🤯 From Zero To A RAG System In 20 Minutes (No Code!)

From Zero to RAG Agent: A Complete Beginner's Course (No Code Required)

Let’s be honest. For the past year, "AI" has mostly been a synonym for a really smart chatbot. It's been impressive but it's also been like having a brilliant new hire who has read the entire internet but has never seen one of your company's documents. They can talk about anything in the world but they know nothing about your business.

That era is over.

Welcome to the world of RAG (Retrieval-Augmented Generation). This is the technology that allows you to give your AI a custom-built brain, a private library filled with your data. It's the difference between having a smart intern and having a seasoned expert who has already memorized every single one of your company's product manuals, support tickets and internal policies.

Ready to build your first AI agent that has a real, working knowledge of your own data? In this guide, you will learn how to create a fully functional RAG system in about 20 minutes, using the powerful no-code combination of n8n and the Supabase vector database.

This guide is designed for the absolute beginner. No coding experience is needed. By the end, you will not only understand what a RAG system is and how vector databases work but you will have a working system that can answer specific questions based on a document that you provide.

What is RAG? (And Why It's Simpler Than You Think)

RAG stands for Retrieval-Augmented Generation. That's a mouthful, so let's use a simpler analogy. It’s like an open-book test.

Imagine someone asks you, "How many feet are in a mile?" If you don't know the answer off the top of your head, you'd probably perform a quick search, retrieve the correct information (5,280 feet) and then use that retrieved fact to generate your answer.

That's all a RAG system does. When you ask an AI a question, it doesn't just rely on its general knowledge. It first retrieves relevant information from a specific data source you've given it and then uses that information to augment (or improve) the generation of its final answer. It's an AI that knows how to do its own research before it speaks.

How a Vector Database Actually Works (The "Mind Palace" Analogy)

The "brain" of our RAG agent, where it stores all its knowledge, is a vector database. This sounds incredibly complex but the concept is surprisingly intuitive.

Imagine a giant, three-dimensional space, like a galaxy. A vector database is a multi-dimensional space where we store "vectors". Think of a vector as a single point of light in this galaxy. Each point of light represents a small chunk of text from your documents and it's placed in the galaxy based on its meaning.

This is the key. The database doesn't organize things alphabetically; it organizes them by semantic similarity. All the points of light related to "fruits" will naturally cluster together in one corner of the galaxy. All the points related to "animals" will form a constellation in another.

When you ask a question like, "What is a kitten?", your question itself is turned into a new point of light and placed into the galaxy. It naturally appears right in the middle of the "animal" constellation. The RAG system then says, "Okay, what are the 10 closest points of light to this new question?" It finds the vectors for "cat", "puppy", and "wolf", retrieves the text associated with them and uses that hyper-relevant information to give you a perfect answer. For our database today, we'll be using Supabase, which has a powerful vector database built right in.

The Two Halves of the System: The Librarian and the Scholar

It's the middle of the work week - the perfect time to focus on the foundational, behind-the-scenes work that makes the real magic happen later. Building a RAG system is exactly that. It's a two-phase project that requires you to build two distinct but complementary, halves of a single brain.

To make this simple, let's use an analogy: we are going to build a magical, intelligent library.

• Part 1: The AI Librarian (The RAG Pipeline). This is the back-office, behind-the-scenes worker. The Librarian's only job is to read every single book (your documents), understand what they're about and then place every single piece of information on the correct shelf in an infinitely large, perfectly organized library (your vector database). This is a one-time process that happens before the library opens to the public.

• Part 2: The AI Scholar (The RAG Agent). This is the front-office, public-facing expert. The Scholar sits at the front desk, ready to help visitors. When a user asks a difficult question, the Scholar instantly zips through the library, pulls the exact pages from the correct books, synthesizes the information and provides a brilliant, custom-written answer.

You cannot have one without the other. A Scholar with no library is just a generic chatbot with no specialized knowledge. A library with no Scholar is just a silent, inaccessible database. To build a truly intelligent RAG agent, you must first hire and train your Librarian.

Building the RAG Pipeline: How to Hire and Train Your AI Librarian

The pipeline is the workflow that takes your raw, unstructured documents and transforms them into a perfectly organized and searchable knowledge base. Its job is to ingest knowledge.

