Merge branch 'staging' into continue-from-reasoning

96d79ac4e963f7dcab07929b7162737720f3862b

Cohee <18619528+Cohee1207@users.noreply.github.com>

Parents: 980ed76cc39b726407948d26bf8fb87d70cdf9c6 b52b11d7bb72dee1f059772812d27cdfbfd9fa3d

public/index.html+10 -2 Viewed

                                             </span>
                                         </div>
                                     </div>
                                     <div class="range-block" data-source="openai,cohere,mistralai,custom,claude,openrouter,groq,deepseek,makersuite,ai21">
                                         <label for="openai_function_calling" class="checkbox_label flexWrap widthFreeExpand">
                                             <input id="openai_function_calling" type="checkbox" />
                                             <span data-i18n="Enable function calling">Enable function calling</span>

                             <div>
                                 <h4 data-i18n="AI21 Model">AI21 Model</h4>
                                 <select id="model_ai21_select">
                                     <optgroup label="Jamba 1.5(Latest)">
 +                                        <option value="jamba-mini">jamba-mini</option>
 +                                        <option value="jamba-large">jamba-large</option>
 +                                    </optgroup>
 +                                    <optgroup label="Jamba 1.6">
 +                                        <option value="jamba-1.6-mini">jamba-1.6-mini</option>
 +                                        <option value="jamba-1.6-large">jamba-1.6-large</option>
 +                                    </optgroup>
 +                                    <optgroup label="Jamba 1.5 (Deprecated)">
                                         <option value="jamba-1.5-mini">jamba-1.5-mini</option>
                                         <option value="jamba-1.5-large">jamba-1.5-large</option>
                                     </optgroup>

public/script.js+16 -9 Viewed

 import { initBulkEdit } from './scripts/bulk-edit.js';
 import { deriveTemplatesFromChatTemplate } from './scripts/chat-templates.js';
 import { getContext } from './scripts/st-context.js';
 import { extractReasoningFromData, initReasoning, parseReasoningInSwipes, PromptReasoning, ReasoningHandler, removeReasoningFromString, updateReasoningUI } from './scripts/reasoning.js';
 import { accountStorage } from './scripts/util/AccountStorage.js';
 // API OBJECT FOR EXTERNAL WIRING

         if (Array.isArray(this.swipes) && this.swipes.length > 0) {
             const message = chat[messageId];
 +            const swipeInfoExtra = structuredClone(message.extra ?? {});
 +            delete swipeInfoExtra.token_count;
 +            delete swipeInfoExtra.reasoning;
 +            delete swipeInfoExtra.reasoning_duration;
             const swipeInfo = {
                 send_date: message.send_date,
                 gen_started: message.gen_started,
                 gen_finished: message.gen_finished,
                 extra: structuredClone(message.extra)swipeInfoExtra,
             };
 -            const swipeInfoArray = [];
 +            const swipeInfoArray = Array(this.swipes.length).fill().map(() => structuredClone(swipeInfo));
 -            swipeInfoArray.length = this.swipes.length;
 +            parseReasoningInSwipes(this.swipes, swipeInfoArray, message.extra?.reasoning_duration);
 -            swipeInfoArray.fill(swipeInfo);
             chat[messageId].swipes.push(...this.swipes);
             chat[messageId].swipe_info.push(...swipeInfoArray);
+        }

             await eventSource.emit(event_types.IMPERSONATE_READY, text);
+        }
 +        syncMesToSwipe(messageId);
         saveLogprobsForActiveMessage(this.messageLogprobs.filter(Boolean), this.continueMessage);
         await saveChatConditional();
         unblockGeneration();

+    }
     if (Array.isArray(swipes) && swipes.length > 0) {
 +        const swipeInfoExtra = structuredClone(item.extra ?? {});
 +        delete swipeInfoExtra.token_count;
 +        delete swipeInfoExtra.reasoning;
 +        delete swipeInfoExtra.reasoning_duration;
         const swipeInfo = {
             send_date: item.send_date,
             gen_started: item.gen_started,
             gen_finished: item.gen_finished,
             extra: structuredClone(item.extra)swipeInfoExtra,
         };
 -        const swipeInfoArray = [];
 +        const swipeInfoArray = Array(swipes.length).fill().map(() => structuredClone(swipeInfo));
 -        swipeInfoArray.length = swipes.length;
 +        parseReasoningInSwipes(swipes, swipeInfoArray, item.extra?.reasoning_duration);
 -        swipeInfoArray.fill(swipeInfo, 0, swipes.length);
         item.swipes.push(...swipes);
         item.swipe_info.push(...swipeInfoArray);
+    }

