Imagine an AI chatbot, you know, brilliant at
general conversation. But then you ask it about
your company's specific policies or maybe the
nuances of your industry. And he either guesses
or maybe worse, just kind of makes things up.
What if you could give that AI a custom brain,
one filled with your verified knowledge? Welcome
to the deep dive. Today, we're diving deep into
exactly that idea. Retrieval Augmented Generation,
or RA for short. And honestly, it's simpler than
it sounds, but it fundamentally changes how we
can trust and actually use AI. We're going to
discover what RA is, how these powerful Mind
Palace databases make it possible. We'll break
down the two crucial parts of any RA system.
Then yeah, we'll walk you through building one,
step by step, no code needed. You'll even learn
how to give your AI a memory, make it remember
stuff, and power its new brain efficiently. It's
really about turning a smart... chatbot into
a true expert on your stuff. Okay, let's unpack
this. So let's start right at the heart of it.
What is ARAG? Retrieval Augmented Generation.
In plain English, it just means your AI does
its own research before it speaks. Think of it
like an open book test. When you ask a question,
maybe how many feet are in a mile? ARAG system
doesn't just pull from its huge general training
data. No, it first retrieves specific accurate
info like 5 ,280 feet from a source you've given
it. Only then does it use that verified fact
to augment or, well, significantly improve its
answer. This is what transforms a generic AI
into one that genuinely knows your company docs,
your support tickets, maybe your internal policies.
Yeah, it changes the AI from just a generalist
into a trusted specialist. OK, so the core idea
is that the AI isn't just generating from like
its initial programming. It's actively seeking
out and pulling in external facts first. Exactly.
It finds the facts, then generates with confidence.
But where does this AI store all the specific
knowledge so it can find it so fast? That's where
the vector database comes in. It's truly the
brain of a rag agent. Imagine this vast... multi
-dimensional space kind of like a galaxy right
where every star is a piece of information from
your documents each chunk of text becomes a single
point of light in this galaxy that's what we
call a vector and the really profound insight
here is semantic similarity this database doesn't
organize things alphabetically not at all it
places data points based on their meaning so
all the points about say fruits cluster together
over here and animals form a totally different
constellation over there when you ask what is
a kitten your question itself gets turned into
a new point of light. And it appears naturally
right within that animal constellation. The database
then super quickly finds the closest points,
maybe cat, puppy, even wolf, and pulls the text
link to them. That gives you a perfect, hyper
-relevant answer. It's all about asking, what
does this mean? And getting an intelligent, context
-aware match. For a deep dive today, we'll be
using Supabase as our vector database example,
which is built right on top of Postgresful. So
this database, it really understands concepts.
then, not just keywords. Yes, precisely. It's
all about meaning and context. Okay. And it's
this clever organization that lets the two crucial
halves of a RIAG system work so well together.
If we kind of connect this to the bigger picture,
building a RIAG system, it's like... assembling
this, I don't know, magical, intelligent library,
it's really a two -phase project, requires two
distinct but totally complementary halves of
a single brain, you could say. First, you've
got what we call the AI librarian. That's your
RA pipeline. This is the behind -the -scenes
worker. Its job is to meticulously read every
single document you give it, understand the content,
and then place each piece of information on the
correct shelf within your, well, infinitely large
perfectly organized vector database. This isn't
an ongoing job, mind you. It's a one -time process
you run to initially stock the library. Right.
And then you have the AI scholar. That's your
R agent. This is the public -facing expert. The
scholar kind of sits at the front desk, ready
to help visitors. When you ask a difficult question,
the scholar instantly zips through the library,
pulls the exact pages from the correct books,
synthesizes the information, and gives you this
brilliant custom -written answer. But these two
parts, they're completely interdependent. A scholar
without a library is, well, just a generic chatbot.
No specialized knowledge. And a library without
a scholar, that's just a silent... inaccessible
database. So one half is organizing all the knowledge
and the other half leverages that organization
to actually provide the answers. Precisely. A
powerful two -phase system, yeah. So let's dig
into the librarian's job then, that RAG pipeline.
It's really all about transforming raw documents
into a searchable, structured knowledge base.
And this process has four key steps. First up,
document input. This is simple. It's just acquiring
the book. It's your source document. It would
be a PDF, a text file, or even data you pull
from other apps, maybe like a CRM. or something.