The process can be broken down into four key steps, which mirror how a real librarian would organize a library.

The Four Steps to Building a Knowledge Base

Document Input (Acquiring the Book): Every great library starts with a book. In our case, this is your source document - a PDF, a text file or even data from another application. This is the raw knowledge we want to teach our AI.

Chunk (Separating the Pages): You wouldn't file a 500-page book under a single topic like "Science". It's too broad. A good librarian would recognize that it contains chapters on physics, biology and chemistry. "Chunking" is the digital equivalent. We take our large document and split it into smaller, more focused pieces of text. This is a critical step, as it creates highly specific, context-rich "pages" for our AI to search through later.

Embed (Translating to the Language of Concepts): This is the most magical part of the process. Computers don't understand words but they can understand relationships between numbers. An "embedding model" is a sophisticated AI that acts as a universal translator. It reads each chunk of text and converts its semantic meaning into a string of numbers called a "vector". This vector is like a coordinate, a specific point in a giant "galaxy of meaning".

Vectorize (Placing the Pages on the Shelf): This is the final step where the Librarian files everything away. Each chunk of text, now represented by its unique vector coordinate, is stored in our Supabase vector database. The database intelligently places it in that "galaxy of meaning", ensuring that chunks about "fruits" are clustered near other chunks about "fruits", and far away from chunks about "car engines".

The n8n Implementation: Your Librarian's 5-Node Assembly Line

In n8n, this entire complex process is handled by a beautiful, simple workflow of just five nodes.

• The Trigger (The "Start Work" Button): A manual trigger to start the indexing process.

• Google Drive (The Book Delivery Service): This node connects to your Google Drive and fetches the raw document (our "book").

• Data Loader (The Page Separator): This node handles the "chunking", automatically splitting your document into perfectly sized pages.

• Embeddings Model (The Universal Translator): This node, typically using an OpenAI model, takes each text chunk and converts it into a numerical vector.

• Supabase Vector Store (The Infinite Library): This final node takes the vectors and stores them in your database, completing the Librarian's job.

You run this workflow once for each document you want to add to your RAG system's brain. With this foundational work done, your library is now stocked organized and ready for your AI Scholar to begin its research.

Our Mission: Building the "AI Golf Caddie"

To make all of this theory concrete, we need a mission. A clear, hands-on project that takes us from start to finish. For our mid-week project, we’re going to build the perfect digital assistant for a very specific and notoriously complex domain: the official rules of golf.

We will be using a 22-page PDF document, “The Rules of Golf Simplified,” as the single source of truth for our AI's brain.

Why is this the perfect example? Because the rules of golf are a dense, specific and self-contained body of knowledge. A generic AI like ChatGPT might know some of the rules but it could easily get them wrong, confuse them with outdated versions or hallucinate a rule that doesn't exist. A RAG agent trained exclusively on the official, simplified rulebook will become a true, reliable expert. It’s the perfect way to demonstrate the power of giving your AI a specific, curated brain.

The goal is simple: once the PDF has been processed through our RAG pipeline, we will have an "AI Caddie" that can instantly and accurately answer tricky, real-world questions that a golfer might have on the course, such as:

• "What am I allowed to do for practice?"

• "Can I hit a practice shot between playing two holes?"

• "What are the specific rules for where I can place my tee?"

Beyond the PDF: This Is a Universal Blueprint

But let's be crystal clear: this tutorial is not really about golf.

The golf PDF is just a placeholder, a stand-in for any body of specialized knowledge you can imagine. The exact same process you are about to learn can be applied to an infinite number of more powerful, business-critical applications.

Your Data Source Can Be Anything

Instead of a PDF of golf rules, your data source could be:

• Your last 5,000 HubSpot support tickets. You could then build an internal RAG system for your support team and ask it questions like, "What are the top three most common product issues our customers in the UK have faced in the last quarter?"

• An Airtable base of all your project management data. You could then build an executive assistant agent and ask it, "Which of our projects have gone over budget this year and what were the common reasons cited in the project notes?"

• The content of 500 of your most recent client emails. You could then build a sales agent and ask it, "What are the most common objections our prospects have raised in the last month?"

Your Agent's Trigger Can Be Anything

Similarly, your agent doesn't have to be triggered by a chat window. The trigger could be:

• An Email: You could set up an email address like [email protected]. When someone sends a question to this address, it triggers the RAG agent to find the answer in your knowledge base and automatically email the response back.

