A multi-strategy LLM-based document parsing pipeline for RAG is described, but the evaluation lacks a baseline comparison, so the claimed improvements are unsubstantiated.
Advanced ingestion process powered by LLM parsing for RAG system
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abstract
Retrieval Augmented Generation (RAG) systems struggle with processing multimodal documents of varying structural complexity. This paper introduces a novel multi-strategy parsing approach using LLM-powered OCR to extract content from diverse document types, including presentations and high text density files both scanned or not. The methodology employs a node-based extraction technique that creates relationships between different information types and generates context-aware metadata. By implementing a Multimodal Assembler Agent and a flexible embedding strategy, the system enhances document comprehension and retrieval capabilities. Experimental evaluations across multiple knowledge bases demonstrate the approach's effectiveness, showing improvements in answer relevancy and information faithfulness.
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Advanced ingestion process powered by LLM parsing for RAG system
A multi-strategy LLM-based document parsing pipeline for RAG is described, but the evaluation lacks a baseline comparison, so the claimed improvements are unsubstantiated.