mirror of
https://github.com/open-webui/open-webui
synced 2025-06-22 18:07:17 +00:00
1161 lines
38 KiB
Svelte
1161 lines
38 KiB
Svelte
<script lang="ts">
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import { toast } from 'svelte-sonner';
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import { onMount, getContext, createEventDispatcher } from 'svelte';
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const dispatch = createEventDispatcher();
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import {
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getQuerySettings,
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updateQuerySettings,
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resetVectorDB,
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getEmbeddingConfig,
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updateEmbeddingConfig,
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getRerankingConfig,
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updateRerankingConfig,
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getRAGConfig,
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updateRAGConfig
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} from '$lib/apis/retrieval';
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import { reindexKnowledgeFiles } from '$lib/apis/knowledge';
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import { deleteAllFiles } from '$lib/apis/files';
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import ResetUploadDirConfirmDialog from '$lib/components/common/ConfirmDialog.svelte';
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import ResetVectorDBConfirmDialog from '$lib/components/common/ConfirmDialog.svelte';
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import ReindexKnowledgeFilesConfirmDialog from '$lib/components/common/ConfirmDialog.svelte';
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import SensitiveInput from '$lib/components/common/SensitiveInput.svelte';
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import Tooltip from '$lib/components/common/Tooltip.svelte';
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import Switch from '$lib/components/common/Switch.svelte';
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import Textarea from '$lib/components/common/Textarea.svelte';
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import Spinner from '$lib/components/common/Spinner.svelte';
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const i18n = getContext('i18n');
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let updateEmbeddingModelLoading = false;
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let updateRerankingModelLoading = false;
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let showResetConfirm = false;
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let showResetUploadDirConfirm = false;
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let showReindexConfirm = false;
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let embeddingEngine = '';
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let embeddingModel = '';
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let embeddingBatchSize = 1;
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let rerankingModel = '';
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let OpenAIUrl = '';
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let OpenAIKey = '';
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let AzureOpenAIUrl = '';
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let AzureOpenAIKey = '';
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let AzureOpenAIVersion = '';
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let OllamaUrl = '';
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let OllamaKey = '';
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let querySettings = {
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template: '',
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r: 0.0,
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k: 4,
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k_reranker: 4,
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hybrid: false
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};
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let RAGConfig = null;
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const embeddingModelUpdateHandler = async () => {
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if (embeddingEngine === '' && embeddingModel.split('/').length - 1 > 1) {
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toast.error(
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$i18n.t(
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'Model filesystem path detected. Model shortname is required for update, cannot continue.'
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)
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);
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return;
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}
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if (embeddingEngine === 'ollama' && embeddingModel === '') {
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toast.error(
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$i18n.t(
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'Model filesystem path detected. Model shortname is required for update, cannot continue.'
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)
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);
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return;
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}
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if (embeddingEngine === 'openai' && embeddingModel === '') {
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toast.error(
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$i18n.t(
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'Model filesystem path detected. Model shortname is required for update, cannot continue.'
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)
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);
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return;
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}
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if (embeddingEngine === 'openai' && (OpenAIKey === '' || OpenAIUrl === '')) {
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toast.error($i18n.t('OpenAI URL/Key required.'));
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return;
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}
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if (
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embeddingEngine === 'azure_openai' &&
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(AzureOpenAIKey === '' || AzureOpenAIUrl === '' || AzureOpenAIVersion === '')
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) {
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toast.error($i18n.t('OpenAI URL/Key required.'));
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return;
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}
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console.debug('Update embedding model attempt:', embeddingModel);
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updateEmbeddingModelLoading = true;
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const res = await updateEmbeddingConfig(localStorage.token, {
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embedding_engine: embeddingEngine,
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embedding_model: embeddingModel,
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embedding_batch_size: embeddingBatchSize,
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ollama_config: {
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key: OllamaKey,
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url: OllamaUrl
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},
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openai_config: {
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key: OpenAIKey,
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url: OpenAIUrl
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},
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azure_openai_config: {
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key: AzureOpenAIKey,
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url: AzureOpenAIUrl,
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version: AzureOpenAIVersion
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}
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}).catch(async (error) => {
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toast.error(`${error}`);
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await setEmbeddingConfig();
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return null;
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});
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updateEmbeddingModelLoading = false;
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if (res) {
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console.debug('embeddingModelUpdateHandler:', res);
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if (res.status === true) {
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toast.success($i18n.t('Embedding model set to "{{embedding_model}}"', res), {
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duration: 1000 * 10
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});
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}
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}
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};
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const submitHandler = async () => {
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if (
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RAGConfig.CONTENT_EXTRACTION_ENGINE === 'external' &&
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RAGConfig.EXTERNAL_DOCUMENT_LOADER_URL === ''
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) {
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toast.error($i18n.t('External Document Loader URL required.'));
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return;
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}
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if (RAGConfig.CONTENT_EXTRACTION_ENGINE === 'tika' && RAGConfig.TIKA_SERVER_URL === '') {
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toast.error($i18n.t('Tika Server URL required.'));
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return;
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}
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if (RAGConfig.CONTENT_EXTRACTION_ENGINE === 'docling' && RAGConfig.DOCLING_SERVER_URL === '') {
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toast.error($i18n.t('Docling Server URL required.'));
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return;
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}
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if (
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RAGConfig.CONTENT_EXTRACTION_ENGINE === 'docling' &&
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((RAGConfig.DOCLING_OCR_ENGINE === '' && RAGConfig.DOCLING_OCR_LANG !== '') ||
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(RAGConfig.DOCLING_OCR_ENGINE !== '' && RAGConfig.DOCLING_OCR_LANG === ''))
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) {
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toast.error(
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$i18n.t('Both Docling OCR Engine and Language(s) must be provided or both left empty.')
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);
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return;
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}
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if (
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RAGConfig.CONTENT_EXTRACTION_ENGINE === 'datalab_marker' &&
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!RAGConfig.DATALAB_MARKER_API_KEY
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) {
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toast.error($i18n.t('Datalab Marker API Key required.'));
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return;
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}
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if (
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RAGConfig.CONTENT_EXTRACTION_ENGINE === 'document_intelligence' &&
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(RAGConfig.DOCUMENT_INTELLIGENCE_ENDPOINT === '' ||
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RAGConfig.DOCUMENT_INTELLIGENCE_KEY === '')
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) {
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toast.error($i18n.t('Document Intelligence endpoint and key required.'));
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return;
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}
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if (
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RAGConfig.CONTENT_EXTRACTION_ENGINE === 'mistral_ocr' &&
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RAGConfig.MISTRAL_OCR_API_KEY === ''
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) {
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toast.error($i18n.t('Mistral OCR API Key required.'));
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return;
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}
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if (!RAGConfig.BYPASS_EMBEDDING_AND_RETRIEVAL) {
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await embeddingModelUpdateHandler();
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}
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RAGConfig.ALLOWED_FILE_EXTENSIONS = (RAGConfig?.ALLOWED_FILE_EXTENSIONS ?? '')
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.split(',')
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.map((ext) => ext.trim())
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.filter((ext) => ext !== '');
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RAGConfig.DATALAB_MARKER_LANGS = RAGConfig.DATALAB_MARKER_LANGS.split(',')
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.map((code) => code.trim())
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.filter((code) => code !== '')
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.join(', ');
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const res = await updateRAGConfig(localStorage.token, RAGConfig);
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dispatch('save');
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};
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const setEmbeddingConfig = async () => {
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const embeddingConfig = await getEmbeddingConfig(localStorage.token);
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if (embeddingConfig) {
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embeddingEngine = embeddingConfig.embedding_engine;
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embeddingModel = embeddingConfig.embedding_model;
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embeddingBatchSize = embeddingConfig.embedding_batch_size ?? 1;
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OpenAIKey = embeddingConfig.openai_config.key;
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OpenAIUrl = embeddingConfig.openai_config.url;
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OllamaKey = embeddingConfig.ollama_config.key;
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OllamaUrl = embeddingConfig.ollama_config.url;
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AzureOpenAIKey = embeddingConfig.azure_openai_config.key;
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AzureOpenAIUrl = embeddingConfig.azure_openai_config.url;
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AzureOpenAIVersion = embeddingConfig.azure_openai_config.version;
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}
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};
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onMount(async () => {
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await setEmbeddingConfig();
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const config = await getRAGConfig(localStorage.token);
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config.ALLOWED_FILE_EXTENSIONS = (config?.ALLOWED_FILE_EXTENSIONS ?? []).join(', ');
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RAGConfig = config;
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});
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</script>
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<ResetUploadDirConfirmDialog
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bind:show={showResetUploadDirConfirm}
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on:confirm={async () => {
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const res = await deleteAllFiles(localStorage.token).catch((error) => {
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toast.error(`${error}`);
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return null;
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});
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if (res) {
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toast.success($i18n.t('Success'));
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}
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}}
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/>
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<ResetVectorDBConfirmDialog
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bind:show={showResetConfirm}
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on:confirm={() => {
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const res = resetVectorDB(localStorage.token).catch((error) => {
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toast.error(`${error}`);
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return null;
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});
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if (res) {
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toast.success($i18n.t('Success'));
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}
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}}
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/>
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<ReindexKnowledgeFilesConfirmDialog
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bind:show={showReindexConfirm}
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on:confirm={async () => {
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const res = await reindexKnowledgeFiles(localStorage.token).catch((error) => {
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toast.error(`${error}`);
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return null;
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});
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if (res) {
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toast.success($i18n.t('Success'));
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}
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}}
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/>
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<form
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class="flex flex-col h-full justify-between space-y-3 text-sm"
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on:submit|preventDefault={() => {
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submitHandler();
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}}
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>
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{#if RAGConfig}
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<div class=" space-y-2.5 overflow-y-scroll scrollbar-hidden h-full pr-1.5">
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<div class="">
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<div class="mb-3">
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<div class=" mb-2.5 text-base font-medium">{$i18n.t('General')}</div>
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<hr class=" border-gray-100 dark:border-gray-850 my-2" />
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<div class="mb-2.5 flex flex-col w-full justify-between">
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<div class="flex w-full justify-between mb-1">
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<div class="self-center text-xs font-medium">
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{$i18n.t('Content Extraction Engine')}
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</div>
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<div class="">
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<select
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class="dark:bg-gray-900 w-fit pr-8 rounded-sm px-2 text-xs bg-transparent outline-hidden text-right"
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bind:value={RAGConfig.CONTENT_EXTRACTION_ENGINE}
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>
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<option value="">{$i18n.t('Default')}</option>
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<option value="external">{$i18n.t('External')}</option>
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<option value="tika">{$i18n.t('Tika')}</option>
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<option value="docling">{$i18n.t('Docling')}</option>
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<option value="datalab_marker">{$i18n.t('Datalab Marker API')}</option>
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<option value="document_intelligence">{$i18n.t('Document Intelligence')}</option>
