Automated Blog Post Tagging with AI and Database Integration

This n8n workflow automates the process of analyzing new blog posts and generating relevant tags using AI models. It begins with a webhook trigger that activates upon a new blog post submission. The content is then split into manageable chunks which are processed through an embedding model to generate semantic vectors. These vectors are stored in a Supabase vector database for efficient similarity search. The workflow uses a language model and retrieval-augmented generation (RAG) technique to determine appropriate tags or categories based on the retrieved data. Results are logged into a Google Sheet for record-keeping, and any errors encountered during the process trigger an alert on Slack. This automated system helps streamline content management, improve tagging accuracy, and enhance site SEO and organization, especially useful for busy content teams managing large volumes of posts.