Then we chunk the document. Think about it. You
wouldn't file an entire 500 -page book under
just one topic, right? You'd separate it into
chapters or sections. Similarly, we split large
documents into smaller, more focused pieces of
text. This is super critical because it creates
these context -rich pages for more accurate searching
later on. A pro tip here, aim for chunks of maybe
about 1 ,000 characters and use a 200 -character
overlap between chunks. That just helps ensure
concepts aren't awkwardly cut off right in the
middle. Okay, and here's where it gets, well...
really interesting. After chunking, you embed.
This is I think the most magical part. An embeddings
model. It's a sophisticated AI, acts like a universal
translator. It converts those text chunks into
vectors, which are basically just long strings
of numbers. Think of it like giving each chunk
a precise coordinate in that huge galaxy of meaning
we talked about, that 1536 number you sometimes
see. That just refers to the number of dimensions
or kind of like traits an AI uses to describe
each piece of information. It's like a super
detailed profile for every single text chunk.
And for your AI to understand your data consistently,
this profile format, it has to match across all
parts of your system. Finally, you vectorize
those chunks. This just means storing these numerical
vectors in your Supabase vector database. They
get intelligently placed by meaning, right? So
all the golf rules about putting are grouped
together, far away from rules about driving.
In a tool like N8AN, this whole process is actually
simplified into like a five -node assembly line.
You've got a trigger to start it, maybe a Google
Drive node to get the document, a data loader
for the chunking part, an embeddings model, like
OpenAI's text embedding 3 small to do the translating,
and the Supabase vector store node to file it
all the way neatly. You just run this workflow
once for each document you want to add. Gotcha.
So it's about breaking down the info, translating
it into this conceptual number language, and
then filing it away smartly. Precisely. Creating
that structured, searchable knowledge base. Yeah.
Okay, to make this all super concrete, let's
talk about our mission for today. We're building
an AI golf caddy using a 22 -page PDF. It's called
The Rules of Golf Simplified. That's our single
source of truth. Why golf rules? Well, because
they are incredibly dense, super specific, and
totally self -contained. A generic AI. You might
just guess or even hallucinate, you know, make
stuff up if you ask a tricky golf question. But
our engagement trained exclusively on this official
rulebook. It'll be a true, reliable expert. Our
goal is an AI caddy that can instantly and accurately
answer very specific questions like, what am
I allowed to do for practice? Or maybe, can I
hit a practice shot between playing two holes?
Yeah, and this isn't really about golf, is it?
What's fascinating here is that this golf PDF,
it's just a placeholder. This whole process is
actually a universal blueprint for any specialized
knowledge you want to give your AI. Your data
source could be anything. Your last 5 ,000 HubSpot
support tickets may be an air table base full
of project data or even recent client emails.
Imagine asking your AI, what are the top three
common product issues we're seeing in the UK?
Or, which projects are running over budget this
year? and your agent's trigger, how you start
the conversation. That can also be anything beyond
just a chat window. It could be an email sent
to askatyourcompany .com or a submission on your
website form, or even a scheduled task that,
say, summarizes the week's support tickets automatically.
The skills you learn building this simple AI
golf caddy, they're absolutely foundational for
building really powerful business systems. So
this simple golf example, it really unlocks vast
possibilities for building custom AI then. Absolutely.
It's a template, a template for pretty much any
custom AI you can think of. All right, let's
get our hands dirty then. Step one, building
the library itself using Supabase. This is going
to be the home for our AI agent. It's built on
Postgresql, which is incredibly solid. Plus,
it's pretty user -friendly and has a very generous
free tier, which is great for getting started.
Think of this step as laying the foundation,
putting up the walls, and installing that magical
shelving system for all your AI's knowledge.
Okay, first up, you'll head over to supebase
.com and create a new project. You give it a
name, obviously, and critically, create a strong
database password. Critical tip. Please, save
this password securely right away, put it in
a password manager, you will definitely need
it later. Once your project is spinning up, might
take a minute or two, you'll configure the vector
database part. Now, this sounds a bit intimidating
because it involves a snippet of code, but trust
me, consider it a one -time magic spell. Exactly.
A magic spell is a great way to put it. You just
navigate to the SQL editor inside Superbase.
You copy and paste a provided code block. We'll
make sure you have access to that. And you click
run that code. It's simply doing three things.
First, create extension vector that's telling
your database, hey, install the add -on to work
with these AI vectors, these numbers representing
meaning. Second, create table documents. This
sets up the main storage shelf for your document
chunks, including that special embedding vector
1536 column we talked about earlier, the profile.
