🧠 DB Studio — AI Database Platform

Design, modify, and query databases using natural language.

DB Studio is an agent-based platform that enables users to:

• 🏗️ Create databases from requirements
• ✏️ Modify schemas safely
• 💬 Query data using natural language (NL → SQL)

Built with LLM agents, validation pipelines, and cloud-backed storage.

---

🚀 Overview

DB Studio is a modular AI-powered platform that allows users to interact with databases without writing SQL manually.

It combines:

• Natural language processing
• Agent-based workflows
• Structured validation pipelines
• Cloud persistence

The system is designed for:

• Rapid database prototyping
• Safe schema evolution
• Intelligent querying with explainability

---

✨ Features

🏗️ Feature 1 — Database Creation

• Convert business requirements → database schema
• Generate tables and relationships
• Produce SQLite database files
• Generate schema documentation / ERD

---

✏️ Feature 2 — Database Modification

• Modify existing schemas safely
• Validate schema changes before applying
• Approval workflow before execution
• Preserve database integrity

---

💬 Feature 3 — Natural Language Querying

• Natural Language → SQL generation
• Query validation pipeline
• Human approval workflow
• SQL execution
• Natural language response generation
• Query traceability/debugging

---

🏗️ Architecture

The project follows a feature-based modular architecture:

app.py
feature1_app.py
feature2_app.py
feature3_app.py

Features/
│
├── Feature1_create_db/
├── Feature2_modify_db/
├── Feature3_chat_db/
│
shared/
├── config.py
├── blob_storage.py
├── workspace.py

🔄 System Flow

User Input (UI)
      ↓
Workspace State Update
      ↓
Agent Pipeline
      ↓
SQL Generation
      ↓
Validation / Approval
      ↓
Execution (SQLite)
      ↓
Results + NL Response
      ↓
Persist to Blob Storage

⚙️ Tech Stack

Python Streamlit Azure OpenAI Azure Blob Storage SQLite Pydantic LangGraph / Agent workflows SQL validation pipelines

⚡ Setup

1) Clone repository

git clone https://github.com/your-username/DB-Studio.git
cd DB-Studio

2) Create virtual environment

python3 -m venv .venv
source .venv/bin/activate

For Windows:

.venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Configure environment variables

Copy the example environment file:

cp .env.example .env

Then open .env and fill in your credentials:

• Azure OpenAI credentials
• Azure Blob Storage credentials
• deployment names
• required environment variables

5. Run the application

streamlit run app.py

---

💬 Usage Example

Input

Show top selling products

Pipeline

NL Query
→ SQL Generation
→ Validation
→ Execution
→ Response

Output

• Generated SQL query
• Query results table
• Natural language explanation

---

🗂️ Project Structure

DB-Studio/
├── app.py
├── feature1_app.py
├── feature2_app.py
├── feature3_app.py

├── Features/
│   ├── Feature1_create_db/
│   ├── Feature2_modify_db/
│   └── Feature3_chat_db/

├── shared/
│   ├── config.py
│   ├── blob_storage.py
│   └── workspace.py

├── requirements.txt
└── README.md

---

🔐 Security Notes

• Never commit .env
• Never expose API keys
• Use secret managers in production
• Secure database credentials

---

🧪 Testing

• Unit tests for agent nodes
• Blob storage mocking
• Integration testing for NL → SQL flow

---

🚀 Deployment

• Dockerize the application
• Use HTTPS proxy
• Inject secrets via CI/CD
• Monitor logs

---