Generate production database schemas from Excel and CSV with OpenAI and LangChain

Overview

This workflow automatically converts CSV or Excel files into a production-ready database schema using AI and rule-based validation.

It analyzes uploaded data, detects column types, relationships, and data quality, then generates a normalized schema. The output includes SQL DDL scripts, ERD diagrams, a data dictionary, and a load plan.

This eliminates manual schema design and accelerates database setup from raw data.

How It Works

1. File Upload (Webhook)

• Accepts CSV or XLSX files via webhook endpoint
• Initializes workflow configuration (thresholds, retry limits)

1. File Extraction

• Detects file format (CSV or Excel)
• Extracts rows into structured JSON
• Merges extracted datasets

1. Data Cleaning & Profiling

• Removes duplicates and normalizes values
• Detects data types (integer, float, date, boolean, string)
• Computes column statistics (nulls, uniqueness, distributions)
• Generates file hash and sample dataset

1. Column Profiling Engine

• Identifies potential primary keys
• Detects cardinality and uniqueness levels
• Suggests foreign key relationships based on value overlap

1. AI Schema Generation

• Uses an AI agent to design normalized tables
• Assigns SQL data types based on real data
• Defines primary keys, foreign keys, constraints, and indexes

1. Validation Layer

• Ensures schema matches actual data
• Validates:
• Data types
• Primary key uniqueness
• Foreign key overlap (>70%)
• Constraint consistency
• Detects circular dependencies

1. Revision Loop

• If validation fails:
• Sends feedback to AI agent
• Regenerates schema
• Retries up to configured limit

1. Schema Output Generation

• Generates:
• SQL DDL scripts
• ERD (Mermaid format)
• Data dictionary
• Load plan with dependency graph

1. Load Plan Engine

• Computes optimal table insertion order
• Detects circular dependencies
• Suggests batching strategy

1. Combine & Explain

• Merges all outputs
• Optional AI explanation of schema decisions

1. Response Output

• Returns structured JSON via webhook:
• SQL schema
• ERD summary
• Data dictionary
• Load plan
• Optional explanation

Setup Instructions

1. Activate the workflow and copy the webhook URL
2. Send a POST request with a CSV or XLSX file
3. Configure OpenAI credentials (used by AI agent)
4. Adjust thresholds if needed (FK overlap, retries, confidence)
5. Execute workflow and review generated outputs

Use Cases

• Auto-generate database schema from CSV/Excel files
• Data migration and onboarding pipelines
• Rapid database prototyping
• Reverse engineering datasets
• AI-assisted data modeling

Requirements

• n8n (latest version recommended)
• OpenAI API credentials
• LangChain nodes enabled
• CSV or XLSX input file