{ const res = await deleteAllFiles(localStorage.token).catch((error) => { toast.error(error); return null; }); if (res) { toast.success($i18n.t('Success')); } }} /> { const res = resetVectorDB(localStorage.token).catch((error) => { toast.error(error); return null; }); if (res) { toast.success($i18n.t('Success')); } }} />
{ submitHandler(); saveHandler(); }} >
{$i18n.t('General Settings')}
{$i18n.t('Scan for documents from {{path}}', { path: 'DOCS_DIR (/data/docs)' })}
{$i18n.t('Embedding Model Engine')}
{#if embeddingEngine === 'openai'}
{$i18n.t('Embedding Batch Size')}
{/if}
{$i18n.t('Hybrid Search')}

{$i18n.t('Embedding Model')}
{#if embeddingEngine === 'ollama'}
{:else}
{#if embeddingEngine === ''} {/if}
{/if}
{$i18n.t( 'Warning: If you update or change your embedding model, you will need to re-import all documents.' )}
{#if querySettings.hybrid === true}
{$i18n.t('Reranking Model')}
{/if}

{$i18n.t('Content Extraction')}
{$i18n.t('Engine')}
{#if showTikaServerUrl}
{/if}

{$i18n.t('Query Params')}
{$i18n.t('Top K')}
{#if querySettings.hybrid === true}
{$i18n.t('Minimum Score')}
{/if}
{#if querySettings.hybrid === true}
{$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.' )}

{/if}
{$i18n.t('RAG Template')}