How To Convert Natural Language to SQL Queries

The amount of data available today on any given subject is staggering. We need powerful, flexible tools to help us query and analyze this data and make sense of it. Structured query language (SQL) is among the most popular data query languages.

However, SQL can be challenging for some users because it requires precise, syntactically correct queries to work properly. Such situations can be a barrier for users unfamiliar with SQL or those who have difficulty writing SQL queries. This is where language models come into play.

‍Natural language processing (NLP) is a field of computer science and artificial intelligence (AI) that deals with computers’ ability to understand human language. In recent years, significant NLP advancements have enabled computers to parse and interpret human language with greater accuracy.

This article acts as a beginner’s guide to converting natural language to SQL queries. You’ll learn the basics of NLP and SQL and how to create simple SQL queries from natural language statements.

What to know before starting

Data storage systems have evolved from flat files to hierarchical and network data storage modes. Today, relational databases are popular options when dealing with complex data relationships. Relational databases store data in tables and can read and retrieve relevant information from diverse tables, assemble that data, and form a unique output.

Each table contains rows and columns. Each row records several pieces of information tied to a key, while the column names represent attributes or data related to a search query.

Basic information about SQL

SQL is a standard language for relational database management. You can store, manipulate, and retrieve large datasets from relational database systems with SQL. Because big data analysis has become a core element of business decisions, the need for more natural communication between humans and computers has become more evident.

NLP enables computers to analyze and interpret text and voice messages. In other words, it allows you to give computer commands using your natural language, leaving the deep learning models to decode your message and execute the command efficiently. This is where large language models (LLMs) excel, offering powerful capabilities for interpreting and generating human-like text.

In data science, storage, retrieval, and manipulation, natural language query processing mitigates the significant challenges of working with SQL (the complex interface making data interaction difficult for beginners). Coding with your natural language helps more people access and interact with relational databases.

Converting natural language queries and commands to SQL commands is an example of semantic parsing, which has several steps. Although you may not see these processes on the front end, understanding how this conversion occurs helps you better approach NLP. Technologies like LangChain can be particularly helpful in building applications that leverage these capabilities.

The following are components involved in converting a natural language to SQL statements.

• Normalization reduces the randomness of a text, making it more consistent with a predefined format. In NLP, this process typically involves two activities: stemming and lemmatization. Stemming cuts words in a text down to the root words or stems to prepare them for processing; lemmatization is the process of using contextual analysis to convert a word to its base form to make it easier to search for the word, or to join it with other words in a sentence. For example, “running” can be lemmatized to “run.”
• Tokenization is the process of breaking text down into words, called tokens. They can be words, phrases, or other units of text.

‍

• Part-of-speech tagging assigns a part-of-speech tag to each token in a text sequence. The part-of-speech tags indicate the grammatical role of each token in the text. As in linguistics, the word “cat” has the tag “noun,” while the word “run” has the tag “verb.”
• Named entity recognition involves identifying named entities in a text and classifying them into predefined categories, such as person names, organizations, or locations.
• When dealing with text data, you’ll sometimes encounter so-called “stop words.” Stop words are common words that carry little meaning, such as “and,” “or,” “the,” and “a.” When analyzing text data, you often want to remove stop words from your dataset to focus on the most meaningful words. Natural Language Toolkit (NLTK), an open-source Python library, provides the functionalities needed to remove stop words.
• Parsing is a process of analyzing a text or sentence to determine its grammatical structure. Parsing is often used in NLP to understand the meaning of a text or sentence. For example, a parser analyzes a sentence to determine the noun and verb phrases representing subject, verb, and object. The information retrieved after parsing is then represented through syntactic mapping, which aids in translating text to SQL queries. The syntax tree gives a clearer picture.

‍

What you need to convert natural languages to SQL queries

You need access to a dataset and pretrained text-to-SQL models before converting natural language questions to SQL.

