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KAZ SATO: Hi.
Konnichiwa.
I'm Kaz Sato, developer advocate for Google Cloud based
in Tokyo, Japan.
HOLT SKINNER: Hi there.
I'm Holt, also a developer advocate from the Google Cloud
team based in Austin, Texas.
One of the most challenging problems
to solve for an organization is discoverability of information.
Many times data will be scattered across a bunch
of different sources.
Some might be in docs, some in databases, others in web pages.
This makes it difficult for employees and customers
to find just what they're looking for,
whether it be internal data or on a public site.
There are three main challenges when building a search service
for an enterprise.
One is the service level requirements for any enterprise
search service is high.
Most companies don't have time or budget
to build a service that is capable of handling
large amounts of data in a wide variety of formats
all while being scalable and available 24/7.
The second challenge is that traditional keyword search
is too restricted, especially for users
who are familiar with AI powered search and recommendations
every day on YouTube, Instagram, and other apps.
These experiences are standard today,
but it's much more difficult to implement.
The third challenge is how to take advantage of generative AI
and LLMs that are the pinnacle of information retrieval
technology.
Generative AI App Builder with enterprise search
from Google Cloud provides a simple to setup
service that unifies all this information together and makes
it straightforward to find.
Gen App Builder supports creating search engines
and chat interfaces.
In this session, we'll be focusing
on how to bring the power of search to your applications.
It's an out of the box solution, which
means you can deploy the fully managed search engine quickly.
You don't have to build and lead an entire DevOps
team to manage it.
It also utilizes semantic search,
which provides the next gen search experience,
as we'll discuss later.
Enterprise search provides LLM based summarization,
multimodal search, and chatbot integration,
utilizing the latest in generative AI technology.
One of the greatest features of Gen App Builder
is its ease of use for developers.
Let's get started by looking at the onboarding experience.
Keep in mind that this product is an early access
and the process might look slightly different
once it's publicly available.
Let's go through the process of setting up a basic search
Engine for a public website, structured data,
and unstructured data.
First you go to the Generative AI App Builder section
in the Google Cloud Console.
Then you enable the API.
Then you select Create a Search Engine.
Then you give it a name and choose the data type.
Let's go through the website process first.
After our search engine has been created,
we just need to add the sites we want to include.
If we open the advanced view, then we
can exclude certain sites as well.
For this example, let's search over the Google Cloud website,
cloud.google.com.
After setting the website, we now
have a view of the URLs included in our search engine.
You can also add more websites if you want.
And just that fast, the search engine has already
indexed our website.
We can now click on Preview Search to try it out.
The Preview page lets you try out your search engine
in a browser.
You can select a desktop or mobile view.
A search for another Google Cloud product, Document AI,
shows lots of relevant results for the product.
We can also look for more detailed information
like how to handle nested entities with Document AI.
Now let's go back and create a new search engine,
this time for structured data.
You can import the data from cloud storage
in a structured format like JSON or from a BigQuery table.
Let's import data from BigQuery.
We'll use a public data set of movies
that can be found on Kaggle.
Importing this data requires very little setup,
because the tool will figure out the schema for you.
All you have to do is provide a link to the data
and it'll get ingested.
Once it finishes importing, we can test the search engine out
just like the previous one.
The process for unstructured data
works pretty similar to structured data.
Unstructured data doesn't follow a schema.
Usually this will be in the form of documents like PDFs.
You create a search engine for unstructured data,
then you can import files from cloud storage.
In this case, I'm importing alphabet earnings reports.
Once they're imported, you can search the files
to find specific information.
Now we've explored the main types of search engines.
Let's see how we can use them in our applications.
If we go to the Integration page,
the console provides simple ways to integrate this new search
engine into an application.
You can go to the API tab to get prewritten API calls and code
samples to integrate this into server applications
or to build your own UI.
Or you can copy paste the HTML code in the widget tab
to embed this directly into your website.
Going to the Configure menu from this page
takes you to the widget settings where
you can configure autocomplete, among other features
for the search engine widget.
For structured data, we can also control more specific settings,
such as the fields that are shown the widget,
like the movie title, description, ratings,
and add thumbnail images.
The Analytics page provides aggregate metrics
for the search engine, including session information,
click through rate, top queries, and devices.
Now I'm going to toss it over to Kaz
to talk about semantic search.
KAZ SATO: So we have learned how enterprise search makes
developers' work much easier to build an AI powered search
engine in a short time.
In my part, I'd like to dive deeper
into this semantic search technology provided
by enterprise search and how it would change the search
experience in a wide variety of businesses.
Many of you might ask, what is semantic search, by the way?
To understand what it is, we need
to know the major shift undergoing in the IT industry.
The traditional IT systems, most data
is organized as structured or tabular data
using simple keywords, labels, and categories in databases
and search engines.
This has been the foundation of the user experience
you'd have with the usual IT system for information
retrieval and recommendation.
In contrast, AI powered search services arrange data
into a simple data structure known as embedding.
What is embedding?
Let's see how AI organizes data using embeddings.
