Merge pull request #101 from HeidiSteen/main

Updated product name references

Author: HeidiSteen

CONTRIBUTING.md

@@ -1,4 +1,4 @@
-# Contributing to Azure Cognitive Search samples
+# Contributing to Azure AI Search samples
 
 This project welcomes contributions and suggestions.  Most contributions require you to agree to a
 Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us

Quickstart/REST/azure-search-quickstart.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This Jupyter Notebook demonstrates index creation, data ingestion, and queries of an Azure Cognitive Search index by calling the REST APIs from Python code. This notebook is a companion document to this [Python quickstart](https://docs.microsoft.com/azure/search/search-get-started-python). \n",
+    "This Jupyter Notebook demonstrates index creation, data ingestion, and queries of an Azure AI Search index by calling the REST APIs from Python code. This notebook is a companion document to this [Python quickstart](https://docs.microsoft.com/azure/search/search-get-started-python). \n",
     "\n",
     "\n",
     "As a first step, load the libraries used for working with JSON and formulating HTTP requests."
@@ -241,7 +241,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You are now ready to run some queries. The next cell contains a query expression that executes an empty search (search=*), returning an unranked list (search score = 1.0) of arbitrary documents. By default, Azure Cognitive Search returns 50 matches at a time. As structured, this query returns an entire document structure and values. Add $count=true to get a count of all documents (4) in the results."
+    "You are now ready to run some queries. The next cell contains a query expression that executes an empty search (search=*), returning an unranked list (search score = 1.0) of arbitrary documents. By default, Azure AI Search returns 50 matches at a time. As structured, this query returns an entire document structure and values. Add $count=true to get a count of all documents (4) in the results."
    ]
   },
   {

Quickstart/v11/README.md

@@ -7,17 +7,17 @@ products:
   - azure
   - azure-cognitive-search
 description: |
-  Learn how to create, load, and query an Azure Cognitive Search index using Python.
+  Learn how to create, load, and query an Azure AI Search index using Python.
 urlFragment: python-quickstart
 ---
 
-# Python quickstart for Azure Cognitive Search
+# Python quickstart for Azure AI Search
 
 ![Flask sample MIT license badge](https://img.shields.io/badge/license-MIT-green.svg)
 
-Demonstrates using Python and the Azure SDK for Python to create an Azure Cognitive Search index, load it with documents, and execute a few queries. The index is modeled on a subset of the Hotels dataset, reduced for readability and comprehension. Index definition and documents are included in the code.
+Demonstrates using Python and the Azure SDK for Python to create an Azure AI Search index, load it with documents, and execute a few queries. The index is modeled on a subset of the Hotels dataset, reduced for readability and comprehension. Index definition and documents are included in the code.
 
-This sample is a Jupyter Python3 .ipynb file to perform the actions against the Cognitive Search service.
+This sample is a Jupyter Python3 .ipynb file to perform the actions against the Azure AI Search service.
 
 ## Prerequisites
 
@@ -46,4 +46,4 @@ This sample is a Jupyter Python3 .ipynb file to perform the actions against the
 
 ## Next steps
 
-You can learn more about Azure Cognitive Search on the [official documentation site](https://docs.microsoft.com/azure/search).
+You can learn more about Azure AI Search on the [official documentation site](https://docs.microsoft.com/azure/search).

Quickstart/v11/azure-search-quickstart.ipynb

@@ -5,15 +5,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Create a search index in Azure Cognitive Search using the Azure SDK for Python"
+    "# Create a search index in Azure AI Search using the Azure SDK for Python"
    ]
   },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This Jupyter Notebook steps through creating, loading, and querying an index in Azure Cognitive Search index by calling the azure-search-documents library in the Azure SDK for Python. "
+    "This Jupyter Notebook steps through creating, loading, and querying an index in Azure AI Search index by calling the azure-search-documents library in the Azure SDK for Python. "
    ]
   },
   {

README.md

@@ -1,12 +1,12 @@
-# Python samples for Azure Cognitive Search
+# Python samples for Azure AI Search
 
-This repository contains Python code samples used in Azure Cognitive Search documentation. Unless noted otherwise, all samples run on the shared (free) pricing tier of an [Azure Cognitive Search service](https://learn.microsoft.com/azure/search/search-create-service-portal).
+This repository contains Python code samples used in Azure AI Search documentation. Unless noted otherwise, all samples run on the shared (free) pricing tier of an [Azure AI Search service](https://learn.microsoft.com/azure/search/search-create-service-portal).
 
