Six months have passed since our last year-end review. As the initial wave of excitement sparked by DeepSeek earlier this year begins to wane, AI seems to have entered a phase of stagnation. This pattern is evident in Retrieval-Augmented Generation (RAG) as well: although academic papers on RAG continue to be plentiful, significant breakthroughs have been few and far between in recent months. Likewise, recent iterations of RAGFlow have focused on incremental improvements rather than major feature releases. Is this the start of future leaps forward, or the beginning of a period of steady, incremental growth? A mid-year assessment is therefore both timely and necessary.`

As 2024 comes to a close, the development of Retrieval-Augmented Generation (RAG) has been nothing short of turbulent. Let's take a comprehensive look back at the year's progress from various perspectives. `

Search technology remains one of the major challenges in computer science, with few commercial products capable of searching effectively. Before the rise of Large Language Models (LLMs), powerful search capabilities weren't considered essential, as they didn't contribute directly to user experience. However, as the LLMs began to gain popularity, a powerful built-in retrieval system became required to apply LLMs to enterprise settings. This is also known as Retrieval-Augmented Generation (RAG)—searching internal knowledge bases for content most relevant to user queries before feeding it to the LLM for final answer generation.`

As of v0.8, RAGFlow is officially entering the Agentic era, offering a comprehensive graph-based task orchestration framework on the back-end and a no-code workflow editor on the front-end. Why agentic? How does this feature differ from existing workflow orchestration systems?`