Build a RAG System with Automatic Citations using Qdrant, Gemini & OpenAI

This workflow implements a Retrieval-Augmented Generation (RAG) system that: Stores vectorized documents in Qdrant, Retrieves relevant content based on user input, Generates AI answers using Google Gemini, Automatically cites the document sources (from Google Drive). Workflow Steps Create Qdrant Collection A REST API node creates a new collection in Qdrant with specified vector size (1536) and cosine similarity. Load Files from Google Drive The workflow lists all files in a Google Drive folder, downloads them as plain text, and loops through each. Text Preprocessing & Embedding Documents are split into chunks (500 characters, with 50-character overlap). Embeddings are created using OpenAI em

Help me set up the n8n workflow "Build a RAG System with Automatic Citations using Qdrant, Gemini & OpenAI" (https://n8n.io/workflows/5023). It uses: HTTP Request, Google Drive, Code, 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.