Hybrid Retrieval-Augmented Generation (RAG) Systems with Embedding Vector Databases

Abstract

This article explores the integration of embedding vector databases into Retrieval-Augmented Generation (RAG) systems to enhance the capabilities of large language models. The article explores how hybrid retrieval strategies combining dense vector search with traditional keyword-based methods can address the limitations of standalone LLMs, particularly regarding knowledge cutoff, hallucinations, and access to domain-specific information. The article presents a comprehensive framework covering theoretical foundations, methodological approaches, implementation considerations, and experimental results across multiple domains. By leveraging vector embeddings for semantic search alongside traditional retrieval techniques, the proposed system demonstrates significant improvements in accuracy, relevance, and factual correctness while maintaining reasonable query response times. The article provides valuable insights for enterprise-scale deployments of RAG systems across various application domains including healthcare, legal, technical support, and financial services.