Merge branch 'neo-server' into parser-v2

post-install.js

@@ -60,7 +60,8 @@ function convertConfig() {
         try {
             console.log(color.blue('Converting config.conf to config.yaml. Your old config.conf will be renamed to config.conf.bak'));
             const config = require(path.join(process.cwd(), './config.conf'));
-            fs.renameSync('./config.conf', './config.conf.bak');
+            fs.copyFileSync('./config.conf', './config.conf.bak');
+            fs.rmSync('./config.conf');
             fs.writeFileSync('./config.yaml', yaml.stringify(config));
             console.log(color.green('Conversion successful. Please check your config.yaml and fix it if necessary.'));
         } catch (error) {

public/scripts/PromptManager.js

@@ -841,7 +841,7 @@ class PromptManager {
             const promptReferences = this.getPromptOrderForCharacter(this.activeCharacter);
             for (let i = promptReferences.length - 1; i >= 0; i--) {
                 const reference = promptReferences[i];
-                if (-1 === this.serviceSettings.prompts.findIndex(prompt => prompt.identifier === reference.identifier)) {
+                if (reference && -1 === this.serviceSettings.prompts.findIndex(prompt => prompt.identifier === reference.identifier)) {
                     promptReferences.splice(i, 1);
                     this.log('Removed unused reference: ' + reference.identifier);
                 }

public/scripts/extensions/stable-diffusion/comfyWorkflowEditor.html

@@ -19,6 +19,8 @@
                     <li data-placeholder="scale" class="sd_comfy_workflow_editor_not_found">"%scale%"</li>
                     <li data-placeholder="width" class="sd_comfy_workflow_editor_not_found">"%width%"</li>
                     <li data-placeholder="height" class="sd_comfy_workflow_editor_not_found">"%height%"</li>
+                    <li data-placeholder="user_avatar" class="sd_comfy_workflow_editor_not_found">"%user_avatar%"</li>
+                    <li data-placeholder="char_avatar" class="sd_comfy_workflow_editor_not_found">"%char_avatar%"</li>
                     <li><hr></li>
                     <li data-placeholder="seed" class="sd_comfy_workflow_editor_not_found">
                         "%seed%"

public/scripts/extensions/stable-diffusion/index.js

@@ -2111,21 +2111,11 @@ async function generateMultimodalPrompt(generationType, quietPrompt) {
     let avatarUrl;
 
     if (generationType == generationMode.USER_MULTIMODAL) {
-        avatarUrl = getUserAvatar(user_avatar);
+        avatarUrl = getUserAvatarUrl();
     }
 
     if (generationType == generationMode.CHARACTER_MULTIMODAL || generationType === generationMode.FACE_MULTIMODAL) {
-        const context = getContext();
+        avatarUrl = getCharacterAvatarUrl();
-
-        if (context.groupId) {
-            const groupMembers = context.groups.find(x => x.id === context.groupId)?.members;
-            const lastMessageAvatar = context.chat?.filter(x => !x.is_system && !x.is_user)?.slice(-1)[0]?.original_avatar;
-            const randomMemberAvatar = Array.isArray(groupMembers) ? groupMembers[Math.floor(Math.random() * groupMembers.length)]?.avatar : null;
-            const avatarToUse = lastMessageAvatar || randomMemberAvatar;
-            avatarUrl = formatCharacterAvatar(avatarToUse);
-        } else {
-            avatarUrl = getCharacterAvatar(context.characterId);
-        }
     }
 
     try {
@@ -2152,6 +2142,24 @@ async function generateMultimodalPrompt(generationType, quietPrompt) {
     }
 }
 
