Build an On-Premises AI Kaggle Competition Assistant with Qdrant RAG and Ollama

LLM/RAG Kaggle Development Assistant An on-premises, domain-specific AI assistant for Kaggle (tested on binary disaster-tweet classification), combining LLM, an n8n workflow engine, and Qdrant-backed Retrieval-Augmented Generation (RAG). Deploy via containerized starter kit. Needs high end GPU support or patience. Initial chat should contain guidelines on what to to produce and the challenge guidelines. Features Coding Assistance • "Real"-time Python code recommendations, debugging help, and data-science best practices • Multi-turn conversational context Workflow Automation • n8n orchestration for LLM calls, document ingestion, and external API integrations Retrieval-Augmented Generation (RA

Help me set up the n8n workflow "Build an On-Premises AI Kaggle Competition Assistant with Qdrant RAG and Ollama" (https://n8n.io/workflows/3967). It uses: AI Agent, Summarization Chain, Ollama Chat Model, Ollama Model, Simple Memory, Recursive Character Text Splitter, Default Data Loader, Qdrant Vector Store, Embeddings Ollama, 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.