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<option value="mistral_ocr">{$i18n.t('Mistral OCR')}</option>
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</select>
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</div>
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</div>
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{#if RAGConfig.CONTENT_EXTRACTION_ENGINE === ''}
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<div class="flex w-full mt-1">
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<div class="flex-1 flex justify-between">
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<div class=" self-center text-xs font-medium">
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{$i18n.t('PDF Extract Images (OCR)')}
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</div>
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<div class="flex items-center relative">
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<Switch bind:state={RAGConfig.PDF_EXTRACT_IMAGES} />
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</div>
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</div>
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</div>
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{:else if RAGConfig.CONTENT_EXTRACTION_ENGINE === 'datalab_marker'}
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<div class="my-0.5 flex gap-2 pr-2">
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<SensitiveInput
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placeholder={$i18n.t('Enter Datalab Marker API Key')}
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required={false}
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bind:value={RAGConfig.DATALAB_MARKER_API_KEY}
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/>
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</div>
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<div class="flex justify-between w-full mt-2">
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<div class="text-xs font-medium">
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{$i18n.t('Languages')}
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</div>
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<input
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class="text-sm bg-transparent outline-hidden"
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type="text"
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bind:value={RAGConfig.DATALAB_MARKER_LANGS}
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placeholder={$i18n.t('e.g.) en,fr,de')}
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/>
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</div>
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<div class="flex justify-between w-full mt-2">
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<div class="self-center text-xs font-medium">
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<Tooltip
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content={$i18n.t(
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'Significantly improves accuracy by using an LLM to enhance tables, forms, inline math, and layout detection. Will increase latency. Defaults to True.'
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)}
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placement="top-start"
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>
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{$i18n.t('Use LLM')}
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</Tooltip>
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</div>
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<div class="flex items-center">
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<Switch bind:state={RAGConfig.DATALAB_MARKER_USE_LLM} />
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</div>
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</div>
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<div class="flex justify-between w-full mt-2">
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<div class="self-center text-xs font-medium">
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<Tooltip
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content={$i18n.t('Skip the cache and re-run the inference. Defaults to False.')}
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placement="top-start"
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>
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{$i18n.t('Skip Cache')}
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</Tooltip>
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</div>
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<div class="flex items-center">
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<Switch bind:state={RAGConfig.DATALAB_MARKER_SKIP_CACHE} />
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</div>
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</div>
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<div class="flex justify-between w-full mt-2">
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<div class="self-center text-xs font-medium">
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<Tooltip
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content={$i18n.t(
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'Force OCR on all pages of the PDF. This can lead to worse results if you have good text in your PDFs. Defaults to False.'
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)}
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placement="top-start"
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>
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{$i18n.t('Force OCR')}
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</Tooltip>
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</div>
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<div class="flex items-center">
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<Switch bind:state={RAGConfig.DATALAB_MARKER_FORCE_OCR} />
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</div>
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</div>
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<div class="flex justify-between w-full mt-2">
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<div class="self-center text-xs font-medium">
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<Tooltip
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content={$i18n.t(
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'Whether to paginate the output. Each page will be separated by a horizontal rule and page number. Defaults to False.'
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)}
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placement="top-start"
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>
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{$i18n.t('Paginate')}
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</Tooltip>
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</div>
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<div class="flex items-center">
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<Switch bind:state={RAGConfig.DATALAB_MARKER_PAGINATE} />
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</div>
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</div>
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<div class="flex justify-between w-full mt-2">
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<div class="self-center text-xs font-medium">
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<Tooltip
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content={$i18n.t(
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'Strip existing OCR text from the PDF and re-run OCR. Ignored if Force OCR is enabled. Defaults to False.'
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)}
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placement="top-start"
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>
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{$i18n.t('Strip Existing OCR')}
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</Tooltip>
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</div>
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<div class="flex items-center">
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<Switch bind:state={RAGConfig.DATALAB_MARKER_STRIP_EXISTING_OCR} />
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</div>
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</div>
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<div class="flex justify-between w-full mt-2">
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<div class="self-center text-xs font-medium">
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<Tooltip
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content={$i18n.t(
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'Disable image extraction from the PDF. If Use LLM is enabled, images will be automatically captioned. Defaults to False.'
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)}
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placement="top-start"
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>
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{$i18n.t('Disable Image Extraction')}
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</Tooltip>
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</div>
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<div class="flex items-center">
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<Switch bind:state={RAGConfig.DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION} />
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</div>
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</div>
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<div class="flex justify-between w-full mt-2">
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|
<div class="self-center text-xs font-medium">
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<Tooltip
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content={$i18n.t(
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"The output format for the text. Can be 'json', 'markdown', or 'html'. Defaults to 'markdown'."
