Hierarchical Indexing and Contextual Enrichment in RAG Systems

In the digital era, companies encounter the task of effectively organizing and retrieving large quantities of data and code. This thesis investigates the improvement of Retrieval-Augmented Generation (RAG) systems by enhancing indexing and incorporating contextual information. The objective is to increase the accuracy and relevance of information retrieval. This study examines the incorporation of sophisticated indexing methods and context enhancement tactics in RAG systems. By improving the indexed information and enhancing the context for generative AI, the system can better comprehend and handle intricate industrial data. The hierarchical indexing method effectively represents the organization of extensive codebases, resulting in better retrieval of content and overall enhancement of the performance of the RAG system. During the development process, multiple components were assessed to guarantee the establishment of a feasible and expandable proof of concept (POC). The effectiveness of the proposed upgrades was evaluated through a number of trials, which showed substantial improvements in information retrieval and user experience. To summarize, this research emphasizes the possibility of improving RAG systems by strengthening indexed information and contextual awareness.