In this video, we'll talk about vector databases.
The AI revolution is here, and it is
about to change the world.
AI is being used in a variety of
industries, and has the potential to improve our lives in many ways, but it
also introduces new challenges.
One of the biggest challenges is
efficient data processing.
AI applications such as LLMs, generative
AI, and semantic search require large amounts of data to train and operate.
Efficient data processing is essential for making AI applications successful.
Many of the latest AI applications rely on vector embeddings, chatbots, question
-answering systems, and machine translation rely on vector embeddings,
which, as you know, means converting text to numbers that carry within themselves
semantic information.
Vector embeddings are a way of
representing text as a set of numbers in a high -dimensional space, and the
numbers represent the meaning of the words in the text.
This is critical for the AI to gain understanding and maintain long -term memory.
If you store text embeddings in a CSV file or in another format that is not
dedicated for embeddings, the size of the file will increase dramatically, and the
performance will drop.
Consequently, we need a specialized
database or data store specifically designed to manage such large quantities
of data in a numeric representation.
There are many vector databases
available, both free and commercial.
Some of the most popular ones include
Pinecone, Chroma, Milvus, or Quadrant.
In this project, we'll use Pinecone.
Pinecone is a vector database designed for storing and querying high
-dimensional vectors.
It provides fast and efficient semantic
search over these vector embeddings.
By integrating OpenAI's LLMs with
Pinecone, we combine deep learning capabilities for embedding generation
with efficient vector storage and retrieval.
Vector databases are a new type of
database designed to store and query unstructured data.
Unstructured data is data that does not have a fixed schema, such as text,
images, and audio.
SQL databases, on the other hand, are
designed to store and query structured data, which has a fixed schema.
In a traditional SQL database, we usually query for rows where the values match
specific criteria.
Think of the SQL SELECT statement.
In vector databases, we apply a similarity metric to find a vector that
is the most similar to our query.
What is the common pipeline for vector databases?
Vector databases use a combination of different optimized algorithms that all
participate in approximate nearest neighbor search.
Take a look at this image.
To understand how vector databases work,
let's break down the image into three steps.
Embedding, Indexing, and Query.
The first step, embedding, is to create vector embeddings for the content we want
to index.
This is done by using an embedding model
such as OpenAI's Text Embedding Ada 0 .0 .2.
The second step, indexing, is to insert
the vector embeddings into the vector database.
This is done by associating each vector
embedding with a reference to the original content that was used to create it.
And the third step is query.
The third step is to query the vector
database for similar content.
This is done by using the same embedding
model used to create the vector embeddings.
The embeddings model is used to create a
vector embedding for the query.
And this vector embedding is then used to
query the database for similar vector embeddings.
The similar vector embeddings are then
associated with the original content that was used to create them.
To illustrate, let's imagine a company that wants to store and query its private documents.
The company would first use an embedding model to create vector embeddings for
each document.
These vector embeddings would then be
inserted into a vector database such as Pinecone.
Each vector embedding would be associated
with a reference to the original document.
Finally, the company can use the vector
database to query for information that is similar to a given query or question.
For instance, the company could query the documents or to a product description.
Vector databases are much more efficient at storing and querying unstructured data
and are heavily used in LLMs applications.
All right.
We are done.
That was a short introduction to vector databases.
In this lecture, you learned what a vector database is, why we need them, and
how they leverage the power of the next generation of AI applications.
We will take a quick break and in the next video, we'll move on to splitting
and embedding text using Lanchain.
You'll also learn about how to upload the
vector embeddings to a vector store like Pinecone.
This is the first step in working with
OPLstack and developing LLMs applications.
The OPLstack is a set of open -source
tools that can be used to build applications that use large -language models.
The stack consists of three main components, OpenAI, Pinecone, and Lanchain.
I'll see you soon.