Save Costs in RAG Workflows with Q&A Tool & Multiple Models

This n8n workflow demonstrates a smart approach to implementing Retrieval-Augmented Generation (RAG) while minimizing costs. It starts with a Form Trigger node where users upload PDF or CSV files containing custom knowledge. These files are then processed through OpenAI Embeddings to create vector representations, stored in a Simple Vector Store for quick retrieval. The core innovation is the Question and Answer (Q&A) Tool, which allows agents to query the vector store efficiently. Instead of r