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

Description

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/