Finally, create function match documents. This
creates the magic search tool that finds similar
documents by comparing their vectors, their meaning.
That little bit that says one, documents .embedding,
query embedding. That's just telling the database
how to measure the conceptual distance between
your question and every piece of info it has.
Think of it like a reverse distance meter. The
closer the concepts match, the higher the similarity
score it gives. And then it brings back the highest
scoring, most relevant information. So by running
this one script, you instantly upgrade your plan.
database into an ai powered knowledge base pretty
cool right and the final part of this setup step
is just getting the keys to the library so to
speak in your super base project settings under
the api section you'll find your project url
and the service role secret key security warning
this service role key it is the master key to
your entire database treat it like a password
like gold Never, ever share it publicly. You'll
need both the URL and that key for connecting
it in 8n later on. OK, so we're essentially building
the AI's personal research library here, getting
it all ready for use. That's it. Building the
database and its intelligence shelves ready to
be filled. Right. So with our library built and
ready to be stocked or maybe already stocked,
if we ran the pipeline, it's time to hire the
scholar. Our ARIG agent. This is a new separate.
and add in workflow. This one's for the interactive
conversations. Think about the anatomy of this
AI scholar. It's got the ears. That's a chat
trigger node. It just listens for user questions.
Then there's the central nervous system, an AI
agent node that coordinates everything, the thinking
and responding. The brain itself is an open AI
chat model, maybe like GPT -40 mini, which does
the actual thinking and generates the responses.
Crucially, we give it the library card. That's
a Supabase vector store tool. You add this as
a tool specifically for the AI agent. You set
its operation to retrieve documents. And you
give it a really clear description, something
like, use this tool to look up the official rules
of golf. The AI needs that clarity. And finally,
the universal translator. That's the embeddings
model again. This must be the exact same model,
say, text embedding three small that you use
when you build a pipeline. That ensures consistent
understanding for accurate searching. It's got
to speak the same language. Now, a brilliant
scholar who can't remember what you just said
five seconds ago, that's incredibly frustrating,
right? It's like the Dory from Finding Nemo problem.
By default, your agent has zero short -term memory,
so we need to give it a notepad. This is step
four, adding conversational memory. And you can
actually do this surprisingly easily. Right inside
your AI agent node, you add the PostgreSQL chat
memory feature. You'll create a new PostgreSQL
credential using your Supabase database settings.
Quick tip here, use the transaction pooler settings
that Supabase provides. It helps with efficiency,
especially if you plan to scale this up later.
The password you need here is the one you set
up for your Supabase project way back at the
start. See? Told you you'd need it. You can also
set the context window how many past messages
it remembers. The default is five recent interactions,
which is usually a good starting point. And this
whole memory system works using session IDs.
Think of it like a unique library card for each
user. That ensures everyone gets their own separate
continuous conversation. Your chat doesn't get
mixed up with someone else's. I still wrestle
with remembering to configure these. memory pieces
myself sometimes it's just it's easy to miss
yeah i can see that and testing this memory is
pretty simple right you just start a new chat
say hello my name is whatever your name is and
then in the next message ask what's my name if
it answers correctly bingo its notepad is working
you can even double check by looking at the engine
histories table directly in superbase if you
want proof So this creates that conversational
expert, one that can actually remember the previous
turns in the conversation. Yes, exactly. It enables
those interactive, context -aware conversations
that feel much more natural. Okay, makes sense.
Mid -roll sponsor read. Now let's talk about
the fuel, the stuff that makes our AI engine
actually run, the OpenAI API key. This is a step
that can sometimes trip up beginners. It's really
crucial to understand the difference between
your ChatGPT Plus subscription, if you have one,
and the OpenAI API. Think of ChatGP Plus like
an all -you -can -eat buffet. You pay a flat
fee, like $20 a month, for direct human -to -AI
chatting through their website. The OpenAI API,
though, that's different. It's a la carte. You
pay for exactly what you use, usually charged
per token, which is kind of like parts of words.
This API is specifically designed for programmatic
use, you know, machine to AI interaction. And
that's exactly what you need for tools like NAI
to work. Right. And getting your API key is pretty
straightforward. You navigate to platform .opni
.com. Notice the platform part. That's the developer
side, not the regular chat GPT site. You'll create
an account if you don't have one at a payment
method. Think of it like opening a tab at a restaurant.
