Implementing Retrieval-Augmented Generation with LangChain, Pgvector and OpenAI

In the previous blog, we explored how Retrieval-Augmented Generation (RAG) can augment the capabilities of GPT models. This post takes it a step further by demonstrating how to build a system that creates and stores embeddings from a document set using LangChain and Pgvector, allowing us to feed these embeddings to OpenAI's GPT for enhanced and contextually relevant responses.
Read more: https://www.codemancers.com/blog/2024-10-24-rag-with-langchain/?utm_source=social+media&utm_medium=dev.to