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)}
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placement="top-start"
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>
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{$i18n.t('Output Format')}
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</Tooltip>
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</div>
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<div class="">
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|
<select
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class="dark:bg-gray-900 w-fit pr-8 rounded-sm px-2 text-xs bg-transparent outline-hidden text-right"
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bind:value={RAGConfig.DATALAB_MARKER_OUTPUT_FORMAT}
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>
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<option value="markdown">{$i18n.t('Markdown')}</option>
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<option value="json">{$i18n.t('JSON')}</option>
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<option value="html">{$i18n.t('HTML')}</option>
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</select>
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</div>
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</div>
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|
{:else if RAGConfig.CONTENT_EXTRACTION_ENGINE === 'external'}
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|
<div class="my-0.5 flex gap-2 pr-2">
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|
<input
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class="flex-1 w-full text-sm bg-transparent outline-hidden"
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placeholder={$i18n.t('Enter External Document Loader URL')}
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bind:value={RAGConfig.EXTERNAL_DOCUMENT_LOADER_URL}
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/>
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<SensitiveInput
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placeholder={$i18n.t('Enter External Document Loader API Key')}
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required={false}
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bind:value={RAGConfig.EXTERNAL_DOCUMENT_LOADER_API_KEY}
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/>
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</div>
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{:else if RAGConfig.CONTENT_EXTRACTION_ENGINE === 'tika'}
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<div class="flex w-full mt-1">
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<div class="flex-1 mr-2">
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<input
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class="flex-1 w-full text-sm bg-transparent outline-hidden"
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placeholder={$i18n.t('Enter Tika Server URL')}
|
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bind:value={RAGConfig.TIKA_SERVER_URL}
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/>
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</div>
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</div>
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{:else if RAGConfig.CONTENT_EXTRACTION_ENGINE === 'docling'}
|
|
<div class="flex w-full mt-1">
|
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<input
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class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
placeholder={$i18n.t('Enter Docling Server URL')}
|
|
bind:value={RAGConfig.DOCLING_SERVER_URL}
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/>
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</div>
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|
<div class="flex w-full mt-2">
|
|
<input
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class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
placeholder={$i18n.t('Enter Docling OCR Engine')}
|
|
bind:value={RAGConfig.DOCLING_OCR_ENGINE}
|
|
/>
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
placeholder={$i18n.t('Enter Docling OCR Language(s)')}
|
|
bind:value={RAGConfig.DOCLING_OCR_LANG}
|
|
/>
|
|
</div>
|
|
|
|
<div class="flex w-full mt-2">
|
|
<div class="flex-1 flex justify-between">
|
|
<div class=" self-center text-xs font-medium">
|
|
{$i18n.t('Describe Pictures in Documents')}
|
|
</div>
|
|
<div class="flex items-center relative">
|
|
<Switch bind:state={RAGConfig.DOCLING_DO_PICTURE_DESCRIPTION} />
|
|
</div>
|
|
</div>
|
|
</div>
|
|
{:else if RAGConfig.CONTENT_EXTRACTION_ENGINE === 'document_intelligence'}
|
|
<div class="my-0.5 flex gap-2 pr-2">
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
placeholder={$i18n.t('Enter Document Intelligence Endpoint')}
|
|
bind:value={RAGConfig.DOCUMENT_INTELLIGENCE_ENDPOINT}
|
|
/>
|
|
<SensitiveInput
|
|
placeholder={$i18n.t('Enter Document Intelligence Key')}
|
|
bind:value={RAGConfig.DOCUMENT_INTELLIGENCE_KEY}
|
|
/>
|
|
</div>
|
|
{:else if RAGConfig.CONTENT_EXTRACTION_ENGINE === 'mistral_ocr'}
|
|
<div class="my-0.5 flex gap-2 pr-2">
|
|
<SensitiveInput
|
|
placeholder={$i18n.t('Enter Mistral API Key')}
|
|
bind:value={RAGConfig.MISTRAL_OCR_API_KEY}
|
|
/>
|
|
</div>
|
|
{/if}
|
|
</div>
|
|
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">
|
|
<Tooltip content={$i18n.t('Full Context Mode')} placement="top-start">
|
|
{$i18n.t('Bypass Embedding and Retrieval')}
|
|
</Tooltip>
|
|
</div>
|
|
<div class="flex items-center relative">
|
|
<Tooltip
|
|
content={RAGConfig.BYPASS_EMBEDDING_AND_RETRIEVAL
|
|
? $i18n.t(
|
|
'Inject the entire content as context for comprehensive processing, this is recommended for complex queries.'
|
|
)
|
|
: $i18n.t(
|
|
'Default to segmented retrieval for focused and relevant content extraction, this is recommended for most cases.'
|
|
)}
|
|
>
|
|
<Switch bind:state={RAGConfig.BYPASS_EMBEDDING_AND_RETRIEVAL} />
|
|
</Tooltip>
|
|
</div>
|
|
</div>
|
|
|
|
{#if !RAGConfig.BYPASS_EMBEDDING_AND_RETRIEVAL}
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">{$i18n.t('Text Splitter')}</div>
|
|
<div class="flex items-center relative">
|
|
<select
|
|
class="dark:bg-gray-900 w-fit pr-8 rounded-sm px-2 text-xs bg-transparent outline-hidden text-right"
|
|
bind:value={RAGConfig.TEXT_SPLITTER}
|
|
>
|
|
<option value="">{$i18n.t('Default')} ({$i18n.t('Character')})</option>
|
|
<option value="token">{$i18n.t('Token')} ({$i18n.t('Tiktoken')})</option>
|
|
<option value="markdown_header">{$i18n.t('Markdown (Header)')}</option>
|
|
</select>
|
|
</div>
|
|
</div>
|
|
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" flex gap-1.5 w-full">
|
|
<div class=" w-full justify-between">
|
|
<div class="self-center text-xs font-medium min-w-fit mb-1">
|
|
{$i18n.t('Chunk Size')}
|
|
</div>
|
|
<div class="self-center">
|
|
<input
|
|
class=" w-full rounded-lg py-1.5 px-4 text-sm bg-gray-50 dark:text-gray-300 dark:bg-gray-850 outline-hidden"
|
|
type="number"
|
|
placeholder={$i18n.t('Enter Chunk Size')}
|
|
bind:value={RAGConfig.CHUNK_SIZE}
|
|
autocomplete="off"
|
|
min="0"
|
|
/>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="w-full">
|
|
<div class=" self-center text-xs font-medium min-w-fit mb-1">
|
|
{$i18n.t('Chunk Overlap')}
|
|
</div>
|
|
|
|
<div class="self-center">
|
|
<input
|
|
class="w-full rounded-lg py-1.5 px-4 text-sm bg-gray-50 dark:text-gray-300 dark:bg-gray-850 outline-hidden"
|
|
type="number"
|
|
placeholder={$i18n.t('Enter Chunk Overlap')}
|
|
bind:value={RAGConfig.CHUNK_OVERLAP}
|
|
autocomplete="off"
|
|
min="0"
|
|
/>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
{/if}
|
|
</div>
|
|
|
|
{#if !RAGConfig.BYPASS_EMBEDDING_AND_RETRIEVAL}
|
|
<div class="mb-3">
|
|
<div class=" mb-2.5 text-base font-medium">{$i18n.t('Embedding')}</div>
|
|
|
|
<hr class=" border-gray-100 dark:border-gray-850 my-2" />
|
|
|
|
<div class=" mb-2.5 flex flex-col w-full justify-between">
|
|
<div class="flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">
|
|
{$i18n.t('Embedding Model Engine')}
|
|
</div>
|
|
<div class="flex items-center relative">
|
|
<select
|
|
class="dark:bg-gray-900 w-fit pr-8 rounded-sm px-2 p-1 text-xs bg-transparent outline-hidden text-right"
|
|
bind:value={embeddingEngine}
|
|
placeholder="Select an embedding model engine"
|
|
on:change={(e) => {
|
|
if (e.target.value === 'ollama') {
|
|
embeddingModel = '';
|
|
} else if (e.target.value === 'openai') {
|
|
embeddingModel = 'text-embedding-3-small';
|
|
} else if (e.target.value === 'azure_openai') {
|
|
embeddingModel = 'text-embedding-3-small';
|
|
} else if (e.target.value === '') {
|
|
embeddingModel = 'sentence-transformers/all-MiniLM-L6-v2';
|
|
}
|
|
}}
|
|
>
|
|
<option value="">{$i18n.t('Default (SentenceTransformers)')}</option>
|
|
<option value="ollama">{$i18n.t('Ollama')}</option>
|
|
<option value="openai">{$i18n.t('OpenAI')}</option>
|
|
<option value="azure_openai">Azure OpenAI</option>
|
|
</select>
|
|
</div>
|
|
</div>
|
|
|
|
{#if embeddingEngine === 'openai'}
|
|
<div class="my-0.5 flex gap-2 pr-2">
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
placeholder={$i18n.t('API Base URL')}
|
|
bind:value={OpenAIUrl}
|
|
required
|
|
/>
|
|
|
|
<SensitiveInput placeholder={$i18n.t('API Key')} bind:value={OpenAIKey} />
|
|
</div>
|
|
{:else if embeddingEngine === 'ollama'}
|
|
<div class="my-0.5 flex gap-2 pr-2">
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
placeholder={$i18n.t('API Base URL')}
|
|
bind:value={OllamaUrl}
|
|
required
|
|
/>
|
|
|
|
<SensitiveInput
|
|
placeholder={$i18n.t('API Key')}
|
|
bind:value={OllamaKey}
|
|
required={false}
|
|
/>
|
|
</div>
|
|
{:else if embeddingEngine === 'azure_openai'}
|
|
<div class="my-0.5 flex flex-col gap-2 pr-2 w-full">
|
|
<div class="flex gap-2">
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
