The conventional use of the Retrieval-Augmented Generation (RAG) architecture has proven effective for retrieving information from diverse documents. However, challenges arise in handling complex table queries, especially within PDF documents containing intricate tabular structures.This research introduces an innovative approach to enhance the accuracy of complex table queries in RAG-based systems. Our methodology involves storing PDFs in the retrieval database and extracting tabular content separately. The extracted tables undergo a process of context enrichment, concatenating headers with corresponding values. To ensure a comprehensive understanding of the enriched data, we employ a fine-tuned version of the Llama-2-chat language model for summarisation within the RAG architecture. Furthermore, we augment the tabular data with contextual sense using the ChatGPT 3.5 API through a one-shot prompt. This enriched data is then fed into the retrieval database alongside other PDFs. Our approach aims to significantly improve the precision of complex table queries, offering a promising solution to a longstanding challenge in information retrieval.

通过存储PDF文档和单独提取表格内容，使用Llama-2-chat语言模型进行汇总，并通过ChatGPT 3.5 API增强表格数据的上下文意义，该研究提出了一种创新方法，以显著提高Retrieval-Augmented Generation中复杂表格查询的准确性，为信息检索中长期存在的挑战提供了有希望的解决方案。

超越提取：通过语言模型对表格数据进行上下文化总结