+function getCharacterAvatarUrl() {
+    const context = getContext();
+
+    if (context.groupId) {
+        const groupMembers = context.groups.find(x => x.id === context.groupId)?.members;
+        const lastMessageAvatar = context.chat?.filter(x => !x.is_system && !x.is_user)?.slice(-1)[0]?.original_avatar;
+        const randomMemberAvatar = Array.isArray(groupMembers) ? groupMembers[Math.floor(Math.random() * groupMembers.length)]?.avatar : null;
+        const avatarToUse = lastMessageAvatar || randomMemberAvatar;
+        return formatCharacterAvatar(avatarToUse);
+    } else {
+        return getCharacterAvatar(context.characterId);
+    }
+}
+
+function getUserAvatarUrl() {
+    return getUserAvatar(user_avatar);
+}
+
 /**
  * Generates a prompt using the main LLM API.
  * @param {string} quietPrompt - The prompt to use for the image generation.
@@ -2636,6 +2644,22 @@ async function generateComfyImage(prompt, negativePrompt) {
     (extension_settings.sd.comfy_placeholders ?? []).forEach(ph => {
         workflow = workflow.replace(`"%${ph.find}%"`, JSON.stringify(substituteParams(ph.replace)));
     });
+    if (/%user_avatar%/gi.test(workflow)) {
+        const response = await fetch(getUserAvatarUrl());
+        if (response.ok) {
+            const avatarBlob = await response.blob();
+            const avatarBase64 = await getBase64Async(avatarBlob);
+            workflow = workflow.replace('"%user_avatar%"', JSON.stringify(avatarBase64));
+        }
+    }
+    if (/%char_avatar%/gi.test(workflow)) {
+        const response = await fetch(getCharacterAvatarUrl());
+        if (response.ok) {
+            const avatarBlob = await response.blob();
+            const avatarBase64 = await getBase64Async(avatarBlob);
+            workflow = workflow.replace('"%char_avatar%"', JSON.stringify(avatarBase64));
+        }
+    }
     console.log(`{
         "prompt": ${workflow}
     }`);
@@ -2649,6 +2673,10 @@ async function generateComfyImage(prompt, negativePrompt) {
             }`,
         }),
     });
+    if (!promptResult.ok) {
+        const text = await promptResult.text();
+        throw new Error(text);
+    }
     return { format: 'png', data: await promptResult.text() };
 }
 

public/scripts/extensions/vectors/index.js

@@ -35,6 +35,7 @@ const settings = {
     include_wi: false,
     togetherai_model: 'togethercomputer/m2-bert-80M-32k-retrieval',
     openai_model: 'text-embedding-ada-002',
+    cohere_model: 'embed-english-v3.0',
     summarize: false,
     summarize_sent: false,
     summary_source: 'main',
@@ -68,6 +69,15 @@ const settings = {
 
 const moduleWorker = new ModuleWorkerWrapper(synchronizeChat);
 
+/**
+ * Gets the Collection ID for a file embedded in the chat.
+ * @param {string} fileUrl URL of the file
+ * @returns {string} Collection ID
+ */
+function getFileCollectionId(fileUrl) {
+    return `file_${getStringHash(fileUrl)}`;
+}
+
 async function onVectorizeAllClick() {
     try {
         if (!settings.enabled_chats) {
@@ -308,7 +318,7 @@ async function processFiles(chat) {
         const dataBankCollectionIds = [];
 
         for (const file of dataBank) {
-            const collectionId = `file_${getStringHash(file.url)}`;
+            const collectionId = getFileCollectionId(file.url);
             const hashesInCollection = await getSavedHashes(collectionId);
             dataBankCollectionIds.push(collectionId);
 
@@ -354,7 +364,7 @@ async function processFiles(chat) {
 
             const fileName = message.extra.file.name;
             const fileUrl = message.extra.file.url;
-            const collectionId = `file_${getStringHash(fileUrl)}`;
+            const collectionId = getFileCollectionId(fileUrl);
             const hashesInCollection = await getSavedHashes(collectionId);
 
             // File is already in the collection
@@ -598,6 +608,9 @@ function getVectorHeaders() {
         case 'openai':
             addOpenAiHeaders(headers);
             break;
+        case 'cohere':
+            addCohereHeaders(headers);
+            break;
         default:
             break;
     }
@@ -636,6 +649,16 @@ function addOpenAiHeaders(headers) {
     });
 }
 
+/**
+ * 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
@@ -647,7 +670,8 @@ async function insertVectorItems(collectionId, items) {
         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 === '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' });
     }
 
@@ -760,7 +784,7 @@ async function purgeFileVectorIndex(fileUrl) {
         }
 
         console.log(`Vectors: Purging file vector index for ${fileUrl}`);
-        const collectionId = `file_${getStringHash(fileUrl)}`;
+        const collectionId = getFileCollectionId(fileUrl);
 
         const response = await fetch('/api/vector/purge', {
             method: 'POST',
@@ -816,6 +840,7 @@ function toggleSettings() {
     $('#vectors_chats_settings').toggle(!!settings.enabled_chats);
     $('#together_vectorsModel').toggle(settings.source === 'togetherai');
     $('#openai_vectorsModel').toggle(settings.source === 'openai');
+    $('#cohere_vectorsModel').toggle(settings.source === 'cohere');
     $('#nomicai_apiKey').toggle(settings.source === 'nomicai');
 }
 
