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

1. 1Design a retrieval-augmented generation system using Python
2. 2Ingest data from Document360 and SharePoint
3. 3Store embeddings in Aurora
4. 4Configure the RAG backend for production
5. 5Test and deploy the RAG backend

Data scientists and software engineers can benefit from this article to design and implement a RAG backend for enterprise knowledge bases

💡 RAG backend can be built using Python and Aurora to ingest data from various sources and store embeddings for efficient retrieval

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