Build a Knowledge Base Chatbot with OpenAI, RAG and MongoDB Vector Embeddings

Who is this for? This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven, retrieval-augmented question answering. What problem is this workflow solving? Support agents often spend too much time manually searching through lengthy documentation, leading to inconsistent or delayed answers. This solution automates importing, chunking, and indexing product manuals, then uses retrieval-augmented generation (RAG) to answer user queries accurately and quickly with AI. What these workflows do Workflow 1: Document Ingestion & Indexing Manually triggered to impo

Help me set up the n8n workflow "Build a Knowledge Base Chatbot with OpenAI, RAG and MongoDB Vector Embeddings" (https://n8n.io/workflows/4526). It uses: Google Docs, AI Agent, Embeddings OpenAI, OpenAI Chat Model, Simple Memory, Recursive Character Text Splitter, Default Data Loader, MongoDB Atlas Vector Store. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.