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Source channel @githubtrending · Post #15038 · Aug 8

#python#agent#agentic#agentic_ai#agents#agents_sdk#ai#ai_agents#aiagentframework#genai#genai_chatbot#llm#llms#multi_agent#multi_agent_systems#multi_agents#multi_agents_collaboration The Agent Development Kit (ADK) is an open-source Python toolkit that helps you easily build, test, and deploy smart AI agents, from simple helpers to complex multi-agent systems. It lets you write agent logic in Python, use many built-in or custom tools, and organize multiple agents to work together. You can deploy agents anywhere, including Google Cloud, and evaluate their performance with built-in tools. ADK supports flexible workflows and works with various AI models, not just Google’s. This means you get full control and flexibility to create powerful AI applications that fit your needs, speeding up development and making it easier to manage AI projects. https://github.com/google/adk-python

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GitHub Trends

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

#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 · 10/17/2025, 10:13 AM

⚡️ 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