@@ -859,6 +884,42 @@ async function onViewStatsClick() {
 
 }
 
+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];
+
+        for (const file of allFiles) {
+            const text = await getFileAttachment(file.url);
+            const collectionId = getFileCollectionId(file.url);
+            await vectorizeFile(text, file.name, collectionId, settings.chunk_size);
+        }
+
+        toastr.success('All files vectorized', 'Vectorization successful');
+    } 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');
+    }
+}
+
 jQuery(async () => {
     if (!extension_settings.vectors) {
         extension_settings.vectors = settings;
@@ -913,6 +974,12 @@ jQuery(async () => {
         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);
@@ -947,6 +1014,8 @@ jQuery(async () => {
     $('#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());

public/scripts/extensions/vectors/settings.html

@@ -10,13 +10,14 @@
                     Vectorization Source
                 </label>
                 <select id="vectors_source" class="text_pole">
-                    <option value="transformers">Local (Transformers)</option>
+                    <option value="cohere">Cohere</option>
                     <option value="extras">Extras</option>
-                    <option value="openai">OpenAI</option>
                     <option value="palm">Google MakerSuite (PaLM)</option>
+                    <option value="transformers">Local (Transformers)</option>
                     <option value="mistral">MistralAI</option>
-                    <option value="togetherai">TogetherAI</option>
                     <option value="nomicai">NomicAI</option>
+                    <option value="openai">OpenAI</option>
+                    <option value="togetherai">TogetherAI</option>
                 </select>
             </div>
             <div class="flex-container flexFlowColumn" id="openai_vectorsModel">
@@ -29,6 +30,20 @@
                     <option value="text-embedding-3-large">text-embedding-3-large</option>
                 </select>
             </div>
+            <div class="flex-container flexFlowColumn" id="cohere_vectorsModel">
+                <label for="vectors_cohere_model">
+                    Vectorization Model
+                </label>
+                <select id="vectors_cohere_model" class="text_pole">
+                    <option value="embed-english-v3.0">embed-english-v3.0</option>
+                    <option value="embed-multilingual-v3.0">embed-multilingual-v3.0</option>
+                    <option value="embed-english-light-v3.0">embed-english-light-v3.0</option>
+                    <option value="embed-multilingual-light-v3.0">embed-multilingual-light-v3.0</option>
+                    <option value="embed-english-v2.0">embed-english-v2.0</option>
+                    <option value="embed-english-light-v2.0">embed-english-light-v2.0</option>
+                    <option value="embed-multilingual-v2.0">embed-multilingual-v2.0</option>
+                </select>
+            </div>
             <div class="flex-container flexFlowColumn" id="together_vectorsModel">
                 <label for="vectors_togetherai_model">
                     Vectorization Model
@@ -167,6 +182,14 @@
                         </label>
                     </div>
                 </div>
+                <div class="flex-container">
+                    <div id="vectors_files_vectorize_all" class="menu_button menu_button_icon" title="Vectorize all files in the Data Bank and current chat.">
+                        Vectorize All
+                    </div>
+                    <div id="vectors_files_purge" class="menu_button menu_button_icon" title="Purge all file vectors in the Data Bank and current chat.">
+                        Purge Vectors
+                    </div>
+                </div>
             </div>
 
             <hr>

public/scripts/instruct-mode.js

@@ -354,7 +354,9 @@ export function formatInstructModeSystemPrompt(systemPrompt) {
     const separator = power_user.instruct.wrap ? '\n' : '';
 
     if (power_user.instruct.system_sequence_prefix) {
-        systemPrompt = power_user.instruct.system_sequence_prefix + separator + systemPrompt;
+        // TODO: Replace with a proper 'System' prompt entity name input
+        const prefix = power_user.instruct.system_sequence_prefix.replace(/{{name}}/gi, 'System');
+        systemPrompt = prefix + separator + systemPrompt;
     }
 
     if (power_user.instruct.system_sequence_suffix) {

public/scripts/popup.js

@@ -119,11 +119,15 @@ export class Popup {
         const keyListener = (evt) => {
             switch (evt.key) {
                 case 'Escape': {
-                    evt.preventDefault();
+                    // does it really matter where we check?
-                    evt.stopPropagation();
+                    const topModal = document.elementFromPoint(window.innerWidth / 2, window.innerHeight / 2)?.closest('.shadow_popup');
-                    this.completeCancelled();
+                    if (topModal == this.dom) {
-                    window.removeEventListener('keydown', keyListenerBound);
+                        evt.preventDefault();
-                    break;
+                        evt.stopPropagation();
+                        this.completeCancelled();
+                        window.removeEventListener('keydown', keyListenerBound);
+                        break;
+                    }
                 }
             }
         };

src/endpoints/assets.js

@@ -227,7 +227,8 @@ router.post('/download', jsonParser, async (request, response) => {
 
