Database Design Patterns

Note

The repository was a part of the ODSC AI Builders' Summit 2025 workshop - "Database Design Patterns: Single Collection vs Multi-Tenancy". If you have any questions, please feel free to reach out to the author, or open an issue on this repository.

Step 1: Preparation & Setup

Clone this repo and navigate into it. This will be your working directory for the workshop.

git clone git@github.com:weaviate-tutorials/odsc-ai-builders-2025.git
cd odsc-ai-builders-2025

1.1 Install Python & set up a virtual environment

Note

If you have a preferred setup (e.g. Conda/Poetry), please feel free to use that. Otherwise, we recommend following these steps.

Install Python 3.9 or higher (e.g. from the Python website, or pyenv).

Then, create & activate a virtual environment:

python -m venv .venv  # Or use `python3` if `python` is Python 2
source .venv/bin/activate

Check that you are using the correct Python:

which python

This should point to the Python binary in the .venv directory.

Install the required Python packages:

pip install -r requirements.txt

Tip

If you have network connectivity issues, the installation may time out part way through. If this happens, just try running the command again. It will re-used cached data, so you will make further

Tip

If you open new terminal or shell window, you will need to activate the virtual environment again. Navigate to the project directory and run source .venv/bin/activate.

1.2 Choose your embedding & LLM provider

The workshop is set up for two different embeddings & LLM providers options (Cohere or Ollama).

1.2.1 Option 1: Cohere

Note

• Recommended if you want to use an API-based solution
  • We will use pre-embedded data for this workshop, so the expense will be for queries only & minimal
  • Still, please set a budget limit in your account for extra safety

You can use your own Cohere API key in the notebook. You can use a free "trial" key, which should be sufficient for this workshop.

1.2.2 Option 2: Ollama

Note

• Recommended if you have 16+ GB of RAM and a modern computer
• We will use pre-embedded data for this workshop, so Ollama will be used for vectorizing queries & LLM use
• No account or API key required

Download & install Ollama from the Ollama website. Make sure Ollama is running, by:

ollama -v

You should see something like:

❯ ollama -v
ollama version is 0.5.7

Then, run the following command:

> ollama pull nomic-embed-text && ollama pull gemma2:2b

This will download the required models for the workshop.

1.3 Install Docker

Install Docker Desktop: https://www.docker.com/products/docker-desktop/

Start up a Weaviate cluster with the following command:

docker compose up -d

This will start a single-node Weaviate cluster.

Check an Weaviate endpoint:

curl http://localhost:8080/v1/meta | jq

You should see a response - this means Weaviate is running!

You should be able to see the memory usage of the Weaviate pod by running:

go tool pprof -top http://localhost:6060/debug/pprof/heap

Step 3: Work with Weaviate

3.1 Work through the notebooks

See: 1_single_collection.ipynb and 2_multi_tenancy.ipynb notebooks and work through them during the workshop.

Tip

Hint: The finished_notebooks branch contains pre-populated versions of the notebooks!

3.2 Run the demo Streamlit app

We have a Streamlit app that will help you to visualise basic cluster statistics, and to make use of the data. (Remember to navigate to the project directory and activate the virtual environment.) Run it with:

streamlit run app.py

This will throw an error, but that's OK. We'll fix that along the way.

Finish up

Docker

When finished with the workshop, you can stop the cluster with:

docker compose -f docker-compose.yml down