🤖 Query PostgreSQL database with natural language using GPT-4o-mini

⚡ 624 views · 🤖 AI Chatbots & Agents

Description

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

🔗 Nodes Used

AI Agent, OpenAI Chat Model, Simple Memory, Chat Trigger

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup