Quickstart: Semantic ranking in Azure AI Search using Python
This sample demonstrates how to set up semantic ranking. You add a semantic configuration to a search index, and then you add semantic parameters to a query.
What's in this sample
| File | Description |
|---|---|
semantic-ranking-quickstart.ipynb |
Jupyter notebook that creates an index with semantic configuration and runs semantic queries |
requirements.txt |
Python package dependencies |
sample.env |
Environment variable template for configuration |
Documentation
This sample accompanies Quickstart: Semantic ranking using Python. Follow the documentation for prerequisites, setup instructions, and detailed explanations.
Next step
You can learn more about Azure AI Search on the official documentation site.