🔗 Building RAG systems in Go with Ent, Atlas, and pgvector

In this blog post, we will explore how to build a RAG (Retrieval Augmented Generation) system using Ent, Atlas, and pgvector.

RAG is a technique that augments the power of generative models by incorporating a retrieval step. Instead of relying solely on the model’s internal knowledge, we can retrieve relevant documents or data from an external source and use that information to produce more accurate, context-aware responses. This approach is particularly useful when building applications such as question-answering systems, chatbots, or any scenario where up-to-date or domain-specific knowledge is needed.