Building a Production-Ready RAG Application with LangChain, pgvector, and Gemini (opens in new tab)

Retrieval-Augmented Generation (RAG) is a powerful pattern to build applications that can query, understand, and extract insights from your custom documents (like PDFs, resumes, and reports) by feeding them as context to Large Language Models (LLMs). This guide walks you through building a complete RAG API step-by-step, explaining the architecture, code, and debugging learnings along the way. 1. Architecture Overview A typical RAG pipeline is divided into two parts: A. Ingestion Phase (Write-...