# SOME DESCRIPTIVE TITLE. # Copyright (C) YEAR THE PACKAGE'S COPYRIGHT HOLDER # This file is distributed under the same license as the PACKAGE package. # FIRST AUTHOR , YEAR. # msgid "" msgstr "" "Project-Id-Version: PACKAGE VERSION\n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-05-16 17:54+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language-Team: LANGUAGE \n" "Language: \n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=UTF-8\n" "Content-Transfer-Encoding: 8bit\n" #: common/auth/authenticate.py:80 msgid "Not logged in, please log in first" msgstr "未登錄，請先登錄" #: common/auth/authenticate.py:82 common/auth/authenticate.py:89 #: common/auth/authenticate.py:95 msgid "Authentication information is incorrect! illegal user" msgstr "身份驗證信息不正確！非法用戶" #: common/auth/authentication.py:96 msgid "No permission to access" msgstr "無權限訪問" #: common/auth/handle/impl/user_token.py:242 msgid "Login expired" msgstr "登錄已過期" #: common/constants/exception_code_constants.py:31 #: users/serializers/login.py:53 msgid "The username or password is incorrect" msgstr "用戶名或密碼不正確" #: common/constants/exception_code_constants.py:32 msgid "Please log in first and bring the user Token" msgstr "請先登錄並攜帶用戶 Token" #: common/constants/exception_code_constants.py:33 msgid "Email sending failed" msgstr "郵件發送失敗" #: common/constants/exception_code_constants.py:34 msgid "Email format error" msgstr "郵箱格式錯誤" #: common/constants/exception_code_constants.py:35 msgid "The email has been registered, please log in directly" msgstr "該郵箱已註冊，請直接登錄" #: common/constants/exception_code_constants.py:36 msgid "The email is not registered, please register first" msgstr "該郵箱未註冊，請先註冊" #: common/constants/exception_code_constants.py:38 msgid "The verification code is incorrect or the verification code has expired" msgstr "驗證碼不正確或已過期" #: common/constants/exception_code_constants.py:39 msgid "The username has been registered, please log in directly" msgstr "用戶名已註冊，請直接登錄" #: common/constants/exception_code_constants.py:41 msgid "" "The username cannot be empty and must be between 6 and 20 characters long." msgstr "用戶名不能為空，且長度在6到20個字符之間。" #: common/constants/exception_code_constants.py:43 msgid "Password and confirmation password are inconsistent" msgstr "密碼和確認密碼不一致" #: common/constants/exception_code_constants.py:44 msgid "The nickname is already registered" msgstr "暱稱已註冊" #: common/constants/permission_constants.py:171 msgid "System Setting" msgstr "系統設置" #: common/constants/permission_constants.py:172 users/views/login.py:24 #: users/views/login.py:36 users/views/user.py:34 users/views/user.py:47 #: users/views/user.py:61 users/views/user.py:76 users/views/user.py:90 #: users/views/user.py:104 users/views/user.py:117 users/views/user.py:128 #: users/views/user.py:139 users/views/user.py:155 users/views/user.py:170 msgid "User Management" msgstr "用戶管理" #: common/constants/permission_constants.py:173 msgid "Role" msgstr "角色" #: common/constants/permission_constants.py:174 msgid "Workspace" msgstr "工作空間" #: common/constants/permission_constants.py:175 msgid "Resource Application" msgstr "資源管理-應用" #: common/constants/permission_constants.py:176 msgid "Resource Knowledge" msgstr "資源管理-知識庫" #: common/constants/permission_constants.py:177 msgid "Resource Tool" msgstr "資源管理-工具" #: common/constants/permission_constants.py:178 msgid "Resource Model" msgstr "資源管理-模型" #: common/constants/permission_constants.py:179 msgid "Resource Permission" msgstr "資源授權" #: common/constants/permission_constants.py:180 msgid "Shared Knowledge" msgstr "共享資源-知識庫" #: common/constants/permission_constants.py:181 msgid "Shared Model" msgstr "共享資源-模型" #: common/constants/permission_constants.py:182 msgid "Shared Tool" msgstr "共享資源-工具" #: common/constants/permission_constants.py:183 msgid "Operation Log" msgstr "操作日誌" #: common/constants/permission_constants.py:184 #: common/constants/permission_constants.py:190 msgid "Other" msgstr "其他" #: common/constants/permission_constants.py:185 msgid "System Management" msgstr "系統管理" #: common/constants/permission_constants.py:186 #: common/constants/permission_constants.py:196 msgid "Application" msgstr "應用" #: common/constants/permission_constants.py:187 #: common/constants/permission_constants.py:197 msgid "Knowledge" msgstr "知識庫" #: common/constants/permission_constants.py:188 #: models_provider/views/model.py:31 models_provider/views/model.py:59 #: models_provider/views/model.py:77 models_provider/views/model.py:90 #: models_provider/views/model.py:102 models_provider/views/model.py:117 #: models_provider/views/model.py:130 models_provider/views/model.py:148 #: models_provider/views/model.py:164 models_provider/views/model_apply.py:29 #: models_provider/views/model_apply.py:42 #: models_provider/views/model_apply.py:55 models_provider/views/provide.py:25 #: models_provider/views/provide.py:49 models_provider/views/provide.py:64 #: models_provider/views/provide.py:83 models_provider/views/provide.py:101 msgid "Model" msgstr "模型" #: common/constants/permission_constants.py:189 tools/views/tool.py:27 #: tools/views/tool.py:42 tools/views/tool.py:60 tools/views/tool.py:79 #: tools/views/tool.py:94 tools/views/tool.py:109 tools/views/tool.py:127 #: tools/views/tool.py:152 tools/views/tool.py:170 tools/views/tool.py:189 msgid "Tool" msgstr "工具" #: common/constants/permission_constants.py:191 msgid "Read" msgstr "查看" #: common/constants/permission_constants.py:192 msgid "Edit" msgstr "編輯" #: common/constants/permission_constants.py:193 msgid "Create" msgstr "創建" #: common/constants/permission_constants.py:194 msgid "Delete" msgstr "刪除" #: common/constants/permission_constants.py:195 msgid "Email Setting" msgstr "郵箱設置" #: common/constants/permission_constants.py:198 msgid "Document" msgstr "文檔" #: common/constants/permission_constants.py:199 msgid "Problem" msgstr "問題" #: common/constants/permission_constants.py:200 msgid "Import" msgstr "導入" #: common/constants/permission_constants.py:201 msgid "Export" msgstr "導出" #: common/constants/permission_constants.py:202 msgid "Debug" msgstr "調試" #: common/constants/permission_constants.py:203 msgid "Sync" msgstr "同步" #: common/constants/permission_constants.py:204 msgid "Generate" msgstr "生成問題" #: common/constants/permission_constants.py:205 msgid "Add Member" msgstr "添加成員" #: common/constants/permission_constants.py:206 msgid "Remove Member" msgstr "移除成員" #: common/constants/permission_constants.py:207 msgid "Vector" msgstr "向量化" #: common/constants/permission_constants.py:208 msgid "Migrate" msgstr "遷移" #: common/constants/permission_constants.py:209 msgid "Relate" msgstr "關聯分段" #: common/event/__init__.py:27 msgid "The download process was interrupted, please try again" msgstr "下載過程被中斷，請重試" #: common/event/listener_manage.py:90 #, python-brace-format msgid "Query vector data: {paragraph_id_list} error {error} {traceback}" msgstr "查詢向量數據：{paragraph_id_list} 錯誤：{error} {traceback}" #: common/event/listener_manage.py:95 #, python-brace-format msgid "Start--->Embedding paragraph: {paragraph_id_list}" msgstr "開始--->向量段落: {paragraph_id_list}" #: common/event/listener_manage.py:107 #, python-brace-format msgid "Vectorized paragraph: {paragraph_id_list} error {error} {traceback}" msgstr "向量段落: {paragraph_id_list} 錯誤：{error} {traceback}" #: common/event/listener_manage.py:113 #, python-brace-format msgid "End--->Embedding paragraph: {paragraph_id_list}" msgstr "結束--->向量段落: {paragraph_id_list}" #: common/event/listener_manage.py:122 #, python-brace-format msgid "Start--->Embedding paragraph: {paragraph_id}" msgstr "開始--->向量段落: {paragraph_id}" #: common/event/listener_manage.py:147 #, python-brace-format msgid "Vectorized paragraph: {paragraph_id} error {error} {traceback}" msgstr "向量段落: {paragraph_id} 錯誤：{error} {traceback}" #: common/event/listener_manage.py:152 #, python-brace-format msgid "End--->Embedding paragraph: {paragraph_id}" msgstr "結束--->向量段落: {paragraph_id}" #: common/event/listener_manage.py:268 #, python-brace-format msgid "Start--->Embedding document: {document_id}" msgstr "開始--->向量文檔: {document_id}" #: common/event/listener_manage.py:288 #, python-brace-format msgid "Vectorized document: {document_id} error {error} {traceback}" msgstr "向量文檔: {document_id} 錯誤：{error} {traceback}" #: common/event/listener_manage.py:293 #, python-brace-format msgid "End--->Embedding document: {document_id}" msgstr "結束--->向量文檔: {document_id}" #: common/event/listener_manage.py:304 #, python-brace-format msgid "Start--->Embedding knowledge: {knowledge_id}" msgstr "開始--->向量知識庫: {knowledge_id}" #: common/event/listener_manage.py:308 #, python-brace-format msgid "Start--->Embedding document: {document_list}" msgstr "開始--->向量文檔: {document_list}" #: common/event/listener_manage.py:312 knowledge/task/embedding.py:116 #, python-brace-format msgid "Vectorized knowledge: {knowledge_id} error {error} {traceback}" msgstr "向量知識庫: {knowledge_id} 錯誤：{error} {traceback}" #: common/event/listener_manage.py:315 #, python-brace-format msgid "End--->Embedding knowledge: {knowledge_id}" msgstr "結束--->向量知識庫: {knowledge_id}" #: common/exception/handle_exception.py:32 common/handle/handle_exception.py:33 msgid "Unknown exception" msgstr "未知錯誤" #: common/forms/base_field.py:64 #, python-brace-format msgid "The field {field_label} is required" msgstr "{field_label} 欄位是必填項" #: common/forms/slider_field.py:56 #, python-brace-format msgid "The {field_label} cannot be less than {min}" msgstr "{field_label} 不能小於{min}" #: common/forms/slider_field.py:62 #, python-brace-format msgid "The {field_label} cannot be greater than {max}" msgstr "{field_label} 不能大於{max}" #: common/handle/impl/qa/zip_parse_qa_handle.py:56 #: common/handle/impl/text/zip_split_handle.py:58 #: knowledge/serializers/document.py:564 knowledge/serializers/document.py:571 #: tools/serializers/tool.py:347 msgid "Unsupported file format" msgstr "不支持的文件格式" #: common/handle/impl/text/pdf_split_handle.py:281 #, python-brace-format msgid "This document has no preface and is treated as ordinary text: {e}" msgstr "該文檔沒有前言，視為普通文本: {e}" #: common/result/api.py:17 common/result/api.py:27 msgid "response code" msgstr "響應碼" #: common/result/api.py:18 common/result/api.py:19 common/result/api.py:28 #: common/result/api.py:29 msgid "error prompt" msgstr "錯誤提示" #: common/result/api.py:43 msgid "total number of data" msgstr "總數據" #: common/result/api.py:44 msgid "current page" msgstr "當前頁" #: common/result/api.py:45 msgid "page size" msgstr "每頁大小" #: common/result/result.py:31 msgid "Success" msgstr "成功" #: common/utils/common.py:86 msgid "Text-to-speech node, the text content must be of string type" msgstr "文本轉語音節點，文本內容必須是字符串類型" #: common/utils/common.py:88 msgid "Text-to-speech node, the text content cannot be empty" msgstr "文本轉語音節點，文本內容不能為空" #: common/utils/common.py:241 #, python-brace-format msgid "Limit {count} exceeded, please contact us (https://fit2cloud.com/)." msgstr "超過限制 {count}，請聯繫我們 (https://fit2cloud.com/)." #: folders/models/folder.py:6 folders/models/folder.py:17 #: folders/serializers/folder.py:100 msgid "folder name" msgstr "文件夾名稱" #: folders/models/folder.py:8 folders/models/folder.py:19 #: folders/serializers/folder.py:101 msgid "folder description" msgstr "文件夾描述" #: folders/models/folder.py:12 folders/models/folder.py:23 #: folders/serializers/folder.py:104 msgid "parent id" msgstr "父級 ID" #: folders/serializers/folder.py:77 msgid "Folder depth cannot exceed 3 levels" msgstr "文件夾深度不能超過3級" #: folders/serializers/folder.py:99 folders/serializers/folder.py:137 #: knowledge/serializers/knowledge.py:46 knowledge/serializers/knowledge.py:53 #: tools/serializers/tool.py:393 msgid "folder id" msgstr "文件夾 ID" #: folders/serializers/folder.py:102 msgid "folder user id" msgstr "文件夾用戶 ID" #: folders/serializers/folder.py:103 folders/serializers/folder.py:138 #: folders/serializers/folder.py:190 knowledge/serializers/document.py:185 #: knowledge/serializers/document.py:244 knowledge/serializers/document.py:331 #: knowledge/serializers/document.py:444 knowledge/serializers/document.py:586 #: knowledge/serializers/document.py:641 knowledge/serializers/document.py:661 #: knowledge/serializers/document.py:805 knowledge/serializers/knowledge.py:173 #: knowledge/serializers/knowledge.py:338 #: knowledge/serializers/knowledge.py:440 #: knowledge/serializers/knowledge.py:518 #: knowledge/serializers/paragraph.py:132 #: knowledge/serializers/paragraph.py:326 knowledge/serializers/problem.py:176 #: knowledge/serializers/problem.py:204 models_provider/api/model.py:40 #: models_provider/api/model.py:53 #: models_provider/serializers/model_serializer.py:262 #: models_provider/serializers/model_serializer.py:326 #: system_manage/serializers/user_resource_permission.py:73 #: tools/serializers/tool.py:196 tools/serializers/tool.py:217 #: tools/serializers/tool.py:275 tools/serializers/tool.py:335 #: tools/serializers/tool.py:365 tools/serializers/tool.py:392 msgid "workspace id" msgstr "工作空間ID" #: folders/serializers/folder.py:107 knowledge/serializers/knowledge.py:104 #: knowledge/serializers/knowledge.py:172 #: knowledge/serializers/knowledge.py:337 #: knowledge/serializers/knowledge.py:442 #: knowledge/serializers/knowledge.py:520 #: models_provider/serializers/model_serializer.py:108 #: models_provider/serializers/model_serializer.py:215 #: models_provider/serializers/model_serializer.py:255 #: tools/serializers/tool.py:195 tools/serializers/tool.py:216 #: tools/serializers/tool.py:395 msgid "user id" msgstr "用戶ID" #: folders/serializers/folder.py:108 folders/serializers/folder.py:139 #: folders/serializers/folder.py:191 tools/serializers/tool.py:120 msgid "source" msgstr "來源" #: folders/serializers/folder.py:121 msgid "Folder name already exists" msgstr "文件夾名稱已存在" #: folders/serializers/folder.py:148 msgid "Folder does not exist" msgstr "文件夾不存在" #: folders/serializers/folder.py:177 msgid "Cannot delete root folder" msgstr "無法刪除根文件夾" #: folders/views/folder.py:19 folders/views/folder.py:20 #: folders/views/folder.py:21 msgid "Create folder" msgstr "創建文件夾" #: folders/views/folder.py:25 folders/views/folder.py:43 #: folders/views/folder.py:63 folders/views/folder.py:79 #: folders/views/folder.py:95 msgid "Folder" msgstr "文件夾" #: folders/views/folder.py:38 folders/views/folder.py:39 #: folders/views/folder.py:40 msgid "Get folder tree" msgstr "獲取文件夾樹" #: folders/views/folder.py:57 folders/views/folder.py:58 #: folders/views/folder.py:59 msgid "Update folder" msgstr "更新文件夾" #: folders/views/folder.py:74 folders/views/folder.py:75 #: folders/views/folder.py:76 msgid "Get folder" msgstr "獲取文件夾" #: folders/views/folder.py:90 folders/views/folder.py:91 #: folders/views/folder.py:92 msgid "Delete folder" msgstr "刪除文件夾" #: knowledge/serializers/common.py:32 knowledge/serializers/knowledge.py:56 msgid "source url" msgstr "來源" #: knowledge/serializers/common.py:33 knowledge/serializers/document.py:141 msgid "selector" msgstr "選擇器" #: knowledge/serializers/common.py:40 #, python-brace-format msgid "URL error, cannot parse [{source_url}]" msgstr "URL 錯誤，無法解析 [{source_url}]" #: knowledge/serializers/common.py:48 knowledge/serializers/document.py:68 #: knowledge/serializers/document.py:159 knowledge/serializers/document.py:171 msgid "id list" msgstr "ID 列表" #: knowledge/serializers/common.py:58 #, python-brace-format msgid "The following id does not exist: {error_id_list}" msgstr "以下ID不存在: {error_id_list}" #: knowledge/serializers/common.py:71 msgid "Model id" msgstr "模型ID" #: knowledge/serializers/common.py:72 msgid "Prompt word" msgstr "提示詞" #: knowledge/serializers/common.py:74 knowledge/serializers/document.py:155 #: knowledge/serializers/document.py:160 knowledge/serializers/document.py:167 msgid "state list" msgstr "狀態列表" #: knowledge/serializers/common.py:117 knowledge/serializers/common.py:141 msgid "The knowledge base is inconsistent with the vector model" msgstr "知識庫與向量模型不一致" #: knowledge/serializers/common.py:119 knowledge/serializers/common.py:143 msgid "Knowledge base setting error, please reset the knowledge base" msgstr "知識庫設置錯誤，請重置知識庫" #: knowledge/serializers/document.py:69 knowledge/serializers/document.py:86 #: knowledge/serializers/document.py:190 msgid "task type" msgstr "任務類型" #: knowledge/serializers/document.py:77 knowledge/serializers/document.py:94 msgid "task type not support" msgstr "任務類型不支持" #: knowledge/serializers/document.py:81 knowledge/serializers/document.py:99 #: knowledge/serializers/document.py:187 msgid "document name" msgstr "文檔名稱" #: knowledge/serializers/document.py:102 knowledge/serializers/document.py:179 msgid "The type only supports optimization|directly_return" msgstr "該類型僅支持優化|直接返回" #: knowledge/serializers/document.py:104 knowledge/serializers/document.py:172 #: knowledge/serializers/document.py:188 msgid "hit handling method" msgstr "命中處理方法" #: knowledge/serializers/document.py:107 knowledge/serializers/document.py:174 msgid "directly return similarity" msgstr "直接返回相似度" #: knowledge/serializers/document.py:109 knowledge/serializers/document.py:189 msgid "document is active" msgstr "文檔已激活" #: knowledge/serializers/document.py:128 knowledge/serializers/document.py:145 #: knowledge/serializers/document.py:150 msgid "file list" msgstr "文件 列表" #: knowledge/serializers/document.py:129 msgid "limit" msgstr "限制" #: knowledge/serializers/document.py:132 knowledge/serializers/document.py:133 msgid "patterns" msgstr "分割符" #: knowledge/serializers/document.py:135 msgid "Auto Clean" msgstr "自動清理" #: knowledge/serializers/document.py:139 knowledge/serializers/document.py:140 msgid "document url list" msgstr "文檔 URL 列表" #: knowledge/serializers/document.py:146 knowledge/serializers/document.py:151 #: knowledge/serializers/file.py:56 tools/serializers/tool.py:333 msgid "file" msgstr "文件" #: knowledge/serializers/document.py:164 msgid "document id list" msgstr "文檔 ID 列表" #: knowledge/serializers/document.py:165 knowledge/serializers/paragraph.py:56 #: models_provider/api/model.py:59 #: models_provider/serializers/model_apply_serializers.py:51 #: models_provider/serializers/model_serializer.py:107 #: models_provider/serializers/model_serializer.py:367 msgid "model id" msgstr "模型ID" #: knowledge/serializers/document.py:166 knowledge/serializers/paragraph.py:57 msgid "prompt" msgstr "提示詞" #: knowledge/serializers/document.py:186 knowledge/serializers/document.py:245 #: knowledge/serializers/document.py:333 knowledge/serializers/document.py:587 #: knowledge/serializers/document.py:642 knowledge/serializers/document.py:662 #: knowledge/serializers/document.py:806 knowledge/serializers/knowledge.py:174 #: knowledge/serializers/knowledge.py:441 knowledge/serializers/paragraph.py:68 #: knowledge/serializers/paragraph.py:136 #: knowledge/serializers/paragraph.py:236 #: knowledge/serializers/paragraph.py:301 #: knowledge/serializers/paragraph.py:327 #: knowledge/serializers/paragraph.py:378 knowledge/serializers/problem.py:62 #: knowledge/serializers/problem.py:126 knowledge/serializers/problem.py:177 #: knowledge/serializers/problem.py:205 msgid "knowledge id" msgstr "知識庫 ID" #: knowledge/serializers/document.py:191 msgid "status" msgstr "狀態" #: knowledge/serializers/document.py:192 msgid "order by" msgstr "排序" #: knowledge/serializers/document.py:246 knowledge/serializers/document.py:332 #: knowledge/serializers/document.py:445 knowledge/serializers/paragraph.py:59 #: knowledge/serializers/paragraph.py:69 knowledge/serializers/paragraph.py:138 #: knowledge/serializers/paragraph.py:237 #: knowledge/serializers/paragraph.py:302 #: knowledge/serializers/paragraph.py:329 #: knowledge/serializers/paragraph.py:379 knowledge/serializers/problem.py:36 #: knowledge/serializers/problem.py:51 msgid "document id" msgstr "文檔 ID" #: knowledge/serializers/document.py:253 knowledge/serializers/document.py:339 msgid "document id not exist" msgstr "文檔 ID 不存在" #: knowledge/serializers/document.py:255 knowledge/serializers/knowledge.py:453 msgid "Synchronization is only supported for web site types" msgstr "僅支持網站類型的同步" #: knowledge/serializers/document.py:421 knowledge/serializers/knowledge.py:186 #: models_provider/serializers/model_serializer.py:116 #: models_provider/serializers/model_serializer.py:132 #: models_provider/serializers/model_serializer.py:151 #: models_provider/serializers/model_serializer.py:178 #: models_provider/serializers/model_serializer.py:373 #: models_provider/tools.py:111 msgid "Model does not exist" msgstr "模型不存在" #: knowledge/serializers/document.py:423 knowledge/serializers/knowledge.py:188 msgid "No permission to use this model" msgstr "無權限使用此模型" #: knowledge/serializers/document.py:441 msgid "The task is being executed, please do not send it repeatedly." msgstr "任務正在執行，請勿重複發送。" #: knowledge/serializers/document.py:450 msgid "knowledge id not exist" msgstr "知識庫 ID 不存在" #: knowledge/serializers/document.py:596 msgid "The maximum size of the uploaded file cannot exceed {}MB" msgstr "上傳文件的最大大小不能超過 {}MB" #: knowledge/serializers/document.py:654 msgid "space" msgstr "空格" #: knowledge/serializers/document.py:655 msgid "semicolon" msgstr "分號" #: knowledge/serializers/document.py:655 msgid "comma" msgstr "逗號" #: knowledge/serializers/document.py:656 msgid "period" msgstr "句號" #: knowledge/serializers/document.py:656 msgid "enter" msgstr "回車" #: knowledge/serializers/document.py:657 msgid "blank line" msgstr "空行" #: knowledge/serializers/document.py:779 msgid "Hit handling method is required" msgstr "命中處理方法是必需的" #: knowledge/serializers/document.py:781 msgid "The hit processing method must be directly_return|optimization" msgstr "命中處理方法必須是直接返回|優化" #: knowledge/serializers/file.py:79 msgid "File not found" msgstr "文件未找到" #: knowledge/serializers/knowledge.py:45 knowledge/serializers/knowledge.py:52 #: knowledge/serializers/knowledge.py:61 knowledge/serializers/knowledge.py:100 msgid "knowledge name" msgstr "知識庫名稱" #: knowledge/serializers/knowledge.py:47 knowledge/serializers/knowledge.py:54 #: knowledge/serializers/knowledge.py:62 knowledge/serializers/knowledge.py:102 msgid "knowledge description" msgstr "知識庫描述" #: knowledge/serializers/knowledge.py:48 knowledge/serializers/knowledge.py:55 msgid "knowledge embedding" msgstr "知識庫向量" #: knowledge/serializers/knowledge.py:57 msgid "knowledge selector" msgstr "知識庫選擇器" #: knowledge/serializers/knowledge.py:66 msgid "application id" msgstr "應用 ID" #: knowledge/serializers/knowledge.py:67 msgid "application id list" msgstr "應用 ID 列表" #: knowledge/serializers/knowledge.py:87 knowledge/serializers/knowledge.py:521 msgid "query text" msgstr "查詢文本" #: knowledge/serializers/knowledge.py:88 knowledge/serializers/knowledge.py:522 msgid "top number" msgstr "Top 數量" #: knowledge/serializers/knowledge.py:89 knowledge/serializers/knowledge.py:523 msgid "similarity" msgstr "相似度" #: knowledge/serializers/knowledge.py:90 knowledge/serializers/knowledge.py:524 msgid "search mode" msgstr "搜索模式" #: knowledge/serializers/knowledge.py:92 knowledge/serializers/knowledge.py:526 msgid "The type only supports embedding|keywords|blend" msgstr "類型僅支持嵌入|關鍵字|混合" #: knowledge/serializers/knowledge.py:144 tools/serializers/tool.py:374 #: tools/serializers/tool.py:404 tools/serializers/tool.py:427 msgid "Folder not found" msgstr "文件夾不存在" #: knowledge/serializers/knowledge.py:204 #: knowledge/serializers/knowledge.py:233 msgid "Failed to send the vectorization task, please try again later!" msgstr "發送向量化任務失敗，請稍後再試！" #: knowledge/serializers/knowledge.py:284 #: knowledge/serializers/knowledge.py:357 #: knowledge/serializers/knowledge.py:417 msgid "Knowledge base name duplicate!" msgstr "知識庫名稱重複！" #: knowledge/serializers/knowledge.py:306 #, python-brace-format msgid "Unknown application id {knowledge_id}, cannot be associated" msgstr "未知應用 ID {knowledge_id}，無法關聯" #: knowledge/serializers/knowledge.py:348 msgid "" "The community version supports up to 50 knowledge bases. If you need more " "knowledge bases, please contact us (https://fit2cloud.com/)." msgstr "" "社區版支持最多50個知識庫，如需更多知識庫，請聯繫我們 (https://" "fit2cloud.com/)." #: knowledge/serializers/knowledge.py:443 msgid "sync type" msgstr "同步類型" #: knowledge/serializers/knowledge.py:445 msgid "The synchronization type only supports:replace|complete" msgstr "同步類型僅支持:replace|complete" #: knowledge/serializers/knowledge.py:451 #: knowledge/serializers/knowledge.py:532 msgid "id does not exist" msgstr "知識庫 ID 不存在" #: knowledge/serializers/knowledge.py:519 users/api/user.py:63 msgid "id" msgstr "ID" #: knowledge/serializers/paragraph.py:37 knowledge/serializers/problem.py:27 #: knowledge/serializers/problem.py:31 knowledge/serializers/problem.py:206 msgid "content" msgstr "內容" #: knowledge/serializers/paragraph.py:39 knowledge/serializers/paragraph.py:46 #: knowledge/serializers/paragraph.py:49 knowledge/serializers/paragraph.py:63 #: knowledge/serializers/paragraph.py:65 knowledge/serializers/paragraph.py:303 msgid "section title" msgstr "章節標題" #: knowledge/serializers/paragraph.py:42 tools/serializers/tool.py:150 #: tools/serializers/tool.py:170 msgid "Is active" msgstr "是否啟用" #: knowledge/serializers/paragraph.py:54 msgid "paragraph id list" msgstr "段落 ID 列表" #: knowledge/serializers/paragraph.py:55 knowledge/serializers/paragraph.py:70 #: knowledge/serializers/paragraph.py:134 #: knowledge/serializers/paragraph.py:330 knowledge/serializers/problem.py:35 #: knowledge/serializers/problem.py:50 msgid "paragraph id" msgstr "段落 ID" #: knowledge/serializers/paragraph.py:75 knowledge/serializers/paragraph.py:143 msgid "Paragraph id does not exist" msgstr "段落 ID 不存在" #: knowledge/serializers/paragraph.py:106 msgid "Already associated, please do not associate again" msgstr "已關聯，請勿再次關聯" #: knowledge/serializers/paragraph.py:179 msgid "Problem id does not exist" msgstr "問題 ID 不存在" #: knowledge/serializers/paragraph.py:243 msgid "The document id is incorrect" msgstr "文檔 ID 不正確" #: knowledge/serializers/paragraph.py:328 knowledge/serializers/problem.py:26 #: knowledge/serializers/problem.py:46 knowledge/serializers/problem.py:56 #: knowledge/serializers/problem.py:127 msgid "problem id" msgstr "問題 ID" #: knowledge/serializers/paragraph.py:338 msgid "Paragraph does not exist" msgstr "段落不存在" #: knowledge/serializers/paragraph.py:340 msgid "Problem does not exist" msgstr "問題不存在" #: knowledge/serializers/paragraph.py:415 msgid "The task is being executed, please do not send it again." msgstr "任務正在執行，請勿重複發送。" #: knowledge/serializers/problem.py:40 msgid "problem list" msgstr "問題列表" #: knowledge/serializers/problem.py:41 msgid "problem" msgstr "問題 ID" #: knowledge/serializers/problem.py:45 knowledge/serializers/problem.py:55 msgid "problem id list" msgstr "問題 ID 列表" #: knowledge/task/embedding.py:24 knowledge/task/embedding.py:74 #, python-brace-format msgid "Failed to obtain vector model: {error} {traceback}" msgstr "向量模型獲取失敗: {error} {traceback}" #: knowledge/task/embedding.py:103 #, python-brace-format msgid "Start--->Vectorized knowledge: {knowledge_id}" msgstr "開始--->向量知識庫: {knowledge_id}" #: knowledge/task/embedding.py:107 #, python-brace-format msgid "Knowledge documentation: {document_names}" msgstr "知識庫文檔: {document_names}" #: knowledge/task/embedding.py:120 #, python-brace-format msgid "End--->Vectorized knowledge: {knowledge_id}" msgstr "結束--->向量知識庫: {knowledge_id}" #: knowledge/task/generate.py:106 #, python-brace-format msgid "" "Generate issue based on document: {document_id} error {error}{traceback}" msgstr "生成問題基於文檔: {document_id} 錯誤 {error}{traceback}" #: knowledge/task/generate.py:110 #, python-brace-format msgid "End--->Generate problem: {document_id}" msgstr "結束--->生成問題: {document_id}" #: knowledge/task/handler.py:121 #, python-brace-format msgid "Association problem failed {error}" msgstr "關聯問題失敗 {error}" #: knowledge/task/sync.py:30 knowledge/task/sync.py:47 #, python-brace-format msgid "Start--->Start synchronization web knowledge base:{knowledge_id}" msgstr "開始--->開始同步 web 知識庫:{knowledge_id}" #: knowledge/task/sync.py:35 knowledge/task/sync.py:51 #, python-brace-format msgid "End--->End synchronization web knowledge base:{knowledge_id}" msgstr "結束--->結束同步 web 知識庫:{knowledge_id}" #: knowledge/task/sync.py:37 knowledge/task/sync.py:53 #, python-brace-format msgid "Synchronize web knowledge base:{knowledge_id} error{error}{traceback}" msgstr "同步 web 知識庫:{knowledge_id} 錯誤{error}{traceback}" #: knowledge/views/document.py:23 knowledge/views/document.py:24 #: knowledge/views/document.py:25 msgid "Create document" msgstr "創建文檔" #: knowledge/views/document.py:29 knowledge/views/document.py:45 #: knowledge/views/document.py:69 knowledge/views/document.py:86 #: knowledge/views/document.py:100 knowledge/views/document.py:122 #: knowledge/views/document.py:152 knowledge/views/document.py:170 #: knowledge/views/document.py:189 knowledge/views/document.py:208 #: knowledge/views/document.py:226 knowledge/views/document.py:244 #: knowledge/views/document.py:263 knowledge/views/document.py:285 #: knowledge/views/document.py:307 knowledge/views/document.py:328 #: knowledge/views/document.py:351 knowledge/views/document.py:372 #: knowledge/views/document.py:399 knowledge/views/document.py:419 #: knowledge/views/document.py:439 msgid "Knowledge Base/Documentation" msgstr "知識庫/文檔" #: knowledge/views/document.py:40 knowledge/views/document.py:41 #: knowledge/views/document.py:42 msgid "Get document" msgstr "獲取文檔" #: knowledge/views/document.py:64 knowledge/views/document.py:65 #: knowledge/views/document.py:66 msgid "Get document details" msgstr "文檔文檔詳情" #: knowledge/views/document.py:80 knowledge/views/document.py:81 #: knowledge/views/document.py:82 msgid "Modify document" msgstr "修改文檔" #: knowledge/views/document.py:95 knowledge/views/document.py:96 #: knowledge/views/document.py:97 msgid "Delete document" msgstr "刪除文檔" #: knowledge/views/document.py:116 knowledge/views/document.py:117 #: knowledge/views/document.py:118 msgid "Segmented document" msgstr "分段文檔" #: knowledge/views/document.py:147 knowledge/views/document.py:148 #: knowledge/views/document.py:149 msgid "Get a list of segment IDs" msgstr "獲取分段列表" #: knowledge/views/document.py:164 knowledge/views/document.py:165 #: knowledge/views/document.py:166 msgid "Modify document hit processing methods in batches" msgstr "批量修改文檔命中處理方法" #: knowledge/views/document.py:183 knowledge/views/document.py:184 #: knowledge/views/document.py:185 msgid "Synchronize web site types" msgstr "同步網站類型" #: knowledge/views/document.py:202 knowledge/views/document.py:203 #: knowledge/views/document.py:204 msgid "Refresh document vector library" msgstr "刷新文檔向量庫" #: knowledge/views/document.py:220 knowledge/views/document.py:221 #: knowledge/views/document.py:222 msgid "Cancel task" msgstr "取消任務" #: knowledge/views/document.py:238 knowledge/views/document.py:239 #: knowledge/views/document.py:240 msgid "Cancel tasks in batches" msgstr "批量取消任務" #: knowledge/views/document.py:257 knowledge/views/document.py:258 #: knowledge/views/document.py:259 msgid "Create documents in batches" msgstr "批量創建文檔" #: knowledge/views/document.py:279 