Airline web check-in data extraction with Ollama AI, Google Sheets & Postgres Vector DB

Overview

This workflow retrieves airline web check-in URLs from Google Sheets, scrapes their content, employs an LLM to generate structured JSON data, refreshes the sheet, creates embeddings, and saves them in a Postgres vector DB for future semantic searches or question-answering.

Quick Notes

• Verify that Google Sheets has accurate URLs for scraping.
• Ensure the Postgres vector DB is set up correctly for embedding storage.

Process Flow

1. Start the workflow with the Chat Trigger - Start node.
2. Retrieve airline check-in URLs using the Fetch Airline URLs node.
3. Scrape webpage data with the Scrape Airline Webpage node.
4. Extract JSON data using the Extract info with LLM node with a Chat Model.
5. Pause for a response with the Wait for Response node.
6. Update Google Sheets with the Store Extracted Data node.
7. Create embeddings with the Generate Embeddings node and store in Postgres vector DB with the Save to Vector DB node.
8. Break down long text with the Split Long Text node and delay the next batch with the Wait Before Next Batch node.

Getting Started

• Import the workflow into n8n and set up Google Sheets and Postgres vector DB credentials.
• Run a test with a sample URL to confirm scraping and embedding storage.

Tailored Adjustments

Tweak the Extract info with LLM node to adjust JSON output or modify the Fetch Airline URLs node to pull from different sheet fields.