Natural Language to SQL Generator: Talking to Your Data, No Coding Required

27 Mar, 2024 | 5 minutes read

Just imagine you could talk to your database. Literally have a conversation with it. A normal back and forth, like with a real person. No more code, no more relying on SQL experts. Just ask a question – in plain English, or whatever language you speak – and get the data you need to make smarter decisions.

That world is here. Large Language Models (LLMs) are able to translate your everyday natural language into precise SQL queries. It’s a leap forward that breaks down barriers and opens up whole new possibilities for businesses.

At our company, we are at the forefront of developing Artificial Intelligence (AI) systems that bridge that same gap between complex database queries and the simplicity of human conversation.

And we’re having amazing results with that.

Why This Matters

Data is your company’s most powerful asset. But only if you can use it. Too often, valuable insights are locked away because most employees don’t have technical skills to write complex SQL queries. An AI-powered translation solution could be the bridge that can change the game for businesses that rely on hard data to predict the future of their company. Now everyone, from marketing managers to executives, can tap into the wisdom buried in their databases.

Natural Language to SQL, How Does It Work?

Think of it like training a brilliant but inexperienced translator. Traditional language models aren’t born knowing about your specific databases. We solve this with a process called Retrieval Augmented Generation (RAG). We feed the AI critical knowledge:

  • The map: Your database schema is like an atlas. It shows tables, columns, and how everything connects.
  • The dictionary: Definitions of each data field add crucial context.
  • Example translations: Sample SQL queries, paired with their natural language equivalents, help the model learn the ‘grammar’ of database language.
NL to SQL Query General Overview

General overview of the architecture.

Once the AI has this foundation, you can ask your question. “How many bikes did we sell in California last month?” The system parses your intent, pulls the relevant data map from its memory, and pieces together the correct SQL code.

The generated SQL query can be saved and used independently of the model, or it can be directly executed in the database with the results presented back to the user in an understandable format.

The Power of User-Driven Learning

Remember that inexperienced translator? The more successful translations they made, the better they got. The same holds for our AI-powered solutions. Every time the system generates a valid SQL query, it gets fed back into the model. This self-learning loop means the system constantly improves, adapting to your specific database and the types of questions your company asks.

Benefits That Transform Businesses

  • True data democracy: Anyone can explore data, ask their own questions, and uncover the insights that drive better decisions.
  • Skyrocket efficiency: No more waiting on IT to write complex queries. Get information in moments, not days.
  • Scalability done right: The system adapts as your database changes. No costly retraining every time you add a new table.

Text-to-SQL: Beyond Simple Queries

We’re just scratching the surface. This technology isn’t limited to just pulling data. It can learn the logic behind aggregations (like SUM and AVERAGE), complex calculations, and even suggest visualizations based on the results. Soon, it won’t just answer questions – it might start finding insights you didn’t know to look for.

Our Company’s Potential: Leading the Charge

At our company, we’re committed to being at the forefront of this revolution. We’re constantly refining our AI models to ensure they understand and translate queries with unmatched accuracy. We believe this technology shouldn’t be a privilege for a select few, but a democratizing force within every organization. That’s why we’ve designed our system to integrate seamlessly with existing ecosystems, requiring minimal disruption to your workflow.

The future belongs to those who can harness the power of their data. Our AI system empowers everyone in your company to ask questions, uncover hidden insights, and make data-driven decisions faster than ever before. It’s a game-changer that levels the playing field and puts powerful analytics within everyone’s reach. This isn’t just about technology; it’s about transforming your company into a data-driven powerhouse.

The Future is Here – Use It Wisely

This is a revolution, plain and simple. But as with any powerful tool, it demands thoughtful use. Work to ensure this technology empowers everyone in your organization ethically and responsibly.

The ability to converse with your database unlocks data’s true potential. It means faster responses to market changes, smarter resource allocation, and the confidence of data-informed decision-making at every level. Your company’s future is being written in data. Now, everyone in the company can help tell that story.

Examples of Usage

Imagine a scenario in a retail company where a marketing manager wants to analyze customer purchase patterns without knowing how to write complex SQL queries. With our AI system, they could simply ask, “What are the top-selling products among males aged 20-30 in the last year?” The system would process this query, translate it into the appropriate SQL command, and retrieve the requested information.

Another example could be in financial analysis, where a financial analyst might query, “Compare the revenue from Q1 to Q2 for the past three years.” The AI system translates this into a series of SQL queries to fetch the relevant data, allowing the analyst to quickly obtain the insights they need without diving into the intricacies of SQL.

Real example demonstration – AdventureWorks Database

For this demonstration, we utilize the well-known built-in SQL Server database, Adventure Works, which supports standard online transaction processing scenarios for a fictitious bicycle manufacturer. These scenarios include Manufacturing, Sales, Purchasing, Product Management, Contact Management, and Human Resources. For our experiment, we will focus on two scenarios from the database: Sales and Product Management. As for the model, we will use GPT-4 from Azure OpenAI.

At the outset, the model’s knowledge must be updated using some of the principles outlined above (in the section “How It Works”). Initially, the model is trained using data available in the system tables of Microsoft SQL Server, which can be queried with: SELECT * FROM INFORMATION_SCHEMA.COLUMNS. As a result of this training, the model becomes capable of understanding tables, columns, and the relationships between them, thus enabling it to create complex joins among several tables.

Since our focus is solely on the Sales and Product Management tables, only the schemas for these tables are extracted and transformed into chunks, where one chunk represents one table, and then integrated into the model. Additionally, the model is trained with a data dictionary that provides descriptions for each column.

Example schema about the SalesTerritoryHistory table

Example data dictionary about the SalesOrderHeader table

Once the model’s setup is complete, users can input questions in natural language, which will be translated into SQL queries.  As the model is utilized, it will be continuously enhanced with additional data, represented by valid and correct queries, thereby enabling the model to train and improve itself. As previously mentioned, the generated SQL query can either be saved for use independently of the model or executed directly to obtain results in an understandable format. In the three examples provided, we utilize the first approach, where the generated query is directly tested in Microsoft SQL Server Management Studio.