From Data to Decisions: Enterprise-Level Domain-Specific Graph Retrieval-Augmented Generation Systems for Advanced Question Answering

Retrieval-Augmented Generation (RAG) is aimed at improving the functionality of large language model (LLM) applications by incorporating specific data. This may include searching for relevant materials or files concerning a particular issue or search provided as background information to the LLM. Some prominent architectures like the Vanilla RAG architecture focuses primarily on retrieval of textual data, primarily utilizing vector databases, thus neglecting the structural intricacies of textual data, resulting in a critical gap in the generation process. To address this gap, we have introduced SAGE-QA (Scalable Advanced Graph RAG for Enterprise Question Answering), which significantly enhances both the retrieval and generation processes by emphasizing the importance of topological information for reasoning tasks on a variety of data sources. For our use case, SAGE-QA significantly outperforms current state-of-the-art RAG methods with its novel retrieval mechanism, which effectively mitigates hallucinations, and acts as a sustainable contextually relevant application.