DEV Community: neehar priydarshi

Implementing Retrieval-Augmented Generation with LangChain, Pgvector and OpenAI

neehar priydarshi — Thu, 21 Nov 2024 06:39:40 +0000

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

Understanding Retrieval-Augmented Generation (RAG) with OpenAI

neehar priydarshi — Thu, 07 Nov 2024 07:34:34 +0000

In this blog, we’ll explore the concept of Retrieval-Augmented Genration (RAG) and how it enhances AI models like GPT-4. While these model excel at generating human like responses, they are limited by the data they have been trained on. RAG overcomes this by integrating real-time knowledge retrieval, allowing models to access and use up-to-date information, improving both accuracy and relevance.
Read more: https://www.codemancers.com/blog/2024-09-17-rag/?utm_source=social+media&utm_medium=dev.to