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White paper: Leveraging SumoGPT for AI-Driven Data Querying and Insights Generation

Srini

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Abstract

This white paper outlines the architecture, process flow, and implementation of SumoGPT, a system that integrates Large Language Models (LLMs) with database querying to provide intelligent, user-friendly insights. By combining natural language understanding with SQL query generation and execution, SumoGPT enables users to extract data-driven answers from a warehouse database seamlessly. The document provides a detailed explanation of the steps involved, supported by architecture diagrams.

Introduction

As organizations increasingly rely on data warehouses for decision-making, querying databases using traditional SQL can be complex for non-technical users. SumoGPT bridges this gap by using AI to interpret natural language questions, generate SQL queries dynamically, and return contextualized responses. This system reduces the barrier to accessing insights while maintaining accuracy and efficiency.

System Overview

Key Features

  • Natural Language Input: Users can ask questions in plain English.
  • Dynamic SQL Generation: LLMs generate SQL queries based on database metadata and user input.
  • Automated Query Execution: The backend executes the generated SQL against the database.
  • Contextualized Responses: LLMs refine raw data into human-readable answers.
  • User-Friendly Interface: Results are displayed in an intuitive UI.

Architecture Diagram

Below is the high-level architecture of SumoGPT:

Workflow Steps

Step 1: User Enters a Question

The user interacts with the UI by typing a natural language question (e.g., “What were the total lines picked today?”). This step requires no technical knowledge.

Step 2: Backend Sends Metadata and Question to LLM

The backend retrieves metadata about the database schema (e.g., table names, column names, data types) and combines it with the user’s question. This information is sent to the LLM for processing.

Step 3: LLM Generates SQL Query

The LLM uses its training in natural language understanding and SQL syntax to generate an appropriate SQL query. For example:

SELECT SUM(quantity) AS total_quantity 
FROM PickLines 
WHERE date = '2025-19-02';

Step 4: Database Query Execution

The generated SQL query is executed against the warehouse database. The backend retrieves the resulting data, ensuring it aligns with the user’s request.

Step 5: Data and Question Resent to LLM for Refinement

The raw data from the database, along with the original question, is sent back to the LLM. The model processes this information to create a polished, human-readable response. For example:

“5000 lines picked today”

Step 6: Display Response in UI

The final response is displayed in the user interface. Users can view clear, concise answers without needing to interpret raw data or write queries manually.

Benefits of SumoGPT

  1. Accessibility: Simplifies access to complex datasets for non-technical users.
  2. Efficiency: Reduces time spent on writing and debugging SQL queries.
  3. Accuracy: Leverages database metadata to ensure precise query generation.
  4. Contextual Insights: Provides answers that are easy to understand and actionable.
  5. Scalability: Supports large-scale datasets typical of modern data warehouses.

Conclusion

SumoGPT represents a significant leap forward in making data querying accessible through AI-powered natural language processing. By integrating LLMs with database systems, it empowers users across technical skill levels to extract meaningful insights from their data warehouses efficiently.

  • This white paper demonstrates how SumoGPT’s architecture and workflow simplify complex processes while ensuring accurate results. As AI continues to evolve, systems like SumoGPT will play a pivotal role in democratizing access to data intelligence across industries.
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Srini