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 (RAG)
  • Qdrant vector-database for competition-specific document lookup
  • On-demand retrieval of Kaggle competition guidelines, tutorials, and notebooks after convertion to HTML and ingestion into RAG
• entirly On-Premises for Privacy
  • Locally hosted LLM (via Ollama) – no external code or data transfer

ALIENTELLIGENCE/contentsummarizer:latest for summarizing qwen3:8b for chat and coding mxbai-embed-large:latest for embedding

• GPU acceleration required

Based on: https://n8n.io/workflows/2339 breakdown documents into study notes using templating mistralai and qdrant/