• A Form Submission: A "Contact Us" form on your website could trigger the agent to provide an instant, helpful answer to a user's question before a human ever has to see it.

• A Scheduled Task: You could have a workflow that runs every Monday morning, asking the agent to summarize all of the customer support tickets from the previous week and send the report to your management team.

The skills you are about to learn by building this simple "AI Golf Caddie" are the exact same skills you need to build any of these more advanced and powerful business systems.

Step-by-Step Build Guide: Let's Get Our Hands Dirty

This is a no-code tutorial. You don't need to be a developer but you do need to be able to follow a checklist to build your RAG system.

Step 1: Building the Library - Your Supabase Setup

Every brain needs a home. For our AI agent, that home will be a powerful and surprisingly user-friendly database called Supabase. It’s the perfect choice for this project because it’s built on top of the rock-solid PostgreSQL, it has a beautiful interface that makes it easy to manage and it offers a generous free tier that is more than enough to build and run your first few agents.

Think of this step as building the physical library building. We're about to lay the foundation, put up the walls and install the special, magical shelving system that will hold all of our AI's knowledge.

It’s the middle of the day in the middle of the week - the perfect time to tackle a foundational, satisfying task. So let's build a database.

1.1 Creating the Project (Laying the Foundation)

First, you need to create your Supabase project. This is the container for your database, your user authentication and all the other powerful tools Supabase offers.

1. Navigate to supabase.com and click “Start your project.”

2. After signing up, create a New Project. You'll need to give it a name and, most importantly, create a strong database password.

3. Critical Tip: Treat this password like the master key to your entire operation. Save it in a secure password manager immediately. You will need it later and recovering it can be a pain.

4. Once you've configured your project, Supabase will begin to spin it up. This might take a minute or two. Go grab a glass of water. You'll know it's ready when the project status turns green.

1.2 Configuring the Vector Database (Installing the Magical Shelves)

This next part looks intimidating because it involves a snippet of code but don't worry. Think of it as a one-time magic spell you recite to give your database its superpowers.

Navigate to the "SQL Editor" in your Supabase dashboard. You'll see a window where you can run commands. You need to copy and paste the following block of code into the editor and click "Run".

-- Enable the pgvector extension to work with embedding vectors
create extension vector;

-- Create a table to store your documents
create table documents (
  id bigserial primary key,
  content text, -- corresponds to Document.pageContent
  metadata jsonb, -- corresponds to Document.metadata
  embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed
);

-- Create a function to search for documents
create function match_documents (
  query_embedding vector(1536),
  match_count int default null,
  filter jsonb DEFAULT '{}'
) returns table (
  id bigint,
  content text,
  metadata jsonb,
  similarity float
)
language plpgsql
as $$
#variable_conflict use_column
begin
  return query
  select
    id,
    content,
    metadata,
    1 - (documents.embedding <=> query_embedding) as similarity
  from documents
  where metadata @> filter
  order by documents.embedding <=> query_embedding
  limit match_count;
end;
$$;

Translating the Magic Spell

That code looks scary but it’s actually doing three very simple and logical things. Let's translate it from nerd-speak into plain English.

Part 1: CREATE EXTENSION vector;

Translation: "Hey database, please install the special add-on so you can work with AI ‘vectors.’” Vectors are just a fancy way of storing the meaning of text as a bunch of numbers. By running this, you’re giving your database the ability to understand and store information based on meaning, not just plain text.

Part 2: CREATE TABLE documents (...)

Translation:
“Now, let’s set up the main storage for our document library. For every entry, I want four things:

• an ID number,

• the actual text of a chunk of the document,

• a spot for extra metadata (like title or tags) and

• a special column called embedding that holds the vector itself.”

Crucial detail:
The vector(1536) part sets how big your vectors are - 1536 numbers per vector. This must match whatever AI model you use for generating embeddings. For example, OpenAI text-embedding-3-small models often use 1536 dimensions.

Part 3: CREATE FUNCTION match_documents (...)

