position_db = Number($('input[name="vectors_file_position_db"]:checked').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_file_depth_db').val(settings.file_depth_db).on('input', () => {
|
|
settings.file_depth_db = Number($('#vectors_file_depth_db').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_file_depth_role_db').val(settings.file_depth_role_db).on('input', () => {
|
|
settings.file_depth_role_db = Number($('#vectors_file_depth_role_db').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_translate_files').prop('checked', settings.translate_files).on('input', () => {
|
|
settings.translate_files = !!$('#vectors_translate_files').prop('checked');
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_enabled_world_info').prop('checked', settings.enabled_world_info).on('input', () => {
|
|
settings.enabled_world_info = !!$('#vectors_enabled_world_info').prop('checked');
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
toggleSettings();
|
|
});
|
|
|
|
$('#vectors_enabled_for_all').prop('checked', settings.enabled_for_all).on('input', () => {
|
|
settings.enabled_for_all = !!$('#vectors_enabled_for_all').prop('checked');
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
$('#vectors_max_entries').val(settings.max_entries).on('input', () => {
|
|
settings.max_entries = Number($('#vectors_max_entries').val());
|
|
Object.assign(extension_settings.vectors, settings);
|
|
saveSettingsDebounced();
|
|
});
|
|
|
|
const validSecret = !!secret_state[SECRET_KEYS.NOMICAI];
|
|
const placeholder = validSecret ? '✔️ Key saved' : '❌ Missing key';
|
|
$('#api_key_nomicai').attr('placeholder', placeholder);
|
|
|
|
toggleSettings();
|
|
eventSource.on(event_types.MESSAGE_DELETED, onChatEvent);
|
|
eventSource.on(event_types.MESSAGE_EDITED, onChatEvent);
|
|
eventSource.on(event_types.MESSAGE_SENT, onChatEvent);
|
|
eventSource.on(event_types.MESSAGE_RECEIVED, onChatEvent);
|
|
eventSource.on(event_types.MESSAGE_SWIPED, onChatEvent);
|
|
eventSource.on(event_types.CHAT_DELETED, purgeVectorIndex);
|
|
eventSource.on(event_types.GROUP_CHAT_DELETED, purgeVectorIndex);
|
|
eventSource.on(event_types.FILE_ATTACHMENT_DELETED, purgeFileVectorIndex);
|
|
});
|