         // Move into asset place
         console.debug('Download finished, moving file from', temp_path, 'to', file_path);
-        fs.renameSync(temp_path, file_path);
+        fs.copyFileSync(temp_path, file_path);
+        fs.rmSync(temp_path);
         response.sendStatus(200);
     }
     catch (error) {

src/endpoints/backgrounds.js

@@ -51,7 +51,8 @@ router.post('/rename', jsonParser, function (request, response) {
         return response.sendStatus(400);
     }
 
-    fs.renameSync(oldFileName, newFileName);
+    fs.copyFileSync(oldFileName, newFileName);
+    fs.rmSync(oldFileName);
     invalidateThumbnail(request.user.directories, 'bg', request.body.old_bg);
     return response.send('ok');
 });
@@ -63,7 +64,8 @@ router.post('/upload', urlencodedParser, function (request, response) {
     const filename = request.file.originalname;
 
     try {
-        fs.renameSync(img_path, path.join(request.user.directories.backgrounds, filename));
+        fs.copyFileSync(img_path, path.join(request.user.directories.backgrounds, filename));
+        fs.rmSync(img_path);
         invalidateThumbnail(request.user.directories, 'bg', filename);
         response.send(filename);
     } catch (err) {

src/endpoints/characters.js

@@ -680,7 +680,8 @@ router.post('/rename', jsonParser, async function (request, response) {
 
         // Rename chats folder
         if (fs.existsSync(oldChatsPath) && !fs.existsSync(newChatsPath)) {
-            fs.renameSync(oldChatsPath, newChatsPath);
+            fs.cpSync(oldChatsPath, newChatsPath, { recursive: true });
+            fs.rmSync(oldChatsPath, { recursive: true, force: true });
         }
 
         // Remove the old character file

src/endpoints/chats.js

@@ -213,8 +213,9 @@ router.post('/rename', jsonParser, async function (request, response) {
         return response.status(400).send({ error: true });
     }
 
+    fs.copyFileSync(pathToOriginalFile, pathToRenamedFile);
+    fs.rmSync(pathToOriginalFile);
     console.log('Successfully renamed.');
-    fs.renameSync(pathToOriginalFile, pathToRenamedFile);
     return response.send({ ok: true });
 });
 

src/endpoints/vectors.js

@@ -12,23 +12,26 @@ const SOURCES = ['transformers', 'mistral', 'openai', 'extras', 'palm', 'togethe
  * @param {string} source - The source of the vector
  * @param {Object} sourceSettings - Settings for the source, if it needs any
  * @param {string} text - The text to get the vector for
+ * @param {boolean} isQuery - If the text is a query for embedding search
  * @param {import('../users').UserDirectoryList} directories - The directories object for the user
  * @returns {Promise<number[]>} - The vector for the text
  */
-async function getVector(source, sourceSettings, text, directories) {
+async function getVector(source, sourceSettings, text, isQuery, directories) {
     switch (source) {
         case 'nomicai':
-            return require('../nomicai-vectors').getNomicAIVector(text, source, directories);
+            return require('../vectors/nomicai-vectors').getNomicAIVector(text, source, directories);
         case 'togetherai':
         case 'mistral':
         case 'openai':
-            return require('../openai-vectors').getOpenAIVector(text, source, directories, sourceSettings.model);
+            return require('../vectors/openai-vectors').getOpenAIVector(text, source, directories, sourceSettings.model);
         case 'transformers':
-            return require('../embedding').getTransformersVector(text);
+            return require('../vectors/embedding').getTransformersVector(text);
         case 'extras':
-            return require('../extras-vectors').getExtrasVector(text, sourceSettings.extrasUrl, sourceSettings.extrasKey);
+            return require('../vectors/extras-vectors').getExtrasVector(text, sourceSettings.extrasUrl, sourceSettings.extrasKey);
         case 'palm':
-            return require('../makersuite-vectors').getMakerSuiteVector(text, directories);
+            return require('../vectors/makersuite-vectors').getMakerSuiteVector(text, directories);
+        case 'cohere':
+            return require('../vectors/cohere-vectors').getCohereVector(text, isQuery, directories, sourceSettings.model);
     }
 
