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Courses/Claude Code + Local AI/Open WebUI + Ollama + Qdrant

Open WebUI + Ollama + Qdrant

This stack gives you a ChatGPT-style interface, local models with Ollama, and a vector database for RAG. It's a practical foundation for learning, running internal demos, or setting up a private lab.

  • Start Open WebUI, Ollama, and Qdrant with Docker Compose.
  • Understand what each service does and how they communicate.
  • Verify that the stack is responding before uploading documents.

Project structure

Terminal
aulafy-stack/
  docker-compose.yml
  data/
    ollama/
    open-webui/
    qdrant/

docker-compose.yml

Terminal
services:
  ollama:
    image: ollama/ollama:latest
    container_name: aulafy-ollama
    ports:
      - "11434:11434"
    volumes:
      - ./data/ollama:/root/.ollama
    restart: unless-stopped

  qdrant:
    image: qdrant/qdrant:latest
    container_name: aulafy-qdrant
    ports:
      - "6333:6333"
      - "6334:6334"
    volumes:
      - ./data/qdrant:/qdrant/storage
    restart: unless-stopped

  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    container_name: aulafy-open-webui
    ports:
      - "3000:8080"
    environment:
      - OLLAMA_BASE_URL=http://ollama:11434
      - VECTOR_DB=qdrant
      - QDRANT_URI=http://qdrant:6333
    volumes:
      - ./data/open-webui:/app/backend/data
    depends_on:
      - ollama
      - qdrant
    restart: unless-stopped

Startup

Terminal
mkdir aulafy-stack
cd aulafy-stack
# create docker-compose.yml with the content above
docker compose up -d
docker compose ps

Download a model

Evaluate before trusting

Uploading documents and receiving a nice answer does not prove that RAG works. Create a small test battery with expected answer, expected source, and failure criteria.

Terminal
rag_evals:
  - id: "manual-001"
    question: "What is the return period?"
    expected_source: "customer_manual.pdf#p12"
    must_contain:
      - "30 days"
      - "purchase receipt"
    fail_if:
      - "does not cite source"
      - "mixes old policy"
      - "answers without evidence"

Test at least ten questions: five with an answer in documents, three that should not be answered, and two with similar but contradictory information. If the system cannot abstain, it is not ready yet.

Terminal
docker exec -it aulafy-ollama ollama pull qwen3:4b
docker exec -it aulafy-ollama ollama run qwen3:4b

Useful commands

Terminal
docker compose logs -f open-webui
docker compose logs -f ollama
docker compose logs -f qdrant
docker compose pull
docker compose up -d
docker compose down
Complete Aulafy mapSee how this lesson fits without leaving your path.

Complete Aulafy map

How all courses connect

This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.

  1. 1Understand
  2. 2Apply or build
  3. 3Operate with confidence
01

Choose an application

Turn the foundation into a visible outcome: a website, a business improvement, media, or an interactive experience.

Continue into the technical branch when you need to maintain code, data, or infrastructure.

02

Build with code

Prepare your environment, work with coding agents, and run models while keeping control of your projects.

This branch prepares you to design and operate reliable AI systems.

03

Take systems to production

Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.

You do not need every course: choose the component your system needs and return as it grows.

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