SQL Agent with Multiple API Providers

Overview

A comprehensive SQL learning and analytics platform that combines natural language database querying, AI-powered explanations, visual analytics, and complete learning resources. Built with Streamlit, LangChain, and supporting both OpenAI and Groq APIs.

Features

Core Database Functionality

• Multiple Database Support: SQLite, MySQL, and custom database creation
• Natural Language Querying: Ask questions about your data in plain English
• AI-Powered SQL Generation: Automatic SQL query generation and execution
• Sample Database: Pre-loaded Chinook music store database for testing
• Custom Database Upload: Support for SQLite file uploads

Advanced Visualization

• Entity-Relationship Diagrams (ERD): Professional Power BI-style database visualizations
o Full-screen viewing mode

AI-Powered Database Creation

• Custom Database Generator: Describe any business domain and AI creates appropriate schema
• Realistic Dummy Data: Auto-generation of sample data with proper relationships
• Instant Visualization: Immediate ERD generation for created databases

SQL Learning Hub

• SQL Q&A Assistant: Ask any SQL/RDBMS question and get detailed explanations
• Curated Tutorials: Links to W3Schools, GeeksforGeeks, SQLBolt, Mode Analytics
• Interview Preparation: Resources from DataCamp, LeetCode, HackerRank
• Practice Challenges: Coding platforms and skill assessments
• Structured Learning Paths: Beginner to advanced progression

Technical Stack

Core Dependencies

streamlit
langchain-community
langchain-openai
langchain-groq
sqlite3
pymysql
requests
faker
Optional Dependencies (for advanced features)
graphviz # For ERD generation
sqlalchemy # For database introspection

Setup Instructions

1. Clone Repository

git clone
cd sql-agent-app

2. Install Dependencies

pip install streamlit langchain-community langchain-openai langchain-groq
pip install plotly pandas pymysql faker requests
pip install graphviz sqlalchemy # Optional for ERD features

3. Run Application

streamlit run app10.py

4. Configuration

1. Choose API provider (OpenAI or Groq)
2. Enter your API key:

o OpenAI: Get from platform.openai.com/api-keys
o Groq: Get from console.groq.com/keys
4. Select your database option
5. Start querying!

Database Options

1. Sample Chinook Database
   • Pre-loaded music store database
   • Ideal for learning and testing
   • Contains customers, tracks, albums, artists, invoices
2. Upload Custom SQLite Database
   • Upload your own .db files
   • Automatic validation and schema analysis
   • Instant querying capabilities
3. Connect to MySQL Database
   • Remote MySQL server connection
   • Full credential management
   • Connection testing and validation
4. Create Custom Database
   • Describe any business domain
   • AI generates appropriate schema
   • Auto-populated with realistic data
   • Example domains: e-commerce, hospital, school, restaurant

5. SQL Learning & Practice

• Interactive tutorials and resources
• Interview preparation materials
• Practice challenges and assessments

Usage Examples

Natural Language Queries

• "How many customers are from each country?"
• "Show me the top 10 best-selling tracks"
• "What are the total sales by year?"
• "Which genres are most popular?" Database Creation Examples
• "Create a database for an online store with customers, products, and orders"
• "Create a hospital management system with patients and doctors"
• "Create a library system with books, authors, and borrowings"

Architecture

Core Components

1. Database Manager: Handles connections to SQLite/MySQL databases
2. AI Agent: LangChain-powered SQL agent for natural language processing
3. Visualization Engine: Automatic chart generation and ERD creation
4. Learning Hub: Comprehensive SQL education resources

Data Flow

1. User inputs natural language question
2. LangChain agent generates appropriate SQL query
3. Query executed against database
4. Results processed and visualized
5. Natural language explanation provided

File Structure

sql-agent-app/
├── app.py # Main Streamlit application
├── requirements.txt # Python dependencies
├── README.md # This file
└── temp/ # Temporary files (auto-created)

API Support

OpenAI Models
• GPT-3.5 Turbo
• GPT-4
• GPT-4 Turbo
• GPT-4o
• GPT-4o Mini
Groq Models
• Llama 3.1 8B Instant
• Llama 3.3 70B Versatile
• Qwen 3 32B
• Gemma2 9B IT

Security Considerations

• API keys handled securely through Streamlit secrets
• No persistent storage of sensitive data
• Temporary database files auto-cleanup
• Connection validation and error handling

Performance Features

• Connection pooling and caching
• Optimized query execution
• Progressive loading for large datasets
• Memory-efficient chart generation

Educational Value

• Perfect for SQL learning and practice
• Database design understanding
• Data visualization principles
• Interview preparation
• Real-world data analysis scenarios

Troubleshooting

Common Issues

1. API Key Validation Failed: Verify your API key is correct and has sufficient credits
2. Database Connection Error: Check file permissions and database format
3. ERD Generation Failed: Install graphviz and sqlalchemy packages
4. MySQL Connection Issues: Verify host, port, credentials, and network access

Error Handling

• Comprehensive error messages with suggestions
• Graceful fallbacks for missing dependencies
• Connection retry mechanisms \ • User-friendly error displays

License

MIT License - See LICENSE file for details