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- GraphRAG: The Marriage of Knowledge Graphs and RAG by Emil Eifrem
GraphRAG: The Marriage of Knowledge Graphs and RAG by Emil Eifrem
Understand how Emil Eifrem is dedicated to enhancing applications through the connection of individual data points by relationships. Explore the evolution of search engines and the significance of utilizing knowledge graphs and RAG in LLMs and Gen AI.
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Video Transcript
I basically dedicated my professional life towards getting developers to be able to build
better applications and build applications better by leveraging not just individual data
points kind of retrieved at once, like one at a time, or summed up or grouped calculated
averages, but individual data points connected by relationships.
And today I'm going to talk about that applied in the world of LLMs and Gen AI.
So before I do that, though, I'm going to take a little bit of a detour.
I'm going to talk about search, the evolution of search.
Everyone here in this room knows that the vast majority of web searches today are handled
with Google.
But some of you know that it didn't start that way.
It started this way.
Who here recognizes this web page?
Right, yeah, who here recognizes Alta Vista as a name?
Like a few people, right?
Back in the mid-90s, there was dozens of web search companies,
dozens plural, like 30, 40, 50 web search companies.
And they all used basically the same technology.
They used keyword-based text search,
inverted index type search, BM25-like, for those of you
who know what that means.
And it worked really, really well until it didn't.
And the Alta Vista effect kicked in,
which was the notion that you search for something,
you got a thousand or thousands of hits back,
and you had to look through page after page
until you found the result that was relevant to you.
The AltaVista effect.
You got too much back from the Internet.
That wasn't a problem in the beginning
because most of the things you searched for
when I went onto the Internet in the beginning
got zero results back
because there was no content about that on the Internet, right?
But the AltaVista effect, too many search results,
was solved by Google.
This is Google's press release, mid-2000.
They talk about a billion URLs they've indexed.
But they also talk about the technology
that they use behind the scenes, the technology called
PageRank that delivers the most important search
results really early on.
In fact, the top 10 blue links on that first page.
That technology, PageRank, is actually
a graph algorithm, which is actually called
eigenvector centrality.
And the innovation that Google did
was applying that to the scale of the internet
and the scale of the web, right?
PageRank.
That ushered in and created, honestly,
