Summary of Advanced Ingestion Process Powered by Llm Parsing For Rag System, By Arnau Perez et al.
Advanced ingestion process powered by LLM parsing for RAG system
by Arnau Perez, Xavier Vizcaino
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel multi-strategy parsing approach for Retrieval Augmented Generation (RAG) systems to process multimodal documents of varying structural complexity. The method employs LLM-powered OCR to extract content from diverse document types, including presentations and high text density files both scanned or not. A node-based extraction technique creates relationships between different information types and generates context-aware metadata. To enhance document comprehension and retrieval capabilities, the system implements a Multimodal Assembler Agent and a flexible embedding strategy. Experimental evaluations across multiple knowledge bases demonstrate the approach’s effectiveness, showing improvements in answer relevancy and information faithfulness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand different types of documents. It uses special AI technology to read and organize text from many kinds of files, including presentations and scanned documents. The method creates connections between different parts of the document and adds important details. This makes it easier for computers to find the right information in these documents. The researchers tested their approach on many different databases and found that it works well, improving how accurate and reliable the answers are. |
Keywords
» Artificial intelligence » Embedding » Parsing » Rag » Retrieval augmented generation