Datasets

A dataset is a collection of data organized in a specific way. Datasets can store data of all kinds, including numerical data, text data, images, and more. Some scientists create them by hand, while others use software to automatically collect data from sources like the internet or sensors.

Since algorithms can’t efficiently work on unstructured data, having the information arranged in datasets becomes crucial for converting text questions to SQL queries. Several datasets are available for semantic parsing, but they’re unique for different use cases.

For example, the IMDB dataset has a large collection of movie titles with data on actors, directors, producers, and more. The IMDB dataset might be an excellent option if you want to ask questions about movies, but it becomes irrelevant if you want to ask questions about marketing data or weather.

Some examples of popular datasets used for semantic parsing to SQL include:

• Spider contains 10,181 questions and 5,693 complex queries. The cross-domain dataset pulls data from 138 domains (including various online news articles, books, and webpages). You can download this dataset from the website.
• WikiSQL has a collection of over 87,000 hand-annotated natural language and SQL question-and-answer pairs. Of all the queries, about 17,000 are designed to test text-to-SQL models. The files are arranged as JSON documents and available for installation on GitHub.
• ATIS, or Airline Travel Information System, is a standard benchmark dataset for intent classification tasks, especially when building chat boxes. The dataset can answer user questions relating to flight information between various cities.

Several other datasets are widely used for NL processing. WordNet contains a vast library of words and sentences derived from thousands of blogs, while GEOQuery contains numerous U.S. geographical facts useful for mapping.

Businesses can also develop private datasets comprising their business operations and customer details. Then they’ll need a trained model for their text-to-SQL tasks.

Trained model

A semantic parsing model provides an intermediate representation between the user’s question and the database. A model contains the instructions for interacting with datasets. Looking at the architecture, a model comprises an encoder and a decoder.

The encoder uses word embeddings to map words into dense vector representations, capturing semantic similarities between them. After encoding, the decoder generates SQL queries based on the encoded semantic information. Some advanced approaches incorporate retrieval-augmented generation (RAG) to enhance the accuracy and relevance of the generated queries.

‍

These models typically don’t have 100% accuracy in interpreting language, but they learn to achieve better accuracy over time with more query generation tasks. This is where continuous fine-tuning and optimization come into play.

Converting natural language to an SQL query

As AI advances, more research is going into producing natural language interfaces and processing tools for better text processing. While several methods are available for converting natural language questions to SQL queries, this end-to-end tutorial focuses on using the Data QnA interface on Google’s Big Query API to interact with databases using natural language.

You’ll need an encoder-decoder framework or trained interpretation model for a more accurate text-to-SQL generation. However, leveraging the robustness of Google’s Big Query platform provides extensive databases and trained algorithms, allowing for real-time query generation and execution.

1. Create a Google Cloud platform account

The first step is to create a Google Cloud Platform account by visiting the sign-up page.

Click Get started for free to begin the sign-up process.

‍

Input your account information and click Continue.

2. Create a new data table

After creating your Google account, you can move on to create a table from your dataset.

Search for Data QnA on the Google Cloud platform.

‍

Go to Manage to create a new table. You must have a new table to request a query from the database.

‍

Next, click Enable New Table.

‍

Enter your table name. (In this case, we’re using “advdata.covid19_usafacts”). Click Enable Table.

‍

3. Set up the table parameters

After enabling the table, fill in the information needed as described by the table’s headers. This includes the name, data type, column type, display name, and synonyms.

The name describes the dataset in general terms, while the column type gives a more detailed description of the name of the dataset. For example, it could be metric or dimension. Metric describes measurable items (i.e., items that can be counted or quantified).

For instance, age might be measured in years, months, days, and so on. The column for Synonyms is also available. You can use synonymous words to describe the dataset. For example, a column “county_name,” which is dimensional, can have a synonym like “county.”

