Build a Document QA System with RAG using Milvus, Cohere, and OpenAI for Google Drive

Template Description This template creates a powerful Retrieval Augmented Generation (RAG) AI agent workflow in n8n. It monitors a specified Google Drive folder for new PDF files, extracts their content, generates vector embeddings using Cohere, and stores these embeddings in a Milvus vector database. Subsequently, it enables a RAG agent that can retrieve relevant information from the Milvus database based on user queries and generate responses using OpenAI, enhanced by the retrieved context. Functionality The workflow automates the process of ingesting documents into a vector database for use with a RAG system. Watch New Files: Triggers when a new file (specifically targeting PDFs) is added

Help me set up the n8n workflow "Build a Document QA System with RAG using Milvus, Cohere, and OpenAI for Google Drive" (https://n8n.io/workflows/3848). It uses: Google Drive, AI Agent, Embeddings Cohere, OpenAI Chat Model, Simple Memory, Recursive Character Text Splitter, Default Data Loader, Milvus Vector Store. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.