AskDB

Side Project

Talk to your database in plain English. Natural language to SQL with safety guardrails.

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Try the Demo

Query a database in plain English

Problem & Context

Non-technical team members constantly ask developers for "quick queries." Product managers need user counts, support needs ticket stats, marketing needs conversion data. Each request interrupts deep work.

AskDB lets anyone query a database by asking questions in plain English. The AI generates safe, read-only SQL and returns results with explanations. No SQL knowledge required.

Constraints

Safety: Generated SQL must never modify data or expose sensitive information
Accuracy: Wrong queries waste time; users need to trust the results
Schema complexity: Must handle diverse database schemas without manual configuration
Latency: Natural conversation requires sub-2-second response times
Explainability: Users should understand what query was run and why

Solution Overview

1. Question
"How many users signed up this month?"
2. Schema Introspection
Tables, columns, relationships
3. AI SQL Generation
GPT-4 with few-shot examples
4. Read-only Execution
SELECT-only permissions
5. Results + Explanation
Data with plain-English summary

User asks a question → system introspects schema → OpenAI generates SQL → query runs on read-only connection → results displayed with the SQL and explanation.

Safety Architecture

Database Level — Read-only user with SELECT-only permissions
Query Validation — Regex patterns block dangerous keywords
Schema Filtering — Sensitive tables excluded from introspection

Example Output

Generated Query

-- Generated SQL (read-only)
SELECT COUNT(*) as signups
FROM users
WHERE created_at >= DATE_TRUNC('month', CURRENT_DATE)
  AND created_at < DATE_TRUNC('month', CURRENT_DATE) + INTERVAL '1 month';

-- Explanation: Counts all users created in the current
-- calendar month using PostgreSQL date functions.

Key Decisions

Read-only connection enforcement

Database connections use a read-only user with SELECT-only permissions. Even if the AI generates a DROP TABLE, the database rejects it. Defense in depth.

Tradeoff: Can't support write operations even when legitimately needed, but eliminates entire category of risk.

Schema introspection + few-shot examples

Before generating SQL, the system introspects the database schema and includes relevant table/column info in the prompt. Combined with few-shot examples of good queries for the specific schema.

Tradeoff: Larger prompts = more tokens = higher cost (~$0.02/query), but dramatically better query accuracy.

Query explanation alongside results

Every response includes the generated SQL with syntax highlighting and a plain-English explanation of what it does. Users can verify before trusting the data.

Tradeoff: More verbose output, but builds trust and helps users learn SQL patterns.

What I'd Improve

Add query history and favorites
Implement chart/visualization generation for aggregate queries
Support for multiple database types (MySQL, SQLite)
Add collaborative features for team query sharing