Microsoft says Agent Framework 1.0 is the production-ready release, with stable APIs and long-term support for both .NET and Python. The framework is presented as a unified successor path that builds ...

Retrieval-Augmented Generation (RAG) is critical for modern AI architecture, serving as an essential framework for building context-aware agents. But moving from a basic prototype to a ...

Ever thought what turns a good idea into a working application? The short and simple answer to this question is selecting the right framework. As Python has gained popularity among web development ...

Index any document into a navigable tree structure, then retrieve relevant sections using any LLM. No vector databases, no embeddings — just structured tree retrieval. Available for both Python and ...

FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 36 pre-processed benchmark RAG datasets and 16 state-of-the-art ...

Most enterprise RAG pipelines are optimized for one search behavior. They fail silently on the others. A model trained to synthesize cross-document reports handles constraint-driven entity search ...

Abstract: The paper presents a novel Retrieval-Augmented Generation (RAG) framework for intelligent banking assistants, integrating structured financial and regulatory data to improve accuracy and ...

In this tutorial, we build an advanced, end-to-end learning pipeline around Atomic-Agents by wiring together typed agent interfaces, structured prompting, and a compact retrieval layer that grounds ...

NVIDIA has published a comprehensive technical guide for building production-ready document processing pipelines using its Nemotron RAG model suite, addressing a persistent pain point for enterprises ...

Abstract: Retrieval Augmented Generation (RAG) has brought a potent way of supplementing the factual accuracy of large language model (LLM) responses through external knowledge sources. Nevertheless, ...