You've been hearing a lot about Azure

SQL database, Vector, and Azure OpenAI.

Learn how you can use them all together

in a demo and a sample that's available

now this week on Data Exposed MVP

edition.

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Hi, I'm Anna Hoffman and welcome to this

episode of Data Exposed MVP edition.

Today we have a special episode because

we're talking about AI, everybody's

favorite topic. Um, and today on MVP

edition, we have Indira joining us uh

today. And as we get started, can you

tell us a little bit about yourself and

what you do? Thanks Anna for this

invite. uh what I do is uh at the moment

I'm doing data architecture and I have

uh spent about 20 more than 20 years in

the data space and uh I'm really

interested in AI and we have been

working on AI um models and different uh

projects on AI for the last 3 years I've

been really uh exploring these and then

enjoying working with AI uh and I'm

excited today about this um uh data

exposed event on Azure SQL DB and

OpenAI.

>> Awesome. Great. Well, we're happy to

have you on the show and we know you

have your own YouTube channel as well.

So, we'll put a link to that in our

description. But, you know, for now, I

say like let's get right into it. I'd

love to understand kind of your

scenario, your the way you're thinking

about Azure SQL database and OpenAI.

>> Yes, let's start.

>> Okay. So uh today what I'm going to show

you is my session is about implementing

rag using Azure SQL DB and Azure OpenAI.

So let's get into the agenda. I'll

quickly explain what uh RAG is in terms

of uh Azure SQL DB and Azure OpenAI uh

and then I'll get straight into the

demo. So uh in the in terms of uh ret

retrieval augmented generation uh with

respect to uh Azure SQL database and um

Azure OpenAI what happens is we have a

lot of data uh in um in Azure SQL and

when we do the queries we just write

normal queries but uh with the new uh

capability uh the we can plug in large

language models which are uh deployed in

um Azure OpenAI. Uh call them and then

we have a new uh vector data type that

that we can uh store in uh uh MSSQL

database um uh Azure SQL database and

then uh for example if I have a products

uh products data in my SQL then we can

extract all products that are similar to

the uh provided question. So provide

question in the sense when you ask when

you when you uh ask in uh in a natural

language uh um the um the what what the

uh question is let's say for example can

you give me similar models to Xbox or

something like that then what it does is

you can uh uh you can create a similar

similarity search and then that that

prompt will go to the open AI uh and

then um then of all the extracted uh

products determines the what are the

most appropriate um and then gives the

pro uh gives the answer to the provided

question in native language. So um that

is the whole process. So here you are uh

storing the vector uh embeddings in the

Azure SQL. So that's the specialtity of

this um uh rag using Azure SQL database.

So uh I'll get into the demo um demo.

But before we get into the demo, there

are some requirements. Obviously when

you are using Azure SQL database you

need to have Azure SQL database um uh

defined and uh also Azure OpenAI

resource um and all and you can uh

deploy any GPT model. So I have I think

GPT4 and GP4 mini um uh and also you

need to have a text model so that you

can uh use the text uh model to uh to

create the embeddings. So the text model

that uh I have used is the uh ADA model

uh that is available in uh Azure uh

OpenAI and then you also uh I create I

uh updated my database using a blob

storage where I stored the CSV files of

the uh of the data and then created a

credential and then uh I uh uh bulk

inserted the data into uh SQL database

which I'm going to show you. So that

those are the demo requirements and then

let's great get straight into the demo.

Um

so I have uh SQL ser uh server here

uh which I have connected using uh SQL

server management studio and um

uh and I have created a database called

Walmart. Um and then in the Walmart

database I have created a table using

the uh using this code here that you can

see um and the database and the table

name is Walmart e-commerce product

details. So I have got this um data from

the internet which uh I have um uh I

have stored the I have uh given the link

of the uh data uh it's it's on Kaggle

database and then after after creating

the database then here the the code that

I I have uh highlighted here is uh uh

creating the external source external

data source so that we can link the

external data source which is in on blob

storage um uh with a shared access

signature that you can see. um we we

create a credential and then we uh do

the bulk uh insert with um here if you

can see the uh code of the bulk insert

uh I am inserting from that particular

uh folder called Walmart and then from

that particular CSV and then the batch

size is uh thousand so it uh includes

the um it it inserts thousand rows per

um per uh each insert

And then after um loading so you can see

the um uh the table uh after loading the

uh so yeah we can see the um top 100 uh

from the Walmart uh data Walmart table

Walmart uh e-commerce product details

table and then here if you uh look at

the all the uh

all the columns You can see at the end

there is a embedding column which is the

uh which is the embedding so that uh

when you ask a question that embedding

column will be used uh by the Azure open

AAI to give you the similarity answers.

