Building a Robust RAG App with Guardrails using Portkey and MongoDB

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external data to provide more accurate and relevant responses, addressing the limitations of LLMs which are restricted to their training data. This tutorial demonstrates how to build a production-ready RAG application in 10 minutes using Portkey, MongoDB Atlas, Patronus AI, and LlamaIndex. Portkey serves as an LLM Ops platform offering AI gateway management, observability, and robust guardrails for responsible AI development. MongoDB Atlas provides a cloud database with native vector search capabilities, while LlamaIndex simplifies data ingestion and indexing. Patronus AI evaluates LLM performance and monitors for hallucinations and other unsafe behaviors. The tutorial covers setting up the environment, building a RAG pipeline using McDonald's SEC 10-K filing, configuring document stores and Portkey, and querying the retrieval system. It also addresses challenges such as hallucinations and lack of observability, offering solutions through Portkey's integration with Patronus AI for enhanced guardrails and observability. The tutorial concludes by suggesting improvements like error handling, performance optimization, and scaling strategies to enhance the RAG application's reliability and efficiency.

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