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Hello guys.

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So we are going to continue the discussion with respect to our graph database.

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Now we are going to see some more amazing examples which will be intermediate to advanced level.

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And we will try to see that how we can upload this particular data in the graph database itself.

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Right.

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So uh, here you will be able to see that I've created a GitHub link where I have uploaded some of the

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database like uh, data sets, like movies, dot CSV posts, dot CSV ratings, dot CSV.

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And then we will take all the CSV file and we'll try to upload this particular data set inside this

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graph database itself.

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So for that what kind of queries will be required.

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All those things we will be discussing.

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So the kind of use case that I'm actually going to show you, it is something related to social media

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platform.

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You know.

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So you know that you will be posting your post.

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There will be various users.

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And based on that, you may be liking a specific post, you know, and you may also have some of the

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friends in your social media platform.

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So considering this, you can see these are some of the important CSV files.

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So here is one user underscore social dot csv.

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So here you have user id name age and city.

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So these are all the details.

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And it is a comma separated file.

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Then uh you have this user ID user ID two type like uh 1 to 2.

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Uh, who all are friends, right.

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Based on the user ID.

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So here you can see if 1 to 2 friend is there.

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So John and Sarah is actually a friend.

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Right.

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And similarly 1 to 3 is a friend then 4 to 1.

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So fourth ID is basically liking the post of the first ID uh, in terms of the user.

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Right.

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So all these information is there.

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Other than that, you also have some post ID content, user ID and time stamp.

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So there is also a post dot csv file.

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Here you have posted something with respect to the time stamp and you have all the information.

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Now what we are going to do.

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First of all, we will insert all the specific data, uh, inside my graph database.

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So how do we insert a data.

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So for this we will be using a inbuilt function which is called as load CSV.

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Right.

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So here I'll say load CSV with headers.

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Okay.

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So I'll say hey with headers we are going to load all the CSV file.

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Then uh it will ask me where the CSV file is available.

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Right.

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So uh right now I have used this.

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So here you'll be able to see that I will be copying this entire link.

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I'll be pasting it over here okay.

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So this will be the link, uh, where my CSV file is present.

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So I will just go ahead and create as a temporary variable called as row.

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Okay.

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This row will be making sure that it will have all rows.

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It will basically iterate to all the records that are available inside the CSV file.

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Okay.

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Then we'll go to the next line and then we will go ahead and create all these users.

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Okay.

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Now here what exactly we have specifically done.

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It's just like you are loading this entire CSV file.

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You are running a for loop on each and every records.

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Okay, so I hope this is very much visible.

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Okay, so I'll be just writing it in this way.

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Now what I will do, I will go ahead and say for every row let's create a user okay I know so there

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is a specific user over here.

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And if you probably click this user, there are some of the users that you will be able to see.

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Okay.

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And uh, let me just zoom out, okay.

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And right now the user is just in this ID format.

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Okay.

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So it was the previous data itself.

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Uh, so what I will do is that I will just delete this user, okay.

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In order to delete it, you just need to instead of writing return and I will go ahead and delete it.

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Delete n okay.

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So just by using this delete n and no need to write limit 25, you'll be able to delete the entire node.

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So once I execute it here you can see deleted five nodes.

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Now you can't you can't see any users over here as the nodes.

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Right.

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So uh, try to do it in that way.

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Now I will go ahead and create my user.

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Okay.

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Now when we are creating this specific user here, I will also go ahead and specify my user ID.

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So let's go ahead and specify my user ID.

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This user id will basically be my properties right here.

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I will be using two integer.

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Now see this.

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Once we use two integer, that basically means what we are doing for every row.

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Whatever will be the ID over there, right?

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So right now this is the user ID right?

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By default when we read it this will be in string.

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But we will try to convert this into an integer.

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So here I'm going to write row dot user id okay.

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So row dot user id will be the thing.

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Then after that let's go ahead and create my name row like a name property.

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So it will be again row dot name okay.

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And finally you have this age which will be again two integer.

