Vector Databases: The Next Evolution
Key Points
- The speaker frames the rise of AI as a transformative wave and introduces vector databases as the latest milestone in the evolution of data storage, following SQL, NoSQL, and graph databases.
- A vector is described as a numerical array that represents complex objects (text, images, etc.), while an embedding is a collection of such vectors organized in a high‑dimensional space for efficient similarity and relationship searching.
- Vector databases store and index these embeddings, enabling large language models and other AI systems to quickly retrieve relevant data points, maintain semantic relationships, and scale with growing datasets.
- Practical applications highlighted include powering chatbots and natural‑language‑processing tasks by providing rapid context‑aware similarity searches that improve conversational understanding.
- Overall, the talk emphasizes that vector databases are essential infrastructure for modern AI, acting as the backbone for storing, comparing, and retrieving the high‑dimensional data that drives LLMs and related technologies.
Full Transcript
# Vector Databases: The Next Evolution **Source:** [https://www.youtube.com/watch?v=t9IDoenf-lo](https://www.youtube.com/watch?v=t9IDoenf-lo) **Duration:** 00:08:13 ## Summary - The speaker frames the rise of AI as a transformative wave and introduces vector databases as the latest milestone in the evolution of data storage, following SQL, NoSQL, and graph databases. - A vector is described as a numerical array that represents complex objects (text, images, etc.), while an embedding is a collection of such vectors organized in a high‑dimensional space for efficient similarity and relationship searching. - Vector databases store and index these embeddings, enabling large language models and other AI systems to quickly retrieve relevant data points, maintain semantic relationships, and scale with growing datasets. - Practical applications highlighted include powering chatbots and natural‑language‑processing tasks by providing rapid context‑aware similarity searches that improve conversational understanding. - Overall, the talk emphasizes that vector databases are essential infrastructure for modern AI, acting as the backbone for storing, comparing, and retrieving the high‑dimensional data that drives LLMs and related technologies. ## Sections - [00:00:00](https://www.youtube.com/watch?v=t9IDoenf-lo&t=0s) **From SQL to Vector Databases** - The speaker outlines the evolution of database technologies—from relational SQL and document‑oriented NoSQL to graph stores—and introduces vector databases as the latest AI‑driven solution for handling embeddings. ## Full Transcript
over the past year we can all agree that
AI applications have really captured the
imagination of everyone this
groundbreaking technology has really
revolutionalized how we will be
Computing now and also in the future now
as I took a deep dive to really
understand how what works in the
background it led me to find our topic
for today what is a vector database now
let's kind of take a stroll down memory
lane and let's look at some other
groundbreaking moments in technology
when it comes to the database area here
first we all know about SQL which stores
structured data in
tables been around for a couple of
decades I think we're all are aware of
that um and where it's been then came
non nosql which takes unstructured data
in the form of documents and this has
been great for a lot of uh real time web
applications as well as Big Data you
know that that really came about and
then the hint of mobile when we needed
to collapse a lot of these better rate
apis the graph which stores data in in
nodes and that's how it formulates a lot
of its
relationships which really takes us to
where we are now with the vector
database uh which is naturally all our
AI
applications very very useful and
supplemental to that so so now let's get
into the characteristics of a vector
database and when I started my research
I realized there are two major Concepts
that I had to really get down the first
is what is a
vector and the second is what is
in
embedding and I'm really going to
simplify things now think about a vector
as an array of data that gets put into
the database now any type of complex
object you put in whether it's images
text documents they all get represented
in some type of numerical value so I'm
going to say this as an
array all right and then at some point
as data scales up in order to keep the
relationships and naturally keep in mind
you're not only going to have user data
that you put in but this is really going
to be a database for a lot of your large
langage models to be able to store its
safe points um it's it its actual data
sets for comparison as we get to the use
cases here so the embedding is lots of
vectors that are staved in a
multi-dimensional i abbreviate that
their format where they can then be used
as groupings of vectors for data sets
that can really start to grow and go
from there now with this understanding
of vector and embeddings now we have the
proper context to really discuss the use
cases that really bring this to
technology to life now we have our large
language models and we've all interacted
with a chatbot in the past I think if
everybody that's the number one way
you've interacted especially if you've
actually used chat GPT and the major
thing that that uses is a concept called
natural language processing so let's
take this from our chat box
it's the number one I would say feature
that you'll see uh this being used um
and and it's going to work a lot by
taking the context of understanding the
semantics of conversation well that
model will be able to leverage a vector
database to keep its ever growing
database to understand a a car is is
similar to a engine or the relationships
between the terms that you have here now
I also have video and image recognition
we've all use these type of applications
to build AI art as they call it but
let's say with the voice
recognition the ability to take sound
waves or audio file and be able to
represent it as a numerical set of data
and then be able to make comparisons
about this equals this particular
semantics of speech all right um and
then also the last but not least let's
talk about search also very important we
may have similarity searches uh being
able to identify certain images you've
all we've all interacted with
recommendation engines all right so
search is another bit a one here and
we'll just
say the similarities all right let's
just summarize that there very important
thing of understanding when I'm
searching for what is related to that
those relationships can definitely be
represented
there now let's get into the benefits of
doing this cuz naturally if you did a
quick search on the internet you'll be
able to see the ability for you to
represent Vector data into some of these
other databases that I discussed earlier
SQL databases no SQL databases all right
but you truly get certain great benefits
when you use the direct Vector databases
to do that number one I would say is
flexibility now flexibility in the terms
to take any type of data whether it's
docs images uh
any type of text Data speech you kind of
heard a lot of the things I was
discussing that all these it doesn't
matter when you use different type of
database you may have to prepare your
data to go in that but with a vector
database it's very easy just to throw in
or insert a bunch of unstructured data
for comparison uh to see now once you
have data in be able to ingest any type
of data the second is the
scalability
all right being able to scale out to
millions and billions of data points of
vectors that you'll be able to have for
comparison and if you think about this
this is really where the power of large
large language models really comes to
shine with this extensive database that
it has for comparison and if you wanted
to start from scratch with your model
you often have to throw give it a bunch
of training data for it to start to grow
and maintain some of his expertise uh to
go so flexibility the ability to put
data in the ability to scale to millions
or billions of data points and once that
data is in let's not forget the Speed
and
Performance of everything here being
able to index a lot of these vectors
these embeddings being able to query in
a low latency mode since it's all in a
numerical format it's very easy to to uh
run queries uh the large language models
to um if you are in chat B is trying to
take the conversation and compare it out
and do some comparison it's going to
leverage this Vector database to put
save points if I want to call them that
or certain like uh you could probably
just say like a cache of of of of data
that it can use to make that operation
go that algorithm work and whatever the
workflow you have uh to really perform
like it should so this has been Vector
databases I'm always an advocate of
having your polyglot meaning that your
architecture can have many different
types of Technologies multiple databases
as a matter of fact you really don't
have to always depend on one type of
database but one thing we can agree
we're all you're all technologist like
myself you all are starting to think
about how you can Infuse AI into your
architecture well I recommend that you
take that next step look at some of the
open source Technologies for Vector
databases and Get Off to the
Races thanks for watching in the
comments below let us know how you've
used Vector databases with your AI
projects and as always please remember
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