nlsql

An AI-powered platform enabling users to query databases using natural language, simplifying data access and analysis without requiring SQL expertise.

NLSQL

Hello and welcome to the NL SQL portal. A central location for your natural language understanding to structure query language code conversion setup. If you're looking for an NL SQL demo based on your own data or just curious about how it works, you're in the right place. 

In this video tutorial, I'm going to show you step-by-step customization uh process for the general for the the generation of NLP to SQL API based on your business requirements and data. Before you want to get St uh started with our demo, I would recommend you to create a separate Google spreadsheet by copy pasting here all the information you want to use in your natural language requests. Hence, include it in the natural language understanding model. 

Before we move forward, you'll need to prepare Google spreadsheet with all the text column distinct values from your database. The structure of these spreadsheets should be the same as your database. Now in this case, we're using a database of import export data from the UK. 

To start with preparing this uh, you want to start with a blank Google spreadsheet like we have here. And then first of all, start by copying the table name to then copy the column headers from your table. And now we want to copy in the distinct values uh from each column. 

And to do so you want to type in a brief command here of distinct to then the column columns with distinct text values that you want to copy into the spreadsheet. So yeah execute that query and copy over the distinct values. 

Now again a reminder that NL SQL is a text based solution. So for numerical values you just want to ensure that the correct formatting is in the Google spreadsheet. To save time we can copy this command and then also just copy the column headers and capture those. Execute that and then then capture those distinct values. 

So Country and this is of course a very simple example that we're using for this database. I'm sure that you'll be using many different database examples which are much more complex. ID would be a numerical value. Prodct code again numerical and year although it's numerically based it still involves distinct values which we would want NL Co to be trained on. 

And finally prodct group great and now you have your Google spreadsheet prepared and ready for sharing with NL with the NLC qul platform. Google spreadsheets usually include product names product groups countries and any other non-confidential information you can share by API. You can feel free to use as many spreadsheets as your initial database has with. 

Data to include into the model correspondingly. Later on, I'll show you how uh to build joins between the tables to fit your internal database structure. 

To get started, all you have to do is to sign up onto our website, click on your username, and then the Google Sheets option. After copying Google's uh API email, share your files with API inter View mode. Click Share. Once done, open the first file, select Google spreadsheet ID from your browser address, copy it, and paste it as a spreadsheet uh Link in a corresponding page on our platform. 

Mark option first line as a column name. If your Google spreadsheet has column names, then choose API as the platform. Select your internal database syntax and type your table name. It should be the same as the table table name in your internal database. 

Mark the suggestions for web chat option only if you want to use a web chat interface with an active directory or any other user inter user authentication. Web chat is a Google like interface which provides you intuitive suggestions during your typing. 

Click on the link above on this video to see how web chat works with our Healthcare bi demonstration based on ICU systems. Once completed, click save and move forward to the next step. The next step is simple by choosing the. 

Columns you want to include in the model based on your table in Google spreadsheet. The next step is adding arguments. The mentioned table in the tutorial doesn't have obvious arguments, so I'm going to show you how to add arguments for the next table. Repeat what you have done over and over with every new table you want to include in the model. At any time, you can go back to linked tables and modify any relevant table settings. 

I'm showing you this demonstration which is based on open-source MySQL database, uh Scalia, which includes such tables as actor, address, category, city, country, customers, and others. Adding all tables from your internal database is crucial for the model understanding of your, uh database structure. Please make a note that data in the Google tables can be dummy ID, i.e., for demo purposes. The purpose of this action is to simulate a real database structure. 

You have actually natural language processing to SQL API token for you will be generated based on simulated database structure by linking Google spreadsheets and using our platform. Once you complete adding all the tables you want to include into the model, you can dig deeper into arguments and join the setup, uh, between your table. In order to build a join between the tables you. 

Need to click on the table name on your platform. Click on edit join columns. Uh choose the master column and join the table name with the corresponding column name. You should repeat it over and over for simulation relations between the same as your internal database has. 

Scal database is a relational uh database with uh multiple tables which is similar to relational databases on Enterprise use. Moreover, ANL SQL can work with non-relational databases as well as existing APIs web services in your corporate IT ecosystem. We support fully on-premise setup for healthcare and for other industries. We are doing on customer Cloud installation using Docker AER web app or AWS Fargate. 

It provides you the ability to integrate NL SQL in a day instead of months. The current setup is not complex as I'm doing uh as I'm doing the same for all tables over and over again. For some of the tables, I'm choosing the option suggest suggestions for web chat and choosing the column to use as suggestions in a web chat Google-like interface. 

Anytime you can stop the video and go back if something is not clear enough. You are welcome to contact our customer support team uh if you need help consultation or technical assistance. Uh if you're interested in the enter priz. 

Version you don't need to do this setup on your own. Our technical team will take care of all the details and get back to you ready to use natural language interface to your data which empowers your Frontline employees with instant access to poorly accessible data to inform and speed business decisions with significant benefits for Enterprises. Just allow some time uh to complete all the joining all the joins for setup.

Okay so let's reconfigure table inventory which requires some new arguments set up. Click on the table edit joins columns uh adding some relation to another table. For instance, table inventory includes current product stock for the Enterprise. In that case, one row equals one stock item on uh one item on stock. So I'm adding arguments stock inventory and Warehouse linked to inventory ID column with the operation for calculation number of rows ID column with the operation for calculating number of rows for the model itself. This setup means that natural language intends uh natural language intends to stock inventory or Warehouse will be calculated based on the inventory ID column.

The next table is payment uh click to edit join columns adding Master column and a table to join with the corresponding column for adding a second join choose.

Another master column and another join table with the corresponding column. If the table must have multiple joins, please feel free to add, uh, as many as required. 

On the next arguments, uh, step I'm adding sales as an argument with, uh, column amount and operation of the total sum for the model itself. This setup, uh, means that natural language intents to intense to sales will be calculated based on the amount column at table payments. 

For the table rentals, click edit join columns for adding joins by rental ID for table payments, by, uh, inventory ID for table inventory, by customer ID for table customers, and by column staff, uh, if it's for, uh, table staff. Once ready, I'm adding argument rental which will calculate the number of rows in column rental ID as well as film, uh, rent intent with the same, um, intents and calculation. 

After I'm adding the dates arguments as the rental date column. The date argument is crucial for natural language requests such as yesterday, last year, or last month. There are also top arguments which are useful for such columns that include products or customers for the requests such as what are the top five customers, uh, for rental last month or what are the top 10 products by sales this year. 

In the next NLC call release, we will also include.

Complex arguments for the configuration of complex KPIs. Uh, such as uh year-to-date growth, average receipts, and others. Uh, that require a complex calculation based on multiple conditions. Let me add a couple more joins to complete this setup. My congratulations, the setup is completed. 

Now you have your API token and link where you can call with natural language requests and get a response with structured query language code for your and users' needs. In the next video, you can see how to set up the bot. Uh, system including data visualization libraries, insight into your IT ecosystem in 15 minutes using Z web app and Docker. 

Note, that there may be slight differences in text and video. Watch video for the original content.

NLSQL Video