Implementing Retrieval Augmented Generation Technique on Unstructured and Structured Data Sources in a Call Center of a Large Financial Institution

Abstract

The retrieval-augmented generation (RAG) technique enables generative AI models to extract accurate facts from external unstructured data sources. For structured data, RAG is further augmented by function calls to query databases. This paper presents an industrial case study that implements RAG in a large financial institution's call center. The study showcases experiences and architecture for a scalable RAG deployment. It also introduces enhancements to RAG for retrieving facts from structured data sources using data embeddings, achieving low latency and high reliability. Our optimized production application demonstrates an average response time of only 7.33 seconds. Additionally, the paper compares various open-source and closed-source models for answer generation in an industrial context.