Strategic Integration of LangChain, Hugging Face Transformers, and OpenAI for Document Intelligence Systems - Oyejide Timothy Odofin; Abraham Ayodeji Abayomi; Ejielo Ogbuefi; Jeffrey Chidera Ogeawuchi; Oluwasanmi Segun Adanigbo; Toluwase Peter Gbenle (International Journal of Scientific Research in Science, Engineering and Technology, 2024)
This paper explores the strategic integration of LangChain, Hugging Face Transformers, and OpenAI’s models to enhance document intelligence systems. Document intelligence, a vital component in automating document understanding, processing, and reasoning, benefits from the synergy between these advanced natural language processing (NLP) tools. LangChain’s chaining capabilities, Hugging Face’s pretrained models, and OpenAI’s foundation models are leveraged to automate and optimize document-related tasks such as retrieval, classification, summarization, and anomaly detection. This research presents a comprehensive overview of the key technologies and their combined power in transforming industries such as law, finance, and research. Through architectural design and case studies, the paper demonstrates how this integration can streamline complex workflows, reduce operational costs, and enhance decision-making. Moreover, it outlines potential use cases, including intelligent contract analysis, financial document intelligence, and knowledge extraction from academic research. Despite its promise, challenges such as interoperability, model performance, and domain-specific fine-tuning are discussed, with future research directions proposed to address these limitations. The findings underline the transformative potential of combining LangChain, Hugging Face, and OpenAI for document intelligence and suggest pathways for future exploration in this rapidly evolving field.