Core Insights - The introduction of SAG (SQL-Retrieval Augmented Generation) technology represents a significant advancement in AI, moving beyond traditional RAG methods to enhance data retrieval and processing capabilities [3][6][29] - SAG combines SQL-driven retrieval with vector-based fuzzy matching, achieving speed, accuracy, and comprehensiveness in data handling [6][18][29] Technology Overview - SAG utilizes SQL for precise retrieval while integrating vector capabilities for semantic matching, addressing the limitations of traditional RAG and GraphRAG methods [3][5][18] - The technology processes data by creating "Natural Language Vectors," which represent events with multi-dimensional attributes, allowing for real-time relationship building during queries [8][9][10] Applications - In enterprise settings, SAG serves as an intelligent decision-making assistant, transforming unstructured data into structured formats for better insights and decision support [22][23] - SAG can function as a universal data processing engine, enhancing existing algorithms in various applications such as e-commerce and financial risk control [24] - For personal use, SAG can create a searchable knowledge base and act as a memory hub for personal AI, maintaining user data privacy [26][27] Future Prospects - The open-source nature of SAG aims to democratize access to this technology, encouraging collaboration and further development within the industry [29][30] - SAG's vision includes transforming isolated data into interconnected assets, potentially revolutionizing productivity across various sectors [30][31]