You only pay for what you order. When you go
to the API keys section, click create new secret
key. It'll generate a key that starts with SCET.
Security 101. Again, this API key is basically
like a credit card number for your AI usage.
Guard it fiercely. Copy it immediately. Save
it in your secure password manager. Never, ever
share it publicly. And once you close that little
window where it shows you the key, you'll never
see the full key again. If you lose it, you just
have to generate a new one. Okay. And the golden
rule of cost control here is really start small
and set limits. Tip number one, use cheaper models
when you're testing and building. GPT -4 -0 Mini
is incredibly capable, especially now, and it's
significantly cheaper than the bigger models
like GPT -4 Turbo. You can always upgrade later
once you know it works and you need more power.
Tip number two, and this is probably the most
important one, set hard usage limits. In your
OpenAI billing settings, you can actually set
a hard spending limit, maybe just $10 a month
to start. If your usage ever hits that limit,
the API will simply stop working until the next
billing cycle. This is your ultimate safety net.
It lets you experiment with total confidence,
knowing you won't get a surprise bill. Got it.
So the API key is the gateway to the AI's brainpower,
and cost control is absolutely essential. Right.
Access the brain, but keep that spending firmly
in check. All right. The moment of truth, then.
Let's test our AI golf caddy. You execute the
agent workflow in NA8N. You open the chat interface
it provides, and you ask a question. Let's try
that one. What am I allowed to do for practice
before a round? Now, behind the scenes, here's
what happens. The agent gets your query. It identifies
its look up the rules of golf tool as the right
one to use. It sends your question to the embeddings
model to get vectorized, turned into numbers.
It searches this super -based database using
those vector numbers. It finds the most relevant
chunks of text about practice from that PDF we
loaded. It feeds those relevant chunks to its
GPD 4 .0 mini brain along with your original
question. And then, it generates a perfect, context
-aware answer based only on the provided rules.
Yeah, and the response you'd likely get, pulled
straight from the source document, would be pretty
detailed. Something like, before a round, you
can practice on the course on the day of a match
play event, but not before a stroke play tournament
or playoff, or between rounds, unless the committee
allows it. During the round itself, no practice
shots are allowed when playing a hole or between
holes. Except for some chipping or putting on
or near the last green you finished, a practice
green, Or the next D -Box, provided it doesn't
hold up play. Let's see. Super specific, right
from the rules. And you can even check the agent's
execution logs in ADN to see this whole thought
process play out, which tool it used, what info
it retrieved. It's great for debugging, but really
this is just the hello world of RAG, you know.
Beyond these basics, you can set up dynamic document
updates. Imagine a workflow where any new file
dropped in a Google Drive folder automatically
triggers an update to the AI's knowledge base.
For multi -user systems, you could use unique
session IDs, like maybe a user's email address
or phone number. to maintain potentially thousands
of separate private conversations simultaneously.
Beep. Whip. I mean, imagine scaling that. Giving
a custom private AI brain to potentially millions
of individual users, all running off one core
system. That's truly remarkable when you think
about it. And of course, there's always performance
optimization you can do later. Things like fine
-tuning the vector search parameters, maybe caching
common queries to reduce API calls and costs.
Lots you can do. So this example, it's really
just scratching the surface of what's possible
with this RAG approach. Absolutely. It's a powerful
foundation. You can build almost infinite applications
on top of this basic. So let's recap. You've
just gone from a general chat bot, one that knows
a lot about everything but nothing specific about
your stuff, to an AI with a custom specialized
brain. RDAG systems, powered by these clever
vector databases, allow AI to do its own context
-specific research within your data. You got
the librarian. building the knowledge base, meticulously
organizing the information, and the scholar using
that knowledge base, retrieving precise facts
to answer your questions accurately. And maybe
the best part, you can actually build this all
without writing a single line of traditional
code, creating powerful, intelligent systems
for anything from, well, golf rules to complex
business data. Yeah, this isn't just some cool
tech demo. It's really about building the future
of how AI interacts with your unique world, your
specific information. You've now got the foundational
skills, the understanding to customize AI and
build truly intelligent agents tailored to your
needs. This deep dive has hopefully equipped
you with a pretty profound understanding of how
to give AI both a memory and a personal library.
So the question for you is, what specific dense
body of knowledge in your life or your work could
you give your AI next? Imagine the expertise
you could unlock. Thank you for diving deep with
us today. Keep exploring, keep building, and
yes, stay curious.