public/scripts/extensions.js+1 -1 Viewed

     toastr.success(t`Extension '${response.display_name}' by ${response.author} (version ${response.version}) has been installed successfully!`, t`Extension installation successful`);
     console.debug(`Extension "${response.display_name}" has been installed successfully at ${response.extensionPath}`);
     await loadExtensionSettings({}, false, false);
     await eventSource.emit(event_types.EXTENSION_SETTINGS_LOADED, response);
+}
 /**

public/scripts/extensions/vectors/index.js+135 -9 Viewed

     modules,
     renderExtensionTemplateAsync,
     doExtrasFetch, getApiUrl,
 +    openThirdPartyExtensionMenu,
 } from '../../extensions.js';
 import { collapseNewlines, registerDebugFunction } from '../../power-user.js';
 import { SECRET_KEYS, secret_state, writeSecret } from '../../secrets.js';

 import { slashCommandReturnHelper } from '../../slash-commands/SlashCommandReturnHelper.js';
 import { callGenericPopup, POPUP_RESULT, POPUP_TYPE } from '../../popup.js';
 import { generateWebLlmChatPrompt, isWebLlmSupported } from '../shared.js';
 +import { WebLlmVectorProvider } from './webllm.js';
 /**
  * @typedef {object} HashedMessage

     ollama_model: 'mxbai-embed-large',
     ollama_keep: false,
     vllm_model: '',
 +    webllm_model: '',
     summarize: false,
     summarize_sent: false,
     summary_source: 'main',

 };
 const moduleWorker = new ModuleWorkerWrapper(synchronizeChat);
+-
 +const webllmProvider = new WebLlmVectorProvider();
 const cachedSummaries = new Map();
 /**

                     return 'Vectorization Source Model is required, but not set.';
                 case 'extras_module_missing':
                     return 'Extras API must provide an "embeddings" module.';
 +                case 'webllm_not_supported':
 +                    return 'WebLLM extension is not installed or the model is not set.';
                 default:
                     return 'Check server console for more details';
+            }

 /**
  * Gets common body parameters for vector requests.
  * @returnsparam {object} args Additional arguments
 + * @returns {object} Request body
  */
 function getVectorsRequestBody(args = {}) {
     const body = Object.assign({}, args);
     switch (settings.source) {
         case 'extras':
             body.extrasUrl =   extension_settings.apiUrl;
             body.extrasKey =   extension_settings.apiKey;
             break;
         case 'togetherai':
             body.model = extension_settings.vectors.togetherai_model;

             body.apiUrl = textgenerationwebui_settings.server_urls[textgen_types.VLLM];
             body.model = extension_settings.vectors.vllm_model;
             break;
 +        case 'webllm':
 +            body.model = extension_settings.vectors.webllm_model;
 +            break;
         default:
             break;
+    }

+}
 /**
 + * Gets additional arguments for vector requests.
 + * @param {string[]} items Items to embed
 + * @returns {Promise<object>} Additional arguments
 + */
 +async function getAdditionalArgs(items) {
 +    const args = {};
 +    switch (settings.source) {
 +        case 'webllm':
 +            args.embeddings = await createWebLlmEmbeddings(items);
 +            break;
 +    }
 +    return args;
 +}
++
 +/**
  * Gets the saved hashes for a collection
 * @param {string} collectionId
 * @returns {Promise<number[]>} Saved hashes

 async function insertVectorItems(collectionId, items) {
     throwIfSourceInvalid();
 +    const args = await getAdditionalArgs(items.map(x => x.text));
     const response = await fetch('/api/vector/insert', {
         method: 'POST',
         headers: getRequestHeaders(),
         body: JSON.stringify({
             ...getVectorsRequestBody(args),
             collectionId: collectionId,
             items: items,
             source: settings.source,

