In this lesson, we are going to talk about the components of the rack technique.
So remember that the main purpose of the drag technique is to overcome the limits of the context window.
A the steps of the technique are very simple.
A first we load in the data, uh, like remember, for example, in the seq uh insights application,
when we loaded the financial documents from a couple of companies Apple and Amazon.
Right.
So we load these documents and then we apply the rack technique.
And the rack technique has um, um, mostly three steps.
The first step is to divide the data into small segments, small chunks.
The second step is to convert the small segments into numbers.
We convert these small segments of text into numbers.
Because computers and vector databases work much better with numbers than with text.
So when we convert the small segments of text into numbers and we call these numbers embeddings.
And the third step of the technique is to load the embeddings into a vector database.
So these are the three initial steps of the RAC technique.
Divide the data into small segments.
Convert the small segments into numbers, and load the embeddings into a vector database.
Now when the user asks anything about the data, what the LM application does is to go to the vector
database and to search for data that only answer the question of the user.
So what we are going to do here is to use a technique called semantic similarity search.
We will learn more about that later.
So.
The rack technique is the essential technique for most LM applications.
The essential the core technique.
That's why we will focus on mastering this technique.
Apart from embeddings, vector databases, and semantic similarity search, we will be learning more
about queries, indexing, orchestration frameworks, etc. but let's.
Learn a little bit more about embeddings and databases now.
So embeddings a.
Ah, if you remember the result to convert small segments of text into numbers.
So remember, computers work better with numbers, and that's why they convert text into numbers.
They do the same with images, audio, video, etc. embeddings are more than numbers.
They are vectors of numbers.
For example, the word hello is converted into an embedding like one comma four comma six.
This is a vector.
This is just an example.
So embeddings are vectors of numbers.
We use embeddings because computers and vector databases are much faster working with numbers than with
text.
A little bit about vector databases.
So vector databases are specialized in working with hundreds of millions of embeddings.
They are much faster than conventional databases.
They are optimized for storing, indexing, and retrieving.
Semantic similarity is a search technique in the vector databases.
So vector databases group embeddings by their semantic similarity.
For example, the embeddings, the embeddings of dog and cat, which are semantically similar as both
are animals, will be grouped together in the vector database.
So using the semantic similarity search vector databases are only going to search for data that are
similar to the question of the user.
So in this lesson we have been started talking about the rack components.
It's just a theory theory theoretical introduction.
But I think it's important for us to become familiar with the main concepts and con and components of
the rack technique before going into practice.
In the next lesson, we are going to talk briefly about the main challenges of the rack technique.
This is important to know at this point.