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Query PostgreSQL Database with Natural Language using GPT-4o-mini

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Created by: Babish Shrestha || bbz

Babish Shrestha

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Last update a month ago

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This Database SQL Query Agent convert natural language into sql query to get results

Turn your PostgreSQL database into a conversational AI agent! Ask questions in plain English and get instant data results without writing SQL.

✨ What It Does

  • Natural Language Queries: "Show laptops under $500 in stock" → Automatic SQL generation
  • Smart Column Mapping: Understands your terms and maps them to actual database columns
  • Conversational Memory: Maintains context across multiple questions
  • Universal Compatibility: Works with any PostgreSQL table structure

🎯 Perfect For

  • Business analysts querying data without SQL knowledge
  • Customer support finding information quickly
  • Product managers analyzing inventory/sales data
  • Anyone who needs database insights fast

🚀 Quick Setup

Step 1: Prerequisites

  • n8n instance (cloud/self-hosted)
  • PostgreSQL database with read access
  • OpenAI API key/You can use other LLM as well

Step 2: Import & Configure

  1. Import this workflow template into n8n
  2. Add Credentials:
    • OpenAI API: Add your API key
    • PostgreSQL: Configure database connection
  3. Set Table Name: Edit "Set Table Name" node → Replace "table_name" with your actual table
  4. Test Connection: Ensure your database user has SELECT permissions

Step 3: Deploy & Use

  1. Start the workflow
  2. Open the chat interface
  3. Ask questions like:
    • "Show all active users"
    • "Find orders from last month over $100"
    • "List products with low inventory"

🔧 Configuration Details

Required Settings

  • Table Name: Update in "Set Table Name" node
  • Database Schema: Default is 'public' (modify SQL if different)
  • Result Limit: Default 50 rows (adjustable in system prompt)

Optional Customizations

  • Multi-table Support: Modify system prompt and add table selection logic
  • Custom Filters: Add business rules to restrict data access
  • Output Format: Customize response formatting in the agent prompt

💡 Example Queries

E-commerce

"Show me all electronics under $200 that are in stock"

HR Database

"List employees hired in 2024 with salary over 70k"

Customer Data

"Find VIP customers from California with recent orders"

🛡️ Security Features

  • Read-only Operations: Only SELECT queries allowed
  • SQL Injection Prevention: Parameterized queries and validation
  • Result Limits: Prevents overwhelming queries
  • Safe Schema Discovery: Uses information_schema tables

🔍 How It Works

  1. Schema Discovery: Agent fetches table structure and column info
  2. Query Planning: Maps natural language to database columns
  3. SQL Generation: Creates safe, optimized queries
  4. Result Formatting: Returns clean, user-friendly data

⚡ Quick Troubleshooting

  • No Results: Check table name and ensure data exists
  • Permission Error: Verify database user has SELECT access
  • Connection Failed: Confirm PostgreSQL credentials and network access
  • Unexpected Results: Try more specific queries with exact column names

🎨 Use Cases

  • Inventory Management: "Show low-stock items by category"
  • Sales Analysis: "Top 10 products by revenue this quarter"
  • Customer Support: "Find customer orders with status 'pending'"
  • Data Exploration: "What are the unique product categories?"

🔧 Advanced Tips

  • Performance: Add database indexes on frequently queried columns
  • Customization: Modify the system prompt for domain-specific terminology
  • Scaling: Use read replicas for high-query volumes
  • Integration: Connect to Slack/Teams for team-wide data access

Tags: AI, PostgreSQL, Natural Language, SQL, Business Intelligence, LangChain, Database Query

Difficulty: Beginner to Intermediate
Setup Time: 10-15 minutes