RAG Chatbot for Company Documents using Google Drive and Gemini

This workflow implements a Retrieval Augmented Generation (RAG) chatbot that answers employee questions based on company documents stored in Google Drive. It automatically indexes new or updated documents in a Pinecone vector database, allowing the chatbot to provide accurate and up-to-date information. The workflow uses Google's Gemini AI for both embeddings and response generation. How it works The workflow uses two Google Drive Trigger nodes: one for detecting new files added to a specified Google Drive folder, and another for detecting file updates in that same folder. Automated Indexing: When a new or updated document is detected The Google Drive node downloads the file. The Default Dat

Help me set up the n8n workflow "RAG Chatbot for Company Documents using Google Drive and Gemini" (https://n8n.io/workflows/2753). It uses: Google Drive, AI Agent, Simple Memory, Recursive Character Text Splitter, Pinecone Vector Store, Default Data Loader, Embeddings Google Gemini, Google Gemini Chat Model, Vector Store Question Answer Tool. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.