  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.
