A unified LangChain, Groq Cloud and Llama-Based Approach to Document Information Retrieval in Multilingual Documents

Abstract

Document information extraction has emerged as a critical component of Natural Language Processing (NLP) as organizations increasingly digitize their operations. This paper provides an in-depth overview of the existing methods applied to retrieve structured information from various document types, including OCR to extract both handwritten and printed text, concept and semantic structure extraction, and Named Entity Recognition (NER). Although there has been an improvement in these areas, most of the currently available solutions have difficulties in handling complex, multilingual documents and usually cannot ensure the preservation of contextual integrity between different formats. The paper introduces a novel system that integrates LangChain to divide the document, Groq Cloud to find the desired data in a short time, and the multilingual model of Llama to improve the quality of extracted information. This experimental finding demonstrates that this method is better than the traditional models particularly where legal and regulatory frameworks are involved and they need a close interpretation. The paper concludes with the discussion of the directions of the research in the future, as more flexible and context-dependent frameworks are needed in document information extraction.