@@ -858,6 +883,10 @@ function throwIfSourceInvalid() {
858	883	if (settings.source === 'extras' && !modules.includes('embeddings')) {
859	884	throw new Error('Vectors: Embeddings module missing', { cause: 'extras_module_missing' });
860	885	}
	886	+
	887	+ if (settings.source === 'webllm' && (!isWebLlmSupported() \|\| !settings.webllm_model)) {
	888	+ throw new Error('Vectors: WebLLM is not supported', { cause: 'webllm_not_supported' });
	889	+ }
861	890	}
862	891
863	892	/**

  * @returns {Promise<{ hashes: number[], metadata: object[]}>} - Hashes of the results
  */
 async function queryCollection(collectionId, searchText, topK) {
 +    const args = await getAdditionalArgs([searchText]);
     const response = await fetch('/api/vector/query', {
         method: 'POST',
         headers: getRequestHeaders(),
         body: JSON.stringify({
             ...getVectorsRequestBody(args),
             collectionId: collectionId,
             searchText: searchText,
             topK: topK,

  * @returns {Promise<Record<string, { hashes: number[], metadata: object[] }>>} - Results mapped to collection IDs
  */
 async function queryMultipleCollections(collectionIds, searchText, topK, threshold) {
 +    const args = await getAdditionalArgs([searchText]);
     const response = await fetch('/api/vector/query-multi', {
         method: 'POST',
         headers: getRequestHeaders(),
         body: JSON.stringify({
             ...getVectorsRequestBody(args),
             collectionIds: collectionIds,
             searchText: searchText,
             topK: topK,

     $('#llamacpp_vectorsModel').toggle(settings.source === 'llamacpp');
     $('#vllm_vectorsModel').toggle(settings.source === 'vllm');
     $('#nomicai_apiKey').toggle(settings.source === 'nomicai');
 +    $('#webllm_vectorsModel').toggle(settings.source === 'webllm');
 +    if (settings.source === 'webllm') {
 +        loadWebLlmModels();
 +    }
 +}
++
 +/**
 + * Executes a function with WebLLM error handling.
 + * @param {function(): Promise<T>} func Function to execute
 + * @returns {Promise<T>}
 + * @template T
 + */
 +async function executeWithWebLlmErrorHandling(func) {
 +    try {
 +        return await func();
 +    } catch (error) {
 +        console.log('Vectors: Failed to load WebLLM models', error);
 +        if (!(error instanceof Error)) {
 +            return;
 +        }
 +        switch (error.cause) {
 +            case 'webllm-not-available':
 +                toastr.warning('WebLLM is not available. Please install the extension.', 'WebLLM not installed');
 +                break;
 +            case 'webllm-not-updated':
 +                toastr.warning('The installed extension version does not support embeddings.', 'WebLLM update required');
 +                break;
 +        }
 +    }
 +}
++
 +/**
 + * Loads and displays WebLLM models in the settings.
 + * @returns {Promise<void>}
 + */
 +function loadWebLlmModels() {
 +    return executeWithWebLlmErrorHandling(() => {
 +        const models = webllmProvider.getModels();
 +        $('#vectors_webllm_model').empty();
 +        for (const model of models) {
 +            $('#vectors_webllm_model').append($('<option>', { value: model.id, text: model.toString() }));
 +        }
 +        if (!settings.webllm_model || !models.some(x => x.id === settings.webllm_model)) {
 +            if (models.length) {
 +                settings.webllm_model = models[0].id;
 +            }
 +        }
 +        $('#vectors_webllm_model').val(settings.webllm_model);
 +        return Promise.resolve();
 +    });
 +}
++
 +/**
 + * Creates WebLLM embeddings for a list of items.
 + * @param {string[]} items Items to embed
 + * @returns {Promise<Record<string, number[]>>} Calculated embeddings
 + */
 +async function createWebLlmEmbeddings(items) {
 +    return executeWithWebLlmErrorHandling(async () => {
 +        const embeddings = await webllmProvider.embedTexts(items, settings.webllm_model);
 +        const result = /** @type {Record<string, number[]>} */ ({});
 +        for (let i = 0; i < items.length; i++) {
 +            result[items[i]] = embeddings[i];
 +        }
 +        return result;
 +    });
+}
 async function onPurgeClick() {