Once lined with specific content like text, images, tweets,
or anything, AI creates a space called
embedding space, which is essentially
a map of the contents' meaning.
AI can identify the location of each content on the map.
That is what embedding is.
Let's take an example where our text discusses movies, music,
and actors with a distribution of 10%, 2%, and 30%
respectively.
In this case, the AI can create an embedding with the three
values 0.1, 0.02, and 0.3 in three dimensional space.
Please note that this is a simplified
example for explanation.
In reality, embedding space may have hundreds or 1,000 or 2,000
of dimensions that can represent millions
of different categories of the content.
And the AI can put content with similar meanings
closely together in the same space.
According to the Google machine learning crash course,
embedding refers to the process of mapping
high dimensional raw data, such as images or text,
which have tens or thousands of meters of dimensions
to a lower dimensional space with hundreds
or thousands of dimensions using deep learning models.
This is done to extract the meaning
or semantics of the data and create
a map of the content's meaning.
And this is how Google organizes data
across various services like Google Search, YouTube,
Play, and many others to provide search result
and recommendations with relevant content.
Embeddings can also be used to represent
different types of things in businesses such as products,
uses, user activities, conversations, music
and videos, signals from IoT sensors, and so on.
Let's see what real embedding space looks like.
This is an embedding space for handwritten digital images.
The embeddings are grouped based on how similar
their shapes are.
This is another embedding space for text sentences.
Even though they are written in different languages,
the sentences with similar meanings are grouped together.
Embeddings is an essential technology
utilized by Google Search to enhance its semantic search
abilities along with keyword search.
When a search query is entered, a language model based on BERT
convert it into an embedding.
Web pages are also transformed into embeddings beforehand,
enabling this service to conduct a vector search to locate
your web pages that matches the query's intention
in the embedding space.
In the past, Google Search used only a keyword search model.
This model sometimes gave you too many math textbooks
for students when someone searched for math practice
books for adults.
But now with semantic search, it can understand the intention
behind the query and show the most helpful result at the top.
This is a major shift in the IT industry,
especially for the information retrieval and recommendations.
AI is now playing a crucial role in the human computer
interaction in those applications.
It organizes data into embeddings
which represent the user's intention,
the meaning of a product, or many other things
you have in your business.
This creates a new level of search experience
that is becoming the new standard.
Now let's examine the semantic search experience provided
to this enterprise search.
In this demo, we use 20 PDF files of Google research papers
that have a total of 400 pages.
What if you are asked by your manager,
can you investigate what is video localized
narratives and annotated diversity
by scrutinizing 400 pages?
The solution is import all files into enterprise search
and run a query.
Let's see how it works.
After importing your files, just enter a text query.
What are video localized narratives?
Then enterprise search quickly finds the relevant pages
from all the files by understanding
the text query and its intent and also
the meaning of all 400 pages.
It's not just a keyword search but a search
by meaning with the AI powered semantic search
technology we built with Google Search and language models.
Also you noticed that enterprise search generates
a summary of the query result. This summarization
is automatically done by a large language
model in the search engine.
Instead of looking at each page one by one,
you can instantly get the gist of the search result.
And you can trust it because the summary is generated
from the real business reports, not a generated text
from nowhere by a chatbot.
This is the way you can use the power of the generative AI
with the responsibility for business use cases.
Here's another demo.
This is a sneak preview that shows the possible use
cases of enterprise search.
Later this year, enterprise search
will enable developers to import their own embeddings
as a search attribute.
That means not only the document or web text.
It can also execute semantic search
on any arbitrary medium or entity represented
with the embeddings.
For example, if you import multimodal embeddings that
capture the meaning of the product images,
then you can run a text query for finding the products
by what the images mean, not only from the product
description.
This demo data set is provided by Mercari,
a marketplace app with over 50 million downloads
in the United States.
It would be possible to enable such a rich user
experience with the multimodal semantic search
not only for our retail businesses like Mercari,
but also for financial, health care,
or other various industries.
Let's recap what we covered this session.
As Holt introduced, enterprise search
gives you an out of the box developer experience.
Whether it's web pages or tabular data
or documents or PDF files, you can just bring them in.
Enterprise search automatically builds the schema and search
index for your data with a simple UI flow.
Second point is the semantic search.
It searches by content's meaning, not just by keywords.
Behind the scene, the documents are converted into embeddings,
enabling the next gen search experience.
The last point is the LLM capability.
With the summarization, enterprise search
saves you time for opening and looking at every pages
for understanding what you get with the search results.
Instead you can get the gist of them
in seconds, dramatically improving the information
retrieval productivity in a wide variety of businesses.
Also the multimodal feature will allow
you to search from images, text, and many different mediums
and entities in your businesses.
These LLM features are based not on the virtual out
of nowhere outputs from AI chatbots
but grounded with business facts with responsibility.
To get started with Gen App Builder and enterprise search,
please check out the Gen AI page on cloud.google.com.
That was our system.
Thank you so much.
HOLT SKINNER: Thank you so much for watching,
and enjoy the rest of I/O.
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