Overview
This workflow automates the extraction and processing of airline web check-in data using advanced AI and seamless integrations. It fetches airline URLs, scrapes web pages, and leverages Ollama AI models for intelligent data extraction.
Key Features
- Automated Data Collection: Loops through airline URLs and scrapes check-in pages.
- AI-Powered Extraction: Utilizes Ollama LLM and embeddings for accurate information parsing.
- Data Storage: Stores structured results in Google Sheets for easy access and in a Postgres vector database for advanced querying.
- Batch Processing: Efficiently handles multiple items with splitInBatches for scalability.
- Real-Time Interaction: Includes chat triggers and wait nodes for responsive, interactive workflows.
Benefits
- Time Savings: Eliminates manual data entry and extraction, reducing operational overhead.
- Accuracy: AI-driven extraction ensures high-quality, reliable data.
- Centralized Insights: Consolidates information in Google Sheets and vector DB for analytics and reporting.
- Scalability: Easily adapts to process large volumes of airline data.
Use Cases
- Airline operations teams automating check-in data collection.
- Travel agencies centralizing client check-in information.
- Analytics teams leveraging structured airline data for insights.
Integrations
- Google Sheets: For accessible data storage and sharing.
- Ollama AI: For LLM-based data extraction and embeddings.
- Postgres Vector DB: For advanced data retrieval and analysis.
