SchemaSense AI

A database intelligence platform that connects to your PostgreSQL database, analyzes schema and data quality, and uses AI to generate business-friendly documentation through a conversational interface.

Features

• Database Connection Management -- Connect multiple PostgreSQL databases, switch between them, and manage connections from a single dashboard.
• Schema Analysis -- Automatically discovers all tables, columns, types, primary keys, and row counts with a visual breakdown.
• Data Quality Scoring -- Runs completeness, consistency, validity, accuracy, and timeliness checks per column and assigns an overall quality grade.
• AI Chat -- Ask natural language questions about your database schema and get streamed, markdown-formatted responses powered by DeepSeek.
• AI Table Explanations -- Generates business context for each table, cached in SQLite so repeated requests are instant.
• Export -- Download schema documentation as JSON, Markdown, or a formatted PDF report. All exports reuse the cached AI analysis.
• Activity Tracking -- Logs database connections, analyses, AI queries, and exports in a recent activity timeline.
• Demo Mode -- One-click demo button pre-fills credentials for a sample e-commerce database to try the platform instantly.

Tech Stack

• Frontend -- Next.js 14 (App Router), TypeScript, Tailwind CSS, shadcn/ui
• Backend -- Python, FastAPI, asyncpg, Pydantic
• AI -- DeepSeek API for chat and table explanations
• Database -- PostgreSQL (user databases), SQLite (explanation and chat cache)
• PDF Generation -- fpdf2

Project Structure

app/                    Next.js pages (dashboard, analysis, chat, exports, etc.)
components/             Reusable UI components (sidebar, markdown renderer, etc.)
hooks/                  React hooks (useChat, useDatabase, useDashboard)
lib/                    API client, utilities
backend/
  routes/               FastAPI route handlers
  utils/                Database, cache, activity, AI client, PDF generator
  schemas/              Pydantic request/response models
  config.py             Environment configuration
  main.py               FastAPI app entry point

Setup

Backend

cd backend
pip install -r requirements.txt
cp .env.example .env   # add your DEEPSEEK_API_KEY
python main.py

The backend runs on port 8000 by default.

Frontend

npm install
cp .env.local.example .env.local   # set NEXT_PUBLIC_API_URL
npm run dev

The frontend runs on port 3000 by default.

Environment Variables

Backend (.env)

Variable	Description
DEEPSEEK_API_KEY	API key for DeepSeek AI
API_HOST	Host to bind (default: 0.0.0.0)
API_PORT	Port to bind (default: 8000)
CORS_ORIGINS	Comma-separated allowed origins

Frontend (.env.local)

Variable	Description
NEXT_PUBLIC_API_URL	Backend URL (default: http://localhost:8000)

License

MIT