Create a Private Document Q&A System with Llama3, Postgres, Qdrant and Google Drive

⚙️ How It Works: LocalRAG.AI ⚠️ Note: This system only works for self-hosted n8n instances. It will not function on n8n.cloud or other remote setups. LocalRAG.AI is a private, on-prem AI assistant that uses your own documents to answer questions intelligently. It combines LangChain, Ollama, Qdrant, and Postgres into a powerful AI pipeline — all running locally for maximum data privacy. 🔄 What It Does Monitors Your Google Drive Folders for new or updated files. Downloads the file, extracts the text, and prepares it. Generates Embeddings using your local Ollama model (e.g., LLaMA 3). Stores them in Qdrant, your local vector database. During a chat, it: Uses vector search to retrieve relevant

Help me set up the n8n workflow "Create a Private Document Q&A System with Llama3, Postgres, Qdrant and Google Drive" (https://n8n.io/workflows/5508). It uses: Google Drive, AI Agent, Ollama Chat Model, Ollama Model, Recursive Character Text Splitter, Default Data Loader, Qdrant Vector Store, Embeddings Ollama, Postgres Chat Memory, 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.