- Vector databases
are specialized databases that are designed
to store high-dimensional vector embeddings,
and you already learned
what are embeddings earlier in the course.
Now, these databases are very, very important,
and they excel for applications
that require similarity search,
like recommendation engines,
image recognition, NLP, retrieval augmented generation
and many others, right?
So now, unlike traditional databases,
these database handle very complex relationship
within data by focusing on vector proximity
or similarity search rather than actually just
the exact matches that you will get from a traditional,
you know, relational database and so on.
Now, in my GitHub repository, I have a section
under the AI research and ML fundamentals related
to vector databases with a few examples in here.
There are many, many of them in there,
but these are some of the most popular.
The first one in here is by Facebook,
is a very popular vector database
that is open-source called Faiss,
and if you click on that link, of course,
it will take you to their GitHub repository.
Very popular.
It is free to use, and for most people
that are actually starting
with retrieval augmented generation,
they actually select whether it's Faiss or Chroma DB,
the second one in the list.
Chroma DB is another vector database that is open-source
and, of course, free to use
and very popular among the community.
There are some commercial vendors in here.
Pinecone is one of them, MongoDB, Atlas Vector Search.
So Pinecone is a managed, you know, database
that you can absolutely use, you know, with a subscription.
MongoDB is a very, very popular,
you know, database solution.
However, within their MongoDB Atlas,
they have a feature for vector search,
basically integrating vector databases,
you know, capabilities,
and this is one of the most popular related
to a production-ready applications.
You know, it is extremely fast.
It's used by many different companies,
but it is not free, right?
So it is definitely not a free database.
It can integrate with many solutions out there,
just like the ones that we mentioned earlier,
like LangChain, Llamaindex and, of course,
you know, Hugging Face models and OpenAI
and Anthropic and so on.
So it is a very, very popular vector database.
Weaviate is another one.
Qdrant is another one, and the the last one is Milvus.
All of them, they actually have
an open-source representation of them.
You know, some of these are companies
that actually provide services around
the open-source repository, or the open-source,
you know, vector database.
If you click on the repository,
you can absolutely get access
to the free open-source variants
of these vector databases.
So again, these are here for your references.
These databases, again,
handle complex relationship within data,
and then they're very popular to, you know,
things like retrieval augmented generation,
you know, semantic search and so on.