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We will use two integer to float whatever numbers you want.

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And here I'm going to basically write row dot age.

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So this way we are actually going to create all our properties right.

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And finally after this we will also create one more.

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That is nothing but city Rho dot city.

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Okay.

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So I hope uh, every user should be getting created directly from the CSV file.

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Okay.

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so now what I'm going to do I'll close this.

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And now let's execute it and let's see whether everything is working fine or not.

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So here you can see hey amazing.

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It has created five nodes 20 properties and added five labels right now.

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Uh, obviously the label is nothing but user.

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And here are all your you have all the element, all the people name like Sarah, John, Mike.

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Right.

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David and Linda.

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Right.

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So all this information is specifically there.

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Perfect.

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So these are the people who are specifically using the social media platform over here right now.

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Coming to the next one.

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For the next one, I will just go back to my post dot CSV.

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And these are the information that are posted with respect to the post ID.

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And here you can see mapping is also there with respect to the user ID okay.

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Okay.

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So let's go ahead and upload this specific file.

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Now what you do is that just pause the video and just try to see that how you will be able to load this

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particular file.

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Okay.

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Now I'm going to probably write down the query over here.

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So here I'm going to say load CSV with headers okay.

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Then I'm going to use from and let me just go ahead and use my URL.

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Okay.

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And here is the URL.

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Please try to understand the syntax okay.

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So the syntax is very much simple.

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We are just using load CSV with headers from this particular URL.

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And here also I will just go ahead and use Azure.

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Okay.

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And uh with respect to the Azure or what we are going to do is that we are going to write this in my

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next query, I will say, hey, go ahead and do the matching.

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And this time when you are doing the matching, please, first of all, let's go ahead and match the

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user.

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So I will write you colon user okay u colon user.

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And again I will go ahead and say, hey, use this user ID whichever property we have actually created,

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let me make some space over here so that it looks good and I'll say, hey, the user ID will try to

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convert this into integer, okay.

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And then we'll say row dot user id you'll understand what we are exactly doing.

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We are matching all the users based on the user ID which we are retrieving it from row dot user id okay,

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after doing this particular match, what we have to do is that we have to create a relation.

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Now let's go ahead and create this.

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I will say, hey, create this user and create a relation, something like Dash.

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And let's keep that relation as something like posted okay.

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So here I'm going to create my relation as posted.

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And this posted should have a relation with what with obviously post.

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So this will basically be my label right with post.

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Oh let me just go ahead and write posts right.

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And here we are going to use some properties like post ID okay.

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What will be the post ID that I have.

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ID okay it will be again two integer.

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And here I'm going to use row dot post id because from this row I will be able to access all the post

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id, so I will just go ahead and write row dot post id right?

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So let's see what are the post IDs there.

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So I will copy this over here.

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All right.

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Perfect.

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Similarly uh the next field that I have is nothing but content.

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So I'll go ahead and write.

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Content is equal to row dot content.

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Right.

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Because that is my sorry row dot content not raw.

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Row dot content.

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Right.

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So this is what is my content is there.

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Then I have my user ID okay.

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So let's go ahead and write the user ID.

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But the user id we have already done the mapping.

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So I will just go ahead and put the timestamp right.

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So here the next thing that I'm actually going to use is time stamp.

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Now inside this time stamp I'll use date time.

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Right.

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So date time is obviously there.

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See over here itself because we need to convert that into date time.

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So here I have a namespace called as date time.

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And here we are going to just go ahead and write row dot time stamp.

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Perfect.

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And then we will close this entire query.

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Step by step.

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See here we are matching with respect to user ID and the row user id right.

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So user id this two rows user id.

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So then mapping has specifically done.

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And we are creating a relationship called as posted.

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So once I execute this here you can see five nodes is specifically there.

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And if I click on post amazing.

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See all the posts are there.

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Right.

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So this is my uh ID also it is mapped right?

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My first post my timestamp.

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So Mike has posted this third and Linda has posted this other post.