knowledge/views/document.py:280 #: knowledge/views/document.py:281 msgid "Batch sync documents" msgstr "批量同步文檔" #: knowledge/views/document.py:301 knowledge/views/document.py:302 #: knowledge/views/document.py:303 msgid "Delete documents in batches" msgstr "批量刪除文檔" #: knowledge/views/document.py:323 knowledge/views/document.py:324 msgid "Batch refresh document vector library" msgstr "批量刷新文檔向量庫" #: knowledge/views/document.py:345 knowledge/views/document.py:346 #: knowledge/views/document.py:347 msgid "Batch generate related documents" msgstr "批量生成相關文檔" #: knowledge/views/document.py:367 knowledge/views/document.py:368 #: knowledge/views/document.py:369 msgid "Get document by pagination" msgstr "分頁獲取文檔" #: knowledge/views/document.py:393 knowledge/views/document.py:394 #: knowledge/views/document.py:395 msgid "Create Web site documents" msgstr "創建網站文檔" #: knowledge/views/document.py:413 knowledge/views/document.py:414 #: knowledge/views/document.py:415 msgid "Import QA and create documentation" msgstr "導入問答並創建文檔" #: knowledge/views/document.py:433 knowledge/views/document.py:434 #: knowledge/views/document.py:435 msgid "Import tables and create documents" msgstr "導入表格並創建文檔" #: knowledge/views/file.py:20 knowledge/views/file.py:21 #: knowledge/views/file.py:22 msgid "Upload file" msgstr "上傳文件" #: knowledge/views/file.py:26 knowledge/views/file.py:39 #: knowledge/views/file.py:51 msgid "File" msgstr "文件" #: knowledge/views/file.py:34 knowledge/views/file.py:35 #: knowledge/views/file.py:36 msgid "Get file" msgstr "獲取文件" #: knowledge/views/file.py:46 knowledge/views/file.py:47 #: knowledge/views/file.py:48 msgid "Delete file" msgstr "刪除文件" #: knowledge/views/knowledge.py:22 knowledge/views/knowledge.py:23 #: knowledge/views/knowledge.py:24 msgid "Get knowledge by folder" msgstr "根據文件夾獲取知識庫" #: knowledge/views/knowledge.py:27 knowledge/views/knowledge.py:52 #: knowledge/views/knowledge.py:68 knowledge/views/knowledge.py:83 #: knowledge/views/knowledge.py:101 knowledge/views/knowledge.py:126 #: knowledge/views/knowledge.py:150 knowledge/views/knowledge.py:177 #: knowledge/views/knowledge.py:196 knowledge/views/knowledge.py:214 #: knowledge/views/knowledge.py:237 knowledge/views/knowledge.py:257 msgid "Knowledge Base" msgstr "知識庫" #: knowledge/views/knowledge.py:46 knowledge/views/knowledge.py:47 #: knowledge/views/knowledge.py:48 msgid "Edit knowledge" msgstr "修改知識庫" #: knowledge/views/knowledge.py:62 knowledge/views/knowledge.py:63 #: knowledge/views/knowledge.py:64 msgid "Delete knowledge" msgstr "刪除知識庫" #: knowledge/views/knowledge.py:78 knowledge/views/knowledge.py:79 #: knowledge/views/knowledge.py:80 msgid "Get knowledge" msgstr "獲取知識庫" #: knowledge/views/knowledge.py:96 knowledge/views/knowledge.py:97 #: knowledge/views/knowledge.py:98 msgid "Get the knowledge base paginated list" msgstr "獲取知識庫分頁列表" #: knowledge/views/knowledge.py:120 knowledge/views/knowledge.py:121 #: knowledge/views/knowledge.py:122 msgid "Synchronize the knowledge base of the website" msgstr "同步網站知識庫" #: knowledge/views/knowledge.py:144 knowledge/views/knowledge.py:145 #: knowledge/views/knowledge.py:146 msgid "Hit test list" msgstr "命中測試列表" #: knowledge/views/knowledge.py:171 knowledge/views/knowledge.py:172 #: knowledge/views/knowledge.py:173 msgid "Re-vectorize" msgstr "重新向量化" #: knowledge/views/knowledge.py:190 knowledge/views/knowledge.py:191 #: knowledge/views/knowledge.py:192 msgid "Generate related" msgstr "生成相關" #: knowledge/views/knowledge.py:209 knowledge/views/knowledge.py:210 #: knowledge/views/knowledge.py:211 msgid "Get model for knowledge base" msgstr "獲取知識庫模型" #: knowledge/views/knowledge.py:231 knowledge/views/knowledge.py:232 #: knowledge/views/knowledge.py:233 msgid "Create base knowledge" msgstr "創建知識庫" #: knowledge/views/knowledge.py:251 knowledge/views/knowledge.py:252 #: knowledge/views/knowledge.py:253 msgid "Create web knowledge" msgstr "創建 web 知識庫" #: knowledge/views/paragraph.py:21 knowledge/views/paragraph.py:22 #: knowledge/views/paragraph.py:23 msgid "Paragraph list" msgstr "段落列表" #: knowledge/views/paragraph.py:26 knowledge/views/paragraph.py:47 #: knowledge/views/paragraph.py:66 knowledge/views/paragraph.py:85 #: knowledge/views/paragraph.py:104 knowledge/views/paragraph.py:126 #: knowledge/views/paragraph.py:148 knowledge/views/paragraph.py:173 #: knowledge/views/paragraph.py:193 knowledge/views/paragraph.py:217 #: knowledge/views/paragraph.py:242 knowledge/views/paragraph.py:266 msgid "Knowledge Base/Documentation/Paragraph" msgstr "知識庫/文檔/段落" #: knowledge/views/paragraph.py:42 knowledge/views/paragraph.py:43 msgid "Create Paragraph" msgstr "創建段落" #: knowledge/views/paragraph.py:60 knowledge/views/paragraph.py:61 #: knowledge/views/paragraph.py:62 msgid "Batch Paragraph" msgstr "批量段落" #: knowledge/views/paragraph.py:79 knowledge/views/paragraph.py:80 #: knowledge/views/paragraph.py:81 msgid "Batch Generate Related" msgstr "批量生成相關" #: knowledge/views/paragraph.py:98 knowledge/views/paragraph.py:99 #: knowledge/views/paragraph.py:100 msgid "Modify paragraph data" msgstr "修改段落數據" #: knowledge/views/paragraph.py:121 knowledge/views/paragraph.py:122 #: knowledge/views/paragraph.py:123 msgid "Get paragraph details" msgstr "獲取段落詳情" #: knowledge/views/paragraph.py:143 knowledge/views/paragraph.py:144 #: knowledge/views/paragraph.py:145 msgid "Delete paragraph" msgstr "刪除段落" #: knowledge/views/paragraph.py:167 knowledge/views/paragraph.py:168 #: knowledge/views/paragraph.py:169 msgid "Add associated questions" msgstr "添加關聯問題" #: knowledge/views/paragraph.py:188 knowledge/views/paragraph.py:189 #: knowledge/views/paragraph.py:190 msgid "Get a list of paragraph questions" msgstr "獲取段落問題列表" #: knowledge/views/paragraph.py:211 knowledge/views/paragraph.py:212 #: knowledge/views/paragraph.py:213 msgid "Disassociation issue" msgstr "取消關聯問題" #: knowledge/views/paragraph.py:236 knowledge/views/paragraph.py:237 #: knowledge/views/paragraph.py:238 msgid "Related questions" msgstr "關聯問題" #: knowledge/views/paragraph.py:261 knowledge/views/paragraph.py:262 #: knowledge/views/paragraph.py:263 msgid "Get paragraph list by pagination" msgstr "獲取段落列表" #: knowledge/views/problem.py:21 knowledge/views/problem.py:22 #: knowledge/views/problem.py:23 msgid "Question list" msgstr "問題列表" #: knowledge/views/problem.py:26 knowledge/views/problem.py:48 #: knowledge/views/problem.py:65 knowledge/views/problem.py:88 #: knowledge/views/problem.py:107 knowledge/views/problem.py:125 #: knowledge/views/problem.py:146 knowledge/views/problem.py:168 msgid "Knowledge Base/Documentation/Paragraph/Question" msgstr "知識庫/文檔/段落/問題" #: knowledge/views/problem.py:42 knowledge/views/problem.py:43 #: knowledge/views/problem.py:44 msgid "Create question" msgstr "創建問題" #: knowledge/views/problem.py:60 knowledge/views/problem.py:61 #: knowledge/views/problem.py:62 msgid "Get a list of associated paragraphs" msgstr "獲取關聯段落列表" #: knowledge/views/problem.py:82 knowledge/views/problem.py:83 #: knowledge/views/problem.py:84 msgid "Batch associated paragraphs" msgstr "批量關聯段落" #: knowledge/views/problem.py:101 knowledge/views/problem.py:102 #: knowledge/views/problem.py:103 msgid "Batch deletion issues" msgstr "批量刪除問題" #: knowledge/views/problem.py:120 knowledge/views/problem.py:121 #: knowledge/views/problem.py:122 msgid "Delete question" msgstr "刪除問題" #: knowledge/views/problem.py:140 knowledge/views/problem.py:141 #: knowledge/views/problem.py:142 msgid "Modify question" msgstr "修改問題" #: knowledge/views/problem.py:163 knowledge/views/problem.py:164 #: knowledge/views/problem.py:165 msgid "Get the list of questions by page" msgstr "分頁獲取問題列表" #: maxkb/settings/base.py:85 msgid "Intelligent customer service platform" msgstr "智能客服平臺" #: models_provider/api/provide.py:17 models_provider/api/provide.py:23 #: models_provider/api/provide.py:28 models_provider/api/provide.py:30 #: models_provider/api/provide.py:82 #: models_provider/serializers/model_serializer.py:40 #: models_provider/serializers/model_serializer.py:218 #: models_provider/serializers/model_serializer.py:256 #: models_provider/serializers/model_serializer.py:321 msgid "model name" msgstr "模型名稱" #: models_provider/api/provide.py:18 models_provider/api/provide.py:38 #: models_provider/api/provide.py:76 models_provider/api/provide.py:104 #: models_provider/api/provide.py:126 #: models_provider/serializers/model_serializer.py:41 #: models_provider/serializers/model_serializer.py:257 #: models_provider/serializers/model_serializer.py:324 msgid "provider" msgstr "供應商" #: models_provider/api/provide.py:19 msgid "icon" msgstr "圖標" #: models_provider/api/provide.py:24 msgid "value" msgstr "值" #: models_provider/api/provide.py:29 models_provider/api/provide.py:70 #: models_provider/api/provide.py:98 #: models_provider/serializers/model_serializer.py:42 #: models_provider/serializers/model_serializer.py:220 #: models_provider/serializers/model_serializer.py:258 #: models_provider/serializers/model_serializer.py:322 msgid "model type" msgstr "模型類型" #: models_provider/api/provide.py:34 tools/serializers/tool.py:130 msgid "input type" msgstr "輸入類型" #: models_provider/api/provide.py:35 msgid "label" msgstr "標籤" #: models_provider/api/provide.py:36 msgid "text field" msgstr "文本欄位" #: models_provider/api/provide.py:37 msgid "value field" msgstr "值" #: models_provider/api/provide.py:39 msgid "method" msgstr "方法" #: models_provider/api/provide.py:40 tools/serializers/tool.py:115 #: tools/serializers/tool.py:129 msgid "required" msgstr "必填" #: models_provider/api/provide.py:41 msgid "default value" msgstr "默認值" #: models_provider/api/provide.py:42 msgid "relation show field dict" msgstr "關係顯示欄位" #: models_provider/api/provide.py:43 msgid "relation trigger field dict" msgstr "關係觸發欄位" #: models_provider/api/provide.py:44 msgid "trigger type" msgstr "觸發類型" #: models_provider/api/provide.py:45 msgid "attrs" msgstr "屬性" #: models_provider/api/provide.py:46 msgid "props info" msgstr "props 信息" #: models_provider/base_model_provider.py:60 msgid "Model type cannot be empty" msgstr "模型類型不能為空" #: models_provider/base_model_provider.py:85 msgid "The current platform does not support downloading models" msgstr "當前平臺不支持下載模型" #: models_provider/base_model_provider.py:143 msgid "LLM" msgstr "大語言模型" #: models_provider/base_model_provider.py:144 msgid "Embedding Model" msgstr "向量模型" #: models_provider/base_model_provider.py:145 msgid "Speech2Text" msgstr "語音識別" #: models_provider/base_model_provider.py:146 msgid "TTS" msgstr "語音合成" #: models_provider/base_model_provider.py:147 msgid "Vision Model" msgstr "視覺模型" #: models_provider/base_model_provider.py:148 msgid "Image Generation" msgstr "圖片生成" #: models_provider/base_model_provider.py:149 msgid "Rerank" msgstr "重排模型" #: models_provider/base_model_provider.py:223 msgid "The model does not support" msgstr "模型不支持" #: models_provider/impl/aliyun_bai_lian_model_provider/aliyun_bai_lian_model_provider.py:42 msgid "" "With the GTE-Rerank text sorting series model developed by Alibaba Tongyi " "Lab, developers can integrate high-quality text retrieval and sorting " "through the LlamaIndex framework." msgstr "" "阿里巴巴通義實驗室開發的GTE-Rerank文本排序系列模型，開發者可以通過LlamaIndex" "框架進行集成高質量文本檢索、排序。" #: models_provider/impl/aliyun_bai_lian_model_provider/aliyun_bai_lian_model_provider.py:45 msgid "" "Chinese (including various dialects such as Cantonese), English, Japanese, " "and Korean support free switching between multiple languages." msgstr "中文（含粵語等各種方言）、英文、日語、韓語支持多個語種自由切換" #: models_provider/impl/aliyun_bai_lian_model_provider/aliyun_bai_lian_model_provider.py:48 msgid "" "CosyVoice is based on a new generation of large generative speech models, " "which can predict emotions, intonation, rhythm, etc. based on context, and " "has better anthropomorphic effects." msgstr "" "CosyVoice基於新一代生成式語音大模型，能根據上下文預測情緒、語調、韻律等，具有" "更好的擬人效果" #: models_provider/impl/aliyun_bai_lian_model_provider/aliyun_bai_lian_model_provider.py:51 msgid "" "Universal text vector is Tongyi Lab's multi-language text unified vector " "model based on the LLM base. It provides high-level vector services for " "multiple mainstream languages around the world and helps developers quickly " "convert text data into high-quality vector data." msgstr "" "通用文本向量，是通義實驗室基於LLM底座的多語言文本統一向量模型，面向全球多個主" "流語種，提供高水準的向量服務，幫助開發者將文本數據快速轉換為高質量的向量數" "據。" #: models_provider/impl/aliyun_bai_lian_model_provider/aliyun_bai_lian_model_provider.py:69 msgid "" "Tongyi Wanxiang - a large image model for text generation, supports " "bilingual input in Chinese and English, and supports the input of reference " "pictures for reference content or reference style migration. Key styles " "include but are not limited to watercolor, oil painting, Chinese painting, " "sketch, flat illustration, two-dimensional, and 3D. Cartoon." msgstr "" "通義萬相-文本生成圖像大模型，支持中英文雙語輸入，支持輸入參考圖片進行參考內容" "或者參考風格遷移，重點風格包括但不限於水彩、油畫、中國畫、素描、扁平插畫、二" "次元、3D卡通。" #: models_provider/impl/aliyun_bai_lian_model_provider/aliyun_bai_lian_model_provider.py:95 msgid "Alibaba Cloud Bailian" msgstr "阿里雲百鍊" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/embedding.py:53 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/image.py:50 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:74 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:77 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/reranker.py:61 #: models_provider/impl/aliyun_bai_lian_model_provider/model/tti.py:43 #: models_provider/impl/aliyun_bai_lian_model_provider/model/tts.py:37 #: models_provider/impl/anthropic_model_provider/credential/image.py:33 #: models_provider/impl/anthropic_model_provider/credential/llm.py:57 #: models_provider/impl/aws_bedrock_model_provider/credential/embedding.py:34 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:53 #: models_provider/impl/azure_model_provider/credential/embedding.py:37 #: models_provider/impl/azure_model_provider/credential/image.py:40 #: models_provider/impl/azure_model_provider/credential/llm.py:69 #: models_provider/impl/deepseek_model_provider/credential/llm.py:57 #: models_provider/impl/gemini_model_provider/credential/embedding.py:36 #: models_provider/impl/gemini_model_provider/credential/image.py:32 #: models_provider/impl/gemini_model_provider/credential/llm.py:57 #: models_provider/impl/gemini_model_provider/model/stt.py:43 #: models_provider/impl/kimi_model_provider/credential/llm.py:57 #: models_provider/impl/local_model_provider/credential/embedding.py:36 #: models_provider/impl/local_model_provider/credential/reranker.py:37 #: models_provider/impl/ollama_model_provider/credential/embedding.py:37 #: models_provider/impl/ollama_model_provider/credential/reranker.py:44 #: models_provider/impl/openai_model_provider/credential/embedding.py:36 #: models_provider/impl/openai_model_provider/credential/image.py:35 #: models_provider/impl/openai_model_provider/credential/llm.py:59 #: models_provider/impl/siliconCloud_model_provider/credential/embedding.py:36 #: models_provider/impl/siliconCloud_model_provider/credential/image.py:35 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:58 #: models_provider/impl/siliconCloud_model_provider/credential/reranker.py:37 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:58 #: models_provider/impl/tencent_model_provider/credential/embedding.py:23 #: models_provider/impl/tencent_model_provider/credential/image.py:37 #: models_provider/impl/tencent_model_provider/credential/llm.py:51 #: models_provider/impl/tencent_model_provider/model/tti.py:54 #: models_provider/impl/vllm_model_provider/credential/embedding.py:36 #: models_provider/impl/vllm_model_provider/credential/llm.py:50 #: models_provider/impl/volcanic_engine_model_provider/credential/embedding.py:36 #: models_provider/impl/volcanic_engine_model_provider/credential/image.py:32 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:57 #: models_provider/impl/volcanic_engine_model_provider/model/tts.py:77 #: models_provider/impl/wenxin_model_provider/credential/embedding.py:31 #: models_provider/impl/wenxin_model_provider/credential/llm.py:60 #: models_provider/impl/xf_model_provider/credential/embedding.py:31 #: models_provider/impl/xf_model_provider/credential/llm.py:76 #: models_provider/impl/xf_model_provider/model/tts.py:101 #: models_provider/impl/xinference_model_provider/credential/embedding.py:31 #: models_provider/impl/xinference_model_provider/credential/image.py:32 #: models_provider/impl/xinference_model_provider/credential/llm.py:50 #: models_provider/impl/xinference_model_provider/credential/reranker.py:34 #: models_provider/impl/xinference_model_provider/model/tts.py:44 #: models_provider/impl/zhipu_model_provider/credential/image.py:31 #: models_provider/impl/zhipu_model_provider/credential/llm.py:56 #: models_provider/impl/zhipu_model_provider/model/tti.py:49 msgid "Hello" msgstr "你好" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/image.py:36 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:59 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/reranker.py:46 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/stt.py:44 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:96 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:89 #: models_provider/impl/anthropic_model_provider/credential/image.py:23 #: models_provider/impl/anthropic_model_provider/credential/llm.py:47 #: models_provider/impl/aws_bedrock_model_provider/credential/embedding.py:21 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:40 #: models_provider/impl/azure_model_provider/credential/embedding.py:27 #: models_provider/impl/azure_model_provider/credential/image.py:30 #: models_provider/impl/azure_model_provider/credential/llm.py:59 #: models_provider/impl/azure_model_provider/credential/stt.py:23 #: models_provider/impl/azure_model_provider/credential/tti.py:58 #: models_provider/impl/azure_model_provider/credential/tts.py:41 #: models_provider/impl/deepseek_model_provider/credential/llm.py:47 #: models_provider/impl/gemini_model_provider/credential/embedding.py:26 #: models_provider/impl/gemini_model_provider/credential/image.py:22 #: models_provider/impl/gemini_model_provider/credential/llm.py:47 #: models_provider/impl/gemini_model_provider/credential/stt.py:21 #: models_provider/impl/kimi_model_provider/credential/llm.py:47 #: models_provider/impl/local_model_provider/credential/embedding.py:27 #: models_provider/impl/local_model_provider/credential/reranker.py:28 #: models_provider/impl/ollama_model_provider/credential/embedding.py:26 #: models_provider/impl/ollama_model_provider/credential/image.py:19 #: models_provider/impl/ollama_model_provider/credential/llm.py:44 #: models_provider/impl/ollama_model_provider/credential/reranker.py:27 #: models_provider/impl/ollama_model_provider/credential/reranker.py:31 #: models_provider/impl/openai_model_provider/credential/embedding.py:26 #: models_provider/impl/openai_model_provider/credential/image.py:25 #: models_provider/impl/openai_model_provider/credential/llm.py:48 #: models_provider/impl/openai_model_provider/credential/stt.py:22 #: models_provider/impl/openai_model_provider/credential/tti.py:61 #: models_provider/impl/openai_model_provider/credential/tts.py:40 #: models_provider/impl/siliconCloud_model_provider/credential/embedding.py:26 #: models_provider/impl/siliconCloud_model_provider/credential/image.py:25 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:47 #: models_provider/impl/siliconCloud_model_provider/credential/reranker.py:28 #: models_provider/impl/siliconCloud_model_provider/credential/stt.py:22 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:61 #: models_provider/impl/siliconCloud_model_provider/credential/tts.py:22 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:47 #: models_provider/impl/tencent_model_provider/credential/embedding.py:19 #: models_provider/impl/tencent_model_provider/credential/image.py:28 #: models_provider/impl/tencent_model_provider/credential/llm.py:31 #: models_provider/impl/tencent_model_provider/credential/tti.py:78 #: models_provider/impl/vllm_model_provider/credential/embedding.py:26 #: models_provider/impl/vllm_model_provider/credential/image.py:22 #: models_provider/impl/vllm_model_provider/credential/llm.py:39 #: models_provider/impl/volcanic_engine_model_provider/credential/embedding.py:26 #: models_provider/impl/volcanic_engine_model_provider/credential/image.py:22 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:47 #: models_provider/impl/volcanic_engine_model_provider/credential/stt.py:25 #: models_provider/impl/volcanic_engine_model_provider/credential/tti.py:41 #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:51 #: models_provider/impl/wenxin_model_provider/credential/embedding.py:27 #: models_provider/impl/wenxin_model_provider/credential/llm.py:46 #: models_provider/impl/xf_model_provider/credential/embedding.py:27 #: models_provider/impl/xf_model_provider/credential/image.py:29 #: models_provider/impl/xf_model_provider/credential/llm.py:66 #: models_provider/impl/xf_model_provider/credential/stt.py:24 #: models_provider/impl/xf_model_provider/credential/tts.py:47 #: models_provider/impl/xinference_model_provider/credential/embedding.py:19 #: models_provider/impl/xinference_model_provider/credential/image.py:22 #: models_provider/impl/xinference_model_provider/credential/llm.py:39 #: models_provider/impl/xinference_model_provider/credential/reranker.py:25 #: models_provider/impl/xinference_model_provider/credential/stt.py:21 #: models_provider/impl/xinference_model_provider/credential/tti.py:59 #: models_provider/impl/xinference_model_provider/credential/tts.py:39 #: models_provider/impl/zhipu_model_provider/credential/image.py:21 #: models_provider/impl/zhipu_model_provider/credential/llm.py:47 #: models_provider/impl/zhipu_model_provider/credential/tti.py:40 #, python-brace-format msgid "{model_type} Model type is not supported" msgstr "{model_type} 模型類型不支持" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/image.py:44 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:67 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/reranker.py:55 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/stt.py:53 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:105 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:98 #, python-brace-format msgid "{key} is required" msgstr "{key} 是必填項" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/image.py:60 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:85 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/reranker.py:69 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/stt.py:67 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:121 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:113 #: models_provider/impl/anthropic_model_provider/credential/image.py:43 #: models_provider/impl/anthropic_model_provider/credential/llm.py:65 #: models_provider/impl/aws_bedrock_model_provider/credential/embedding.py:42 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:61 #: models_provider/impl/azure_model_provider/credential/image.py:50 #: models_provider/impl/azure_model_provider/credential/stt.py:40 #: models_provider/impl/azure_model_provider/credential/tti.py:77 #: models_provider/impl/azure_model_provider/credential/tts.py:58 #: models_provider/impl/deepseek_model_provider/credential/llm.py:65 #: models_provider/impl/gemini_model_provider/credential/embedding.py:43 #: models_provider/impl/gemini_model_provider/credential/image.py:42 #: models_provider/impl/gemini_model_provider/credential/llm.py:66 #: models_provider/impl/gemini_model_provider/credential/stt.py:38 #: models_provider/impl/kimi_model_provider/credential/llm.py:64 #: models_provider/impl/local_model_provider/credential/embedding.py:44 #: models_provider/impl/local_model_provider/credential/reranker.py:45 #: models_provider/impl/ollama_model_provider/credential/reranker.py:51 #: models_provider/impl/openai_model_provider/credential/embedding.py:43 #: models_provider/impl/openai_model_provider/credential/image.py:45 #: models_provider/impl/openai_model_provider/credential/llm.py:67 #: models_provider/impl/openai_model_provider/credential/stt.py:39 #: models_provider/impl/openai_model_provider/credential/tti.py:80 #: models_provider/impl/openai_model_provider/credential/tts.py:58 #: models_provider/impl/siliconCloud_model_provider/credential/embedding.py:43 #: models_provider/impl/siliconCloud_model_provider/credential/image.py:45 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:66 #: models_provider/impl/siliconCloud_model_provider/credential/reranker.py:44 #: models_provider/impl/siliconCloud_model_provider/credential/stt.py:39 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:80 #: models_provider/impl/siliconCloud_model_provider/credential/tts.py:40 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:66 #: models_provider/impl/tencent_model_provider/credential/embedding.py:30 #: models_provider/impl/tencent_model_provider/credential/image.py:47 #: models_provider/impl/tencent_model_provider/credential/llm.py:57 #: models_provider/impl/tencent_model_provider/credential/tti.py:104 #: models_provider/impl/vllm_model_provider/credential/embedding.py:43 #: models_provider/impl/vllm_model_provider/credential/image.py:42 #: models_provider/impl/vllm_model_provider/credential/llm.py:55 #: models_provider/impl/volcanic_engine_model_provider/credential/embedding.py:43 #: models_provider/impl/volcanic_engine_model_provider/credential/image.py:42 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:66 #: models_provider/impl/volcanic_engine_model_provider/credential/stt.py:42 #: models_provider/impl/volcanic_engine_model_provider/credential/tti.py:58 #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:68 #: models_provider/impl/wenxin_model_provider/credential/embedding.py:38 #: models_provider/impl/xf_model_provider/credential/embedding.py:38 #: models_provider/impl/xf_model_provider/credential/image.py:50 #: models_provider/impl/xf_model_provider/credential/llm.py:84 #: models_provider/impl/xf_model_provider/credential/stt.py:41 #: models_provider/impl/xf_model_provider/credential/tts.py:65 #: models_provider/impl/xinference_model_provider/credential/image.py:41 #: models_provider/impl/xinference_model_provider/credential/reranker.py:40 #: models_provider/impl/xinference_model_provider/credential/stt.py:37 #: models_provider/impl/xinference_model_provider/credential/tti.py:77 #: models_provider/impl/xinference_model_provider/credential/tts.py:56 #: models_provider/impl/zhipu_model_provider/credential/image.py:41 #: models_provider/impl/zhipu_model_provider/credential/llm.py:64 #: models_provider/impl/zhipu_model_provider/credential/tti.py:59 #, python-brace-format msgid "" "Verification failed, please check whether the parameters are correct: {error}" msgstr "認證失敗，請檢查參數是否正確：{error}" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:17 #: models_provider/impl/anthropic_model_provider/credential/llm.py:22 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:14 #: models_provider/impl/azure_model_provider/credential/llm.py:23 #: models_provider/impl/deepseek_model_provider/credential/llm.py:22 #: models_provider/impl/gemini_model_provider/credential/llm.py:22 #: models_provider/impl/kimi_model_provider/credential/llm.py:22 #: models_provider/impl/ollama_model_provider/credential/llm.py:20 #: models_provider/impl/openai_model_provider/credential/llm.py:23 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:22 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:22 #: models_provider/impl/tencent_model_provider/credential/llm.py:14 #: models_provider/impl/vllm_model_provider/credential/llm.py:15 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:22 #: models_provider/impl/wenxin_model_provider/credential/llm.py:22 #: models_provider/impl/xf_model_provider/credential/llm.py:22 #: models_provider/impl/xf_model_provider/credential/llm.py:41 #: models_provider/impl/xinference_model_provider/credential/llm.py:15 #: models_provider/impl/zhipu_model_provider/credential/llm.py:22 msgid "Temperature" msgstr "溫度" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:18 #: models_provider/impl/anthropic_model_provider/credential/llm.py:23 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:15 #: models_provider/impl/azure_model_provider/credential/llm.py:24 #: models_provider/impl/deepseek_model_provider/credential/llm.py:23 #: models_provider/impl/gemini_model_provider/credential/llm.py:23 #: models_provider/impl/kimi_model_provider/credential/llm.py:23 #: models_provider/impl/ollama_model_provider/credential/llm.py:21 #: models_provider/impl/openai_model_provider/credential/llm.py:24 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:23 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:23 #: models_provider/impl/tencent_model_provider/credential/llm.py:15 #: models_provider/impl/vllm_model_provider/credential/llm.py:16 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:23 #: models_provider/impl/wenxin_model_provider/credential/llm.py:23 #: models_provider/impl/xf_model_provider/credential/llm.py:23 #: models_provider/impl/xf_model_provider/credential/llm.py:42 #: models_provider/impl/xinference_model_provider/credential/llm.py:16 #: models_provider/impl/zhipu_model_provider/credential/llm.py:23 msgid "" "Higher values make the output more random, while lower values make it more " "focused and deterministic" msgstr "較高的數值會使輸出更加隨機，而較低的數值會使其更加集中和確定" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:30 #: models_provider/impl/anthropic_model_provider/credential/llm.py:31 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:23 #: models_provider/impl/azure_model_provider/credential/llm.py:32 #: models_provider/impl/azure_model_provider/credential/llm.py:43 #: models_provider/impl/deepseek_model_provider/credential/llm.py:31 #: models_provider/impl/gemini_model_provider/credential/llm.py:31 #: models_provider/impl/kimi_model_provider/credential/llm.py:31 #: models_provider/impl/ollama_model_provider/credential/llm.py:29 #: models_provider/impl/openai_model_provider/credential/llm.py:32 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:31 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:31 #: models_provider/impl/vllm_model_provider/credential/llm.py:24 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:31 #: models_provider/impl/wenxin_model_provider/credential/llm.py:31 #: models_provider/impl/xf_model_provider/credential/llm.py:31 #: models_provider/impl/xf_model_provider/credential/llm.py:50 #: models_provider/impl/xinference_model_provider/credential/llm.py:24 #: models_provider/impl/zhipu_model_provider/credential/llm.py:31 msgid "Output the maximum Tokens" msgstr "輸出最大Token數" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:31 msgid "Specify the maximum number of tokens that the model can generate." msgstr "指定模型可以生成的最大 tokens 數" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:43 #: models_provider/impl/anthropic_model_provider/credential/image.py:15 #: models_provider/impl/anthropic_model_provider/credential/llm.py:74 msgid "API URL" msgstr "" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:44 #: models_provider/impl/anthropic_model_provider/credential/image.py:16 #: models_provider/impl/anthropic_model_provider/credential/llm.py:75 msgid "API Key" msgstr "" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:20 #: models_provider/impl/azure_model_provider/credential/tti.py:15 #: models_provider/impl/openai_model_provider/credential/tti.py:15 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:15 #: models_provider/impl/volcanic_engine_model_provider/credential/tti.py:15 #: models_provider/impl/xinference_model_provider/credential/tti.py:14 #: models_provider/impl/zhipu_model_provider/credential/tti.py:15 msgid "Image size" msgstr "每頁大小" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:20 #: models_provider/impl/azure_model_provider/credential/tti.py:15 msgid "Specify the size of the generated image, such as: 1024x1024" msgstr "指定生成圖片的尺寸, 如: 1024x1024" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:34 #: models_provider/impl/azure_model_provider/credential/tti.py:40 #: models_provider/impl/openai_model_provider/credential/tti.py:43 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:43 #: models_provider/impl/xinference_model_provider/credential/tti.py:41 msgid "Number of pictures" msgstr "圖片數量" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:34 #: models_provider/impl/azure_model_provider/credential/tti.py:40 msgid "Specify the number of generated images" msgstr "指定生成圖片的數量" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:44 msgid "Style" msgstr "風格" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:44 msgid "Specify the style of generated images" msgstr "指定生成圖片的風格" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:48 msgid "Default value, the image style is randomly output by the model" msgstr "默認值，圖片風格由模型隨機輸出" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:49 msgid "photography" msgstr "攝影" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:50 msgid "Portraits" msgstr "人像寫真" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:51 msgid "3D cartoon" msgstr "3D卡通" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:52 msgid "animation" msgstr "動畫" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:53 msgid "painting" msgstr "油畫" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:54 msgid "watercolor" msgstr "水彩" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:55 msgid "sketch" msgstr "素描" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:56 msgid "Chinese painting" msgstr "中國畫" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:57 msgid "flat illustration" msgstr "扁平插畫" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:20 msgid "Timbre" msgstr "音色" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:20 #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:15 msgid "Chinese sounds can support mixed scenes of Chinese and English" msgstr "中文音色支持中英文混合場景" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:26 msgid "Long Xiaochun" msgstr "龍小淳" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:27 msgid "Long Xiaoxia" msgstr "龍小夏" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:28 msgid "Long Xiaochen" msgstr "龍小誠" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:29 msgid "Long Xiaobai" msgstr "龍小白" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:30 msgid "Long Laotie" msgstr "龍老鐵" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:31 msgid "Long Shu" msgstr "龍書" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:32 msgid "Long Shuo" msgstr "龍碩" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:33 msgid "Long Jing" msgstr "龍婧" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:34 msgid "Long Miao" msgstr "龍妙" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:35 msgid "Long Yue" msgstr "龍悅" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:36 msgid "Long Yuan" msgstr "龍媛" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:37 msgid "Long Fei" msgstr "龍飛" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:38 msgid "Long Jielidou" msgstr "龍傑力豆" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:39 msgid "Long Tong" msgstr "龍彤" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:40 msgid "Long Xiang" msgstr "龍祥" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:47 msgid "Speaking speed" msgstr "語速" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:47 msgid "[0.5, 2], the default is 1, usually one decimal place is enough" msgstr "[0.5,2]，默認為1，通常一位小數就足夠了" #: models_provider/impl/anthropic_model_provider/credential/image.py:28 #: models_provider/impl/anthropic_model_provider/credential/llm.py:52 #: models_provider/impl/azure_model_provider/credential/embedding.py:32 #: models_provider/impl/azure_model_provider/credential/image.py:35 #: models_provider/impl/azure_model_provider/credential/llm.py:64 #: models_provider/impl/azure_model_provider/credential/stt.py:28 #: models_provider/impl/azure_model_provider/credential/tti.py:63 #: models_provider/impl/azure_model_provider/credential/tts.py:46 #: models_provider/impl/deepseek_model_provider/credential/llm.py:52 #: models_provider/impl/gemini_model_provider/credential/embedding.py:31 #: models_provider/impl/gemini_model_provider/credential/image.py:27 #: models_provider/impl/gemini_model_provider/credential/llm.py:52 #: models_provider/impl/gemini_model_provider/credential/stt.py:26 #: models_provider/impl/kimi_model_provider/credential/llm.py:52 #: models_provider/impl/local_model_provider/credential/embedding.py:31 #: models_provider/impl/local_model_provider/credential/reranker.py:32 #: models_provider/impl/ollama_model_provider/credential/embedding.py:46 #: models_provider/impl/ollama_model_provider/credential/llm.py:62 #: models_provider/impl/ollama_model_provider/credential/reranker.py:63 #: models_provider/impl/openai_model_provider/credential/embedding.py:31 #: models_provider/impl/openai_model_provider/credential/image.py:30 #: models_provider/impl/openai_model_provider/credential/llm.py:53 #: models_provider/impl/openai_model_provider/credential/stt.py:27 #: models_provider/impl/openai_model_provider/credential/tti.py:66 #: models_provider/impl/openai_model_provider/credential/tts.py:45 #: models_provider/impl/siliconCloud_model_provider/credential/embedding.py:31 #: models_provider/impl/siliconCloud_model_provider/credential/image.py:30 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:52 #: models_provider/impl/siliconCloud_model_provider/credential/reranker.py:32 #: models_provider/impl/siliconCloud_model_provider/credential/stt.py:27 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:66 #: models_provider/impl/siliconCloud_model_provider/credential/tts.py:27 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:52 #: models_provider/impl/tencent_model_provider/credential/image.py:32 #: models_provider/impl/vllm_model_provider/credential/embedding.py:31 #: models_provider/impl/vllm_model_provider/credential/image.py:27 #: models_provider/impl/vllm_model_provider/credential/llm.py:65 #: models_provider/impl/volcanic_engine_model_provider/credential/embedding.py:31 #: models_provider/impl/volcanic_engine_model_provider/credential/image.py:27 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:52 #: models_provider/impl/volcanic_engine_model_provider/credential/stt.py:30 #: models_provider/impl/volcanic_engine_model_provider/credential/tti.py:46 #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:56 #: models_provider/impl/wenxin_model_provider/credential/llm.py:55 #: models_provider/impl/wenxin_model_provider/credential/llm.py:72 #: models_provider/impl/xf_model_provider/credential/image.py:34 #: models_provider/impl/xf_model_provider/credential/llm.py:71 #: models_provider/impl/xf_model_provider/credential/stt.py:29 #: models_provider/impl/xf_model_provider/credential/tts.py:52 #: models_provider/impl/xinference_model_provider/credential/embedding.py:40 #: models_provider/impl/xinference_model_provider/credential/image.py:27 #: models_provider/impl/xinference_model_provider/credential/llm.py:59 #: models_provider/impl/xinference_model_provider/credential/reranker.py:29 #: models_provider/impl/xinference_model_provider/credential/stt.py:26 #: models_provider/impl/xinference_model_provider/credential/tti.py:64 #: models_provider/impl/xinference_model_provider/credential/tts.py:44 #: models_provider/impl/zhipu_model_provider/credential/image.py:26 #: models_provider/impl/zhipu_model_provider/credential/llm.py:51 #: models_provider/impl/zhipu_model_provider/credential/tti.py:45 #, python-brace-format msgid "{key} is required" msgstr "{key} 是必填項" #: models_provider/impl/anthropic_model_provider/credential/llm.py:32 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:24 #: models_provider/impl/azure_model_provider/credential/llm.py:33 #: models_provider/impl/azure_model_provider/credential/llm.py:44 #: models_provider/impl/deepseek_model_provider/credential/llm.py:32 #: models_provider/impl/gemini_model_provider/credential/llm.py:32 #: models_provider/impl/kimi_model_provider/credential/llm.py:32 #: models_provider/impl/ollama_model_provider/credential/llm.py:30 #: models_provider/impl/openai_model_provider/credential/llm.py:33 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:32 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:32 #: models_provider/impl/vllm_model_provider/credential/llm.py:25 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:32 #: models_provider/impl/wenxin_model_provider/credential/llm.py:32 #: models_provider/impl/xf_model_provider/credential/llm.py:32 #: models_provider/impl/xf_model_provider/credential/llm.py:51 #: models_provider/impl/xinference_model_provider/credential/llm.py:25 #: models_provider/impl/zhipu_model_provider/credential/llm.py:32 msgid "Specify the maximum number of tokens that the model can generate" msgstr "指定模型可以生成的最大 tokens 數" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:36 msgid "" "An update to Claude 2 that doubles the context window and improves " "reliability, hallucination rates, and evidence-based accuracy in long " "documents and RAG contexts." msgstr "" "Claude 2 的更新，採用雙倍的上下文窗口，並在長文檔和 RAG 上下文中提高可靠性、" "幻覺率和循證準確性。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:43 msgid "" "Anthropic is a powerful model that can handle a variety of tasks, from " "complex dialogue and creative content generation to detailed command " "obedience." msgstr "" "Anthropic 功能強大的模型，可處理各種任務，從複雜的對話和創意內容生成到詳細的" "指令服從。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:50 msgid "" "The Claude 3 Haiku is Anthropic's fastest and most compact model, with near-" "instant responsiveness. The model can answer simple queries and requests " "quickly. Customers will be able to build seamless AI experiences that mimic " "human interactions. Claude 3 Haiku can process images and return text " "output, and provides 200K context windows." msgstr "" "Claude 3 Haiku 是 Anthropic 最快速、最緊湊的模型，具有近乎即時的響應能力。該" "模型可以快速回答簡單的查詢和請求。客戶將能夠構建模仿人類交互的無縫人工智慧體" "驗。 Claude 3 Haiku 可以處理圖像和返回文本輸出，並且提供 200K 上下文窗口。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:57 msgid "" "The Claude 3 Sonnet model from Anthropic strikes the ideal balance between " "intelligence and speed, especially when it comes to handling enterprise " "workloads. This model offers maximum utility while being priced lower than " "competing products, and it's been engineered to be a solid choice for " "deploying AI at scale." msgstr "" "Anthropic 推出的 Claude 3 Sonnet 模型在智能和速度之間取得理想的平衡，尤其是在" "處理企業工作負載方面。該模型提供最大的效用，同時價格低於競爭產品，並且其經過" "精心設計，是大規模部署人工智慧的可靠選擇。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:64 msgid "" "The Claude 3.5 Sonnet raises the industry standard for intelligence, " "outperforming competing models and the Claude 3 Opus in extensive " "evaluations, with the speed and cost-effectiveness of our mid-range models." msgstr "" "Claude 3.5 Sonnet提高了智能的行業標準，在廣泛的評估中超越了競爭對手的型號和" "Claude 3 Opus，具有我們中端型號的速度和成本效益。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:71 msgid "" "A faster, more affordable but still very powerful model that can handle a " "range of tasks including casual conversation, text analysis, summarization " "and document question answering." msgstr "" "一種更快速、更實惠但仍然非常強大的模型，它可以處理一系列任務，包括隨意對話、" "文本分析、摘要和文檔問題回答。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:78 msgid "" "Titan Text Premier is the most powerful and advanced model in the Titan Text " "series, designed to deliver exceptional performance for a variety of " "enterprise applications. With its cutting-edge features, it delivers greater " "accuracy and outstanding results, making it an excellent choice for " "organizations looking for a top-notch text processing solution." msgstr "" "Titan Text Premier 是 Titan Text 系列中功能強大且先進的型號，旨在為各種企業應" "用程序提供卓越的性能。憑藉其尖端功能，它提供了更高的準確性和出色的結果，使其" "成為尋求一流文本處理解決方案的組織的絕佳選擇。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:85 msgid "" "Amazon Titan Text Lite is a lightweight, efficient model ideal for fine-" "tuning English-language tasks, including summarization and copywriting, " "where customers require smaller, more cost-effective, and highly " "customizable models." msgstr "" "Amazon Titan Text Lite 是一種輕量級的高效模型，非常適合英語任務的微調，包括摘" "要和文案寫作等，在這種場景下，客戶需要更小、更經濟高效且高度可定製的模型" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:91 msgid "" "Amazon Titan Text Express has context lengths of up to 8,000 tokens, making " "it ideal for a variety of high-level general language tasks, such as open-" "ended text generation and conversational chat, as well as support in " "retrieval-augmented generation (RAG). At launch, the model is optimized for " "English, but other languages are supported." msgstr "" "Amazon Titan Text Express 的上下文長度長達 8000 個 tokens，因而非常適合各種高" "級常規語言任務，例如開放式文本生成和對話式聊天，以及檢索增強生成（RAG）中的支" "持。在發布時，該模型針對英語進行了優化，但也支持其他語言。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:97 msgid "" "7B dense converter for rapid deployment and easy customization. Small in " "size yet powerful in a variety of use cases. Supports English and code, as " "well as 32k context windows." msgstr "" "7B 密集型轉換器，可快速部署，易於定製。體積雖小，但功能強大，適用於各種用例。" "支持英語和代碼，以及 32k 的上下文窗口。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:103 msgid "" "Advanced Mistral AI large-scale language model capable of handling any " "language task, including complex multilingual reasoning, text understanding, " "transformation, and code generation." msgstr "" "先進的 Mistral AI 大型語言模型，能夠處理任何語言任務，包括複雜的多語言推理、" "文本理解、轉換和代碼生成。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:109 msgid "" "Ideal for content creation, conversational AI, language understanding, R&D, " "and enterprise applications" msgstr "非常適合內容創作、會話式人工智慧、語言理解、研發和企業應用" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:115 msgid "" "Ideal for limited computing power and resources, edge devices, and faster " "training times." msgstr "非常適合有限的計算能力和資源、邊緣設備和更快的訓練時間。