         $('#dialogue_popup_input').val(presetModel);
     });
 +    $('#vectors_webllm_install').on('click', (e) => {
 +        e.preventDefault();
 +        e.stopPropagation();
++
 +        if (Object.hasOwn(SillyTavern, 'llm')) {
 +            toastr.info('WebLLM is already installed');
 +            return;
 +        }
++
 +        openThirdPartyExtensionMenu('https://github.com/SillyTavern/Extension-WebLLM');
 +    });
++
 +    $('#vectors_webllm_model').on('input', () => {
 +        settings.webllm_model = String($('#vectors_webllm_model').val());
 +        Object.assign(extension_settings.vectors, settings);
 +        saveSettingsDebounced();
 +    });
++
 +    $('#vectors_webllm_load').on('click', async () => {
 +        if (!settings.webllm_model) return;
 +        await webllmProvider.loadModel(settings.webllm_model);
 +        toastr.success('WebLLM model loaded');
 +    });
++
     $('#api_key_nomicai').toggleClass('success', !!secret_state[SECRET_KEYS.NOMICAI]);
     toggleSettings();

     eventSource.on(event_types.CHAT_DELETED, purgeVectorIndex);
     eventSource.on(event_types.GROUP_CHAT_DELETED, purgeVectorIndex);
     eventSource.on(event_types.FILE_ATTACHMENT_DELETED, purgeFileVectorIndex);
 +    eventSource.on(event_types.EXTENSION_SETTINGS_LOADED, async (manifest) => {
 +        if (settings.source === 'webllm' && manifest?.display_name === 'WebLLM') {
 +            await loadWebLlmModels();
 +        }
 +    });
     SlashCommandParser.addCommandObject(SlashCommand.fromProps({
         name: 'db-ingest',

public/scripts/extensions/vectors/settings.html+16 -0 Viewed

                     <option value="openai">OpenAI</option>
                     <option value="togetherai">TogetherAI</option>
                     <option value="vllm">vLLM</option>
 +                    <option value="webllm" data-i18n="WebLLM Extension">WebLLM Extension</option>
                 </select>
             </div>
 +            <div class="flex-container flexFlowColumn" id="webllm_vectorsModel">
 +                <label for="vectors_webllm_model" data-i18n="Vectorization Model">
 +                    Vectorization Model
 +                </label>
 +                <div class="flex-container">
 +                    <select id="vectors_webllm_model" class="text_pole flex1">
 +                    </select>
 +                    <div id="vectors_webllm_load" class="menu_button menu_button_icon" title="Verify and load the selected model.">
 +                        <i class="fa-solid fa-check-to-slot"></i>
 +                    </div>
 +                </div>
 +                <div>
 +                    Requires the WebLLM extension to be installed. Click <a href="#" id="vectors_webllm_install">here</a> to install.
 +                </div>
 +            </div>
             <div class="flex-container flexFlowColumn" id="ollama_vectorsModel">
                 <label for="vectors_ollama_model" data-i18n="Vectorization Model">
                     Vectorization Model

public/scripts/extensions/vectors/webllm.js+64 -0 Viewed

 +export class WebLlmVectorProvider {
 +    /** @type {object?} WebLLM engine */
 +    #engine = null;
++
 +    constructor() {
 +        this.#engine = null;
 +    }
++
 +    /**
 +     * Check if WebLLM is available and up-to-date
 +     * @throws {Error} If WebLLM is not available or not up-to-date
 +     */
 +    #checkWebLlm() {
 +        if (!Object.hasOwn(SillyTavern, 'llm')) {
 +            throw new Error('WebLLM is not available', { cause: 'webllm-not-available' });
 +        }
++
 +        if (typeof SillyTavern.llm.generateEmbedding !== 'function') {
 +            throw new Error('WebLLM is not updated', { cause: 'webllm-not-updated' });
 +        }
 +    }
++
 +    /**
 +     * Initialize the engine with a model.
 +     * @param {string} modelId Model ID to initialize the engine with
 +     * @returns {Promise<void>} Promise that resolves when the engine is initialized
 +     */
 +    #initEngine(modelId) {
 +        this.#checkWebLlm();
 +        if (!this.#engine) {
 +            this.#engine = SillyTavern.llm.getEngine();
 +        }
++
 +        return this.#engine.loadModel(modelId);
 +    }
++
 +    /**
 +     * Get available models.
 +     * @returns {{id:string, toString: function(): string}[]} Array of available models
 +     */
 +    getModels() {
 +        this.#checkWebLlm();
 +        return SillyTavern.llm.getEmbeddingModels();
 +    }
++
 +    /**
 +     * Generate embeddings for a list of texts.
 +     * @param {string[]} texts Array of texts to generate embeddings for
 +     * @param {string} modelId Model to use for generating embeddings
 +     * @returns {Promise<number[][]>} Array of embeddings for each text
 +     */
 +    async embedTexts(texts, modelId) {
 +        await this.#initEngine(modelId);
 +        return this.#engine.generateEmbedding(texts);
 +    }
++
 +    /**
 +     * Loads a model into the engine.
 +     * @param {string} modelId Model ID to load
 +     */
 +    async loadModel(modelId) {
 +        await this.#initEngine(modelId);
 +    }
 +}

