Building a Production-Ready RAG Application with LangChain, pgvector, and Gemini

Retrieval-Augmented Generation (RAG) is a powerful pattern to build applications that can query,...

mercoledì 17 giugno 2026 New tab

982 words~4 min read

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-Path)

Building a Production-Ready RAG Application with LangChain, pgvector, and Gemini

Building a Production-Ready RAG Application with LangChain, pgvector, and Gemini

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