So after that the next step is creating

the uh credentials. Uh so um so that we

we can um uh create what what the uh

credential is for the um uh Azure OpenAI

instance so that the uh the instance is

used from the uh credentials from from

within the database. So um and then the

next step is creating the uh

transforming the search text into a

vector. So here uh you can see that the

the text asking being asked is anything

for a teenager boy passionate about

racing cars. So here uh you are uh

creating the uh you are running the um

uh while loop if there are any uh this

thing and then you create the HTTP

response and insert into the HTTP

response. Um so that is the response

that that it gives the search vector

gives uh which is in a JSON format. Um

and when you uh look at the

um table so you can see the HTTP

response table and uh uh the Walmart uh

uh e-commerce uh product details table

as well in the table list of tables. So

if if you look at the uh top one JSON

query so you can see the embeddings that

you can you are uh creating you have

created in the uh htt http response. uh

so the the the JSON query has passed

what and then how uh the number of uh

records in that table is 30 30,000 that

I have inserted.

So now this this code is uh uh getting

similar items. So here what uh this

query does is it creates a um uh uh

dbo.imaril

items table uh where you pass the um uh

and then that will be used and here this

one this query is you is chatting with

the data. So you declare a JSON array

and then you pass two prompts. One is

the system prompt and the and the other

prompt is the anything for a teenager

boy and then it gives you the um uh it

gives you the uh result in a again in a

JSON format.

So uh name and yeah you are a system uh

you are the system assistant who helps

yeah you can give you that. And then uh

the next one is uh uh uh you uh if you

run the entire uh query you get the

difference. So that the top result is

the JSON and the bottom result is the uh

actual u uh uh actual result and this

query is uh uh chat with the data

structured data. So here we are g

parsing the JSON query and then uh

running the um running the uh

details. So now we we get the uh similar

products uh which are which are there in

um as per the question. So, teenage boy

uh Sony PlayStation and uh you can uh if

I go back again so and pause this so you

can uh see that the Sony PlayStation and

Lego Racers uh scratch building toy set

uh building build your vehicle racing

car. So these are the similar things

that uh the um uh the open AI has

identified and given you the results and

uh based on the um the based on the uh

results that were stored in the sim in

the uh similar objects uh uh data table

in the uh in the similar objects table.

So that's what I wanted to show you. Uh

there is also uh if I go into the SQL

database, you can also create a uh store

procedure um called uh get embedding or

something. Um and then you can pass the

uh URL of the embedding of your

embedding model and also your credential

and then uh where where the if you

insert some new records call this get

get embedding um procedure then uh you

can uh create the you can create the

embedding based on uh based on your um

which is which is of a vector type which

uh of 1 36 and then uh you can store

that in the uh database in the into in

the embedding column that I have showed

you. So if I show you the uh

if I show you the

columns again so here

you can call this embedding uh get

embedding store procedure and then

create the embeddings. So that's what I

wanted to show you uh as uh this thing

and if you if I um this is the data set

that I have uh no this is not the data

set but the the there is a GitHub link

that I have given here. Um

see here so this is the uh GitHub link

that I have used where I got the uh

Walmart data and then there are clear

steps as well. Um, so maybe if I go in

this.

>> Awesome. Thanks, Andira. I feel like I

learned a lot by walking through your

demo. So, just to kind of summarize what

we saw is you imported an existing data

set which already had embedding. So, in

that case, you're using Azure SQL

database as a vector store. Now, then

what you were able to do is create store

procedures that call out to Azure OpenAI

and take in new requests. So like if

someone says find me something similar

to you know this Xbox thing for a gift

for racing cars then it converts that so

it's able to call out get the embeddings

for that and then you also showed how it

does a similarity search so it used I

think you s used cosign distance there

to go between

>> cosign yes

>> um the search and the items available in

the products list. Um, and then you also

showed us kind of full circle how you

could if you wanted uh create additional

embeddings maybe as new products come in

or some or something like that.

>> Um, so I think it's an awesome sample. I

would say you know as someone who's been

working in the AI space for some time

like uh do you have any final tips or

tricks for folks who are just getting

started with rag or using AI and SQL

databases?

So uh I would say that you uh you can

use different um uh rag techniques that

uh you that that are uh there. I usually

follow Pamela from Microsoft and she has

she really uh explains the rag concepts

very clearly. So maybe you also uh

follow Pamela Fox. uh she does uh really

good um videos and also I think the as

an as next steps uh if you are a data

person maybe you should uh look at this

public preview of uh um the native

vector support in Azure SQL database

where you have some uh uh examples as

well to go through uh and get started.

>> Awesome. Cool. Well, Indier, thanks so

much for coming on the show. Uh for our

users, if you like this episode, go

ahead, give it a like, give us a

comment, let us know what you think.

We'll put links to the GitHub sample uh

and some of the other things that we

talked about in the description. Note

that this is available in Azure SQL

database, SQL database and fabric and

SQL server uh 2025 which at the time of

recording uh all of these things are in

preview. Um so if you yeah if you like

the episode go ahead and give it a like.

Indira again thanks so much for coming

on this episode of Data Exposed and we

hope to see you next time on Data

Exposed.

>> Thank you. Thank you Anna.

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