Structured RAG: Overcoming Traditional Retrieval Limitations to Build Enterprise-Grade Trustworthy AI Decision Engines
7 hours ago 高效码农In the wave of enterprise digital transformation, Retrieval-Augmented Generation technology has become a crucial bridge connecting large language models with private knowledge bases. However, when this technology is applied to enterprise environments with extremely high accuracy requirements, its inherent limitations gradually become apparent, potentially even triggering serious business risks. The RAG Dilemma in Enterprise Applications: Why Traditional Methods Fall Short Traditional embedding-based retrieval-augmented generation methods retrieve relevant information by calculating semantic similarity between queries and document fragments. While this approach performs well with narrative, open-ended questions, it proves inadequate for the structured, precise query scenarios common in enterprises. The Natural …
MMDocRAG: How Multimodal Retrieval-Augmented Generation Transforms Document QA Systems
5 months ago 高效码农MMDocRAG: Revolutionizing Multimodal Document QA with Retrieval-Augmented Generation The Dual Challenge in Document Understanding Today’s Document Visual Question Answering (DocVQA) systems grapple with processing lengthy, multimodal documents (text, images, tables) while performing cross-modal reasoning. Traditional text-centric approaches often miss critical visual information, creating significant knowledge gaps. Worse still? The field lacks standardized benchmarks to evaluate how well models integrate multimodal evidence. MMDocRAG Architecture Diagram Introducing the MMDocRAG Benchmark Developed by leading researchers, MMDocRAG provides a breakthrough solution with: 4,055 expert-annotated QA pairs anchored to multi-page evidence chains Novel evaluation metrics for multimodal quote selection Hybrid answer generation combining text and …