Automated Document Embedding and AI Query Workflow

This n8n workflow streamlines the process of preparing documents for AI-based querying and embedding, automating data ingestion, processing, and storage. It starts with a manual trigger or webhook to initiate document analysis, then extracts content using Google Docs. The content is split into manageable chunks, which are embedded via HTTP requests. These embeddings are stored in a database (Supabase). Additionally, the workflow enables querying an AI language model, specifically Google Vertex Chat, to generate responses based on uploaded document content. This setup is ideal for applications like knowledge bases, document search enhancements, or AI-powered support systems, ensuring seamless integration of document data with AI processing for intelligent answer generation.