‍

The Name column represents the unique tags  for each search, meaning the algorithm tracks the data on the name column to sum up the total number of deaths recorded. Applying two targets like county name and date ensures the algorithm fetches only relevant data.

Click Save to create an index of the dataset.

‍

4. Launch Big Query

Search for Big Query and look for the name you used to save your dataset. In this case, we saved ours with advdata.covid19_usafacts.

‍

5. Ask natural language questions

Click the Ask Question icon to ask different questions about the entries made regarding the table you initially created.

‍

Select the table you initially saved with entries you want to ask questions about.

If you need to change another table, click the drop-down arrow and select your table of choice.

‍

You can now type questions like, “Which county has the maximum number of cases?” and select Generate Equivalent SQL. The algorithm will now generate a semantic interpretation of the question and convert it to SQL format.

Recall that your target is the unique keywords that appear in your natural language question. Data QnA reviews those keywords and conducts other text classification processes to translate the data to equivalent SQL commands.

Click Open in Query Editor.

‍

Click Run to analyze the data and get your values.

‍

The results now display the counties with the most COVID-19 cases alongside the number of confirmed cases. From the image below, you can see that Cook, Queens, and Kings counties make up the top three on the list.

‍

Natural language to SQL queries FAQ

To help you better understand the process of converting natural language to SQL queries, we answer some frequently asked questions.

What are the main benefits of using natural language to generate SQL queries?

Using natural language to generate SQL queries improves accessibility for nontechnical users, allowing them to interact with databases without extensive SQL knowledge. This leads to faster data analytics processes as more team members can directly query data.

The reduced learning curve for interacting with SQL databases means organizations can leverage their data more effectively. Additionally, it enhances productivity for data analysts and other professionals by streamlining the query creation process, allowing them to focus on data interpretation rather than syntax.

Can NLP be used with any SQL database?

Yes, NLP techniques can be applied to various SQL databases, including popular systems like MySQL, PostgreSQL, and SQL Server. While the specific implementation may vary depending on the database system, the general principles of converting natural language to SQL remain consistent across different platforms. This flexibility allows organizations to implement natural language interfaces regardless of their chosen database technology.

What are some common use cases for natural language to SQL conversion?

Natural language to SQL conversion has a wide range of use cases across various industries. It’s particularly useful for ad-hoc data analysis by business users who may not have strong SQL skills. Data scientists often use it for rapid prototyping of database queries, allowing them to quickly explore datasets.

Another common application is in chatbots and virtual assistants that need to interact with databases to provide information or perform actions. Additionally, it can simplify the construction of complex queries for data scientists, allowing them to focus more on analysis and less on SQL syntax.

What skills do I need to implement natural language to SQL conversion in my projects?

Implementing natural language to SQL conversion requires a diverse skill set. A solid understanding of NLP and machine learning concepts is crucial, as is familiarity with SQL and database management. Proficiency in Python and relevant libraries like NLTK or spaCy is typically necessary for processing natural language.

Knowledge of language models and how to fine-tune them is important for achieving high accuracy. Familiarity with frameworks like LangChain or similar tools can greatly facilitate the development process. Additionally, a good grasp of data structures and algorithms helps optimize the conversion process for efficiency and accuracy.

Need help? Find a natural language developer

One of the most powerful features of SQL is its ability to query data stored in databases. This can be a complex and time-consuming task, especially for those unfamiliar with the SQL language.

In recent years, significant NLP advancements have enabled semantic parsing and interpretation of human language with greater accuracy. Such strides allow end users to interact with databases and extract information without learning to code. However, you’ll need a NLP expert to help you train machine learning models that can interpret human language and translate it to SQL queries and database schemas.

Upwork is a place to find a pool of NLP experts. Hire an independent NLP developer to help with your natural language processing projects.

NLP developers can also leverage Upwork’s services to get jobs. Create an Upwork account and a job profile to find and apply for jobs. Visit Upwork to get new NLP job opening updates.

‍

Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.