     throw new Error(`Unknown vector source ${source}`);
@@ -39,10 +42,11 @@ async function getVector(source, sourceSettings, text, directories) {
  * @param {string} source - The source of the vector
  * @param {Object} sourceSettings - Settings for the source, if it needs any
  * @param {string[]} texts - The array of texts to get the vector for
+ * @param {boolean} isQuery - If the text is a query for embedding search
  * @param {import('../users').UserDirectoryList} directories - The directories object for the user
  * @returns {Promise<number[][]>} - The array of vectors for the texts
  */
-async function getBatchVector(source, sourceSettings, texts, directories) {
+async function getBatchVector(source, sourceSettings, texts, isQuery, directories) {
     const batchSize = 10;
     const batches = Array(Math.ceil(texts.length / batchSize)).fill(undefined).map((_, i) => texts.slice(i * batchSize, i * batchSize + batchSize));
 
@@ -50,21 +54,24 @@ async function getBatchVector(source, sourceSettings, texts, directories) {
     for (let batch of batches) {
         switch (source) {
             case 'nomicai':
-                results.push(...await require('../nomicai-vectors').getNomicAIBatchVector(batch, source, directories));
+                results.push(...await require('../vectors/nomicai-vectors').getNomicAIBatchVector(batch, source, directories));
                 break;
             case 'togetherai':
             case 'mistral':
             case 'openai':
-                results.push(...await require('../openai-vectors').getOpenAIBatchVector(batch, source, directories, sourceSettings.model));
+                results.push(...await require('../vectors/openai-vectors').getOpenAIBatchVector(batch, source, directories, sourceSettings.model));
                 break;
             case 'transformers':
-                results.push(...await require('../embedding').getTransformersBatchVector(batch));
+                results.push(...await require('../vectors/embedding').getTransformersBatchVector(batch));
                 break;
             case 'extras':
-                results.push(...await require('../extras-vectors').getExtrasBatchVector(batch, sourceSettings.extrasUrl, sourceSettings.extrasKey));
+                results.push(...await require('../vectors/extras-vectors').getExtrasBatchVector(batch, sourceSettings.extrasUrl, sourceSettings.extrasKey));
                 break;
             case 'palm':
-                results.push(...await require('../makersuite-vectors').getMakerSuiteBatchVector(batch, directories));
+                results.push(...await require('../vectors/makersuite-vectors').getMakerSuiteBatchVector(batch, directories));
+                break;
+            case 'cohere':
+                results.push(...await require('../vectors/cohere-vectors').getCohereBatchVector(batch, isQuery, directories, sourceSettings.model));
                 break;
             default:
                 throw new Error(`Unknown vector source ${source}`);
@@ -106,7 +113,7 @@ async function insertVectorItems(directories, collectionId, source, sourceSettin
 
     await store.beginUpdate();
 
-    const vectors = await getBatchVector(source, sourceSettings, items.map(x => x.text), directories);
+    const vectors = await getBatchVector(source, sourceSettings, items.map(x => x.text), false, directories);
 
     for (let i = 0; i < items.length; i++) {
         const item = items[i];
@@ -165,7 +172,7 @@ async function deleteVectorItems(directories, collectionId, source, hashes) {
  */
 async function queryCollection(directories, collectionId, source, sourceSettings, searchText, topK) {
     const store = await getIndex(directories, collectionId, source);
-    const vector = await getVector(source, sourceSettings, searchText, directories);
+    const vector = await getVector(source, sourceSettings, searchText, true, directories);
 
     const result = await store.queryItems(vector, topK);
     const metadata = result.map(x => x.item.metadata);
@@ -184,7 +191,7 @@ async function queryCollection(directories, collectionId, source, sourceSettings
  * @returns {Promise<Record<string, { hashes: number[], metadata: object[] }>>} - The top K results from each collection
  */
 async function multiQueryCollection(directories, collectionIds, source, sourceSettings, searchText, topK) {
-    const vector = await getVector(source, sourceSettings, searchText, directories);
+    const vector = await getVector(source, sourceSettings, searchText, true, directories);
     const results = [];
 
