Build a self-updating RAG system with OpenAI, Qdrant and Google Drive

This workflow implements a Retrieval-Augmented Generation (RAG) system that integrates Google Drive and Qdrant. This setup creates a powerful, self-updating knowledge base that provides accurate, context-aware answers to user queries. Key Advantages Automated Knowledge Base Updates No manual intervention is required—documents in Google Drive are automatically synchronized with Qdrant. Efficient Search and Retrieval Vector embeddings enable fast and precise retrieval of relevant information. Scalable and Flexible Works with multiple documents and supports continuous growth of your dataset. Seamless AI Integration Combines OpenAI embeddings for vectorization and Google Gemini for high-quality

Help me set up the n8n workflow "Build a self-updating RAG system with OpenAI, Qdrant and Google Drive" (https://n8n.io/workflows/7647). 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.