 | Sample | Description |
 |--------|-------------|
-| quickstart | "Day One" introduction to the fundamental tasks of working with a search index: create, load, and query. This sample is a Jupyter Python3 .ipynb file. The index is modeled on a subset of the Hotels dataset, widely used in Cognitive Search samples, but reduced here for readability and comprehension. |
+| quickstart | "Day One" introduction to the fundamental tasks of working with a search index: create, load, and query. This sample is a Jupyter Python3 .ipynb file. The index is modeled on a subset of the Hotels dataset, widely used in Azure AI Search samples, but reduced here for readability and comprehension. |
 | quickstart-semantic-search | Extends the quickstart through modifications that invoke semantic search. This notebook adds a semantic configuration to the index and semantic query options that formulate the query and response. |
-| tutorial-ai-enrichment | This sample is a Jupyter Python3 .ipynb file used in the [Python Tutorial: Call Cognitive Services APIs in an Azure Cognitive Search indexing pipeline](https://docs.microsoft.com/azure/search/cognitive-search-tutorial-blob-python). This sample demonstrates cognitive search functionality, adding AI enrichments from Cognitive Services to extract, detect, and analyze information from image files or large unstructured document files. |
+| tutorial-ai-enrichment | This sample is a Jupyter Python3 .ipynb file used in the [Python Tutorial: Call Azure AI Services APIs in an Azure AI Search indexing pipeline](https://docs.microsoft.com/azure/search/cognitive-search-tutorial-blob-python). This sample demonstrates Azure AI functionality, adding AI enrichments from Azure AI services to extract, detect, and analyze information from image files or large unstructured document files. |
 | search-website-functions-v4 | Shows how to create, load, and query a search index in Python using the Azure.Search.Documents library in the Azure SDK for Python. It also includes application code and sample data so that you can see search integration in the context of a full app. The data is from [https://github.com/zygmuntz/goodbooks-10k](https://github.com/zygmuntz/goodbooks-10k). The app is an Azure Static Web app, using the React library for user interaction, and Azure Function to handle the query requests and responses in the application layer. |
 
 ## Archived samples

Tutorial-AI-Enrichment/PythonTutorial-AzureSearch-AIEnrichment.ipynb

@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Enrichment sample in Python for Azure Cognitive Search\n",
+    "# Enrichment sample in Python for Azure AI Search\n",
     "\n",
-    "In this Jupyter Notebook, create and run enrichment steps to unlock searchable content in Azure blobs. It performs operations over mixed content in Azure Storage, such as images and application files, using a skillset that analyzes and extracts text information that becomes searchable in Azure Cognitive Search. The full documentation for this sample can be found at [Tutorial: Use Python and AI to generate searchable content from Azure blobs](https://docs.microsoft.com/azure/search/cognitive-search-tutorial-blob-python).\n",
+    "In this Jupyter Notebook, create and run enrichment steps to unlock searchable content in Azure blobs. It performs operations over mixed content in Azure Storage, such as images and application files, using a skillset that analyzes and extracts text information that becomes searchable in Azure AI Search. The full documentation for this sample can be found at [Tutorial: Use Python and AI to generate searchable content from Azure blobs](https://docs.microsoft.com/azure/search/cognitive-search-tutorial-blob-python).\n",
     "\n",
     "This sample creates the following objects on your search service:\n",
     "\n",
@@ -75,7 +75,7 @@
     "datasourceConnectionString = \"<YOUR-BLOB-RESOURCE-CONNECTION-STRING>\"\n",
     "datasource_payload = {\n",
     "    \"name\": datasource_name,\n",
-    "    \"description\": \"Demo files to demonstrate cognitive search capabilities.\",\n",
+    "    \"description\": \"Demo files to demonstrate search capabilities.\",\n",
     "    \"type\": \"azureblob\",\n",
     "    \"credentials\": {\n",
     "        \"connectionString\": datasourceConnectionString\n",
@@ -339,7 +339,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This step concludes the walkthrough, showing you how to invoke skills that extract searchable content and information from image and application files in Azure Storage. The processes run on Azure Cognitive Search, and the resulting index can be used in client apps to provide a search experience. For more information, visit the [official docs](https://docs.microsoft.com/azure/search/)."
+    "This step concludes the walkthrough, showing you how to invoke skills that extract searchable content and information from image and application files in Azure Storage. The processes run on Azure AI Search, and the resulting index can be used in client apps to provide a search experience. For more information, visit the [official docs](https://docs.microsoft.com/azure/search/)."
    ]
   }
  ],

Tutorial-AI-Enrichment/README.md

@@ -7,15 +7,15 @@ products:
   - azure-cognitive-search
 name: AI enrichment tutorial - Jupyter Notebook
 description: |
-  Create an AI enrichment pipeline in Azure Cognitive Search to extract text, structure, and information from raw content, including images and unstructured text.
+  Create an AI enrichment pipeline in Azure AI Search to extract text, structure, and information from raw content, including images and unstructured text.
 urlFragment: python-tutorial-cognitive-search
 ---
 
