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We built some great AI power chat
interfaces together. The models know
about programming languages, historical
events, scientific concepts, and
millions of other topics to respond to
user questions. Let's say you want to
create an AI chat interface as an
internal tool where anyone can ask
questions about your company. For
example, developers need to know about
architecture decisions. Designers want
to look up brand guidelines and user
research findings. Product managers need
roadmap information and customer
feedback. Support teams need to quickly
find solutions to customer issues and
product documentation. The AI model has
no information about any of this. It
doesn't know your tech stack, your
design system, your product strategy, or
your customer history. And even for
public information, there's always a
cutoff date. The model doesn't know
about yesterday's product launch or this
morning's incident resolution. Now, you
might think, well, I'll just store all
my data in a database and search when
needed. Let me tell you, traditional
search has a huge problem. Imagine
you're on Netflix trying to find
something to watch. You remember
watching this great movie about AI
taking over the world, but you can't
remember the title. So, you search for
AI Apocalypse or Robots Destroying
Humanity. If the movie's title and
description don't have those exact
words, you get nothing. Zero results.
Maybe the movie description says
"Machines gain consciousness and
threaten mankind." Totally different
words, but similar meaning. Traditional
keyword search just doesn't work here.
This is exactly what Netflix, Spotify
YouTube, basically every content
platform has to deal with. How do you
help users find content based on
meaning, not just matching keywords?
This is where embeddings come in.
Embeddings are a way to turn text into
numbers that capture what the text
actually means. When you give text to an
embedding model, it gives you back a
list of numbers. We call this a vector.
These numbers represent what your text
means in mathematical terms. And here's
the cool part. Similar meanings end up
close together in this mathematical
space even if they use completely
different words. So AI apocalypse and
machines gain consciousness would be
really close in this embedding space
even though they don't share a single
word. And this is how Netflix can show
you the matrix when you search for
reality simulation, even though the
movie description might never use those
words. Now, let's talk about what these
vectors actually are. A vector is just a
list of decimal numbers. For OpenAI's
text embedding 3 small model, you
get,536
numbers. We call that,536
dimensions. Even the simplest text like
the matrix will give you an array of,536
dimensions. Of course, we can't
visualize thousands of dimensions. Our
brains max out at 3D, but the idea is
the same. Imagine a simple 2D graph
where we plot movies. On the x-axis
maybe we measure action versus drama
and on the y-axis, realistic versus
sci-fi. The Matrix would be high on
sci-fi and high on action. The Godfather
would be high on drama and high on
realistic. With,500
plus dimensions, the model can capture
superdetailed aspects of meaning, which
means similar content naturally groups
together. All sci-fi movies about AI
would be in one area. Romantic comedies
would be grouped in another area.
Historical documentaries would be
somewhere else entirely. But it is
smarter than just genre. The Matrix
Inception, and Blade Runner, for
example, would be super close together
because they're all philosophical sci-fi
that explores reality. Meanwhile, Star
Wars would be further away because it's
sci-fi, but more adventure than
philosophy. When Netflix wants to
recommend movies, they find the movies
closest to what you just watched in this
embedding space. When you search, they
convert your search into an embedding
and find the closest content. No keyword
matching is needed. But how do we
measure closeness in this space? The
most common way is called cosine
similarity. Without getting too deep
into the math, cosine similarity
measures the angle between two vectors.
It gives us a score from minus1 to one
where one means identical meaning, zero
means unrelated and minus one means
opposite meaning. So for example, the
matrix versus inception could be.92 very
similar. The matrix versus godfather
could be 31 pretty different. and matrix
versus the matrix reloaded could be.98
almost identical. Of course, these
numbers are hypothetical and will depend
on the model you use. Embeddings and
similarity search actually have many use
cases. In customer support, when a
customer says, "My internet is slow,"
you can find similar resolve tickets
even if they said connection lagging
pages loading forever, or bandwidth
issues. In content moderation, you can
detect harmful content even when people
use creative spelling or code words to
avoid keyword filters. In duplicate
detection, you can find similar bug
reports or support tickets even when
people describe the same problem
differently. In retrieval augmented
generation or rag for short, which is
what we are building toward, you can
store your documents as embeddings, find
the most relevant ones for a user's
question, and give them to the AI as
context to generate an accurate answer.
All right,, now, that, we, understand, the
theory, let's see how to actually
implement this in code. In the next
lesson, we will use OpenAI's embedding
models to convert text into vectors.
Github - https://github.com/gopinav/Next.js-AI-Tutorials Become a Fullstack Developer with Scrimba - https://scrimba.com/fullstack-path-c0fullstack?via=Codevolution Embeddings In this lesson, we dive into the power of AI embeddings and how they can transform searches beyond keyword matching. Learn how to build an AI chat interface for internal company use, handling diverse information needs from developers to support teams. Discover the limitations of traditional search and explore how embeddings represent text in a multi-dimensional space, making it possible to find content based on meaning. We discuss embeddings, vectors, and cosine similarity, and their real-world applications like customer support, content moderation, and retrieval-augmented generation. We'll also cover practical implementation with OpenAI's embedding models. Join us to uncover the future of intelligent searching. 00:00 Introduction 00:59 Challenges with Traditional Search 01:56 Understanding Embeddings 02:43 Vectors and Dimensions 05:18 Applications of Embeddings Follow me + Twitter - https://twitter.com/CodevolutionWeb Business - codevolution.business@gmail.com