Skip to content

Latest commit

 

History

History

advanced_app

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
app
app
 
 
data
data
 
 
vespa_shopping_assistant
vespa_shopping_assistant
 
 
README.md
README.md
 
 
requirements.txt
requirements.txt
 
 
template-secrets.toml
template-secrets.toml
 
 

README.md

Prerequisites

  • Clone this repo and navigate to this directory.
  • 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.
  • Uncompress the data file: unzstd data/*.zst

Deploy the Vespa Application

  • Navigate to the app subdirectory
  • 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:
    • Skip Step 5. as you have already cloned the app directory.
    • 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.
    • Return to the parent directory. To feed the application, run:
    cd ..
vespa feed data/* --progress 30
  • You can test the following query from the Vespa CLI: 
    vespa query "select id, category, title, price, description  from sources * where default contains 'screwdriver'"
  • You will need the URL of your MTL endpoint for you 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 for the MTLS end-point.

Configure and Launch your Streamlit Application

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

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

Optional. Launch random real-time updates for inventory:

cd vespa_shopping_assistant
python vespa_module/update_vespa.py

Launch your streamlit application from vespa_shopping_assistant:

streamlit run streamlit_app.py

You can now start asking some conversational questions, like for example looking for a glue, or rain gear.