Translation: Finally, let’s create a magic search tool! This function lets you search for documents that are most similar to your question or prompt. It does this by comparing the meaning (vector) of your search with every document’s vector, then gives you the closest matches, sorted by similarity. You can also filter by document metadata if you want

By running this script, you’ve upgraded your database into a mini AI-powered knowledge base. Now it can store, filter and instantly search documents based on what they mean, not just what words they contain.

1.3 Getting the Keys to the Library (Your Credentials)

The final step is to get the secret keys that will allow n8n to connect to your new database.

1. In your Supabase project, go to Settings (the gear icon) and then click on API.

2. You will see two crucial pieces of information you need to copy:

• The Project URL. This is the public address of your database.

• The service_role secret key. This is a long, powerful API key.

3. Security Warning: The service_role key is the master key to your entire database. Treat it like a password. Never share it publicly or commit it to a public code repository. Save it securely in your password manager.

And that's it. You now have a powerful, secure and professionally configured vector database, ready to be stocked with knowledge. The library is built. Next, we'll hire the librarian to start filling the shelves.

Step 2: Hiring the Librarian - Building the RAG Pipeline in n8n

With our beautiful, empty library built in Supabase, it's time for the real work to begin. It's the middle of the day, a perfect time to focus on the foundational, behind-the-scenes work that makes the magic happen later. We need to hire our AI Librarian and start stocking the shelves.

This "Librarian" is your RAG Pipeline - a simple n8n workflow whose only job is to take your raw documents, process them and file them away perfectly in your vector database. This is a one-time (or occasional) task that prepares your knowledge base for use.

The Librarian's Assembly Line: A Five-Node Workflow

The entire process of ingesting a document is handled by a beautiful, simple workflow of just five nodes working in perfect sequence.

1. The "Start Work" Button (The Trigger): The workflow starts with a simple Manual Trigger node that allows you to run the process on demand.

2. The Book Delivery Service (Google Drive Node). Every library needs a way to get new books. For this, we'll use the Google Drive node. You simply create a new n8n workflow, add the node, set its action to "Download File", and connect it to your Google account. Then, you can select the PDF of the golf rules (or any other document) you want to add to your library.

3. The Page Separator (Data Loader Node) A 22-page document is too large for an AI to understand in one go. You need to break it down. Add a "Default Data Loader" node after your Google Drive node. This is your "chunker". Its job is to act like a precise machine that splits your large document into smaller, more manageable "pages" or chunks of text.

• Pro Tip: A good starting configuration is to set the chunk size to 1000 characters with a 200-character overlap. This overlap is crucial. It acts as a safety net, ensuring that a single, important concept isn't awkwardly cut in half between two different chunks, which dramatically improves the accuracy of your AI's search results later.

4. The Universal Translator (Embeddings Model Node) This is the most magical part of the process. Computers don't understand words; they understand the mathematical relationships between numbers. The "OpenAI Embeddings" node is your universal translator. It reads each chunk of text and converts its semantic meaning into a list of numbers called a "vector". This vector is a coordinate that represents that chunk's unique position in a vast "galaxy of meaning". You'll need to add your OpenAI API key here and select a cost-effective model like text-embedding-3-small.

5. The Infinite Library (Supabase Vector Store Node) This is the final destination. Add a "Supabase Vector Store" node, set its operation to "Add documents to vector store", and configure it with the Supabase credentials you saved in Step 1. This node takes the translated vectors from the previous step and files them away in your database, placing them on the correct "shelf" next to other concepts with similar meanings.

When you are ready, you execute the workflow. You can watch as n8n downloads the file, splits it, translates it and files it away. Your library is now stocked.

Step 3: Hiring the Scholar - Creating the RAG Agent

The library is built and the shelves are full. Now, we need to hire a brilliant, public-facing research scholar who will sit at the front desk and help your users. This is your RAG Agent.

This is a separate workflow in n8n, handling all the interactive conversations.

The Anatomy of an AI Scholar

Your agent is composed of several key parts that work together like a central nervous system.

• The Ears (Chat Trigger Node): This node's job is to listen for incoming questions from your users.

• The Central Nervous System (AI Agent Node): This is the coordinator that manages the entire process of thinking and responding.

• The Brain (OpenAI Chat Model): Inside the AI Agent, you'll add an AI model like gpt-4o-mini. This is the part that does the actual thinking, reasoning and answer generation.