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:123 msgid "" "Titan Embed Text is the largest embedding model in the Amazon Titan Embed " "series and can handle various text embedding tasks, such as text " "classification, text similarity calculation, etc." msgstr "" "Titan Embed Text 是 Amazon Titan Embed 系列中最大的嵌入模型，可以處理各種文本" "嵌入任務，如文本分類、文本相似度計算等。" #: models_provider/impl/aws_bedrock_model_provider/credential/embedding.py:28 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:47 #, python-brace-format msgid "The following fields are required: {keys}" msgstr "以下欄位是必填項: {keys}" #: models_provider/impl/azure_model_provider/credential/embedding.py:44 #: models_provider/impl/azure_model_provider/credential/llm.py:76 msgid "Verification failed, please check whether the parameters are correct" msgstr "認證失敗，請檢查參數是否正確" #: models_provider/impl/azure_model_provider/credential/tti.py:28 #: models_provider/impl/openai_model_provider/credential/tti.py:29 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:29 #: models_provider/impl/xinference_model_provider/credential/tti.py:28 msgid "Picture quality" msgstr "圖片質量" #: models_provider/impl/azure_model_provider/credential/tts.py:17 #: models_provider/impl/openai_model_provider/credential/tts.py:17 msgid "" "Try out the different sounds (Alloy, Echo, Fable, Onyx, Nova, and Sparkle) " "to find one that suits your desired tone and audience. The current voiceover " "is optimized for English." msgstr "" "嘗試不同的聲音（合金、回聲、寓言、縞瑪瑙、新星和閃光），找到一種適合您所需的" "音調和聽眾的聲音。當前的語音針對英語進行了優化。" #: models_provider/impl/deepseek_model_provider/deepseek_model_provider.py:24 msgid "Good at common conversational tasks, supports 32K contexts" msgstr "擅長通用對話任務，支持 32K 上下文" #: models_provider/impl/deepseek_model_provider/deepseek_model_provider.py:29 msgid "Good at handling programming tasks, supports 16K contexts" msgstr "擅長處理編程任務，支持 16K 上下文" #: models_provider/impl/gemini_model_provider/gemini_model_provider.py:32 msgid "Latest Gemini 1.0 Pro model, updated with Google update" msgstr "最新的 Gemini 1.0 Pro 模型，更新了 Google 更新" #: models_provider/impl/gemini_model_provider/gemini_model_provider.py:36 msgid "Latest Gemini 1.0 Pro Vision model, updated with Google update" msgstr "最新的Gemini 1.0 Pro Vision模型，隨Google更新而更新" #: models_provider/impl/gemini_model_provider/gemini_model_provider.py:43 #: models_provider/impl/gemini_model_provider/gemini_model_provider.py:47 #: models_provider/impl/gemini_model_provider/gemini_model_provider.py:54 #: models_provider/impl/gemini_model_provider/gemini_model_provider.py:58 msgid "Latest Gemini 1.5 Flash model, updated with Google updates" msgstr "最新的Gemini 1.5 Flash模型，隨Google更新而更新" #: models_provider/impl/gemini_model_provider/model/stt.py:53 msgid "convert audio to text" msgstr "將音頻轉換為文本" #: models_provider/impl/local_model_provider/credential/embedding.py:53 #: models_provider/impl/local_model_provider/credential/reranker.py:54 msgid "Model catalog" msgstr "模型目錄" #: models_provider/impl/local_model_provider/local_model_provider.py:39 msgid "local model" msgstr "本地模型" #: models_provider/impl/ollama_model_provider/credential/embedding.py:30 #: models_provider/impl/ollama_model_provider/credential/image.py:23 #: models_provider/impl/ollama_model_provider/credential/llm.py:48 #: models_provider/impl/ollama_model_provider/credential/reranker.py:35 #: models_provider/impl/vllm_model_provider/credential/llm.py:43 #: models_provider/impl/xinference_model_provider/credential/embedding.py:24 #: models_provider/impl/xinference_model_provider/credential/llm.py:44 msgid "API domain name is invalid" msgstr "API 域名無效" #: models_provider/impl/ollama_model_provider/credential/embedding.py:35 #: models_provider/impl/ollama_model_provider/credential/image.py:28 #: models_provider/impl/ollama_model_provider/credential/llm.py:53 #: models_provider/impl/ollama_model_provider/credential/reranker.py:40 #: models_provider/impl/vllm_model_provider/credential/llm.py:47 #: models_provider/impl/xinference_model_provider/credential/embedding.py:30 #: models_provider/impl/xinference_model_provider/credential/llm.py:48 msgid "The model does not exist, please download the model first" msgstr "模型不存在，請先下載模型" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:56 msgid "" "Llama 2 is a set of pretrained and fine-tuned generative text models ranging " "in size from 7 billion to 70 billion. This is a repository of 7B pretrained " "models. Links to other models can be found in the index at the bottom." msgstr "" "Llama 2 是一組經過預訓練和微調的生成文本模型，其規模從 70 億到 700 億個不等。" "這是 7B 預訓練模型的存儲庫。其他模型的連結可以在底部的索引中找到。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:60 msgid "" "Llama 2 is a set of pretrained and fine-tuned generative text models ranging " "in size from 7 billion to 70 billion. This is a repository of 13B pretrained " "models. Links to other models can be found in the index at the bottom." msgstr "" "Llama 2 是一組經過預訓練和微調的生成文本模型，其規模從 70 億到 700 億個不等。" "這是 13B 預訓練模型的存儲庫。其他模型的連結可以在底部的索引中找到。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:64 msgid "" "Llama 2 is a set of pretrained and fine-tuned generative text models ranging " "in size from 7 billion to 70 billion. This is a repository of 70B pretrained " "models. Links to other models can be found in the index at the bottom." msgstr "" "Llama 2 是一組經過預訓練和微調的生成文本模型，其規模從 70 億到 700 億個不等。" "這是 70B 預訓練模型的存儲庫。其他模型的連結可以在底部的索引中找到。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:68 msgid "" "Since the Chinese alignment of Llama2 itself is weak, we use the Chinese " "instruction set to fine-tune meta-llama/Llama-2-13b-chat-hf with LoRA so " "that it has strong Chinese conversation capabilities." msgstr "" "由於Llama2本身的中文對齊較弱，我們採用中文指令集，對meta-llama/Llama-2-13b-" "chat-hf進行LoRA微調，使其具備較強的中文對話能力。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:72 msgid "" "Meta Llama 3: The most capable public product LLM to date. 8 billion " "parameters." msgstr "Meta Llama 3：迄今為止最有能力的公開產品LLM。80億參數。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:76 msgid "" "Meta Llama 3: The most capable public product LLM to date. 70 billion " "parameters." msgstr "Meta Llama 3：迄今為止最有能力的公開產品LLM。700億參數。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:80 msgid "" "Compared with previous versions, qwen 1.5 0.5b has significantly enhanced " "the model's alignment with human preferences and its multi-language " "processing capabilities. Models of all sizes support a context length of " "32768 tokens. 500 million parameters." msgstr "" "qwen 1.5 0.5b 相較於以往版本，模型與人類偏好的對齊程度以及多語言處理能力上有" "顯著增強。所有規模的模型都支持32768個tokens的上下文長度。5億參數。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:84 msgid "" "Compared with previous versions, qwen 1.5 1.8b has significantly enhanced " "the model's alignment with human preferences and its multi-language " "processing capabilities. Models of all sizes support a context length of " "32768 tokens. 1.8 billion parameters." msgstr "" "qwen 1.5 1.8b 相較於以往版本，模型與人類偏好的對齊程度以及多語言處理能力上有" "顯著增強。所有規模的模型都支持32768個tokens的上下文長度。18億參數。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:88 msgid "" "Compared with previous versions, qwen 1.5 4b has significantly enhanced the " "model's alignment with human preferences and its multi-language processing " "capabilities. Models of all sizes support a context length of 32768 tokens. " "4 billion parameters." msgstr "" "qwen 1.5 4b 相較於以往版本，模型與人類偏好的對齊程度以及多語言處理能力上有顯" "著增強。所有規模的模型都支持32768個tokens的上下文長度。40億參數。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:93 msgid "" "Compared with previous versions, qwen 1.5 7b has significantly enhanced the " "model's alignment with human preferences and its multi-language processing " "capabilities. Models of all sizes support a context length of 32768 tokens. " "7 billion parameters." msgstr "" "qwen 1.5 7b 相較於以往版本，模型與人類偏好的對齊程度以及多語言處理能力上有顯" "著增強。所有規模的模型都支持32768個tokens的上下文長度。70億參數。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:97 msgid "" "Compared with previous versions, qwen 1.5 14b has significantly enhanced the " "model's alignment with human preferences and its multi-language processing " "capabilities. Models of all sizes support a context length of 32768 tokens. " "14 billion parameters." msgstr "" "qwen 1.5 14b 相較於以往版本，模型與人類偏好的對齊程度以及多語言處理能力上有顯" "著增強。所有規模的模型都支持32768個tokens的上下文長度。140億參數。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:101 msgid "" "Compared with previous versions, qwen 1.5 32b has significantly enhanced the " "model's alignment with human preferences and its multi-language processing " "capabilities. Models of all sizes support a context length of 32768 tokens. " "32 billion parameters." msgstr "" "qwen 1.5 32b 相較於以往版本，模型與人類偏好的對齊程度以及多語言處理能力上有顯" "著增強。所有規模的模型都支持32768個tokens的上下文長度。320億參數。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:105 msgid "" "Compared with previous versions, qwen 1.5 72b has significantly enhanced the " "model's alignment with human preferences and its multi-language processing " "capabilities. Models of all sizes support a context length of 32768 tokens. " "72 billion parameters." msgstr "" "qwen 1.5 72b 相較於以往版本，模型與人類偏好的對齊程度以及多語言處理能力上有顯" "著增強。所有規模的模型都支持32768個tokens的上下文長度。720億參數。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:109 msgid "" "Compared with previous versions, qwen 1.5 110b has significantly enhanced " "the model's alignment with human preferences and its multi-language " "processing capabilities. Models of all sizes support a context length of " "32768 tokens. 110 billion parameters." msgstr "" "qwen 1.5 110b 相較於以往版本，模型與人類偏好的對齊程度以及多語言處理能力上有" "顯著增強。所有規模的模型都支持32768個tokens的上下文長度。1100億參數。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:153 #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:193 msgid "" "Phi-3 Mini is Microsoft's 3.8B parameter, lightweight, state-of-the-art open " "model." msgstr "Phi-3 Mini是Microsoft的3.8B參數，輕量級，最先進的開放模型。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:162 #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:197 msgid "" "A high-performance open embedding model with a large token context window." msgstr "一個具有大 tokens上下文窗口的高性能開放嵌入模型。" #: models_provider/impl/openai_model_provider/credential/tti.py:16 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:16 msgid "" "The image generation endpoint allows you to create raw images based on text " "prompts. When using the DALL·E 3, the image size can be 1024x1024, 1024x1792 " "or 1792x1024 pixels." msgstr "" "圖像生成端點允許您根據文本提示創建原始圖像。使用 DALL·E 3 時，圖像的尺寸可以" "為 1024x1024、1024x1792 或 1792x1024 像素。" #: models_provider/impl/openai_model_provider/credential/tti.py:29 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:29 msgid "" " \n" "By default, images are produced in standard quality, but with DALL·E 3 you " "can set quality: \"hd\" to enhance detail. Square, standard quality images " "are generated fastest.\n" " " msgstr "" "默認情況下，圖像以標準質量生成，但使用 DALL·E 3 時，您可以設置質量：「hd」以增" "強細節。方形、標準質量的圖像生成速度最快。" #: models_provider/impl/openai_model_provider/credential/tti.py:44 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:44 msgid "" "You can use DALL·E 3 to request 1 image at a time (requesting more images by " "issuing parallel requests), or use DALL·E 2 with the n parameter to request " "up to 10 images at a time." msgstr "" "您可以使用 DALL·E 3 一次請求 1 個圖像（通過發出並行請求來請求更多圖像），或者" "使用帶有 n 參數的 DALL·E 2 一次最多請求 10 個圖像。" #: models_provider/impl/openai_model_provider/openai_model_provider.py:35 #: models_provider/impl/openai_model_provider/openai_model_provider.py:119 #: models_provider/impl/siliconCloud_model_provider/siliconCloud_model_provider.py:118 msgid "The latest gpt-3.5-turbo, updated with OpenAI adjustments" msgstr "最新的gpt-3.5-turbo，隨OpenAI調整而更新" #: models_provider/impl/openai_model_provider/openai_model_provider.py:38 msgid "Latest gpt-4, updated with OpenAI adjustments" msgstr "最新的gpt-4，隨OpenAI調整而更新" #: models_provider/impl/openai_model_provider/openai_model_provider.py:40 #: models_provider/impl/openai_model_provider/openai_model_provider.py:99 msgid "" "The latest GPT-4o, cheaper and faster than gpt-4-turbo, updated with OpenAI " "adjustments" msgstr "最新的GPT-4o，比gpt-4-turbo更便宜、更快，隨OpenAI調整而更新" #: models_provider/impl/openai_model_provider/openai_model_provider.py:43 #: models_provider/impl/openai_model_provider/openai_model_provider.py:102 msgid "" "The latest gpt-4o-mini, cheaper and faster than gpt-4o, updated with OpenAI " "adjustments" msgstr "最新的gpt-4o-mini，比gpt-4o更便宜、更快，隨OpenAI調整而更新" #: models_provider/impl/openai_model_provider/openai_model_provider.py:46 msgid "The latest gpt-4-turbo, updated with OpenAI adjustments" msgstr "最新的gpt-4-turbo，隨OpenAI調整而更新" #: models_provider/impl/openai_model_provider/openai_model_provider.py:49 msgid "The latest gpt-4-turbo-preview, updated with OpenAI adjustments" msgstr "最新的gpt-4-turbo-preview，隨OpenAI調整而更新" #: models_provider/impl/openai_model_provider/openai_model_provider.py:53 msgid "" "gpt-3.5-turbo snapshot on January 25, 2024, supporting context length 16,385 " "tokens" msgstr "2024年1月25日的gpt-3.5-turbo快照，支持上下文長度16,385 tokens" #: models_provider/impl/openai_model_provider/openai_model_provider.py:57 msgid "" "gpt-3.5-turbo snapshot on November 6, 2023, supporting context length 16,385 " "tokens" msgstr "2023年11月6日的gpt-3.5-turbo快照，支持上下文長度16,385 tokens" #: models_provider/impl/openai_model_provider/openai_model_provider.py:61 msgid "" "[Legacy] gpt-3.5-turbo snapshot on June 13, 2023, will be deprecated on June " "13, 2024" msgstr "[Legacy] 2023年6月13日的gpt-3.5-turbo快照，將於2024年6月13日棄用" #: models_provider/impl/openai_model_provider/openai_model_provider.py:65 msgid "" "gpt-4o snapshot on May 13, 2024, supporting context length 128,000 tokens" msgstr "2024年5月13日的gpt-4o快照，支持上下文長度128,000 tokens" #: models_provider/impl/openai_model_provider/openai_model_provider.py:69 msgid "" "gpt-4-turbo snapshot on April 9, 2024, supporting context length 128,000 " "tokens" msgstr "2024年4月9日的gpt-4-turbo快照，支持上下文長度128,000 tokens" #: models_provider/impl/openai_model_provider/openai_model_provider.py:72 msgid "" "gpt-4-turbo snapshot on January 25, 2024, supporting context length 128,000 " "tokens" msgstr "2024年1月25日的gpt-4-turbo快照，支持上下文長度128,000 tokens" #: models_provider/impl/openai_model_provider/openai_model_provider.py:75 msgid "" "gpt-4-turbo snapshot on November 6, 2023, supporting context length 128,000 " "tokens" msgstr "2023年11月6日的gpt-4-turbo快照，支持上下文長度128,000 tokens" #: models_provider/impl/tencent_cloud_model_provider/tencent_cloud_model_provider.py:58 msgid "Tencent Cloud" msgstr "騰訊雲" #: models_provider/impl/tencent_model_provider/credential/llm.py:41 #: models_provider/impl/tencent_model_provider/credential/tti.py:88 #, python-brace-format msgid "{keys} is required" msgstr "{keys} 是必填項" #: models_provider/impl/tencent_model_provider/credential/tti.py:14 msgid "painting style" msgstr "繪畫風格" #: models_provider/impl/tencent_model_provider/credential/tti.py:14 msgid "If not passed, the default value is 201 (Japanese anime style)" msgstr "如果未傳遞，則默認值為201（日本動漫風格）" #: models_provider/impl/tencent_model_provider/credential/tti.py:18 msgid "Not limited to style" msgstr "不限於風格" #: models_provider/impl/tencent_model_provider/credential/tti.py:19 msgid "ink painting" msgstr "水墨畫" #: models_provider/impl/tencent_model_provider/credential/tti.py:20 msgid "concept art" msgstr "概念藝術" #: models_provider/impl/tencent_model_provider/credential/tti.py:21 msgid "Oil painting 1" msgstr "油畫1" #: models_provider/impl/tencent_model_provider/credential/tti.py:22 msgid "Oil Painting 2 (Van Gogh)" msgstr "油畫2（梵谷）" #: models_provider/impl/tencent_model_provider/credential/tti.py:23 msgid "watercolor painting" msgstr "水彩畫" #: models_provider/impl/tencent_model_provider/credential/tti.py:24 msgid "pixel art" msgstr "像素畫" #: models_provider/impl/tencent_model_provider/credential/tti.py:25 msgid "impasto style" msgstr "厚塗風格" #: models_provider/impl/tencent_model_provider/credential/tti.py:26 msgid "illustration" msgstr "插圖" #: models_provider/impl/tencent_model_provider/credential/tti.py:27 msgid "paper cut style" msgstr "剪紙風格" #: models_provider/impl/tencent_model_provider/credential/tti.py:28 msgid "Impressionism 1 (Monet)" msgstr "印象派1（莫奈）" #: models_provider/impl/tencent_model_provider/credential/tti.py:29 msgid "Impressionism 2" msgstr "印象派2" #: models_provider/impl/tencent_model_provider/credential/tti.py:31 msgid "classical portraiture" msgstr "古典肖像畫" #: models_provider/impl/tencent_model_provider/credential/tti.py:32 msgid "black and white sketch" msgstr "黑白素描畫" #: models_provider/impl/tencent_model_provider/credential/tti.py:33 msgid "cyberpunk" msgstr "賽博朋克" #: models_provider/impl/tencent_model_provider/credential/tti.py:34 msgid "science fiction style" msgstr "科幻風格" #: models_provider/impl/tencent_model_provider/credential/tti.py:35 msgid "dark style" msgstr "暗黑風格" #: models_provider/impl/tencent_model_provider/credential/tti.py:37 msgid "vaporwave" msgstr "蒸汽波" #: models_provider/impl/tencent_model_provider/credential/tti.py:38 msgid "Japanese animation" msgstr "日系動漫" #: models_provider/impl/tencent_model_provider/credential/tti.py:39 msgid "monster style" msgstr "怪獸風格" #: models_provider/impl/tencent_model_provider/credential/tti.py:40 msgid "Beautiful ancient style" msgstr "唯美古風" #: models_provider/impl/tencent_model_provider/credential/tti.py:41 msgid "retro anime" msgstr "復古動漫" #: models_provider/impl/tencent_model_provider/credential/tti.py:42 msgid "Game cartoon hand drawing" msgstr "遊戲卡通手繪" #: models_provider/impl/tencent_model_provider/credential/tti.py:43 msgid "Universal realistic style" msgstr "通用寫實風格" #: models_provider/impl/tencent_model_provider/credential/tti.py:50 msgid "Generate image resolution" msgstr "生成圖像解析度" #: models_provider/impl/tencent_model_provider/credential/tti.py:50 msgid "If not transmitted, the default value is 768:768." msgstr "不傳默認使用768:768。" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:38 msgid "" "The most effective version of the current hybrid model, the trillion-level " "parameter scale MOE-32K long article model. Reaching the absolute leading " "level on various benchmarks, with complex instructions and reasoning, " "complex mathematical capabilities, support for function call, and " "application focus optimization in fields such as multi-language translation, " "finance, law, and medical care" msgstr "" "當前混元模型中效果最優版本，萬億級參數規模 MOE-32K 長文模型。在各種 " "benchmark 上達到絕對領先的水平，複雜指令和推理，具備複雜數學能力，支持 " "functioncall，在多語言翻譯、金融法律醫療等領域應用重點優化" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:45 msgid "" "A better routing strategy is adopted to simultaneously alleviate the " "problems of load balancing and expert convergence. For long articles, the " "needle-in-a-haystack index reaches 99.9%" msgstr "" "採用更優的路由策略，同時緩解了負載均衡和專家趨同的問題。長文方面，大海撈針指" "標達到99.9%" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:51 msgid "" "Upgraded to MOE structure, the context window is 256k, leading many open " "source models in multiple evaluation sets such as NLP, code, mathematics, " "industry, etc." msgstr "" "升級為 MOE 結構，上下文窗口為 256k ，在 NLP，代碼，數學，行業等多項評測集上領" "先眾多開源模型" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:57 msgid "" "Hunyuan's latest version of the role-playing model, a role-playing model " "launched by Hunyuan's official fine-tuning training, is based on the Hunyuan " "model combined with the role-playing scene data set for additional training, " "and has better basic effects in role-playing scenes." msgstr "" "混元最新版角色扮演模型，混元官方精調訓練推出的角色扮演模型，基於混元模型結合" "角色扮演場景數據集進行增訓，在角色扮演場景具有更好的基礎效果" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:63 msgid "" "Hunyuan's latest MOE architecture FunctionCall model has been trained with " "high-quality FunctionCall data and has a context window of 32K, leading in " "multiple dimensions of evaluation indicators." msgstr "" "混元最新 MOE 架構 FunctionCall 模型，經過高質量的 FunctionCall 數據訓練，上下" "文窗口達 32K，在多個維度的評測指標上處於領先。" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:69 msgid "" "Hunyuan's latest code generation model, after training the base model with " "200B high-quality code data, and iterating on high-quality SFT data for half " "a year, the context long window length has been increased to 8K, and it " "ranks among the top in the automatic evaluation indicators of code " "generation in the five major languages; the five major languages In the " "manual high-quality evaluation of 10 comprehensive code tasks that consider " "all aspects, the performance is in the first echelon." msgstr "" "混元最新代碼生成模型，經過 200B 高質量代碼數據增訓基座模型，迭代半年高質量 " "SFT 數據訓練，上下文長窗口長度增大到 8K，五大語言代碼生成自動評測指標上位居前" "列；五大語言10項考量各方面綜合代碼任務人工高質量評測上，性能處於第一梯隊" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:77 msgid "" "Tencent's Hunyuan Embedding interface can convert text into high-quality " "vector data. The vector dimension is 1024 dimensions." msgstr "" "騰訊混元 Embedding 接口，可以將文本轉化為高質量的向量數據。向量維度為1024維。" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:87 msgid "Mixed element visual model" msgstr "混元視覺模型" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:94 msgid "Hunyuan graph model" msgstr "混元生圖模型" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:125 msgid "Tencent Hunyuan" msgstr "騰訊混元" #: models_provider/impl/vllm_model_provider/vllm_model_provider.py:24 #: models_provider/impl/vllm_model_provider/vllm_model_provider.py:42 msgid "Facebook’s 125M parameter model" msgstr "Facebook的125M參數模型" #: models_provider/impl/vllm_model_provider/vllm_model_provider.py:25 msgid "BAAI’s 7B parameter model" msgstr "BAAI的7B參數模型" #: models_provider/impl/vllm_model_provider/vllm_model_provider.py:26 msgid "BAAI’s 13B parameter mode" msgstr "BAAI的13B參數模型" #: models_provider/impl/volcanic_engine_model_provider/credential/tti.py:16 msgid "" "If the gap between width, height and 512 is too large, the picture rendering " "effect will be poor and the probability of excessive delay will increase " "significantly. Recommended ratio and corresponding width and height before " "super score: width*height" msgstr "" "寬、高與512差距過大，則出圖效果不佳、延遲過長概率顯著增加。超分前建議比例及對" "應寬高：width*height" #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:15 #: models_provider/impl/xinference_model_provider/credential/tts.py:15 msgid "timbre" msgstr "音色" #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:31 #: models_provider/impl/xf_model_provider/credential/tts.py:28 msgid "speaking speed" msgstr "語速" #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:31 msgid "[0.2,3], the default is 1, usually one decimal place is enough" msgstr "[0.2,3]，默認為1，通常保留一位小數即可" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:39 #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:44 #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:88 msgid "" "The user goes to the model inference page of Volcano Ark to create an " "inference access point. Here, you need to enter ep-xxxxxxxxxx-yyyy to call " "it." msgstr "" "用戶前往火山方舟的模型推理頁面創建推理接入點，這裡需要輸入ep-xxxxxxxxxx-yyyy" "進行調用" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:59 msgid "Universal 2.0-Vincent Diagram" msgstr "通用2.0-文生圖" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:64 msgid "Universal 2.0Pro-Vincent Chart" msgstr "通用2.0Pro-文生圖" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:69 msgid "Universal 1.4-Vincent Chart" msgstr "通用1.4-文生圖" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:74 msgid "Animation 1.3.0-Vincent Picture" msgstr "動漫1.3.0-文生圖" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:79 msgid "Animation 1.3.1-Vincent Picture" msgstr "動漫1.3.1-文生圖" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:113 msgid "volcano engine" msgstr "火山引擎" #: models_provider/impl/wenxin_model_provider/credential/llm.py:51 #, python-brace-format msgid "{model_name} The model does not support" msgstr "{model_name} 模型不支持" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:24 #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:53 msgid "" "ERNIE-Bot-4 is a large language model independently developed by Baidu. It " "covers massive Chinese data and has stronger capabilities in dialogue Q&A, " "content creation and generation." msgstr "" "ERNIE-Bot-4是百度自行研發的大語言模型，覆蓋海量中文數據，具有更強的對話問答、" "內容創作生成等能力。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:27 msgid "" "ERNIE-Bot is a large language model independently developed by Baidu. It " "covers massive Chinese data and has stronger capabilities in dialogue Q&A, " "content creation and generation." msgstr "" "ERNIE-Bot是百度自行研發的大語言模型，覆蓋海量中文數據，具有更強的對話問答、內" "容創作生成等能力。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:30 msgid "" "ERNIE-Bot-turbo is a large language model independently developed by Baidu. " "It covers massive Chinese data, has stronger capabilities in dialogue Q&A, " "content creation and generation, and has a faster response speed." msgstr "" "ERNIE-Bot-turbo是百度自行研發的大語言模型，覆蓋海量中文數據，具有更強的對話問" "答、內容創作生成等能力，響應速度更快。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:33 msgid "" "BLOOMZ-7B is a well-known large language model in the industry. It was " "developed and open sourced by BigScience and can output text in 46 languages " "and 13 programming languages." msgstr "" "BLOOMZ-7B是業內知名的大語言模型，由BigScience研發並開源，能夠以46種語言和13種" "程式語言輸出文本。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:39 msgid "" "Llama-2-13b-chat was developed by Meta AI and is open source. It performs " "well in scenarios such as coding, reasoning and knowledge application. " "Llama-2-13b-chat is a native open source version with balanced performance " "and effect, suitable for conversation scenarios." msgstr "" "Llama-2-13b-chat由Meta AI研發並開源，在編碼、推理及知識應用等場景表現優秀，" "Llama-2-13b-chat是性能與效果均衡的原生開源版本，適用於對話場景。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:42 msgid "" "Llama-2-70b-chat was developed by Meta AI and is open source. It performs " "well in scenarios such as coding, reasoning, and knowledge application. " "Llama-2-70b-chat is a native open source version with high-precision effects." msgstr "" "Llama-2-70b-chat由Meta AI研發並開源，在編碼、推理及知識應用等場景表現優秀，" "Llama-2-70b-chat是高精度效果的原生開源版本。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:45 msgid "" "The Chinese enhanced version developed by the Qianfan team based on " "Llama-2-7b has performed well on Chinese knowledge bases such as CMMLU and C-" "EVAL." msgstr "" "千帆團隊在Llama-2-7b基礎上的中文增強版本，在CMMLU、C-EVAL等中文知識庫上表現優" "異。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:49 msgid "" "Embedding-V1 is a text representation model based on Baidu Wenxin large " "model technology. It can convert text into a vector form represented by " "numerical values and can be used in text retrieval, information " "recommendation, knowledge mining and other scenarios. Embedding-V1 provides " "the Embeddings interface, which can generate corresponding vector " "representations based on input content. You can call this interface to input " "text into the model and obtain the corresponding vector representation for " "subsequent text processing and analysis." msgstr "" "Embedding-V1是一個基於百度文心大模型技術的文本表示模型，可以將文本轉化為用數" "值表示的向量形式，用於文本檢索、信息推薦、知識挖掘等場景。 Embedding-V1提供了" "Embeddings接口，可以根據輸入內容生成對應的向量表示。您可以通過調用該接口，將" "文本輸入到模型中，獲取到對應的向量表示，從而進行後續的文本處理和分析。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:66 msgid "Thousand sails large model" msgstr "千帆大模型" #: models_provider/impl/xf_model_provider/credential/image.py:42 msgid "Please outline this picture" msgstr "請描述這張圖片" #: models_provider/impl/xf_model_provider/credential/tts.py:15 msgid "Speaker" msgstr "發音人" #: models_provider/impl/xf_model_provider/credential/tts.py:16 msgid "" "Speaker, optional value: Please go to the console to add a trial or purchase " "speaker. After adding, the speaker parameter value will be displayed." msgstr "" "發音人，可選值：請到控制臺添加試用或購買發音人，添加後即顯示發音人參數值" #: models_provider/impl/xf_model_provider/credential/tts.py:21 msgid "iFlytek Xiaoyan" msgstr "訊飛小燕" #: models_provider/impl/xf_model_provider/credential/tts.py:22 msgid "iFlytek Xujiu" msgstr "訊飛許久" #: models_provider/impl/xf_model_provider/credential/tts.py:23 msgid "iFlytek Xiaoping" msgstr "訊飛小萍" #: models_provider/impl/xf_model_provider/credential/tts.py:24 msgid "iFlytek Xiaojing" msgstr "訊飛小婧" #: models_provider/impl/xf_model_provider/credential/tts.py:25 msgid "iFlytek Xuxiaobao" msgstr "訊飛許小寶" #: models_provider/impl/xf_model_provider/credential/tts.py:28 msgid "Speech speed, optional value: [0-100], default is 50" msgstr "語速，可選值：[0-100]，默認為50" #: models_provider/impl/xf_model_provider/xf_model_provider.py:39 #: models_provider/impl/xf_model_provider/xf_model_provider.py:50 msgid "Chinese and English recognition" msgstr "中英文識別" #: models_provider/impl/xf_model_provider/xf_model_provider.py:66 msgid "iFlytek Spark" msgstr "訊飛星火" #: models_provider/impl/xinference_model_provider/credential/tti.py:15 msgid "" "The image generation endpoint allows you to create raw images based on text " "prompts. The dimensions of the image can be 1024x1024, 1024x1792, or " "1792x1024 pixels." msgstr "" "圖像生成端點允許您根據文本提示創建原始圖像。圖像的尺寸可以為 1024x1024、" "1024x1792 或 1792x1024 像素。" #: models_provider/impl/xinference_model_provider/credential/tti.py:29 msgid "" "By default, images are generated in standard quality, you can set quality: " "\"hd\" to enhance detail. Square, standard quality images are generated " "fastest." msgstr "" "默認情況下，圖像以標準質量生成，您可以設置質量：「hd」以增強細節。方形、標準質" "量的圖像生成速度最快。" #: models_provider/impl/xinference_model_provider/credential/tti.py:42 msgid "" "You can request 1 image at a time (requesting more images by making parallel " "requests), or up to 10 images at a time using the n parameter." msgstr "" "您可以一次請求 1 個圖像（通過發出並行請求來請求更多圖像），或者使用 n 參數一" "次最多請求 10 個圖像。" #: models_provider/impl/xinference_model_provider/credential/tts.py:20 msgid "Chinese female" msgstr "中文女" #: models_provider/impl/xinference_model_provider/credential/tts.py:21 msgid "Chinese male" msgstr "中文男" #: models_provider/impl/xinference_model_provider/credential/tts.py:22 msgid "Japanese male" msgstr "日語男" #: models_provider/impl/xinference_model_provider/credential/tts.py:23 msgid "Cantonese female" msgstr "粵語女" #: models_provider/impl/xinference_model_provider/credential/tts.py:24 msgid "English female" msgstr "英文女" #: models_provider/impl/xinference_model_provider/credential/tts.py:25 msgid "English male" msgstr "英文男" #: models_provider/impl/xinference_model_provider/credential/tts.py:26 msgid "Korean female" msgstr "韓語女" #: models_provider/impl/xinference_model_provider/xinference_model_provider.py:37 msgid "" "Code Llama is a language model specifically designed for code generation." msgstr "Code Llama 是一個專門用於代碼生成的語言模型。" #: models_provider/impl/xinference_model_provider/xinference_model_provider.py:44 msgid "" " \n" "Code Llama Instruct is a fine-tuned version of Code Llama's instructions, " "designed to perform specific tasks.\n" " " msgstr "" "Code Llama Instruct 是 Code Llama 的指令微調版本，專為執行特定任務而設計。" #: models_provider/impl/xinference_model_provider/xinference_model_provider.py:53 msgid "" "Code Llama Python is a language model specifically designed for Python code " "generation." msgstr "Code Llama Python 是一個專門用於 Python 代碼生成的語言模型。" #: models_provider/impl/xinference_model_provider/xinference_model_provider.py:60 msgid "" "CodeQwen 1.5 is a language model for code generation with high performance." msgstr "CodeQwen 1.5 是一個用於代碼生成的語言模型，具有較高的性能。" #: models_provider/impl/xinference_model_provider/xinference_model_provider.py:67 msgid "CodeQwen 1.5 Chat is a chat model version of CodeQwen 1.5." msgstr "CodeQwen 1.5 Chat 是一個聊天模型版本的 CodeQwen 1.5。" #: models_provider/impl/xinference_model_provider/xinference_model_provider.py:74 msgid "Deepseek is a large-scale language model with 13 billion parameters." msgstr "Deepseek Chat 是一個聊天模型版本的 Deepseek。" #: models_provider/impl/zhipu_model_provider/credential/tti.py:16 msgid "" "Image size, only cogview-3-plus supports this parameter. Optional range: " "[1024x1024,768x1344,864x1152,1344x768,1152x864,1440x720,720x1440], the " "default is 1024x1024." msgstr "" "圖片尺寸，僅 cogview-3-plus 支持該參數。