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Speak Your Data Needs: Demystifying Databases with SQL/NoSQL GPT

Save yourself from data management nighmare
Save yourself from data management nighmare

The world of data is ever-evolving, and the professionals who navigate it require efficient tools to keep pace. SQL GPT, a powerful language model, emerges as a game-changer, bridging the gap between natural language and database interaction. By leveraging advanced AI techniques, SQL GPT empowers professionals across various roles to generate efficient SQL queries for diverse databases like MySQL, PostgreSQL, Microsoft SQL Server (T-SQL), and even NoSQL solutions like Elasticsearch and MongoDB.

What is SQL GPT?

Imagine a tool that understands your data needs expressed in plain English and translates them into optimized SQL queries. That's the essence of SQL GPT. It analyzes your natural language prompts and generates the corresponding SQL code, streamlining interactions with your databases.

A tool for every data professional

SQL GPT caters to various data professionals, enhancing their workflows:

  • DBAs (Database Administrators): Automate routine tasks like data extraction and table management, freeing up valuable time for complex database administration. Generate initial drafts of intricate SQL queries, allowing DBAs to focus on optimization and troubleshooting.

  • Data Engineers: Expedite data wrangling and pre-processing by generating code for data cleaning, transformation, and manipulation. Experiment with diverse data manipulation techniques by swiftly iterating through various SQL queries suggested by SQL GPT.

  • BI Analysts and Data Scientists: Translate business requirements into clear SQL queries, even for those with limited SQL expertise. Explore data from various angles by effortlessly generating queries for different analytical tasks, saving crucial time and effort.

GPT based AI Co-pilot for you

SQL GPT offers more than just automation:

  • Suggestive Power: Receive multiple query options tailored to your specific needs, fostering creativity and exploration in your data analysis.

  • Error Detection: Get notified of potential syntax errors or logical inconsistencies within your SQL queries, ensuring code quality and accuracy.

  • Adaptive Learning: As you interact with SQL GPT, it learns your preferences and adapts its suggestions over time, transforming into a personalized productivity partner.

A Note of Caution:

While SQL GPT is an invaluable asset, it's essential to remember that it serves as a tool, not a replacement for human expertise. Always meticulously review and comprehend the generated code before execution, ensuring alignment with your data security and privacy protocols.

Examples of Prompt-to-Code Generation:

Prompt (MySQL):

I need a SQL query to find all customers in my database who purchased a product category named 'Electronics' in the last quarter with an order total exceeding $500. Please display the customer name, product name, order date, and order total.

Generated SQL:

SELECT AS customer_name, AS product_name, o.order_date,
FROM customers c
INNER JOIN orders o ON = o.customer_id
INNER JOIN order_items oi ON = oi.order_id
INNER JOIN products p ON oi.product_id =
WHERE p.category = 'Electronics'
AND > 500;

Prompt (PostgreSQL)

Write a PostgreSQL query to retrieve the top 5 most popular products based on total quantity sold in the past year. Show the product name, total quantity sold, and the average unit price.

Generated SQL:

SELECT product_name, SUM(quantity) AS total_quantity_sold, AVG(unit_price) AS average_unit_price
FROM order_items
GROUP BY product_id
ORDER BY total_quantity_sold DESC

Prompt-to-Query Examples for NoSQL Databases



Find all documents in the 'products' index with a 'price' greater than $100 and a 'category' of 'electronics' within the 'description' field. Sort the results by 'price' in descending order and display only the first 10 documents.

General Query Format:

  "size": 10,
  "sort": [
    { "price": { "order": "desc" } }
  "query": {
    "bool": {
      "must": [
        { "match": { "category": "electronics" } },
        { "range": { "price": { "gt": 100 } } },
        { "match": { "description": "your_search_term" } }  // Replace "your_search_term" with actual search term


  • size: Limits the number of returned documents to 10.

  • sort: Sorts results by price in descending order.

  • query: Defines the filtering criteria using a boolean query with "must" clauses for each filtering condition.

  • match: Matches documents where the "category" field equals "electronics".

  • range: Finds documents where the "price" field is greater than 100.

  • match: Matches documents where the "description" field contains the specified search term (replace with your actual search terms).



Using the 'customers' collection in my MongoDB database, find all customers located in 'California' whose 'order_total' is greater than $500. Project only the 'name', 'city', and 'order_total' fields and sort the results by 'order_total' in descending order.

General Query Format (using hypothetical library):

results = dbgpt.mb.find(
    {"location": "California", "order_total": {"$gt": 500}},
    projection={"name": 1, "city": 1, "order_total": 1}
).sort("order_total", pymongo.DESCENDING)


  • Replace "your_library" with the actual library used for MongoDB interaction (e.g., Motor).

  • find: Retrieves documents matching the specified criteria.

  • The first argument defines the filtering criteria using a dictionary where keys are field names and values are the desired values.

  • The second argument specifies the fields to be included in the projected results using a dictionary where keys are field names and values are set to 1.

  • sort: Sorts the results by "order_total" in descending order (using pymongo.DESCENDING).


In my DynamoDB table 'users', query for users with a 'username' starting with 'A' and a 'creation_date' between '2024-01-01' and '2024-02-29'. Additionally, retrieve only the 'username' and 'email' attributes.

General Query Format :

response = dpgpt.dynamodb.query(
    IndexName="username-index",  # Assuming an index on "username"
        Key("username").begins_with("A") & Key("creation_date").between(
            LowerBound="2024-01-01", UpperBound="2024-02-29"
    ProjectionExpression="username, email",

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