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RAG

Building a Robust RAG App with Guardrails using Portkey and MongoDB

Building a Robust RAG App with Guardrails using Portkey and MongoDB

Introduction Retrieval-Augmented Generation (RAG) has become a go-to technique for enhancing LLMs with external data. In this tutorial, we'll build a production-ready RAG application in just 10 minutes using Portkey, MongoDB Atlas, Patronus AI, and LlamaIndex. But first, what is RAG? Retrieval-Augmented Generation (RAG) is a technique that enhances LLMs

Open Sourcing Guardrails on the Gateway Framework

We are solving the *biggest missing component* in taking AI apps to prod → Now, enforce LLM behavior and route requests with precision, in one go.

Understanding RAG: A Deeper Dive into the Fusion of Retrieval and Generation

Understanding RAG: A Deeper Dive into the Fusion of Retrieval and Generation

Retrieval-Augmented Generation (RAG) models represent a fascinating marriage of two distinct but complementary components: retrieval systems and generative models. By seamlessly integrating the retrieval of relevant information with the generation of contextually appropriate responses, RAG models achieve a level of sophistication that sets them apart in the realm of artificial

Building Production-Ready RAG Apps

Building Production-Ready RAG Apps

💡This is Portkey's first collaboration with the Hasura Team. Hasura helps you build robust RAG data pipelines by unifying multiple private data sources (relational DB, vector DB, etc.) and letting you query the data securely with production-grade controls. LLMs have been around for some time now and have shown that