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Upload a .sql file containing CREATE TABLE and INSERT statements
MySQL/PostgreSQL syntax will be automatically converted to SQLite
Upload a pre-existing SQLite database file
Supported extensions: .db, .sqlite, .sqlite3
Comprehensive guide to AI-powered database analysis
Demonstrating intelligent SQL assistance capabilities
This system demonstrates an Agentic AI-powered SQL Assistant that intelligently converts natural language queries to SQL, explains query logic, and executes SQL across different database dialects. The system can handle both SQL files and SQLite databases with automatic syntax conversion.
Addressing the complexity of database interaction
Traditional database interaction requires deep SQL knowledge and understanding of different database dialects. Non-technical users struggle with writing complex queries, understanding database schemas, and working across different SQL variants (MySQL, PostgreSQL, Oracle, SQL Server). This system bridges that gap by providing an intelligent assistant that understands natural language and generates appropriate SQL queries.
Technical foundation and framework overview
Four-stage intelligent query processing workflow
Upload and parse SQL files or SQLite databases with automatic schema extraction
Convert natural language questions into executable SQL queries
Execute SQL queries against the database and return formatted results
Generate equivalent queries for MySQL, PostgreSQL, Oracle, SQL Server, and Amazon Aurora DB dialects
Modern frameworks and tools powering the system
Core functionality and intelligent features
Understanding how SQL queries are processed step-by-step
The Order of Execution explains how a database engine processes SQL queries internally. Understanding this helps optimize queries and debug performance issues.
Our AI analyzes each generated SQL query and provides a detailed breakdown of the execution order, highlighting key SQL keywords with visual badges.
Step-by-step process flow
Value proposition and real-world use cases
Try these sample queries with your data
ecommerce_sample.sqlmulti_table_sample.dbThese questions work with ecommerce_sample.sql and multi_table_sample.db
Combine data from multiple tables
Find specific data with conditions
Calculate totals, averages, and counts
Complex business insights
These questions work with any SQL/DB file structure
Understand your data structure
Discover patterns in your data
Work with related data
🎨 Designed and Developed by Aradhya Pavan H S ✨