• The Library Card (Supabase Vector Store Tool): This is the agent's most important tool. You'll add the "Supabase Vector Store" node again but this time, you'll set its operation to "Retrieve documents" and add it as a tool for the AI Agent.

Crucially, you must give it a clear description, such as: "Use this tool to look up the official rules of golf". This description is how the agent's brain learns what the tool is for.

• The Universal Translator (Embeddings Model): You must connect the exact same embeddings model you used in your pipeline (text-embedding-3-small) to this tool. This ensures the scholar can translate the user's question into the same "language" that the librarian used to organize the books, which is essential for an accurate search.

Step 4: Giving Your Scholar a Notepad - Adding Conversational Memory

A brilliant scholar who can't remember what you said five seconds ago is incredibly frustrating. It's the Dory from Finding Nemo problem. By default, your RAG system has no short-term memory. We need to give it a notepad.

The Setup: PostgreSQL Chat Memory

This is surprisingly easy to do. Inside your AI Agent node, you can directly add the "PostgreSQL Chat Memory" feature.

1. Create a New Credential: You'll need to create a new PostgreSQL credential.

2. Get Connection Details: All the details you need (host, user, port, etc.) are available directly in your Supabase project's database settings.

A key tip is to use the "transaction pooler" settings, as this is a more efficient way to handle many simultaneous conversations, which is important if you plan to scale your agent.

(The password is the one you set up when you created your project in the earlier step).

3. Set the Context Window: You can set how many of the most recent interactions the agent should remember. The default of 5 is a good starting point.

The "Library Card" System: How Session IDs Work

This memory system works using Session IDs. A session ID is like a unique library card assigned to each user. It ensures that the scholar's "notepad" for its conversation with "Jim" is completely separate from its notepad for its conversation with "Sarah". This is what allows your single agent to have thousands of unique, private and continuous conversations at the same time.

Testing the Memory

The test is simple. Start a new chat session.

• You: "Hello, my name is [Your Name]".

• Agent: "Hello [Your Name], how can I help you today?"

• You: "What's my name?"

If the agent answers correctly, its notepad is working perfectly. You can even go into your Supabase project and see the new "n8n_chat_histories" table that has been automatically created to store these conversations.

Powering the Brain: Your OpenAI API Setup Guide

It's the middle of the day and it's time to talk about the fuel that makes our AI engine run: the OpenAI API key. Just like a powerful machine needs electricity, your AI agent's "brain" needs access to an API to be able to think, reason and generate responses.

This is a step that trips up many beginners, so let's make it crystal clear.

ChatGPT Plus vs. The API: A Crucial Distinction

First, it's essential to understand that the $20/month ChatGPT Plus subscription and the OpenAI API are two completely different products, designed for different purposes.

• Think of ChatGPT Plus as an "all-you-can-eat" buffet. You pay a flat monthly fee and you get to have as many conversations (or "meals") as you want through the official web interface. It's designed for direct, human-to-AI interaction.

• Think of the OpenAI API as ordering "à la carte" from a restaurant menu. You only pay for exactly what you use. Every time your n8n workflow sends a piece of text to be processed, you are charged a tiny amount based on the number of "tokens" (words) used. This is designed for programmatic, machine-to-AI interaction.

To build automations in n8n, you need to set up an account for the OpenAI API.

Getting Your Key: A Quick Walkthrough

Getting your API key is a straightforward process.

1. Navigate to platform.openai.com (note the "platform" subdomain; this is the developer side).

2. Create an account. You will need to add a payment method (like a credit card) to your billing settings. This is like opening a tab at a restaurant; you won't be charged until you actually order something.

3. Once your billing is set up, go to your account Dashboard and find the "API Keys" section.

4. Click "Create new secret key". Give it a descriptive name, like "n8n_RAG_Project".

5. OpenAI will generate a new, long string of characters that starts with sk-. This is your secret key.

Security 101: Guard Your API Key Like a Password

This next part is critical. Your OpenAI API key is not just a key; it's a credit card number for your AI usage.

If this key is leaked or stolen, someone else can use it to make API calls and the bill will come to you. You must treat it with the same level of security as a password or your bank account details.

• Copy it immediately and save it in a secure password manager.

• Never share it publicly or paste it into a public code repository like GitHub.

