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Build an on-premises AI Kaggle competition assistant with Qdrant RAG and Ollama

n8nn8n21 modulesv1.0
agentchainSummarizationchatTriggerdocumentDefaultDataLoaderembeddingsOllamaextractFromFilelmChatOllamalmOllamalocalFileTriggermarkdownmemoryBufferWindowmergereadWriteFilesetswitchtextSplitterRecursiveCharacterTextSplittertoolVectorStorevectorStoreQdrant

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,

At a glance

Build an on-premises AI Kaggle competition assistant with Qdrant RAG and Ollama is a ready-made n8n workflow you import as a workflow JSON file — no build required. It connects agent, chainSummarization, chatTrigger, documentDefaultDataLoader. It's free to download. Follow the 5-step import below to go live in minutes.

Platform
n8n
Connects
agent, chainSummarization, chatTrigger, documentDefaultDataLoader, embeddingsOllama, extractFromFile
Modules
21
Price
Free
Version
v1.0
Build an on-premises AI Kaggle competition assistant with Qdrant RAG and Ollama workflow diagram

About this workflow

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: breakdown documents into study notes using templating mistralai and qdrant/

n8n

How to import this n8n workflow

  1. 1

    Download the workflow JSON file after purchase.

  2. 2

    Open n8n → click the menu → Import from File.

  3. 3

    Select the downloaded JSON and import.

  4. 4

    Set up credentials for each node that requires them.

  5. 5

    Click Execute Workflow to test, then activate.

Setup guide

Setup guide included

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  • JSON blueprint — instant download
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