     for (const collectionId of collectionIds) {
@@ -223,18 +230,24 @@ async function multiQueryCollection(directories, collectionIds, source, sourceSe
  */
 function getSourceSettings(source, request) {
     if (source === 'togetherai') {
-        let model = String(request.headers['x-togetherai-model']);
+        const model = String(request.headers['x-togetherai-model']);
 
         return {
             model: model,
         };
     } else if (source === 'openai') {
-        let model = String(request.headers['x-openai-model']);
+        const model = String(request.headers['x-openai-model']);
 
         return {
             model: model,
         };
-    } else {
+    } else if (source === 'cohere') {
+        const model = String(request.headers['x-cohere-model']);
+
+        return {
+            model: model,
+        };
+    }else {
         // Extras API settings to connect to the Extras embeddings provider
         let extrasUrl = '';
         let extrasKey = '';

src/users.js

@@ -286,12 +286,22 @@ async function migrateUserData() {
                 // Copy the file to the new location
                 fs.cpSync(migration.old, migration.new, { force: true });
                 // Move the file to the backup location
-                fs.renameSync(migration.old, path.join(backupDirectory, path.basename(migration.old)));
+                fs.cpSync(
+                    migration.old,
+                    path.join(backupDirectory, path.basename(migration.old)),
+                    { recursive: true, force: true }
+                );
+                fs.rmSync(migration.old, { recursive: true, force: true });
             } else {
                 // Copy the directory to the new location
                 fs.cpSync(migration.old, migration.new, { recursive: true, force: true });
                 // Move the directory to the backup location
-                fs.renameSync(migration.old, path.join(backupDirectory, path.basename(migration.old)));
+                fs.cpSync(
+                    migration.old,
+                    path.join(backupDirectory, path.basename(migration.old)),
+                    { recursive: true, force: true }
+                );
+                fs.rmSync(migration.old, { recursive: true, force: true });
             }
         } catch (error) {
             console.error(color.red(`Error migrating ${migration.old} to ${migration.new}:`), error.message);

src/vectors/cohere-vectors.js

@@ -0,0 +1,65 @@
+const fetch = require('node-fetch').default;
+const { SECRET_KEYS, readSecret } = require('../endpoints/secrets');
+
+/**
+ * Gets the vector for the given text batch from an OpenAI compatible endpoint.
+ * @param {string[]} texts - The array of texts to get the vector for
+ * @param {boolean} isQuery - If the text is a query for embedding search
+ * @param {import('../users').UserDirectoryList} directories - The directories object for the user
+ * @param {string} model - The model to use for the embedding
+ * @returns {Promise<number[][]>} - The array of vectors for the texts
+ */
+async function getCohereBatchVector(texts, isQuery, directories, model) {
+    const key = readSecret(directories, SECRET_KEYS.COHERE);
+
+    if (!key) {
+        console.log('No API key found');
+        throw new Error('No API key found');
+    }
+
+    const response = await fetch('https://api.cohere.ai/v1/embed', {
+        method: 'POST',
+        headers: {
+            'Content-Type': 'application/json',
+            Authorization: `Bearer ${key}`,
+        },
+        body: JSON.stringify({
+            texts: texts,
+            model: model,
+            input_type: isQuery ? 'search_query' : 'search_document',
+            truncate: 'END',
+        }),
+    });
+
+    if (!response.ok) {
+        const text = await response.text();
+        console.log('API request failed', response.statusText, text);
+        throw new Error('API request failed');
+    }
+
+    const data = await response.json();
+    if (!Array.isArray(data?.embeddings)) {
+        console.log('API response was not an array');
+        throw new Error('API response was not an array');
+    }
+
+    return data.embeddings;
+}
+
+/**
+ * Gets the vector for the given text from an OpenAI compatible endpoint.
+ * @param {string} text - The text to get the vector for
+ * @param {boolean} isQuery - If the text is a query for embedding search
+ * @param {import('../users').UserDirectoryList} directories - The directories object for the user
+ * @param {string} model - The model to use for the embedding
+ * @returns {Promise<number[]>} - The vector for the text
+ */
+async function getCohereVector(text, isQuery, directories, model) {
+    const vectors = await getCohereBatchVector([text], isQuery, directories, model);
+    return vectors[0];
+}
+
+module.exports = {
+    getCohereBatchVector,
+    getCohereVector,
+};

