智能体驱动的检索

Search documents
长上下文窗口、Agent崛起,RAG已死?
机器之心· 2025-10-19 09:17
Core Viewpoint - The article discusses the evolving landscape of Retrieval-Augmented Generation (RAG) and its potential obsolescence due to advancements in context engineering and agent capabilities, suggesting that RAG is not dead but rather transforming into a more sophisticated retrieval paradigm [2][5][21]. Group 1: RAG's Evolution and Current Status - RAG has become a standard solution for addressing the limitations of LLM input lengths, acting as an external knowledge base since 2022 [3][4]. - The emergence of long context windows and agent capabilities is challenging RAG's traditional role, leading to debates about its relevance [5][6]. - RAG is evolving into "agentic retrieval," where AI agents play a central role in advanced retrieval systems, moving beyond basic block retrieval [8][21]. Group 2: Stages of RAG Development - The first stage of RAG involves basic "Top-k" retrieval, where documents are split into chunks, and the most relevant chunks are retrieved based on user queries [10][11]. - The second stage introduces lightweight agents for automatic routing, allowing the system to intelligently select the appropriate retrieval method based on user queries [15]. - The third stage expands to composite retrieval APIs, enabling the system to handle multiple document formats efficiently [17][19]. Group 3: RAG's Future and Integration with Agents - The ultimate goal is to create a fully agent-driven knowledge system that can make intelligent decisions at every stage of the retrieval process [18][21]. - RAG is being redefined as a powerful component within an agent toolbox, rather than the default architecture for all applications [54]. - The future landscape will likely see a combination of various technologies tailored to specific application scenarios, emphasizing the importance of understanding the strengths and weaknesses of each paradigm [52][54].