Thread by @avikumart_

Natural language to SQL is one of the most exciting applications of large language models

Here's the step by step guide to build such application👇

1. User Input:

• Users provide natural language queries or requests as input to the application.

• Example: "Retrieve all customers who purchased in the last month." 2.

2. LLM Processing:

• Use LLM (Large Language Model) to process and understand the user's natural language input. • Extract the intent and key entities from the user query.

• Example: Identify the intent as "Retrieve" and entities like "customers" and "last month."

3. NL2SQL Translation with llamaindex's nl2sqlqueryengine:

• Utilize llamaindex's nl2sqlqueryengine to convert the processed natural language query into a SQL query.

• Map the intent and entities to appropriate SQL operations and conditions.

• Example: Generate SQL query like "SELECT * FROM customers WHERE purchase_date >= '2023-11-01'."

4. MySQL Database Connection: • Establish a connection to the MySQL database using appropriate credentials.

• Example: Connect to the MySQL database hosted at a specific IP address with a username and password.

5. SQL Execution:

• Execute the generated SQL query on the connected MySQL database. • Retrieve the result set containing the requested data.

• Example: Execute "SELECT * FROM customers WHERE purchase_date >= '2023-11-01'" on the MySQL database.

6. Data Processing:

• Process the retrieved data, if necessary, based on the requirements of the user query.

• Example: Extract relevant information from the customer data such as names, purchase dates, etc.

7. Response Generation:

• Construct a response to the user based on the processed data.

• Example: Generate a response like "Here are the customers who made a purchase in the last month: [List of customers]."

8. User Output:

• Present the response to the user through the application's interface. • Example: Display the response on a web page or return it as an API response.

9. Error Handling:

• Implement error handling mechanisms to address issues such as invalid queries, database connection errors, or SQL execution failures.

• Example: Notify the user if the query is invalid or if there's an issue connecting to the database.

10. Logging and Monitoring:

• Implement logging and monitoring to track user queries, system performance, and potential issues for analysis and improvement.

• Example: Log queries, execution times, and any errors encountered for further analysis.

Read more about NL2SQL models using below blog👇
blog.futuresmart.ai/fine-tuning-gp…

End of this thread!👍

If you are looking to learn more about

📊Data Science
🤖ML/DL/AI
📈Analytics
🔢Math & Statistics
📚Resources
📝LLMs
🛠MLOps

Then, Don't forget to follow me at @avikumart_ for upcoming posts🔥👍

https://x.com/avikumart_/status/1739292430132994220?s=20