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David has posted this right.

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All this information is John has posted this right.

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So this posting has been done.

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And here we can just go ahead and check match P is equal to some blank space Like give nothing over

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here and try to see like where is this specific relation.

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Once I click on this posted you are able to see this.

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Okay now let's do one more thing.

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There is also one called as relation dot csv.

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Right?

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We need to understand who are whose friends right.

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So for this we need to load this entire relationship dot dot csv file.

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Again let's go ahead and write it down.

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So I'll write load underscore CSV with headers.

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Okay.

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From my file name.

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File name is nothing.

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But where is my relationship file.

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So it is present in this GitHub location.

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And here also I will just go ahead and create a temporary row and let's press enter okay.

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Now this is just a simple match query.

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First of all we need to match with the user ID with row is your ID then YouTube user with a user id

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two.

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See there are two users ID right over here.

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See this user id one.

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User id two.

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User id one to user id two.

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What type the friends are.

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Right.

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So this is the kind of mapping that you will be able to see.

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So that is the reason we made two mappings.

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One is UU1 with user id, one uh user id and then YouTube u2 with user id where we are mapping user

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id to user id two, and then we are mapping this user id to user id one.

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Okay.

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Then we are creating this called as create U1 friends friends relationship likes relationship with U1

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to U2.

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Okay, so just go ahead and explore this.

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You will get a complete idea what exactly we have done over here.

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So once I execute this So created 22 relationships.

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This is amazing.

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Right?

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So, uh, if you go ahead and see.

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Friends see nicely.

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All the graph is basically coming.

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Sarah is a friend of Mike.

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John is a.

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Friend of Mike.

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Then.

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And remember this user ID and user one uh, user ID is basically getting mapped.

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Right 1 to 2.

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Right 1 to 2 basically means one is the friend of two.

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So one is John is friend of Sarah.

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So if you go ahead and see John is a friend of Sarah, then Sarah is a friend of Mike.

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This entire friend circle is basically getting created perfect.

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So, uh, I hope you have understood of probably creating all the tables and how you can basically define

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all the relationships.

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Now it's time we start retrieving like start.

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Let's start from some basic queries.

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We will go ahead and retrieve all users.

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00:12:58,000 --> 00:13:01,000
So for this I'm going to write my match keyword.

254
00:13:01,000 --> 00:13:05,000
Let me go ahead and write you colon user okay.

255
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This will basically be user.

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And let's return return you okay.

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So once I execute this here you will be able to see that I'm able to get all the users over here Sara,

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John, David Linda and Mike.

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Okay.

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Now the next one, what I will do is that I will retrieve all the post.

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00:13:24,000 --> 00:13:27,000
Okay, so here again we will use a match query.

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00:13:28,000 --> 00:13:32,000
Let me go ahead and write P colon post post okay.

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And here we are going to return P okay.

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Sorry we have to use this capital letter.

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It's good return p.

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P.

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So I'm just trying to check which all posts are there.

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So so many different posts are there.

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Love New York beautiful.

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And this is basically given with the help of this post ID okay, perfect.

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Now let's retrieve a friend of a specific user.

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00:13:56,000 --> 00:13:56,000
Okay.

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If I say hey for this user John who is the friend.

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00:13:59,000 --> 00:14:03,000
So here what I'm actually going to do again, I'll go ahead and write my match query.

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And with respect to this match query again I will go and write u is equal to u colon user.

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Okay.

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00:14:09,000 --> 00:14:14,000
And then I will go ahead and use the property name colon name colon.

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Nothing but John.

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00:14:16,000 --> 00:14:16,000
Okay.

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00:14:16,000 --> 00:14:20,000
So once I do this and now I need to probably create that relation.

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The relation is very simple friend.

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00:14:23,000 --> 00:14:23,000
Right.

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00:14:23,000 --> 00:14:27,000
So I will use this I will write colon friend okay.

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And then I will say hey give me f user.

