Build a PDF Document RAG System with Mistral OCR, Qdrant and Gemini AI

This workflow is designed to process PDF documents using Mistral's OCR capabilities, store the extracted text in a Qdrant vector database, and enable Retrieval-Augmented Generation (RAG) for answering questions. Here’s how it functions: Once configured, the workflow automates document ingestion, vectorization, and intelligent querying, enabling powerful RAG applications. Benefits End-to-End Automation No manual interaction is needed: documents are read, processed, and made queryable with minimal setup. Scalable and Modular The workflow uses subflows and batching, making it easy to scale and customize. Multi-Model Support Combines Mistral for OCR, OpenAI for embeddings, and Gemini for intelli

Help me set up the n8n workflow "Build a PDF Document RAG System with Mistral OCR, Qdrant and Gemini AI" (https://n8n.io/workflows/4400). It uses: HTTP Request, Google Drive, Code, Summarization Chain, Question and Answer Chain, Embeddings OpenAI, Vector Store Retriever, Token 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.