Build and Update RAG System with Google Drive, Qdrant, and Gemini Chat

This workflow automates the creation and management of a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as the document source. It enables full or incremental updates to documents in the Qdrant vector database and integrates with a chatbot using Google Gemini for question answering. Here is a clear and professional description in English of the n8n workflow “Create a RAG with Qdrant and update single files”, including its benefits: Benefits Efficient RAG Setup Seamlessly integrates OpenAI, Qdrant, and Google Drive to create a scalable RAG pipeline. Single File Update You can replace the vector representation of a single file without reprocessing t

Help me set up the n8n workflow "Build and Update RAG System with Google Drive, Qdrant, and Gemini Chat" (https://n8n.io/workflows/5140). It uses: HTTP Request, Google Drive, Question and Answer Chain, Embeddings OpenAI, Vector Store Retriever, Recursive Character Text Splitter, Default Data Loader, Qdrant Vector Store, Google Gemini Chat Model. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.