In the intriguing world of modern digital technology, artificial intelligence (AI) chatbots elevate people’s online experiences. Artificial intelligence chatbots have been trained to have conversations that resemble those of humans using natural language processing (NLP). NLP enables the AI chatbot to comprehend written human language, allowing them to function independently. They are capable of handling any task, be it assisting you with a pizza order, responding to special inquiries, or assisting you with a challenging B2B sales process.
Beyond these use cases, Lasse, a full-stack developer, just released AIHelperBot. This tool lets people and businesses quickly write SQL queries, increase productivity, and pick up new SQL techniques. Lasse has over ten years of experience developing web and mobile applications.
Working with SQL Server is made much easier with the help of SQL Server Management Studio (SSMS). Although it has many functions, being able to write SQL queries is one of the most crucial ones. But creating SQL queries can be time-consuming, and users should be familiar with the database’s tables, columns, and relationships among them.
The AI-powered SQL query builder steps in at this point. Based on the user’s input, AIHeplerBot creates SQL queries using OpenAI. The query’s input consists of a plain language description of what they want. AIHelperBot then produces a SQL query that matches the input. The created SQL query has been formatted and is prepared for usage. The AIHelperBot supports several databases, including PostgreSQL, MSSQL, Oracle, MySQL, BigQuery, MariaDB, etc.
By enabling users to perform the following actions, AI Bot helps to improve productivity and other insights:
- Users can export their database schema.
- AI Bot is well-versed in SQL. From a straightforward utterance in plain language, produce SQL queries. It is simple to understand and translate a sentence like “clients with their orders and remarks from the last three months” into:
However, since the input doesn’t provide much information about the potential database schema, AI Bot must “guess” the names of the tables and columns.
This can still be useful as a model for constructing a challenging query or manually changing particular table and column names afterward.
- When creating a custom database schema, users can use autosuggest after the database schema has been imported. This enables supplementing the natural language input with crucial metadata like table and column names. The AI Bot will be able to grasp the database schema and produce extremely accurate SQL queries.
- From user-provided natural language words, AI Bot creates SQL JOIN statements. Normally, an AI bot will decide for itself which tables to JOIN and which JOIN type to employ.
Check out the Tool. All Credit For This Research Goes To Researchers on This Project. Also, don’t forget to join our Reddit page and discord channel, where we share the latest AI research news, cool AI projects, and more.
Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world making everyone’s life easy.
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