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/. An n8n automation workflow template by JHH.

Community Metrics

Categories

• Automation
• AI

Tags

• n8n
• workflow
• ai-agent
• summarization-chain
• ollama-chat-model
• ollama-model
• simple-memory
• recursive-character-text-splitter
• default-data-loader
• qdrant-vector-store

Capabilities

• 10 nodes
• 2,052 views

Information