public/scripts/openai.js+11 -7 Viewed

     openai_model: 'gpt-4-turbo',
     claude_model: 'claude-3-5-sonnet-20240620',
     google_model: 'gemini-1.5-pro',
     ai21_model: 'jamba-1.56-large',
     mistralai_model: 'mistral-large-latest',
     cohere_model: 'command-r-plus',
     perplexity_model: 'sonar-pro',

     openai_model: 'gpt-4-turbo',
     claude_model: 'claude-3-5-sonnet-20240620',
     google_model: 'gemini-1.5-pro',
     ai21_model: 'jamba-1.56-large',
     mistralai_model: 'mistral-large-latest',
     cohere_model: 'command-r-plus',
     perplexity_model: 'sonar-pro',

         generate_data['logprobs'] = 5;
+    }
     // Remove logit bias, /logprobs and /stop -strings if it's not supported by the model
 -    if (isOAI && oai_settings.openai_model.includes('vision') || isOpenRouter && oai_settings.openrouter_model.includes('vision') || isOAI && oai_settings.openai_model.includes('gpt-4.5-preview')) {
 +    const isVision = (m) => ['gpt', 'vision'].every(x => m.includes(x));
 +    if (isOAI && isVision(oai_settings.openai_model) || isOpenRouter && isVision(oai_settings.openrouter_model)) {
         delete generate_data.logit_bias;
         delete generate_data.stop;
         delete generate_data.logprobs;
+    }
 +    if (isOAI && oai_settings.openai_model.includes('gpt-4.5-preview') || isOpenRouter && oai_settings.openrouter_model.includes('gpt-4.5-preview')) {
 +        delete generate_data.logprobs;
 +    }
     if (isClaude) {
         generate_data['top_k'] = Number(oai_settings.top_k_openai);

@@ -3251,7 +3255,7 @@ function loadOpenAISettings(data, settings) {
3251	3255	}
3252	3256
3253	3257	if (oai_settings.ai21_model.startsWith('j2-')) {
3254	3258	oai_settings.ai21_model = 'jamba-1.56-large';
3255	3259	}
3256	3260
3257	3261	if (settings.wrap_in_quotes !== undefined) oai_settings.wrap_in_quotes = !!settings.wrap_in_quotes;

     if ($(this).is('#model_ai21_select')) {
         if (value === '' || value.startsWith('j2-')) {
             value = 'jamba-1.56-large';
             $('#model_ai21_select').val(value);
+        }

     if (oai_settings.chat_completion_source == chat_completion_sources.AI21) {
         if (oai_settings.max_context_unlocked) {
             $('#openai_max_context').attr('max', unlocked_max);
         } else if (oai_settings.ai21_model.includes('jamba-1.5') || oai_settings.ai21_model.includesstartsWith('jamba-instruct')) {
             $('#openai_max_context').attr('max', max_256k);
+        }

public/scripts/reasoning.js+26 -0 Viewed

+    }
+}
 +/**
 + * Parse reasoning in an array of swipe strings if auto-parsing is enabled.
 + * @param {string[]} swipes Array of swipe strings
 + * @param {{extra: {reasoning: string, reasoning_duration: number}}[]} swipeInfoArray Array of swipe info objects
 + * @param {number?} duration Duration of the reasoning
 + */
 +export function parseReasoningInSwipes(swipes, swipeInfoArray, duration) {
 +    if (!power_user.reasoning.auto_parse) {
 +        return;
 +    }
++
 +    // Something ain't right, don't parse
 +    if (!Array.isArray(swipes) || !Array.isArray(swipeInfoArray) || swipes.length !== swipeInfoArray.length) {
 +        return;
 +    }
++
 +    for (let index = 0; index < swipes.length; index++) {
 +        const parsedReasoning = parseReasoningFromString(swipes[index]);
 +        if (parsedReasoning) {
 +            swipes[index] = parsedReasoning.content;
 +            swipeInfoArray[index].extra.reasoning = parsedReasoning.reasoning;
 +            swipeInfoArray[index].extra.reasoning_duration = duration;
 +        }
 +    }
 +}
++
 function registerReasoningAppEvents() {
     const eventHandler = (/** @type {string} */ type, /** @type {number} */ idx) => {
         if (!power_user.reasoning.auto_parse) {