• Once you close the window in OpenAI, you will never be able to see the full key again. If you lose it, you'll have to generate a new one.

You will paste this key into the "Credential" section for the OpenAI nodes in your n8n workflow.

The Golden Rule of Cost Control: Start Small and Set Limits

The "pay-as-you-go" model of the API is incredibly powerful but it can also be intimidating. Nobody wants a surprise thousand-dollar bill at the end of the month. Here’s how you stay in complete control of your spending.

Tip #1: Use Cheaper Models for Testing. When you're building and testing your agent, always start with a more cost-effective model, like gpt-4o-mini. It is incredibly capable for most tasks and is significantly cheaper than the full-blown gpt-4-turbo. You can always upgrade to the more powerful model later once you've confirmed your workflow is running perfectly.

Tip #2 (The Most Important One): Set Hard Usage Limits. Inside your OpenAI billing settings, you have the power to set hard spending limits. You can, for example, set a hard limit of just $10 per month. If your usage ever hits that amount, the API will simply stop working until the next month. This is your ultimate safety net. It makes it impossible to accidentally get a massive bill and allows you to experiment with confidence, knowing your costs are always capped.

Set your usage limits before you start experimenting to avoid any surprise bills. OpenAI’s dashboard makes this easy.

The Moment of Truth: Testing Your New Agent

It's time to see if all this work paid off. Execute your agent workflow and open the chat interface.

Your Question: "What am I allowed to do for practice before a round?"

Behind the Scenes:

1. The agent receives your query.

2. It looks at its tools and sees the "look up the rules of golf" tool. It decides this is the right tool for the job.

3. It sends your question to the embeddings model to get vectorized.

4. It searches the Supabase database and finds the most relevant chunks of text from the PDF about "practice".

5. It feeds these retrieved chunks to its main "brain" (the GPT-4o-mini model) and generates a perfect, context-aware answer.

The Expected Response:

Before a round, according to the Rules of Golf:

You are allowed to practice on the course on the day of a match.
However, you cannot practice on the course on the day of a stroke play tournament or before a playoff on the course and you cannot play or practice on the course between rounds unless the Committee allows it.
During the round, you cannot hit a practice shot when playing a hole or between holes, except you are allowed to practice chipping or putting on the last green you played (unless prohibited), on a practice green or on the tee box of the next hole as long as it does not hold up play.

So, practice before a match on the day itself is generally allowed but there are restrictions during tournaments and between rounds based on the type of play and Committee decisions.

You can look at the agent's logs to see this entire thought process play out. It's a fantastic way to debug any issues and see how the AI is "thinking".

Beyond the Basics: Your Next Steps

What you've just built is the "Hello World" of RAG agents. It's the foundation upon which you can build incredibly powerful and sophisticated systems.

• Dynamic Document Updates: You can create a workflow that automatically triggers whenever a new file is added to your Google Drive, keeping your agent's knowledge base constantly up-to-date in real-time.

• Multi-User Systems: The memory system uses "session IDs" to keep conversations separate. You can use a user's email address or phone number as their unique session ID, allowing you to have a single agent that maintains a separate, private memory for thousands of different users.

• Performance Optimization: As your database grows, you can fine-tune the vector search limits, cache frequent queries and monitor your API usage to keep costs low.

Congratulations! You Are Now an AI Builder

In the span of about 20 minutes, you've gone from zero to having a fully functional, intelligent RAG system with a custom brain and conversational memory. You've learned the core concepts of RAG, vector databases and no-code AI automation.

This is more than just a cool tech demo. This is the future of how businesses will interact with their data and how customers will interact with businesses. The possibilities are truly endless and you now have the foundational skills to start building that future yourself.

Try this with a real company FAQ or support doc and see how fast you can build custom knowledge bots for any need.

If you are interested in other topics and how AI is transforming different aspects of our lives or even in making money using AI with more detailed, step-by-step guidance, you can find our other articles here:

• Stop "Prompting" AI Coding Assistants! Do THIS Instead

• Mastering DALL-E 3: More Than Just Image Generator

• Stop Building n8n AI Agents Manually! THIS Does It For You*

• Create Your AI-Generated Video in Just 10 Minutes with $0 Budget: A Complete Guide

• You’re Wasting Your YouTube Content - Here’s How to Fix That*

  *indicates a premium content, if any