Building a Production RAG Backend for Enterprise Knowledge Bases: Architecture, Decisions, and…

1. 1Design a retrieval-augmented generation system that can ingest data from multiple sources
2. 2Configure data ingestion from Document360 and SharePoint using APIs or data connectors
3. 3Build an embedding storage system using Aurora or a similar database
4. 4Implement a query interface to retrieve relevant embeddings and generate responses
5. 5Test and optimize the RAG backend for production deployment

This article benefits software engineers and data scientists working on enterprise knowledge base projects, as it provides a detailed overview of the architecture and design decisions for a production RAG backend

💡 A well-designed RAG backend can efficiently ingest and store embeddings from multiple sources, enabling effective retrieval-augmented generation for enterprise knowledge bases

🚀 Build a production RAG backend for enterprise knowledge bases with Aurora and Document360