public/scripts/tool-calling.js+1 -0 Viewed

             chat_completion_sources.COHERE,
             chat_completion_sources.DEEPSEEK,
             chat_completion_sources.MAKERSUITE,
 +            chat_completion_sources.AI21,
         ];
         return supportedSources.includes(oai_settings.chat_completion_source);
+    }

src/endpoints/backends/chat-completions.js+8 -1 Viewed

 async function sendAI21Request(request, response) {
     if (!request.body) return response.sendStatus(400);
 +    const apiKey = readSecret(request.user.directories, SECRET_KEYS.AI21);
 +    if (!apiKey) {
 +        console.warn('AI21 API key is missing.');
 +        return response.status(400).send({ error: true });
 +    }
++
     const controller = new AbortController();
     console.debug(request.body.messages);
     request.socket.removeAllListeners('close');

         top_p: request.body.top_p,
         stop: request.body.stop,
         stream: request.body.stream,
 +        tools: request.body.tools,
     };
     const options = {
         method: 'POST',
         headers: {
             accept: 'application/json',
             'content-type': 'application/json',
             Authorization: `Bearer ${readSecret(request.user.directories, SECRET_KEYS.AI21)apiKey}`,
         },
         body: JSON.stringify(body),
         signal: controller.signal,

src/endpoints/characters.js+2 -0 Viewed

         date_last_chat: character.date_last_chat,
         chat_size: character.chat_size,
         data_size: character.data_size,
 +        tags: character.tags,
         data: {
             name: _.get(character, 'data.name', ''),
             character_version: _.get(character, 'data.character_version', ''),
             creator: _.get(character, 'data.creator', ''),
             creator_notes: _.get(character, 'data.creator_notes', ''),
 +            tags: _.get(character, 'data.tags', []),
             extensions: {
                 fav: _.get(character, 'data.extensions.fav', false),
             },

src/endpoints/users-admin.js+2 -2 Viewed

 import express from 'express';
 import lodash from 'lodash';
 import { jsonParser } from '../express-common.js';
 import { checkForNewContent, CONTENT_TYPES } from './content-manager.js';
 import {
     KEY_PREFIX,
     toKey,

         console.info('Creating data directories for', newUser.handle);
         await ensurePublicDirectoriesExist();
         const directories = getUserDirectories(newUser.handle);
         await checkForNewContent([directories], [CONTENT_TYPES.SETTINGS]);
         return response.json({ handle: newUser.handle });
     } catch (error) {
         console.error('User create failed:', error);

src/endpoints/vectors.js+11 -0 Viewed

     'ollama',
     'llamacpp',
     'vllm',
 +    'webllm',
 ];
 /**

             return getVllmVector(text, sourceSettings.apiUrl, sourceSettings.model, directories);
         case 'ollama':
             return getOllamaVector(text, sourceSettings.apiUrl, sourceSettings.model, sourceSettings.keep, directories);
 +        case 'webllm':
 +            return sourceSettings.embeddings[text];
+    }
     throw new Error(`Unknown vector source ${source}`);

             case 'ollama':
                 results.push(...await getOllamaBatchVector(batch, sourceSettings.apiUrl, sourceSettings.model, sourceSettings.keep, directories));
                 break;
 +            case 'webllm':
 +                results.push(...texts.map(x => sourceSettings.embeddings[x]));
 +                break;
             default:
                 throw new Error(`Unknown vector source ${source}`);
+        }

             return {
                 model: 'nomic-embed-text-v1.5',
             };
 +        case 'webllm':
 +            return {
 +                model: String(request.body.model),
 +                embeddings: request.body.embeddings ?? {},
 +            };
         default:
             return {};
+    }