Vibe Coding: Build AI Apps with OpenAI, Supabase & n8n

Agents operate in an environment, observe and remember context, reason about information, and autonomously act within a bounded rationality, guided by an internal memory cycle.

Discover agentic workflows that fuse knowledge bases, case history, memory, and user profiles to decide actions such as email, ticketing, or payment API, with human escalation when needed.

Get and safeguard your OpenAI API key, create a secret with project permissions, and set up Supabase as a Postgres-backed Firebase alternative.

Explore the full n8n workflow for AI apps using OpenAI and Supabase, covering webhooks, triggers, Rag-based retrieval, vector stores, embeddings, and code nodes.

Create a simple n8n webhook workflow, configure a trigger with test and production URLs, and use a field node to edit incoming data for an upload.

Test a webhook end-to-end by building a modern upload page for CSV, PDF, and TXT files, executing the workflow, and debugging post requests in Vibe coding.

Add a switch and file extractor in a vibe coding workflow to route incoming files by mime type (pdf, csv, text) and extract fields, validating binary file input.

Learn to align the uploaded binary file name with the input field by adjusting form data keys (files to data) for reliable file uploads in ai app workflows.

Demonstrate how to extend a webhook workflow by embedding metadata such as a user ID with a binary upload, and view the JSON payload and extracted fields.

Follow a hands-on code node to extract data from pdf, csv, and text inputs and combine them into a content array and JSON structure using JavaScript.

Troubleshoot a workflow to reliably pull the user id from a pdf upload by correcting the webhook node configuration, using extract field steps, and validating json metadata.

Explore retrieval augmented generation (rag), which blends retrieval and generation to answer questions from your data using document chunking, embeddings, and a vector store.

Explore how Supabase functions as a Postgres SQL based persistence layer with ACID compliance, REST APIs, auth, real time updates, and a vector store for embeddings and semantic search.

Discover how vector databases encode unstructured data into vectors for efficient similarity search in high-dimensional space, and compare them to traditional SQL databases and their limitations.

Transform unstructured data, such as PDFs or images, into embeddings and store them in a vector database. Compare embeddings to retrieve semantically similar content for querying large language models.

Set up your supabase account by signing in with email or GitHub, confirm your email, create an organization, and initialize a new project with a strong password and region selection.

Set up Supabase in n8n and create a documents table with embeddings for a vector store. Learn to connect credentials, enable the PG vector extension, and use match_documents for retrieval.

Create embeddings with OpenAI to vectorize text chunks and store them in documents table of the Supabase vector store, using a recursive character splitter with 1000-character chunks and 20% overlap.

Set up a chat AI agent within an n8n workflow, enabling a retrieval augmented generation system that uses Supabase vector store for embeddings and Postgres memory to store chat history.

Set up a full rag system with Supabase vector store, embeddings, and OpenAI embeddings to enable semantic search and retrieve real documents for an AI agent.

Learn how supabase authentication creates unique user ids and secure sessions, enabling users to upload files and chat with only their own documents.

Learn to configure Supabase authentication with email sign-in, integrate Bolt for frontend login and registration, and display the signed-in user name while preparing document-based chat access.

Implement metadata enrichment by extracting the authenticated user ID from the webhook and attaching it to each uploaded document in Supabase for secure, user-specific access.

Build and test a chat interface integrated with an n8n webhook that passes user and session data to an OpenAI-powered AI agent and retrieves document-based answers from Supabase.

Verify that adding a user ID as metadata and filtering vector store results confines chat to the current user's documents.

Overview of building a YouTube transcript workflow in a Rag-based AI app: extract transcripts, chunk text, generate embeddings, and store in a vector database for user-specific chat.

Explore RapidAPI to find and test a YouTube transcript API, sign up, and obtain an API key. Test the endpoint with a YouTube video URL to retrieve its transcript.

Set up a webhook to call the RapidAPI YouTube transcript endpoint via HTTP request, configure headers and lang parameters, and send a body with video URL and user ID.

Add an if node to validate a successful payload before continuing, else stop with an error; then save the transcript and metadata to a database table named transcript.

Create a transcript table in Supabase, save transcripts with user and video details, and store embeddings in the Supabase vector store using OpenAI embeddings.

Practice testing the internal chat node with youtube transcript documents by validating json payload handling, session and user identifiers, and vector store integration to retrieve transcripts during chat.

Builds the backend and frontend, with YouTube transcript and document processing workflows. Enables chat with transcripts and uploads via a vector database, plus a polished docu chat UI with authentication.

Activate production webhook URLs for chat, extraction, YouTube transcripts, and uploads, then test the app to validate automatic production executions and correct transcript processing from Supabase to the frontend.

Refine the user interface by updating the top bar to show docu chat title, user name, and a logout option. Add a light/dark mode toggle and improve transcripts upload, history.

Showcase a transcript manager that retrieves saved transcripts from a Supabase transcripts table and displays YouTube thumbnails, video URLs, and playback options, with a user-friendly history view and transcript download.

Integrate stripe to upgrade free users to pro after they reach three items and three transcripts, using a webhook and Supabase to update status.

Develop and test a user usage limit engine that starts authenticated users with three uploads and three transcript extractions, tracks usage, and prompts upgrade to a pro plan.

Set up Stripe for testing in your app by creating a Stripe account, using the sandbox, and accessing the developer dashboard to obtain the secret key and configure basic payments.

Learn to set up Stripe recurring payments in a Supabase app by creating a recurring product, configuring API keys and environment variables, and connecting checkout with edge functions and webhooks.

deploy the app live with Netlify, connect GitHub, configure Stripe payments and authentication, and validate the pro upgrade flow with proper routing and a success page.

Troubleshoot deployment and debugging of the success page and url redirection for the vibe coding app, using Netlify, Vite, Supabase env vars, and Stripe upgrades to the pro tier.

Switch from stripe test mode to live mode by configuring live API keys, webhooks, and edge functions with supabase. Learn to create a live checkout flow and validate payments end-to-end.

Consolidate learning by turning agent workflows into production-ready apps, leveraging vibe coding, ai coding tools, Supabase authentication, vector embeddings, and Stripe payments.