Enterprise RAG at scale: search techniques for million-document databases

Reducto is a cutting-edge document processing product designed to handle the complexities of Retrieval-Augmented Generation (RAG) at an enterprise scale, capable of managing vast document volumes across various formats with high-speed user query responses. RAG enhances large language model outputs by indexing documents into vector embeddings, retrieving relevant content via similarity searches, and augmenting these with language models for accurate information retrieval. The challenges of scaling RAG involve ingestion, latency, and data relevancy, especially when dealing with diverse document types like multilingual invoices and scanned contracts. Effective RAG implementation at scale necessitates a robust ingestion process, strategic retrieval techniques such as pure vector retrieval, hybrid search, and vector search with metadata filtering, and contextual retrieval to ensure precision and context. Reducto's approach emphasizes high accuracy, multi-modal processing, and enterprise-grade reliability, offering a foundation for enterprises to develop scalable and effective RAG systems, supported by rigorous evaluation frameworks to maintain quality and performance in real-world applications.