How to Build an AI-Powered RAG Search Application with Next.js, Supabase, and OpenAI

A comprehensive step-by-step guide to building a RAG (Retrieval-Augmented Generation) search application. Covers document upload and processing (PDF, DOCX, TXT), text extraction and chunking, embedding generation with OpenAI, vector storage in Supabase PostgreSQL with pgvector, semantic search implementation, and AI-powered

#openai

#nextjs

#rag

#vector-search

#supabase

Jan 27•37m read time• From freecodecamp.org

Table of contents

Table of Contents What You'll Learn Prerequisites Understanding the Technologies Project Overview Step 1: Create Your Next.js Project Step 2: Install Required Dependencies Step 3: Set Up Your Supabase Project Step 4: Configure Environment Variables Step 5: Create the Upload API Route Step 6: Create the RAG Search API Route Step 7: Create the Documents API Route Step 8: Create the Upload Modal Component Step 9: Create the PDF Viewer Modal Component Step 10: Create the Navigation Component Step 11: Create the Home Page (Search Interface)Step 12: Create the Documents Page Step 13: Test Your Application Step 14: Deploy Your Application How RAG Search Works Troubleshooting Common Issues Next Steps Conclusion

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