And I'm actually guessing it's going to be no surprise to you at all.
Is the is the obvious next step to rag rag in traditional rag.
We understand the process.
You have chat with a user that goes to vector based retrieval that your code is written that is doing
this, this lookup based on, uh, semantic search using vector similarity.
I'm already using Pro Pro terms with you as if you've been doing this for years.
Uh, and that is then sent to an LLM in order to get the LLM to respond to it, using the LLM to respond
to this question with this extra context that is traditional Rag.
So if you think about like bringing agents into this, the natural next step, uh, would wouldn't be
necessarily this.
This feels very linear.
It feels very much as if you're doing you're writing code to do this vector based retrieval.
And when you're thinking in the rag world, things are much more iterative and interactive.
So what is the the agentic twist on this process?
So here it is.
The Agentic twist is to say, okay, we've got chat.
The user messages us as before.
What do we do with it?
Well that goes to an agent.
We it doesn't go yet to a vector based retrieval.
It goes to an agent.
And when I say it goes to an agent, what I mean is we're using an LLM.
We're using an LLM not just for the response as we did before, but also the workflow, figuring out
what to do next, what tools to use, how does it best answer those questions.
This question, and one of those tools that we equip it with, is a tool that is able to do vector based
retrieval.
It's able it's given access to a vector database and said, hey, you can make vector queries of this
vector database, and maybe you give it access to some of the different clever techniques around Rag
that it could explore.
If it wants to.
It could, it could try it and then try a few other twists and turns as it tries to to find the relevant
contexts.
And hey, maybe we'll give it some other tools as well.
You could imagine that maybe we just give it straight up SQL.
Maybe if it turns out that there is our database table has has a country or a city destination column
in it, and there's a table called Ticket prices, maybe it would be like, hey, I don't need to do
all this fancy vector based stuff.
I can just do like a SQL statement to select ticket prices where the city equals London.
And so I'll write that SQL statement and I'll use my SQL tool to call the database and retrieve the
information a more traditional kind of retrieval.
And we give the the Lem the ability to control the workflow and do any of those things in order to find
the best context for answering the question.
And then that same Lem can then go ahead and answer the question.
Now, of course, there are other ways of doing this.
You can have a whole separate agent whose only job it is to to do the the retrieval part of it, and
then it passes control on to another LM to actually answer the question.
Of course, all of those things are possible.
But in its simplest form, this is just a straight up comparison between traditional rag, which is
a simple workflow, a linear workflow.
Get the message.
Look up relevant context, pass it to an LM versus a genetic rag, which is more iterative, where you
let an LM decide how to do different kinds of techniques, to retrieve relevant context, to build up
the relevant context in order to answer the question.
So that is traditional rag versus a genetic rag.
And no prizes for guessing which one we are going to be doing.
Yeah.
So it's going to be a genetic rag obviously.
Okay.
So today was it was very much a core expertise day.
It might have felt like it was a lot of theory, but hopefully it was like applied theory very practical
theory.
But just to leave you with, with something to to do that isn't just slides.
Uh, I wanted to spend a moment to mention super bass.
So, uh, Nan comes with with lots of sort of, like, built in database stuff and some vector database
things as well.
But, but for this project, for this week, as we look to, to work with data and databases and even
vector databases, I wanted to use a third party database that's incredibly popular and that's used
by a lot of agencies and a lot of startups to midsize companies that are looking to quickly build products
out there.
Super bass, very, very popular.
Definitely one that I thought it was worth us integrating with.
And of course Ann has a super bass node.
It had to because a super bass is really, really top.
Okay so what what is super bass?
Super bass is basically it uses it's it's a cloud version, a managed version of a type of database
called Postgres, which is a fiendishly popular relational database for storing data.
And super bass is so great because it's really easy to set up and use.
It's very user friendly, it's very startup friendly and great for this kind of experience for you to
get up and running.
So I would like you to go to Super Bass, and I'd like you to spend a bit of time clicking around it.
It also has a generous free tier of course, which is a must must have for this course.
Uh, so I would like you to come on in here and just read about it, explore it.
Uh, it does.
They have a few different, um, service offerings, but the Postgres database is the big one.
You can see they've got some pretty big name clients.
Uh, and you'll see, amongst other things, that they store vector embeddings, which is the name for
the vectors that come from embedding models, vector embeddings.
They store vector embeddings in their database, which is just the kind of database that we're looking
for in, in our project to build out a genetic rag.
So that's your homework.
Come on in here.
Explore.
Set up your account.
Read about it.
You can read.
Read some of the docs.
Get yourself somewhat familiar.
See on the pricing page about the free plan and the paid plans, so that you get some sense of what
we can do.
Unlimited API requests 50,000 monthly active users.
We're probably probably not going to get that with our project this week.
Uh, and but you get a decent sense of what you can get.
And then if you did want to pay more, then then this is what you would be able to do.
Um, but that should be no need for this, this course.
Okay.
So I will leave you to do that, but also I will go back to the slides for the wrap.
Okay.
And I should mention that when you're setting up your super base account, I do believe it asks you
to name your organization.
And I recommend that what I did was I, I call my organization like donor research.
Like my name and then research or education or whatever you want.
Because this is you're using this as a personal, uh, playpen for, for your own research and education
rather than for for a corporate purpose.
So that's what I did.
Uh, and different regions.
You might have a slightly different sign up flow, but but I seem to remember it was it was super easy.
And if you have any problems with that, of course, message me or look in the resources in case I've
put any notes there.
All right.
So the next two days are where we are going to be building out a rag pipeline with a voice agent.
The idea is we want to build an expert voice agent that could accelerate a business, because it could
act as something that can handle more.
Volumes of questions from people about your company, allow you to scale up, accelerate, because you
are able to to do things in a streamlined way, because you can have experts working for you that are
voice agents with complete knowledge of everything about your company.
And that's that's what we're going to be building out.
Just small steps, but leaving you with the ability to expand this so much, whether you're building
that for your own company or whether it's something that you could build for others.
You could offer that as a commercial product for other people that want to do this.
It's a classic agentic use case and a rag use case.
And so tomorrow we're going to be working with Superbase and integrating with Superbase for the first
time and then taking data, turning it into vectors, and putting it into superbase.
So it's going to be about data ingest and building automated pipes for that.
And then that's going to be that's going to be great.
And finally we're going to wrap up the week by building out our voice expert.
That can then allow us.
It's going to be just like what we did yesterday.
We're going to have a voice agent that then calls a webhook running in.
That's going to carry out a workflow to answer an expert question.
So all of that is what's ahead.
I hope you're excited, I certainly am.
And with that, you're over halfway.
53% of the way through this program, you're on on the path to being an Nanh Pro.
See you tomorrow.