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소스 채널 @phpdevelopersuz · Post #2990 · 12월 31일

Bot API was updated to version 6.4 Forums • Bots can now open, close, edit and toggle the visibility of the General Topic. • Added support for new service messages, like ForumTopicEdited, GeneralForumTopicHidden and more. • The method sendChatAction can now send actions to any thread or topic via the message_thread_id parameter. Spoilers • Added spoiler detection via the new has_media_spoiler field in the Message class. • Bots can send media covered by a spoiler animation via the has_spoiler field in sendPhoto, sendVideo and sendAnimation. Web Apps • Added a native QR scanner popup, controllable via showScanQrPopup and closeScanQrPopup. • Web Apps launched from the attachment menu can request clipboard text via readTextFromClipboard. • Added a platform field, showing which platform the web app is being used on. General • Added the is_persistent field, to keep ReplyKeyboards open by default. See the full changelog for details on the official website. #update#BotAPI https://t.me/+VMLgtEPNL49jZmNh

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GitHub Trends

@githubtrending · Post #15265 · 2025. 11. 03. PM 12:00

#python#ai#llm#rag#reasoning#retrieval PageIndex is an advanced AI tool that helps you find the most relevant information in long professional documents by thinking and reasoning like a human expert, rather than just matching keywords. It organizes documents into a clear tree structure, similar to a table of contents, and searches through this structure to give precise, trustworthy answers with exact page references. This method avoids the common problems of traditional vector-based search, making it ideal for complex reports, legal texts, or financial filings. You can use it easily via cloud services or run it locally, improving your ability to analyze and understand large documents quickly and accurately. https://github.com/VectifyAI/PageIndex

Machinelearning

@ai_machinelearning_big_data · Post #8801 · 2025. 10. 17. AM 10:13

⚡️ Omni-Embed-Nemotron - новая единая модель от NVIDIA для поиска по тексту, изображениям, аудио и видео Модель обучена на разнообразных мультимодальных данных и может объединять разные типы входных сигналов в общее векторное представление. - Поддержка всех типов данных: текст, изображение, аудио, видео. - Основана на архитектуре Qwen Omni (Thinker-модуль, без генерации текста). - Контекст - до 32 768 токенов, размер embedding — 2048. - Оптимизирована под GPU, поддерживает FlashAttention 2. Это делает её идеальной для: - кросс-модального поиска (поиск текста по видео или изображению); - улучшения RAG-проектов; - систем мультимодального понимания контента. Просто, быстро и эффективно - всё в одном открытом решении. 🌐 Открытая модель: https://huggingface.co/nvidia/omni-embed-nemotron-3b @ai_machinelearning_big_data #crossmodal#retrieval#openAI#NVIDIA#OmniEmbed#multimodal#AIModels#OpenSource#Search#UnifiedEmbedding