placeholder={$i18n.t('API Base URL')}
|
|
bind:value={AzureOpenAIUrl}
|
|
required
|
|
/>
|
|
<SensitiveInput placeholder={$i18n.t('API Key')} bind:value={AzureOpenAIKey} />
|
|
</div>
|
|
<div class="flex gap-2">
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
placeholder="Version"
|
|
bind:value={AzureOpenAIVersion}
|
|
required
|
|
/>
|
|
</div>
|
|
</div>
|
|
{/if}
|
|
</div>
|
|
|
|
<div class=" mb-2.5 flex flex-col w-full">
|
|
<div class=" mb-1 text-xs font-medium">{$i18n.t('Embedding Model')}</div>
|
|
|
|
<div class="">
|
|
{#if embeddingEngine === 'ollama'}
|
|
<div class="flex w-full">
|
|
<div class="flex-1 mr-2">
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
bind:value={embeddingModel}
|
|
placeholder={$i18n.t('Set embedding model')}
|
|
required
|
|
/>
|
|
</div>
|
|
</div>
|
|
{:else}
|
|
<div class="flex w-full">
|
|
<div class="flex-1 mr-2">
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
placeholder={$i18n.t('Set embedding model (e.g. {{model}})', {
|
|
model: embeddingModel.slice(-40)
|
|
})}
|
|
bind:value={embeddingModel}
|
|
/>
|
|
</div>
|
|
|
|
{#if embeddingEngine === ''}
|
|
<button
|
|
class="px-2.5 bg-transparent text-gray-800 dark:bg-transparent dark:text-gray-100 rounded-lg transition"
|
|
on:click={() => {
|
|
embeddingModelUpdateHandler();
|
|
}}
|
|
disabled={updateEmbeddingModelLoading}
|
|
>
|
|
{#if updateEmbeddingModelLoading}
|
|
<div class="self-center">
|
|
<svg
|
|
class=" w-4 h-4"
|
|
viewBox="0 0 24 24"
|
|
fill="currentColor"
|
|
xmlns="http://www.w3.org/2000/svg"
|
|
>
|
|
<style>
|
|
.spinner_ajPY {
|
|
transform-origin: center;
|
|
animation: spinner_AtaB 0.75s infinite linear;
|
|
}
|
|
|
|
@keyframes spinner_AtaB {
|
|
100% {
|
|
transform: rotate(360deg);
|
|
}
|
|
}
|
|
</style>
|
|
<path
|
|
d="M12,1A11,11,0,1,0,23,12,11,11,0,0,0,12,1Zm0,19a8,8,0,1,1,8-8A8,8,0,0,1,12,20Z"
|
|
opacity=".25"
|
|
/>
|
|
<path
|
|
d="M10.14,1.16a11,11,0,0,0-9,8.92A1.59,1.59,0,0,0,2.46,12,1.52,1.52,0,0,0,4.11,10.7a8,8,0,0,1,6.66-6.61A1.42,1.42,0,0,0,12,2.69h0A1.57,1.57,0,0,0,10.14,1.16Z"
|
|
class="spinner_ajPY"
|
|
/>
|
|
</svg>
|
|
</div>
|
|
{:else}
|
|
<svg
|
|
xmlns="http://www.w3.org/2000/svg"
|
|
viewBox="0 0 16 16"
|
|
fill="currentColor"
|
|
class="w-4 h-4"
|
|
>
|
|
<path
|
|
d="M8.75 2.75a.75.75 0 0 0-1.5 0v5.69L5.03 6.22a.75.75 0 0 0-1.06 1.06l3.5 3.5a.75.75 0 0 0 1.06 0l3.5-3.5a.75.75 0 0 0-1.06-1.06L8.75 8.44V2.75Z"
|
|
/>
|
|
<path
|
|
d="M3.5 9.75a.75.75 0 0 0-1.5 0v1.5A2.75 2.75 0 0 0 4.75 14h6.5A2.75 2.75 0 0 0 14 11.25v-1.5a.75.75 0 0 0-1.5 0v1.5c0 .69-.56 1.25-1.25 1.25h-6.5c-.69 0-1.25-.56-1.25-1.25v-1.5Z"
|
|
/>
|
|
</svg>
|
|
{/if}
|
|
</button>
|
|
{/if}
|
|
</div>
|
|
{/if}
|
|
</div>
|
|
|
|
<div class="mt-1 mb-1 text-xs text-gray-400 dark:text-gray-500">
|
|
{$i18n.t(
|
|
'Warning: If you update or change your embedding model, you will need to re-import all documents.'
|
|
)}
|
|
</div>
|
|
</div>
|
|
|
|
{#if embeddingEngine === 'ollama' || embeddingEngine === 'openai' || embeddingEngine === 'azure_openai'}
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">
|
|
{$i18n.t('Embedding Batch Size')}
|
|
</div>
|
|
|
|
<div class="">
|
|
<input
|
|
bind:value={embeddingBatchSize}
|
|
type="number"
|
|
class=" bg-transparent text-center w-14 outline-none"
|
|
min="-2"
|
|
max="16000"
|
|
step="1"
|
|
/>
|
|
</div>
|
|
</div>
|
|
{/if}
|
|
</div>
|
|
|
|
<div class="mb-3">
|
|
<div class=" mb-2.5 text-base font-medium">{$i18n.t('Retrieval')}</div>
|
|
|
|
<hr class=" border-gray-100 dark:border-gray-850 my-2" />
|
|
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">{$i18n.t('Full Context Mode')}</div>
|
|
<div class="flex items-center relative">
|
|
<Tooltip
|
|
content={RAGConfig.RAG_FULL_CONTEXT
|
|
? $i18n.t(
|
|
'Inject the entire content as context for comprehensive processing, this is recommended for complex queries.'
|
|
)
|
|
: $i18n.t(
|
|
'Default to segmented retrieval for focused and relevant content extraction, this is recommended for most cases.'
