Semi-Structured Data Parsing and Vectorization Using Retrieval-Augmented Generation

Parsing and vectorizing semi-structured data has long posed significant challenges due to its hybrid nature, which includes both structured and unstructured elements. Introducing a novel approach that integrates Retrieval-Augmented Generation (RAG) within the LLama model, this research significantly enhances the model's capability to handle diverse and complex data formats. The modified LLama model, through the integration of a retrieval mechanism, dynamically accesses and incorporates relevant external information during the generation process, resulting in marked improvements in accuracy, efficiency, and robustness. Experimental results demonstrate substantial enhancements in precision, recall, and F1 score, alongside reduced processing time and optimized resource utilization. This hybrid architecture provides a scalable solution for parsing and vectorizing semi-structured data, offering practical applications across domains such as healthcare, finance, and customer service. The research highlights the potential of integrating retrieval mechanisms within generative models to address the complexities of semi-structured data, paving the way for future advancements in natural language processing.