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Using Azure OpenAI to retrieve SQL data

Science & Technology

Introduction

In this article, we will explore how to use Azure OpenAI in conjunction with SQL databases to enable non-technical users to easily query and retrieve data through natural language prompts. This guide will focus on an introductory approach, ensuring that anyone, regardless of their technical background, can grasp the concepts and applications discussed herein.

Introduction to Azure OpenAI

Azure OpenAI is a suite of AI modules provided by Microsoft that goes beyond chat interfaces like ChatGPT. It allows developers to build applications that not only leverage advanced language predictions but also interact with real-world data through structured queries. This capability is particularly useful in enterprise scenarios where employees may need to access complex data without comprehensive knowledge of SQL or other programming languages.

The Problem

Imagine an accountant named Bob who frequently uses ChatGPT for various queries. However, ChatGPT does not have access to Bob's company's internal data stored in SQL databases. This disconnect means that when Bob asks questions such as, "What were my profits this month?" ChatGPT is unable to retrieve the necessary information. The goal of this article is to bridge that gap using Azure OpenAI.

Solution Overview

To facilitate data retrieval from SQL databases, we need two main components:

  1. Prompt Engineering: Crafting effective prompts that allow Azure OpenAI to interpret user queries correctly.

  2. SQL Executor: Connecting the OpenAI model with a backend SQL function capable of executing SQL commands.

When these two elements are effectively combined, even a user with no SQL experience can retrieve relevant data from an SQL database.

Demonstrating the Solution

To illustrate how this works, we will walk through a simple web application that utilizes Azure OpenAI to execute SQL queries based on natural language inputs.

Setting Up the Application

  1. User Input: The application accepts a user query in natural language. For example, the user might input: "Show me all the rows where our expenses were greater than $ 1,000."

  2. SQL Query Generation: The application leverages Azure OpenAI to convert the natural language input into an SQL query. This step is essential as it enables non-technical users to access database information easily.

  3. Executing the Query: The SQL command generated from the user's query is executed against the SQL database to return relevant results.

Example Queries

  • To fetch rows where expenses exceed $ 1,000.
  • To find instances of negative profit margins.
  • Queries that involve comparative calculations, such as "Show me when revenue exceeds twice the expenses."

Underlying Code Structure

The application consists of several key components:

  1. Azure OpenAI Script: This script handles interactions with Azure's OpenAI API to process user queries.

  2. Database Connection: Utility functions manage database connections and querying processes.

  3. Schema Representation: It's crucial to define the schema of the database accurately so the OpenAI model can construct SQL queries effectively.

  4. Prompt Formatting: By structuring prompts correctly, the system can guide OpenAI to output SQL commands in a defined JSON format, making it easier to pass results to the SQL executor for execution.

Security Considerations

Using Azure OpenAI provides an additional layer of security compared to traditional OpenAI APIs. The Azure platform allows organizations to disable content moderation, ensuring sensitive company data remains secure and is not stored or reviewed externally.

Conclusion

As organizations look for innovative ways to empower employees with data access, combining Azure OpenAI with SQL database querying is a promising solution. By enabling users like Bob to interact with databases using natural language prompts, businesses can drastically improve efficiency and increase data-driven decision-making.

Keywords

  • Azure OpenAI
  • SQL databases
  • Query generation
  • Prompt engineering
  • User interface
  • Data retrieval
  • Data security

FAQ

1. How does Azure OpenAI connect with SQL databases?
Azure OpenAI uses carefully crafted prompts to interpret user queries and generate SQL commands, which are then executed against a SQL database.

2. Can the solution handle complex SQL queries?
Yes, with proper prompt engineering and understanding of the database schema, the application can handle complex queries, including those involving multiple tables.

3. Is any programming knowledge required to use this application?
No, the goal of this application is to allow non-technical users to retrieve data using plain language prompts, removing the need for SQL knowledge.

4. How secure is my data when using Azure OpenAI?
Using Azure OpenAI allows you to turn off content moderation, meaning your data will not be stored externally. This provides a higher level of security for sensitive company information.