Transform PostgreSQL to AI-Ready Vector Base with Gemini

Transform Your PostgreSQL Database into an AI-Powered Vector Knowledge Base

Automatically convert your existing PostgreSQL databases into searchable AI vector knowledge bases using Pinecone and Google Gemini embeddings—without manually mapping every table and column. This intelligent n8n workflow discovers your database schema, extracts text-rich content, and creates semantic search capabilities for RAG applications and AI assistants.

What This Workflow Does

The workflow performs complete PostgreSQL-to-Pinecone transformation through automated stages:

• Dynamic Schema Discovery: Uses information_schema to automatically detect your PostgreSQL structure, primary keys, and timestamp columns
• Intelligent Table Analysis: Identifies text-rich tables suitable for embedding while excluding sensitive columns by default
• Smart Content Processing: Builds readable embedding documents from database rows with configurable filtering options
• Google Gemini Integration: Generates high-quality embeddings using gemini-embedding-001 model
• Pinecone Vector Storage: Upserts vectors with namespace organization and metadata preservation
• Incremental Sync Tracking: Maintains per-table sync state in n8n workflow data for efficient updates
• Semantic Search API: Includes companion webhook workflow for testing RAG retrieval with user queries

Use Cases

• Customer Support AI: Transform support ticket databases into searchable knowledge bases for intelligent chatbots
• Document Intelligence: Convert product documentation stored in PostgreSQL into semantic search systems
• Knowledge Management: Enable AI-powered search across organizational data without complex manual setup
• RAG Applications: Power retrieval-augmented generation systems with existing database content

Technical Details

Built with robust n8n automation components including PostgreSQL nodes for database connectivity, HTTP request nodes for Pinecone and Google Gemini API integration, and code nodes for intelligent schema analysis. Features configurable parameters for allowed schemas, table filtering, column exclusions, and row limits. Includes fail-fast error handling to ensure data integrity across PostgreSQL, Gemini, and Pinecone operations.