|
|
)}
|
|
>
|
|
<Switch bind:state={RAGConfig.RAG_FULL_CONTEXT} />
|
|
</Tooltip>
|
|
</div>
|
|
</div>
|
|
|
|
{#if !RAGConfig.RAG_FULL_CONTEXT}
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">{$i18n.t('Hybrid Search')}</div>
|
|
<div class="flex items-center relative">
|
|
<Switch
|
|
bind:state={RAGConfig.ENABLE_RAG_HYBRID_SEARCH}
|
|
on:change={() => {
|
|
submitHandler();
|
|
}}
|
|
/>
|
|
</div>
|
|
</div>
|
|
|
|
{#if RAGConfig.ENABLE_RAG_HYBRID_SEARCH === true}
|
|
<div class=" mb-2.5 flex flex-col w-full justify-between">
|
|
<div class="flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">
|
|
{$i18n.t('Reranking Engine')}
|
|
</div>
|
|
<div class="flex items-center relative">
|
|
<select
|
|
class="dark:bg-gray-900 w-fit pr-8 rounded-sm px-2 p-1 text-xs bg-transparent outline-hidden text-right"
|
|
bind:value={RAGConfig.RAG_RERANKING_ENGINE}
|
|
placeholder="Select a reranking model engine"
|
|
on:change={(e) => {
|
|
if (e.target.value === 'external') {
|
|
RAGConfig.RAG_RERANKING_MODEL = '';
|
|
} else if (e.target.value === '') {
|
|
RAGConfig.RAG_RERANKING_MODEL = 'BAAI/bge-reranker-v2-m3';
|
|
}
|
|
}}
|
|
>
|
|
<option value="">{$i18n.t('Default (SentenceTransformers)')}</option>
|
|
<option value="external">{$i18n.t('External')}</option>
|
|
</select>
|
|
</div>
|
|
</div>
|
|
|
|
{#if RAGConfig.RAG_RERANKING_ENGINE === 'external'}
|
|
<div class="my-0.5 flex gap-2 pr-2">
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
placeholder={$i18n.t('API Base URL')}
|
|
bind:value={RAGConfig.RAG_EXTERNAL_RERANKER_URL}
|
|
required
|
|
/>
|
|
|
|
<SensitiveInput
|
|
placeholder={$i18n.t('API Key')}
|
|
bind:value={RAGConfig.RAG_EXTERNAL_RERANKER_API_KEY}
|
|
required={false}
|
|
/>
|
|
</div>
|
|
{/if}
|
|
</div>
|
|
|
|
<div class=" mb-2.5 flex flex-col w-full">
|
|
<div class=" mb-1 text-xs font-medium">{$i18n.t('Reranking Model')}</div>
|
|
|
|
<div class="">
|
|
<div class="flex w-full">
|
|
<div class="flex-1 mr-2">
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
placeholder={$i18n.t('Set reranking model (e.g. {{model}})', {
|
|
model: 'BAAI/bge-reranker-v2-m3'
|
|
})}
|
|
bind:value={RAGConfig.RAG_RERANKING_MODEL}
|
|
/>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
{/if}
|
|
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">{$i18n.t('Top K')}</div>
|
|
<div class="flex items-center relative">
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
type="number"
|
|
placeholder={$i18n.t('Enter Top K')}
|
|
bind:value={RAGConfig.TOP_K}
|
|
autocomplete="off"
|
|
min="0"
|
|
/>
|
|
</div>
|
|
</div>
|
|
|
|
{#if RAGConfig.ENABLE_RAG_HYBRID_SEARCH === true}
|
|
<div class="mb-2.5 flex w-full justify-between">
|
|
<div class="self-center text-xs font-medium">{$i18n.t('Top K Reranker')}</div>
|
|
<div class="flex items-center relative">
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
type="number"
|
|
placeholder={$i18n.t('Enter Top K Reranker')}
|
|
bind:value={RAGConfig.TOP_K_RERANKER}
|
|
autocomplete="off"
|
|
min="0"
|
|
/>
|
|
</div>
|
|
</div>
|
|
{/if}
|
|
|
|
{#if RAGConfig.ENABLE_RAG_HYBRID_SEARCH === true}
|
|
<div class=" mb-2.5 flex flex-col w-full justify-between">
|
|
<div class=" flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">
|
|
{$i18n.t('Relevance Threshold')}
|
|
</div>
|
|
<div class="flex items-center relative">
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
type="number"
|
|
step="0.01"
|
|
placeholder={$i18n.t('Enter Score')}
|
|
bind:value={RAGConfig.RELEVANCE_THRESHOLD}
|
|
autocomplete="off"
|
|
min="0.0"
|
|
title={$i18n.t(
|
|
'The score should be a value between 0.0 (0%) and 1.0 (100%).'
|
|
)}
|
|
/>
|
|
</div>
|
|
</div>
|
|
<div class="mt-1 text-xs text-gray-400 dark:text-gray-500">
|
|
{$i18n.t(
|
|
'Note: If you set a minimum score, the search will only return documents with a score greater than or equal to the minimum score.'
|
|
)}
|
|
</div>
|
|
</div>
|
|
{/if}
|
|
|
|
{#if RAGConfig.ENABLE_RAG_HYBRID_SEARCH === true}
|
|
<div class="mb-2.5 flex w-full justify-between">
|
|
<div class="self-center text-xs font-medium">
|
|
{$i18n.t('Weight of BM25 Retrieval')}
|
|
</div>
|
|
<div class="flex items-center relative">
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
type="number"
|
|
step="0.01"
|
|
placeholder={$i18n.t('Enter BM25 Weight')}
|
|
bind:value={RAGConfig.HYBRID_BM25_WEIGHT}
|
|
autocomplete="off"
|
|
min="0.0"
|
|
max="1.0"
|
|
/>
|
|
</div>
|
|
</div>
|
|
{/if}
|
|
{/if}
|
|
|
|
<div class=" mb-2.5 flex flex-col w-full justify-between">
|
|
<div class=" mb-1 text-xs font-medium">{$i18n.t('RAG Template')}</div>
|
|
<div class="flex w-full items-center relative">
|
|
<Tooltip
|
|
content={$i18n.t(
|
|
'Leave empty to use the default prompt, or enter a custom prompt'
|
|
)}
|
|
placement="top-start"
|
|
className="w-full"
|
|
>
|
|
<Textarea
|
|
bind:value={RAGConfig.RAG_TEMPLATE}
|
|
placeholder={$i18n.t(
|
|
'Leave empty to use the default prompt, or enter a custom prompt'
|
|
)}
|
|
/>
|
|
</Tooltip>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
{/if}
|
|
|
|
<div class="mb-3">
|
|
<div class=" mb-2.5 text-base font-medium">{$i18n.t('Files')}</div>
|
|
|
|
<hr class=" border-gray-100 dark:border-gray-850 my-2" />
|
|
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">{$i18n.t('Allowed File Extensions')}</div>
|
|
<div class="flex items-center relative">
|
|
<Tooltip
|
|
content={$i18n.t(
|
|
'Allowed file extensions for upload. Separate multiple extensions with commas. Leave empty for all file types.'
|
|
)}
|
|
placement="top-start"
|
|
>
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
type="text"
|
|
placeholder={$i18n.t('e.g. pdf, docx, txt')}
|
|
bind:value={RAGConfig.ALLOWED_FILE_EXTENSIONS}
|
|
autocomplete="off"
|
|
/>
|
|
</Tooltip>
|
|
</div>
|
|
</div>
|
|
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">{$i18n.t('Max Upload Size')}</div>
|
|
<div class="flex items-center relative">
|
|
<Tooltip
|
|
content={$i18n.t(
|
|
'The maximum file size in MB. If the file size exceeds this limit, the file will not be uploaded.'
|
|
)}
|
|
placement="top-start"
|
|
>
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
type="number"
|
|
placeholder={$i18n.t('Leave empty for unlimited')}
|
|
bind:value={RAGConfig.FILE_MAX_SIZE}
|
|
autocomplete="off"
|
|
min="0"
|
|
/>
|
|
</Tooltip>
|
|
</div>
|
|
</div>
|
|
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">{$i18n.t('Max Upload Count')}</div>
|
|
<div class="flex items-center relative">
|
|
<Tooltip
|
|
content={$i18n.t(
|
|
'The maximum number of files that can be used at once in chat. If the number of files exceeds this limit, the files will not be uploaded.'
|
|
)}
|
|
placement="top-start"
|
|
>
|
|
<input
|
|
class="flex-1 w-full text-sm bg-transparent outline-hidden"
|
|
type="number"
|
|
placeholder={$i18n.t('Leave empty for unlimited')}
|
|
bind:value={RAGConfig.FILE_MAX_COUNT}
|
|
autocomplete="off"
|
|
min="0"
|
|
/>
|
|
</Tooltip>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="mb-3">
|
|
<div class=" mb-2.5 text-base font-medium">{$i18n.t('Integration')}</div>
|
|
|
|
<hr class=" border-gray-100 dark:border-gray-850 my-2" />
|
|
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">{$i18n.t('Google Drive')}</div>
|
|
<div class="flex items-center relative">
|
|
<Switch bind:state={RAGConfig.ENABLE_GOOGLE_DRIVE_INTEGRATION} />
|
|
</div>
|
|
</div>
|
|
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">{$i18n.t('OneDrive')}</div>
|
|
<div class="flex items-center relative">
|
|
<Switch bind:state={RAGConfig.ENABLE_ONEDRIVE_INTEGRATION} />
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="mb-3">
|
|
<div class=" mb-2.5 text-base font-medium">{$i18n.t('Danger Zone')}</div>
|
|
|
|
<hr class=" border-gray-100 dark:border-gray-850 my-2" />
|
|
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">{$i18n.t('Reset Upload Directory')}</div>
|
|
<div class="flex items-center relative">
|
|
<button
|
|
class="text-xs"
|
|
on:click={() => {
|
|
showResetUploadDirConfirm = true;
|
|
}}
|
|
>
|
|
{$i18n.t('Reset')}
|
|
</button>
|
|
</div>
|
|
</div>
|
|
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">
|
|
{$i18n.t('Reset Vector Storage/Knowledge')}
|
|
</div>
|
|
<div class="flex items-center relative">
|
|
<button
|
|
class="text-xs"
|
|
on:click={() => {
|
|
showResetConfirm = true;
|
|
}}
|
|
>
|
|
{$i18n.t('Reset')}
|
|
</button>
|
|
</div>
|
|
</div>
|
|
<div class=" mb-2.5 flex w-full justify-between">
|
|
<div class=" self-center text-xs font-medium">
|
|
{$i18n.t('Reindex Knowledge Base Vectors')}
|
|
</div>
|
|
<div class="flex items-center relative">
|
|
<button
|
|
class="text-xs"
|
|
on:click={() => {
|
|
showReindexConfirm = true;
|
|
}}
|
|
>
|
|
{$i18n.t('Reindex')}
|
|
</button>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
<div class="flex justify-end pt-3 text-sm font-medium">
|
|
<button
|
|
class="px-3.5 py-1.5 text-sm font-medium bg-black hover:bg-gray-900 text-white dark:bg-white dark:text-black dark:hover:bg-gray-100 transition rounded-full"
|
|
type="submit"
|
|
>
|
|
{$i18n.t('Save')}
|
|
</button>
|
|
</div>
|
|
{:else}
|
|
<div class="flex items-center justify-center h-full">
|
|
<Spinner />
|
|
</div>
|
|
{/if}
|
|
</form>
|