src/vectors/embedding.js

@@ -6,7 +6,7 @@ const TASK = 'feature-extraction';
  * @returns {Promise<number[]>} - The vectorized text in form of an array of numbers
  */
 async function getTransformersVector(text) {
-    const module = await import('./transformers.mjs');
+    const module = await import('../transformers.mjs');
     const pipe = await module.default.getPipeline(TASK);
     const result = await pipe(text, { pooling: 'mean', normalize: true });
     const vector = Array.from(result.data);

src/vectors/makersuite-vectors.js

@@ -1,10 +1,10 @@
 const fetch = require('node-fetch').default;
-const { SECRET_KEYS, readSecret } = require('./endpoints/secrets');
+const { SECRET_KEYS, readSecret } = require('../endpoints/secrets');
 
 /**
  * Gets the vector for the given text from gecko model
  * @param {string[]} texts - The array of texts to get the vector for
- * @param {import('./users').UserDirectoryList} directories - The directories object for the user
+ * @param {import('../users').UserDirectoryList} directories - The directories object for the user
  * @returns {Promise<number[][]>} - The array of vectors for the texts
  */
 async function getMakerSuiteBatchVector(texts, directories) {
@@ -16,7 +16,7 @@ async function getMakerSuiteBatchVector(texts, directories) {
 /**
  * Gets the vector for the given text from PaLM gecko model
  * @param {string} text - The text to get the vector for
- * @param {import('./users').UserDirectoryList} directories - The directories object for the user
+ * @param {import('../users').UserDirectoryList} directories - The directories object for the user
  * @returns {Promise<number[]>} - The vector for the text
  */
 async function getMakerSuiteVector(text, directories) {

src/vectors/nomicai-vectors.js

@@ -1,5 +1,5 @@
 const fetch = require('node-fetch').default;
-const { SECRET_KEYS, readSecret } = require('./endpoints/secrets');
+const { SECRET_KEYS, readSecret } = require('../endpoints/secrets');
 
 const SOURCES = {
     'nomicai': {
@@ -13,7 +13,7 @@ const SOURCES = {
  * Gets the vector for the given text batch from an OpenAI compatible endpoint.
  * @param {string[]} texts - The array of texts to get the vector for
  * @param {string} source - The source of the vector
- * @param {import('./users').UserDirectoryList} directories - The directories object for the user
+ * @param {import('../users').UserDirectoryList} directories - The directories object for the user
  * @returns {Promise<number[][]>} - The array of vectors for the texts
  */
 async function getNomicAIBatchVector(texts, source, directories) {
@@ -64,7 +64,7 @@ async function getNomicAIBatchVector(texts, source, directories) {
  * Gets the vector for the given text from an OpenAI compatible endpoint.
  * @param {string} text - The text to get the vector for
  * @param {string} source - The source of the vector
- * @param {import('./users').UserDirectoryList} directories - The directories object for the user
+ * @param {import('../users').UserDirectoryList} directories - The directories object for the user
  * @returns {Promise<number[]>} - The vector for the text
  */
 async function getNomicAIVector(text, source, directories) {

src/vectors/openai-vectors.js

@@ -1,5 +1,5 @@
 const fetch = require('node-fetch').default;
-const { SECRET_KEYS, readSecret } = require('./endpoints/secrets');
+const { SECRET_KEYS, readSecret } = require('../endpoints/secrets');
 
 const SOURCES = {
     'togetherai': {
@@ -23,7 +23,7 @@ const SOURCES = {
  * Gets the vector for the given text batch from an OpenAI compatible endpoint.
  * @param {string[]} texts - The array of texts to get the vector for
  * @param {string} source - The source of the vector
- * @param {import('./users').UserDirectoryList} directories - The directories object for the user
+ * @param {import('../users').UserDirectoryList} directories - The directories object for the user
  * @param {string} model - The model to use for the embedding
  * @returns {Promise<number[][]>} - The array of vectors for the texts
  */
@@ -79,7 +79,7 @@ async function getOpenAIBatchVector(texts, source, directories, model = '') {
  * Gets the vector for the given text from an OpenAI compatible endpoint.
  * @param {string} text - The text to get the vector for
  * @param {string} source - The source of the vector
- * @param {import('./users').UserDirectoryList} directories - The directories object for the user
+ * @param {import('../users').UserDirectoryList} directories - The directories object for the user
  * @param {string} model - The model to use for the embedding
  * @returns {Promise<number[]>} - The vector for the text
  */