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Prerequisites

  • You need a python virtual environment. For this demo, Python 3.13.1 was used, but any Python environment 3.11+ should work.
  • You will need Vespa CLI that you can deploy on MacOS with brew install vespa-cli
  • Libraries dependencies can be installed with: pip install -R requirements.txt
  • Sign-up with Tavily and Get an API key.
  • Spin-up a Vespa Cloud Trial account:
  • A Valid OpenAI API key. Note that you have the option to use any other LLM, which may support Langgraph tools binding.
  • Git clone the repo https://github.com/vespa-engine/system-test.git
  • The ecommerce_hybrid_search app will be deployed. For more information about the app, please review the README.md. You do not have to follow the data prep steps there. Follow the instructions below instead.
  • Uncompress the data file: zstd -d data/vespa_update-96k.json.zst

Deploy the Ecommerce Vespa Application

  • In the system-test repo you just cloned, navigate to tests/performance/ecommerce_hybrid_search/app
  • Choose a name for your app. For example ecommercebot
  • Follow instructions in the Getting Started document. Please note the following as you go through the documentation:
    • You will need your tenant name you created previously.
    • When adding the public certificates with vespa auth cert, it will give you the absolute path of the certificate and the private key. Please note them down.
    • To feed the application, return to the original directory and run:
    vespa feed data/vespa_update-96k.json
  • You can test the following query from the Vespa CLI: 
    vespa query "select id, category, title, price  from sources * where default contains 'screwdriver'"
  • You will need the URL of your Vespa application. Run the following command:
vespa status

This should return you an output like:

Container container at https://xxxxx.yyyyy.z.vespa-app.cloud/ is ready

Note down the URL.

Configure and Launch your Streamlit Application

A template for secrets.toml file to store streamlit secrets has been provided. Please create a subdirectory .streamlit and copy the template there.

Update all the fields with all the information collected previously and save the file as secrets.toml

Launch your streamlit application:

streamlit run streamlit_vespa_app.py

Testing the Application

You can try a mix of questions like:

What is the weather in Toronto ?

Followed by:

I'm looking for a screwdriver

And then:

Which one do you recommend to fix a watch?