Welcome.
In this episode, we are diving into a really important topic for building agents and AI automations
vector databases.
So let's jump in.
This is something you really need to understand, especially for building agents.
And later in this course we will build agents with long term memory and vector databases are key for
that.
So we will see how the vector databases work in practice and why they are so crucial.
So in this video you will learn what vector databases are, what they are essential for large language
models like GPT, and how they help AI systems understand meaning, not just exact words.
So let's get into it.
All right.
Let's start with the basics.
What is a vector database?
A vector database is a new kind of database designed especially for large language models and AI systems.
Traditional databases store data in rows and columns and match exact keywords.
Vector databases, on the other hand, store information as vectors, allowing them to find results
based on meaning and context, not just exact text.
So instead of matching exact words, they retrieve data that's similar in context, which is super useful
for large language models.
So think of it like this.
Traditional databases work like looking for an exact street address.
Vector databases are more like searching for places similar to your favorite coffee shop so they understand
the vibe or meaning, not just the exact name.
Now what about vectors and vector stores?
Vector store is just a group of numbers that captures the meaning of some text or data.
So you can think of it like a summary of meaning, not what something says word for word, but what
it's about.
So the meaning and they focus on meaning, similarity and context, not just exact matches of words.
A vector store is where all these vectors are saved, kind of like a searchable memory for LMS.
So when you ask a question, it can go look through the stored meanings and find the most relevant information.
So when you input text or data vector databases store it as a as vectors which are like unique fingerprints
representing the meaning of your data.
I will explain how that works visually in a moment, but for now, just keep in mind that vectors equal
meaning and vector stores equal memory banks for meaning.
Let's move on.
Now via vector database is important for agents because large language models like GPT, the brains
behind ChatGPT, rely on understanding context and meaning.
So vector databases help agents with that because they store relevant knowledge, and they act as a
memory for AI and enable Llms to store and retrieve information dynamically.
Now semantic search.
So instead of just finding exact words, they let llms search by meaning.
So basically semantic search is a technique which allows llms searching, binding and real time insights.
So they retrieve the most relevant data, whether it's customer frequently asked questions or personalized
recommendations.
And the next slide.
Now let's quickly go over how Vectorizing process works.
And this is what actually powers things like RAC agents and smart retrieval.
So first we start by chunking the data.
So this just means breaking large documents into smaller, more manageable pieces of text.
Next we create vectors.
So each chunk is converted into a numerical Representation called an embedding.
And this captures the meaning of that chunk.
Then we store these vectors in a vector database.
This is like saving all those meanings into in a searchable memory.
When a user asks a question, the system turns the question into its own vector and compares it against
the stored vectors to find the closest matches.
And finally, it uses the best matching chunks to generate a response that's accurate relevant to to
your data.
Now let's take a closer look at how the vectorization process actually works.
And I will walk you through this diagram.
I created this diagram for my paper for my AI system.
It all starts with your source documents.
This can be PDFs, JSON, or plain text files.
These are the raw materials we want to make searchable by meaning.
So first we extract the content from those files, just the useful text inside.
Then we chunk that content into smaller, more manageable pieces.
So it's much easier for llms to work with smaller chunks.
Think of this like breaking a big book into individual paragraphs or sections.
Then each chunk is then converted into a vector, a group of numbers that represents the meaning of
that chunk.
And this is done using an LLM, which generates what we call an embedding.
And these embeddings are stored in a vector database, also called a vector store or knowledge base.
So it's like saving the meaning of every chunk into a memory bank that can be searched later.
Now let's say a user asks a question and that question is also turned into a vector.
So this is the question embedding.
Then the system performs semantic search.
So in other words, compares the questions meaning to distort vectors and finds the most relevant matches
and the best matching chunks are passed into the LLM like GPT, which uses them to generate a final
answer back to the user.
Now let's talk about popular vector databases pinecone, Supabase, and quadrant.
So pinecone is specifically designed for vector search and is widely recognized as one of the easiest
tools to use for working with vector data.
While it's great for agents, pinecone excels at semantic search, so it's perfect for agents that need
to retrieve contextually relevant information quickly.
It's a fully managed solution, so you don't need to worry about infrastructure.
Just focus on your.
Just focus on your agent's functionality.
So it's plug and play for fast implementation.
It's optimized for high speed searches working with real time agents.
Plus it can be on the higher side for enterprise size solution, but for individual users or small teams
is very affordable.
And because it's fully managed, there is less control over customization.
And best use case are chatbots, semantic search for customer support, and personalized recommendations.
We will use Python later in this course.
Now super bass.
Super bass is not strictly a vector database.
It's an open source alternative to Firebase.
However, with extensions like PG vector, it can handle vector embeddings.
So Super Bass combines the flexibility of traditional relational databases with the capability to store
vectors.
So it's great if your agent needs both structured data like user profiles and vector based search.
So it's open source.
You can self-host it, giving you full control.
It combines relational and vector storage so you can centralize all your data.
It requires more setup and maintenance compared to fully managed options, and may not match Bitcoin's
speed for large scale vector searches.
And the best use case are agents that handle hybrid data, so combining customer information with semantic
search for ask questions.
We will be using Supabase later in this course, so we will see how it works in practice now.
Quadrant.
Quadrant is an open source vector database designed specifically for AI and machine learning applications.
So it's a favorite among developers who value full control over the database.
So built from the ground up for vector search is great for self-hosted solutions.
It offers integration with embeddings from models like OpenAI and is perfect for cost efficient AI workflows.
It's fast and efficient for mid to large sized datasets.
It integrates with frameworks like Long Chain so you can build multi-agent systems because it's he's
self-hosted.
It requires technical expertise to set up and maintain, so it does not provide a full managed service
like Bitcoin does.
And the best use case and best use case is self-hosted agents for semantic search, such as customer
recommendations or knowledge management systems.
To conclude, which one should you choose?
In my opinion, Bitcoin is perfect if you are building agents with a focus on semantic search and need
a fully managed solution.
Supabase is great for hybrid data workflows, especially if you are already using relational data alongside
vectors and could run is great if you want full control and need a cost efficient open source solution
for your agents.
So to wrap up, vector databases are the backbone of modern agents and rack systems.
They help your agents remember, understand, and respond with context.
Now that you know the basics, you will learn how to use vector databases in projects later in this
course.