Labspace - Building an AI Product Reviewer with Docker Model Runner

AI-powered applications don't require cloud APIs or API keys. With Docker Model Runner, you can run large language models locally and build production-quality AI pipelines entirely on your own machine.

In this Labspace, you'll build a complete feedback analysis pipeline for a fictional AI product called Jarvis — using local LLMs and embeddings via Docker Model Runner.

Learning objectives

By the end of this Labspace, you will have learned the following:

• How to use Docker Model Runner to run LLMs locally via an OpenAI-compatible API
• How to connect a Node.js app to Docker Model Runner using the OpenAI SDK and the Compose models: integration
• How to perform sentiment analysis using low-temperature LLM classification
• What embeddings are and how to use them for semantic clustering with cosine similarity
• How to extract structured data from an LLM using response_format: { type: 'json_object' }
• How to generate context-aware responses informed by extracted features

Launch the Labspace

To launch the Labspace, run the following command:

docker compose -f oci://dockersamples/labspace-creating-ai-product-reviewer up -d

And then open your browser to http://localhost:3030.

Using the Docker Desktop extension

If you have the Labspace extension installed (docker extension install dockersamples/labspace-extension if not), you can also click this link to launch the Labspace.

Contributing

If you find something wrong or something that needs to be updated, feel free to submit a PR. If you want to make a larger change, feel free to fork the repo into your own repository.

Important note: If you fork it, you will need to update the GHA workflow to point to your own Hub repo.

1. Clone this repo

2. Start the Labspace in content development mode:

   # On Mac/Linux
   CONTENT_PATH=$PWD docker compose up --watch
   
   # On Windows with PowerShell
   $Env:CONTENT_PATH = (Get-Location).Path; docker compose up --watch

3. Open the Labspace at http://localhost:3030.

4. Make the necessary changes and validate they appear as you expect in the Labspace

   Be sure to check out the docs for additional information and guidelines.