可選範圍：" "[1024x1024,768x1344,864x1152,1344x768,1152x864,1440x720,720x1440]，默認是" "1024x1024。" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:34 msgid "" "Have strong multi-modal understanding capabilities. Able to understand up to " "five images simultaneously and supports video content understanding" msgstr "具有強大的多模態理解能力。能夠同時理解多達五張圖像，並支持視頻內容理解" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:37 msgid "" "Focus on single picture understanding. Suitable for scenarios requiring " "efficient image analysis" msgstr "專注於單圖理解。適用於需要高效圖像解析的場景" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:40 msgid "" "Focus on single picture understanding. Suitable for scenarios requiring " "efficient image analysis (free)" msgstr "專注於單圖理解。適用於需要高效圖像解析的場景(免費)" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:46 msgid "" "Quickly and accurately generate images based on user text descriptions. " "Resolution supports 1024x1024" msgstr "根據用戶文字描述快速、精準生成圖像。解析度支持1024x1024" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:49 msgid "" "Generate high-quality images based on user text descriptions, supporting " "multiple image sizes" msgstr "根據用戶文字描述生成高質量圖像，支持多圖片尺寸" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:52 msgid "" "Generate high-quality images based on user text descriptions, supporting " "multiple image sizes (free)" msgstr "根據用戶文字描述生成高質量圖像，支持多圖片尺寸(免費)" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:75 msgid "zhipu AI" msgstr "智譜 AI" #: models_provider/serializers/model_apply_serializers.py:32 #: models_provider/serializers/model_apply_serializers.py:37 msgid "vector text" msgstr "向量文本" #: models_provider/serializers/model_apply_serializers.py:33 msgid "vector text list" msgstr "向量文本列表" #: models_provider/serializers/model_apply_serializers.py:41 msgid "text" msgstr "文本" #: models_provider/serializers/model_apply_serializers.py:42 msgid "metadata" msgstr "元數據" #: models_provider/serializers/model_apply_serializers.py:47 msgid "query" msgstr "查詢" #: models_provider/serializers/model_serializer.py:43 #: models_provider/serializers/model_serializer.py:222 #: models_provider/serializers/model_serializer.py:259 #: models_provider/serializers/model_serializer.py:323 msgid "base model" msgstr "基礎模型" #: models_provider/serializers/model_serializer.py:44 #: models_provider/serializers/model_serializer.py:260 msgid "parameter configuration" msgstr "參數配置" #: models_provider/serializers/model_serializer.py:45 #: models_provider/serializers/model_serializer.py:225 #: models_provider/serializers/model_serializer.py:261 msgid "certification information" msgstr "認證信息" #: models_provider/serializers/model_serializer.py:233 #: models_provider/serializers/model_serializer.py:272 #, python-brace-format msgid "base model【{model_name}】already exists" msgstr "模型【{model_name}】已存在" #: models_provider/serializers/model_serializer.py:312 msgid "Model saving failed" msgstr "模型保存失敗" #: models_provider/serializers/model_serializer.py:325 msgid "create user" msgstr "創建用戶" #: models_provider/views/model.py:28 models_provider/views/model.py:29 #: models_provider/views/model.py:30 msgid "Create model" msgstr "創建模型" #: models_provider/views/model.py:54 models_provider/views/model.py:55 #: models_provider/views/model.py:56 msgid "Query model list" msgstr "查詢模型列表" #: models_provider/views/model.py:71 models_provider/views/model.py:72 #: models_provider/views/model.py:73 msgid "Update model" msgstr "更新模型" #: models_provider/views/model.py:85 models_provider/views/model.py:86 #: models_provider/views/model.py:87 msgid "Delete model" msgstr "刪除模型" #: models_provider/views/model.py:97 models_provider/views/model.py:98 #: models_provider/views/model.py:99 msgid "Query model details" msgstr "查詢模型詳情" #: models_provider/views/model.py:112 models_provider/views/model.py:113 #: models_provider/views/model.py:114 msgid "Get model parameter form" msgstr "獲取模型參數表單" #: models_provider/views/model.py:124 models_provider/views/model.py:125 #: models_provider/views/model.py:126 msgid "Save model parameter form" msgstr "保存模型參數表單" #: models_provider/views/model.py:141 models_provider/views/model.py:143 #: models_provider/views/model.py:145 msgid "" "Query model meta information, this interface does not carry authentication " "information" msgstr "查詢模型元信息，該接口不攜帶認證信息" #: models_provider/views/model.py:158 models_provider/views/model.py:159 #: models_provider/views/model.py:160 msgid "Pause model download" msgstr "下載模型暫停" #: models_provider/views/model_apply.py:25 #: models_provider/views/model_apply.py:26 #: models_provider/views/model_apply.py:27 #: models_provider/views/model_apply.py:38 #: models_provider/views/model_apply.py:39 #: models_provider/views/model_apply.py:40 msgid "Vectorization documentation" msgstr "向量化文檔" #: models_provider/views/model_apply.py:51 #: models_provider/views/model_apply.py:52 #: models_provider/views/model_apply.py:53 msgid "Reorder documents" msgstr "重新排序文檔" #: models_provider/views/provide.py:21 models_provider/views/provide.py:22 #: models_provider/views/provide.py:23 msgid "Get a list of model suppliers" msgstr "獲取模型供應商列表" #: models_provider/views/provide.py:44 models_provider/views/provide.py:45 #: models_provider/views/provide.py:46 msgid "Get a list of model types" msgstr "獲取模型類型列表" #: models_provider/views/provide.py:59 models_provider/views/provide.py:60 #: models_provider/views/provide.py:61 msgid "Example of obtaining model list" msgstr "獲取模型列表示例" #: models_provider/views/provide.py:78 models_provider/views/provide.py:79 #: models_provider/views/provide.py:80 msgid "Get model default parameters" msgstr "獲取模型默認參數" #: models_provider/views/provide.py:96 models_provider/views/provide.py:97 #: models_provider/views/provide.py:98 msgid "Get the model creation form" msgstr "獲取模型創建表單" #: system_manage/serializers/email_setting.py:28 msgid "SMTP host" msgstr "" #: system_manage/serializers/email_setting.py:29 msgid "SMTP port" msgstr "" #: system_manage/serializers/email_setting.py:30 #: system_manage/serializers/email_setting.py:34 msgid "Sender's email" msgstr "發送者郵箱" #: system_manage/serializers/email_setting.py:31 users/api/user.py:80 #: users/serializers/login.py:28 users/serializers/user.py:42 #: users/serializers/user.py:100 users/serializers/user.py:250 msgid "Password" msgstr "密碼" #: system_manage/serializers/email_setting.py:32 msgid "Whether to enable TLS" msgstr "是否啟用 TLS" #: system_manage/serializers/email_setting.py:33 msgid "Whether to enable SSL" msgstr "是否啟用 SSL" #: system_manage/serializers/email_setting.py:49 msgid "Email verification failed" msgstr "郵件認證失敗" #: system_manage/serializers/user_resource_permission.py:52 msgid "target id" msgstr "當前 ID" #: system_manage/serializers/user_resource_permission.py:69 msgid "Non-existent application|knowledge base id[" msgstr "不存在的應用|知識庫 ID[" #: system_manage/views/email_setting.py:30 #: system_manage/views/email_setting.py:31 #: system_manage/views/email_setting.py:32 msgid "Create or update email settings" msgstr "創建或更新郵件設置" #: system_manage/views/email_setting.py:35 #: system_manage/views/email_setting.py:48 #: system_manage/views/email_setting.py:61 msgid "Email Settings" msgstr "郵箱設置" #: system_manage/views/email_setting.py:44 #: system_manage/views/email_setting.py:45 msgid "Test email settings" msgstr "測試郵箱設置" #: system_manage/views/email_setting.py:57 #: system_manage/views/email_setting.py:58 #: system_manage/views/email_setting.py:59 msgid "Get email settings" msgstr "獲取郵箱設置" #: system_manage/views/user_resource_permission.py:29 #: system_manage/views/user_resource_permission.py:30 msgid "Obtain resource authorization list" msgstr "獲取資源授權列表" #: system_manage/views/user_resource_permission.py:33 #: system_manage/views/user_resource_permission.py:48 msgid "Resources authorization" msgstr "資源授權" #: system_manage/views/user_resource_permission.py:43 #: system_manage/views/user_resource_permission.py:44 msgid "Modify the resource authorization list" msgstr "修改資源授權列表" #: tools/serializers/tool.py:114 tools/serializers/tool.py:176 msgid "variable name" msgstr "變量名稱" #: tools/serializers/tool.py:116 msgid "type" msgstr "類型" #: tools/serializers/tool.py:118 msgid "fields only support string|int|dict|array|float" msgstr "欄位僅支持字符串|整數|字典|數組|浮點數" #: tools/serializers/tool.py:122 msgid "The field only supports custom|reference" msgstr "欄位僅支持自定義|引用" #: tools/serializers/tool.py:127 msgid "field name" msgstr "欄位名稱" #: tools/serializers/tool.py:128 msgid "field label" msgstr "標籤" #: tools/serializers/tool.py:138 tools/serializers/tool.py:156 #: tools/serializers/tool.py:394 msgid "tool name" msgstr "工具名稱" #: tools/serializers/tool.py:141 tools/serializers/tool.py:159 msgid "tool description" msgstr "工具描述" #: tools/serializers/tool.py:143 tools/serializers/tool.py:161 #: tools/serializers/tool.py:181 msgid "tool content" msgstr "工具內容" #: tools/serializers/tool.py:146 tools/serializers/tool.py:164 #: tools/serializers/tool.py:183 msgid "input field list" msgstr "輸入欄位列表" #: tools/serializers/tool.py:148 tools/serializers/tool.py:166 #: tools/serializers/tool.py:184 msgid "init field list" msgstr "內置欄位列表" #: tools/serializers/tool.py:168 tools/serializers/tool.py:185 msgid "init params" msgstr "內置參數" #: tools/serializers/tool.py:177 msgid "variable value" msgstr "變量名稱" #: tools/serializers/tool.py:190 msgid "function content" msgstr "工具內容" #: tools/serializers/tool.py:245 msgid "field has no value set" msgstr "欄位未設置值" #: tools/serializers/tool.py:261 tools/serializers/tool.py:266 msgid "type error" msgstr "類型錯誤" #: tools/serializers/tool.py:269 #, python-brace-format msgid "Field: {name} Type: {_type} Value: {value} Type conversion error" msgstr "欄位：{name} 類型：{_type} 值：{value} 類型轉換錯誤" #: tools/serializers/tool.py:274 msgid "tool id" msgstr "工具 ID" #: tools/serializers/tool.py:282 msgid "Tool not found" msgstr "工具不存在" #: tools/serializers/tool.py:334 users/api/user.py:39 users/api/user.py:93 #: users/api/user.py:109 users/serializers/user.py:284 msgid "User ID" msgstr "用戶 ID" #: tools/serializers/tool.py:396 msgid "tool type" msgstr "工具類型" #: tools/views/tool.py:21 tools/views/tool.py:22 tools/views/tool.py:23 msgid "Create tool" msgstr "創建工具" #: tools/views/tool.py:37 tools/views/tool.py:38 tools/views/tool.py:39 msgid "Get tool by folder" msgstr "通過文件夾獲取工具" #: tools/views/tool.py:55 tools/views/tool.py:56 tools/views/tool.py:57 msgid "Debug Tool" msgstr "調試工具" #: tools/views/tool.py:73 tools/views/tool.py:74 tools/views/tool.py:75 msgid "Update tool" msgstr "更新工具" #: tools/views/tool.py:89 tools/views/tool.py:90 tools/views/tool.py:91 msgid "Get tool" msgstr "獲取工具" #: tools/views/tool.py:104 tools/views/tool.py:105 tools/views/tool.py:106 msgid "Delete tool" msgstr "刪除工具" #: tools/views/tool.py:122 tools/views/tool.py:123 tools/views/tool.py:124 msgid "Get tool list by pagination" msgstr "獲取工具列表" #: tools/views/tool.py:146 tools/views/tool.py:147 tools/views/tool.py:148 msgid "Import tool" msgstr "導入工具" #: tools/views/tool.py:165 tools/views/tool.py:166 tools/views/tool.py:167 msgid "Export tool" msgstr "導出工具" #: tools/views/tool.py:183 tools/views/tool.py:184 tools/views/tool.py:185 msgid "Check code" msgstr "檢查代碼" #: users/api/user.py:51 users/api/user.py:148 msgid "Workspace ID" msgstr "工作空間 ID" #: users/api/user.py:64 users/serializers/login.py:27 #: users/serializers/user.py:41 users/serializers/user.py:88 msgid "Username" msgstr "用戶名" #: users/api/user.py:132 msgid "Email or Username" msgstr "郵箱或用戶名" #: users/serializers/login.py:29 users/serializers/login.py:69 msgid "captcha" msgstr "驗證碼" #: users/serializers/login.py:36 msgid "token" msgstr "令牌" #: users/serializers/login.py:50 msgid "Captcha code error or expiration" msgstr "驗證碼錯誤或過期" #: users/serializers/login.py:55 msgid "The user has been disabled, please contact the administrator!" msgstr "用戶已被禁用，請聯繫管理員！" #: users/serializers/user.py:32 msgid "Is Edit Password" msgstr "是否編輯密碼" #: users/serializers/user.py:33 msgid "permissions" msgstr "無權限訪問" #: users/serializers/user.py:43 users/serializers/user.py:80 #: users/serializers/user.py:209 msgid "Email" msgstr "郵箱" #: users/serializers/user.py:44 users/serializers/user.py:114 msgid "Nick name" msgstr "暱稱" #: users/serializers/user.py:45 users/serializers/user.py:119 #: users/serializers/user.py:222 msgid "Phone" msgstr "手機" #: users/serializers/user.py:94 msgid "Username must be 6-20 characters long" msgstr "用戶名必須為6-20個字符" #: users/serializers/user.py:107 users/serializers/user.py:257 msgid "" "The password must be 6-20 characters long and must be a combination of " "letters, numbers, and special characters." msgstr "密碼必須為6-20個字符，且必須包含字母、數字和特殊字符。" #: users/serializers/user.py:144 msgid "Email or username" msgstr "郵箱或用戶名" #: users/serializers/user.py:171 msgid "" "The community version supports up to 2 users. If you need more users, please " "contact us (https://fit2cloud.com/)." msgstr "" "社區版支持最多2個用戶，如需更多用戶，請聯繫我們（https://fit2cloud.com/）。" #: users/serializers/user.py:217 msgid "Name" msgstr "用戶名" #: users/serializers/user.py:229 msgid "Is Active" msgstr "是否啟用" #: users/serializers/user.py:240 msgid "Nickname is already in use" msgstr "Nickname已被使用" #: users/serializers/user.py:245 msgid "Email is already in use" msgstr "郵箱已被使用" #: users/serializers/user.py:264 msgid "Re Password" msgstr "確認密碼" #: users/serializers/user.py:269 msgid "" "The confirmation password must be 6-20 characters long and must be a " "combination of letters, numbers, and special characters." msgstr "確認密碼必須為6-20個字符，且必須包含字母、數字和特殊字符。" #: users/serializers/user.py:292 msgid "User does not exist" msgstr "用戶不存在" #: users/serializers/user.py:307 msgid "Unable to delete administrator" msgstr "無法刪除管理員" #: users/serializers/user.py:338 msgid "Cannot modify administrator status" msgstr "不能修改管理員狀態" #: users/views/login.py:21 users/views/login.py:22 users/views/login.py:23 msgid "Log in" msgstr "登錄" #: users/views/login.py:33 users/views/login.py:34 users/views/login.py:35 msgid "Get captcha" msgstr "獲取驗證碼" #: users/views/user.py:31 users/views/user.py:32 users/views/user.py:33 #: users/views/user.py:44 users/views/user.py:45 msgid "Get current user information" msgstr "獲取當前用戶信息" #: users/views/user.py:73 users/views/user.py:74 users/views/user.py:75 msgid "Get user list under workspace" msgstr "獲取工作空間下用戶列表" #: users/views/user.py:87 users/views/user.py:88 users/views/user.py:89 msgid "Create user" msgstr "創建者" #: users/views/user.py:101 users/views/user.py:102 users/views/user.py:103 msgid "Get default password" msgstr "獲取默認密碼" #: users/views/user.py:114 users/views/user.py:115 users/views/user.py:116 msgid "Delete user" msgstr "刪除用戶" #: users/views/user.py:125 users/views/user.py:126 users/views/user.py:127 msgid "Get user information" msgstr "獲取用戶信息" #: users/views/user.py:136 users/views/user.py:137 users/views/user.py:138 msgid "Update user information" msgstr "更新當前用戶信息" #: users/views/user.py:152 users/views/user.py:153 users/views/user.py:154 msgid "Change password" msgstr "修改密碼" #: users/views/user.py:167 users/views/user.py:168 users/views/user.py:169 msgid "Get user paginated list" msgstr "獲取用戶分頁列表"