Build an Optimized RAG System with Reranking & Recall/MRR Evaluation | PgVector, LangSmith, FastAPI

Description

Learn how to build a Retrieval-Augmented Generation (RAG) system that’s truly production-ready! In this video, I’ll guide you step-by-step through: 1. Building a FastAPI-based backend for RAG 2. Integrating PgVector for vector storage 3. Using LangSmith for evaluation, tracing, and debugging 4. Measuring Recall and MRR (Mean Reciprocal Rank) for performance optimization 5. Implementing Reranking to improve response accuracy This tutorial is ideal for developers, researchers, and AI engineers looking to master RAG pipelines and performance metrics. 👉 GitHub Repository: https://github.com/Sarfaraz021/optimization-rag-system