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00:14:33,000 --> 00:14:33,000
Okay.

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00:14:33,000 --> 00:14:35,000
I'm just creating this another label.

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00:14:36,000 --> 00:14:39,000
So here let me go ahead and write F user.

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This will be another label.

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This will be another label to distinguish between them.

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00:14:43,000 --> 00:14:43,000
Right.

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00:14:43,000 --> 00:14:47,000
And then we are just going to return f dot name.

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So once I do this you will be able to see that you'll be getting all the f uh f dot names over here.

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00:14:55,000 --> 00:15:00,000
So David, Sara, Mike, Linda are the friends of John.

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And if you don't believe me, see David, Sara, Mike and Linda.

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00:15:04,000 --> 00:15:07,000
So if I go and click on friend David.

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00:15:08,000 --> 00:15:09,000
Uh, David is the friend.

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00:15:09,000 --> 00:15:10,000
Sara is the friend.

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00:15:10,000 --> 00:15:12,000
Mike is the friend.

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00:15:12,000 --> 00:15:12,000
Right?

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00:15:12,000 --> 00:15:13,000
And Linda is also a friend.

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00:15:13,000 --> 00:15:14,000
Right.

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00:15:14,000 --> 00:15:19,000
All this information is basically getting extracted and that is how you are able to see to it right

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00:15:19,000 --> 00:15:20,000
now.

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00:15:20,000 --> 00:15:27,000
Uh, let's go ahead and see how many, uh, let's see the post made by the friends of a specific user.

305
00:15:27,000 --> 00:15:27,000
Okay.

306
00:15:28,000 --> 00:15:31,000
So here you will be able to see that I will go ahead and write my match query.

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00:15:31,000 --> 00:15:33,000
I will say, hey, let's go ahead and create this.

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00:15:33,000 --> 00:15:35,000
You label user.

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00:15:36,000 --> 00:15:37,000
Uh, sorry user is my label.

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00:15:37,000 --> 00:15:41,000
And then I am just going to write you is just a node okay.

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00:15:41,000 --> 00:15:44,000
Name with respect to John.

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00:15:44,000 --> 00:15:44,000
Okay.

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00:15:45,000 --> 00:15:49,000
Now we know that John, first of all we need to retrieve all the friends right.

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00:15:49,000 --> 00:15:51,000
So I will go ahead and write friend okay.

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00:15:51,000 --> 00:15:56,000
And then I will create another label F colon user okay.

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00:15:57,000 --> 00:16:00,000
And I'll say hey let's go ahead with posted.

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00:16:00,000 --> 00:16:01,000
Right.

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00:16:01,000 --> 00:16:02,000
So here I'm going to write.

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00:16:03,000 --> 00:16:11,000
It's just a posted one Okay, I now see there will be a little bit of confusion with respect to the

320
00:16:11,000 --> 00:16:14,000
queries, but you need to practice these things, right?

321
00:16:14,000 --> 00:16:19,000
But this is very simple when compared to MySQL I feel still this is very very comfortable.

322
00:16:19,000 --> 00:16:20,000
Very very much easy.

323
00:16:20,000 --> 00:16:21,000
Yeah.

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00:16:21,000 --> 00:16:25,000
Then you can go ahead and write p colon post.

325
00:16:25,000 --> 00:16:25,000
Right.

326
00:16:25,000 --> 00:16:27,000
So here what we are doing.

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00:16:27,000 --> 00:16:33,000
We are basically matching first of all user name who is a friend of the other user like of the same

328
00:16:33,000 --> 00:16:38,000
user with respect to comparing with all the other users, then we are basically considering whether

329
00:16:38,000 --> 00:16:40,000
they have actually posted, right?

330
00:16:40,000 --> 00:16:43,000
So posted is one of the relationship with respect to P is equal to post.

331
00:16:43,000 --> 00:16:44,000
Okay.

332
00:16:44,000 --> 00:16:52,000
And here I will just go ahead and write return f dot name and p dot content is just like inner queries

333
00:16:52,000 --> 00:16:52,000
right.

