Get Started#

Set up the AI Teaching Assistant on Windows and ingest your first course materials.

Confirm your machine meets the System Requirements before starting.

Important: Use Windows PowerShell (not Command Prompt/CMD) for all steps in this guide. PowerShell scripts (.ps1 files) will not execute in CMD — they will only open as text files.

Step 1: Prerequisites#

  • Git for Windows — Download here

  • Python 3.10+ — Download here (check “Add Python to PATH”)

  • Visual C++ Build Tools — Required for some Python packages

Step 2: Clone The Repository#

Open PowerShell and run:

git clone --filter=blob:none --sparse https://github.com/open-edge-platform/edge-ai-suites.git `
; cd edge-ai-suites `
; git sparse-checkout set education-ai-suite/ai-teaching-assistant `
; cd education-ai-suite/ai-teaching-assistant

Step 3: Run Windows Setup#

PowerShell script handles all setup (Python venv, dependencies, models):

# If prompted about execution policy, run:
# Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

.\setup_windows.ps1

Note: This setup script also initializes the required submodules automatically.

The script will:

  1. Create and activate a Python virtual environment

  2. Download and install model files (~30-50 GB)

  3. Install all dependencies for the five services

First run may take 10-30 minutes while models are downloaded and cached.

Step 4: Start the Application#

.\start_ata.ps1

Services will start in sequence:

  • audio-analyzer (8010)

  • text-to-speech (8011)

  • rag-service (8020)

  • kiosk-core (8012)

  • ai-teaching-assistant ui (7860)

Wait for all services to show “ready” in the terminal.

Step 5: Verify All Services Are Running#

Open PowerShell and verify health:

# Audio-to-text
curl http://127.0.0.1:8010/health

# Text-to-speech
curl http://127.0.0.1:8011/health

# RAG service
curl http://127.0.0.1:8020/health

# Session orchestrator
curl http://127.0.0.1:8012/health

Each response should be: {"status": "ok"}

Step 6: Access the Web Interface#

Open your browser and navigate to:

http://127.0.0.1:7860

You should see the AI Teaching Assistant interface.

Step 7: Ingest Course Materials#

  1. In the browser, go to the “Knowledge Base” panel

  2. Click “Choose Files” and select your course material (.txt, .md, .docx, or .pdf)

  3. Click “Upload” — wait for “Upload successful” confirmation

Stopping the Application#

.\stop_ata.ps1

To stop individual services, use Ctrl+C in their respective terminal windows.

Uninstall#

The application has no installer — all files live inside the cloned repository. To uninstall, stop the services and delete the Python virtual environments (venv) along with the downloaded models, storage, and cache folders.

Warning: Deleting the storage/ folders permanently removes user data, including the RAG vector database of your ingested course materials (rag-service/storage/vector_db). Back up anything you want to keep first.

  1. Stop all services:

    .\stop_ata.ps1

  2. Run the uninstall script from the ai-teaching-assistant directory:

    .\uninstall_ata.ps1

    The script lists the folders it will delete and asks for confirmation before removing the venv, models, storage, and .cache folders for every service.

    Options:

    # Skip the confirmation prompt
.\uninstall_ata.ps1 -Yes

# Also delete the Hugging Face cache in your user profile
.\uninstall_ata.ps1 -Yes -RemoveHfCache

  3. (Optional) To remove the entire application, delete the cloned repository folder.

To reinstall later, re-run .\setup_windows.ps1 — it will recreate the virtual environments and re-download the models. Ingested course materials will need to be uploaded again.

Note: If you deleted the cloned repository folder in step 3, re-run Step 2: Clone The Repository first — setup_windows.ps1 lives inside the repo, so running it alone is not enough.

Next Steps#