334
00:16:52,000 --> 00:16:59,000
So now if I just go ahead and execute this, you'll be able to see David has posted San Francisco is

335
00:16:59,000 --> 00:17:00,000
amazing.

336
00:17:00,000 --> 00:17:02,000
Sarah has posted my first post.

337
00:17:02,000 --> 00:17:04,000
Mike has posted beautiful day in Chicago.

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00:17:04,000 --> 00:17:10,000
Linda love New York and all right, um, this is, uh, how you can basically do this.

339
00:17:10,000 --> 00:17:15,000
But again, here the main aim is not to become pro in Cypher Query language.

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00:17:15,000 --> 00:17:21,000
This is just for your purpose to show you because later on we will be when we implementing or when we

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00:17:21,000 --> 00:17:27,000
are creating our generative AI applications with the help of Lang Chain right there, we will be using

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00:17:27,000 --> 00:17:28,000
all this kind of queries.

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00:17:28,000 --> 00:17:31,000
Some of the queries will be using in order to insert the data and all.

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00:17:31,000 --> 00:17:39,000
So guys now let's go ahead and see one more example where we are going to find users who likes a specific

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00:17:39,000 --> 00:17:40,000
user post.

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00:17:40,000 --> 00:17:44,000
So here you will be able to see uh with respect to the post, right?

347
00:17:44,000 --> 00:17:46,000
How many people have liked it.

348
00:17:46,000 --> 00:17:46,000
Right.

349
00:17:46,000 --> 00:17:48,000
4 to 1, 4 to 5, 1 to 5 like that.

350
00:17:48,000 --> 00:17:49,000
Right.

351
00:17:49,000 --> 00:17:52,000
So we will try to create another relationship with respect to that.

352
00:17:52,000 --> 00:17:53,000
Right.

353
00:17:53,000 --> 00:17:58,000
So uh, let's go over here and let's see how to probably write this.

354
00:17:58,000 --> 00:18:05,000
So here I'm going to write a match keyword again u colon user okay.

355
00:18:05,000 --> 00:18:09,000
And uh, let's say the user name is John.

356
00:18:09,000 --> 00:18:11,000
So I will just go ahead and write John okay.

357
00:18:12,000 --> 00:18:17,000
And here we will go ahead and write colon posted.

358
00:18:17,000 --> 00:18:18,000
Oops.

359
00:18:19,000 --> 00:18:21,000
Colon posted relation okay.

360
00:18:22,000 --> 00:18:31,000
Um then this will be mapped to p colon post P colon push.

361
00:18:31,000 --> 00:18:36,000
And with respect to this I will also go ahead and specify my likes.

362
00:18:36,000 --> 00:18:40,000
So let's go ahead and see with respect to likes okay.

363
00:18:40,000 --> 00:18:46,000
Because we are comparing inside this p post itself we have this specific likes okay.

364
00:18:46,000 --> 00:18:51,000
Uh see when you give this kind of direction that basically means we are just trying to find out the

365
00:18:51,000 --> 00:18:57,000
relationship we are traversing the relationship between this user and post and inside this post, you

366
00:18:57,000 --> 00:19:00,000
know that, uh, there are something called as likes, right?

367
00:19:00,000 --> 00:19:06,000
So we are just going to use a reverse direction right where we are saying this as likes.

368
00:19:06,000 --> 00:19:09,000
And this is another relationship that we are trying to create.

369
00:19:09,000 --> 00:19:10,000
Okay.

370
00:19:10,000 --> 00:19:16,000
And this likes will be with respect to another user, another user.

371
00:19:16,000 --> 00:19:19,000
Uh uh, I can just consider another user label.

372
00:19:19,000 --> 00:19:20,000
Right.

373
00:19:20,000 --> 00:19:23,000
And then we will go ahead and return L dot name.

374
00:19:24,000 --> 00:19:27,000
Along with this we will also return p dot content.

375
00:19:28,000 --> 00:19:31,000
So this way you will be able to see everything as such.

376
00:19:31,000 --> 00:19:32,000
Let's execute this.

377
00:19:32,000 --> 00:19:36,000
So it says uh no changes, no records.

378
00:19:36,000 --> 00:19:37,000
No records has been found.

379
00:19:38,000 --> 00:19:40,000
Um, let's again see this.

380
00:19:40,000 --> 00:19:43,000
Uh, you can see user with respect to name is equal to John.

381
00:19:43,000 --> 00:19:44,000
Okay.

382
00:19:44,000 --> 00:19:50,000
And whatever John has specifically posted with respect to that particular post and likes has been compared

383
00:19:50,000 --> 00:19:52,000
with L dot user over here.

384
00:19:52,000 --> 00:19:54,000
And then we are returning L dot name.

385
00:19:54,000 --> 00:19:58,000
So right now no match is specifically happening.

386
00:19:58,000 --> 00:20:04,000
Um, so with respect to any John post, uh, I think this is there.

387
00:20:04,000 --> 00:20:07,000
Let's verify the ID itself.

388
00:20:07,000 --> 00:20:07,000
Okay?

389
00:20:07,000 --> 00:20:11,000
So I will just, uh, make sure to copy the same thing.

390
00:20:11,000 --> 00:20:13,000
Let's execute it.

391
00:20:13,000 --> 00:20:17,000
So it says John posted p post.

392
00:20:17,000 --> 00:20:25,000
So, uh, John is over here with respect to John one comma, user id comma user id one, comma user

393
00:20:25,000 --> 00:20:27,000
id two right.

394
00:20:27,000 --> 00:20:28,000
With respect to likes.

395
00:20:29,000 --> 00:20:33,000
So I think there will be no record that is specifically matching.

396
00:20:33,000 --> 00:20:35,000
Okay, let's try one more query over here.

397
00:20:35,000 --> 00:20:39,000
Let's count the number of friends each user has.

398
00:20:39,000 --> 00:20:45,000
And now I'm going to use this specific query I'm writing match u colon user with respect to friend f

399
00:20:45,000 --> 00:20:45,000
colon user.

400
00:20:45,000 --> 00:20:51,000
Again F is another label uh another node written u dot name count f as number of friends.

401
00:20:51,000 --> 00:20:55,000
See all the inbuilt function like how we use in uh, MySQL and all.

402
00:20:55,000 --> 00:20:56,000
Similarly, we are able to do this.

403
00:20:56,000 --> 00:20:58,000
So order by number of friends describing.

404
00:20:58,000 --> 00:21:01,000
So it will just show you that David has five friends.

405
00:21:01,000 --> 00:21:05,000
Sarah has five friends, John has four friends, Mike has four friends, and Linda has four friends.

406
00:21:05,000 --> 00:21:06,000
Right.

407
00:21:07,000 --> 00:21:11,000
Um, these are some of the good examples that I've actually shown you guys.

408
00:21:11,000 --> 00:21:15,000
Uh, but again, it is up to you to go ahead and try it out again here.

409
00:21:15,000 --> 00:21:22,000
The main intention of creating this module is not to become perfectionist in a cypher query languages,

410
00:21:22,000 --> 00:21:27,000
because obviously there are different different scenarios, but you can see just the power of this,

411
00:21:27,000 --> 00:21:27,000
right?

412
00:21:28,000 --> 00:21:33,000
Our main aim of understanding this was that we should be able to work with long chain whenever we are

413
00:21:33,000 --> 00:21:35,000
creating a generative AI application.

414
00:21:35,000 --> 00:21:38,000
So yes, uh, this was it from my side.

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00:21:38,000 --> 00:21:40,000
I hope you liked this particular video.

416
00:21:40,000 --> 00:21:43,000
I will be see you all in the next video.

417
00:21:43,000 --> 00:21:43,000
Thank you.

418
00:21:43,000 --> 00:21:44,000
Take care.