-# Get started with skillsets and AI enrichment in Azure Cognitive Search
+# Get started with skillsets and AI enrichment in Azure AI Search
 
-Demonstrates AI enrichment by building a [skillset](https://docs.microsoft.com/azure/search/cognitive-search-working-with-skillsets) that detects and extracts text and text representations of images and scanned documents stored as blobs in Azure Blob storage. This sample leverages cognitive skills that are based on he Cognitive Services APIs, such as entity recognition and language detection. It uses the REST APIs to make calls to Azure Cognitive Search, including index definition, data ingestion and AI enrichment, and query execution.
+Demonstrates AI enrichment by building a [skillset](https://docs.microsoft.com/azure/search/cognitive-search-working-with-skillsets) that detects and extracts text and text representations of images and scanned documents stored as blobs in Azure Blob storage. This sample leverages cognitive skills that are based on the Azure AI Services APIs, such as entity recognition and language detection. It uses the REST APIs to make calls to Azure AI Search, including index definition, data ingestion and AI enrichment, and query execution.
 
-This Python sample is in a notebook. For an explanation of each step, see [Python Tutorial: Call Cognitive Services APIs in an enrichment pipeline](https://docs.microsoft.com/azure/search/cognitive-search-tutorial-blob-python). 
+This Python sample is in a notebook. For an explanation of each step, see [Python Tutorial: Call Azure AI Services APIs in an enrichment pipeline](https://docs.microsoft.com/azure/search/cognitive-search-tutorial-blob-python). 
 
 ## Contents
 
@@ -32,7 +32,7 @@ This Python sample is in a notebook. For an explanation of each step, see [Pytho
 - [Anaconda 3.x](https://www.anaconda.com/distribution/#download-section) providing Python 3.x and Jupyter Notebooks
 - [Sample file set (mixed content types)](https://github.com/Azure-Samples/azure-search-sample-data/tree/master/mixedContent)
 - [Azure Storage account](https://docs.microsoft.com/azure/storage/common/storage-quickstart-create-account) 
-- [Azure Cognitive Search service](https://docs.microsoft.com/en-us/azure/search/search-create-service-portal)
+- [Azure AI Search service](https://docs.microsoft.com/en-us/azure/search/search-create-service-portal)
 
 ## Setup
 

search-website-functions-v4/README.md

@@ -16,9 +16,9 @@ urlFragment: python-sample-search-web-app
 
 ![Flask sample MIT license badge](https://img.shields.io/badge/license-MIT-green.svg)
 
-This Python sample shows you how to add document search to a web app using Azure Cognitive Search.
+This Python sample shows you how to add document search to a web app using Azure AI Search.
 
-For this sample, you will use the [**azure-search-documents**](https://pypi.org/project/azure-search-documents/) library in the Azure SDK for Python to create, load, and query a search index containing the goodbooks-10k dataset, publicly available at [https://github.com/zygmuntz/goodbooks-10k](https://github.com/zygmuntz/goodbooks-10k). The search index runs on an [Azure Cognitive Search](https://docs.microsoft.com/azure/search/search-what-is-azure-search) service that you create. You can use the free tier for this sample.
+For this sample, you will use the [**azure-search-documents**](https://pypi.org/project/azure-search-documents/) library in the Azure SDK for Python to create, load, and query a search index containing the goodbooks-10k dataset, publicly available at [https://github.com/zygmuntz/goodbooks-10k](https://github.com/zygmuntz/goodbooks-10k). The search index runs on an [Azure AI Search](https://docs.microsoft.com/azure/search/search-what-is-azure-search) service that you create. You can use the free tier for this sample.
 
 Optionally, this sample includes a devcontainer.json file so that you can run the code locally, as a developer, with the assurance that the environment is correctly configured and your local system doesn't need anything beyond docker. You'll need the [Docker extension](https://code.visualstudio.com/docs/containers/overview) to do this. If you don't want to us Docker, you can run your code in a virtual environment instead. 
 
@@ -49,7 +49,7 @@ You can deploy the sample onto Azure or run it locally by following the steps be
 * [Visual Studio  Code extension: Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
 * [Visual Studio  Code extension: Azure Functions](https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-azurefunctions&WT.mc_id=shopathome-github-jopapa)
 * [Visual Studio Code extension: Azure Static Web Apps](https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-azurestaticwebapps)
-* [Azure Cognitive Search](https://learn.microsoft.com/azure/search/search-create-service-portal)
+* [Azure AI Search](https://learn.microsoft.com/azure/search/search-create-service-portal)
 
 [Visual Studio Code extension: Docker](https://code.visualstudio.com/docs/containers/overview) is optional, but necessary if you want to run the Python code in a container.
 
@@ -140,7 +140,11 @@ The following instructions create and configure the Azure Static Web App resourc
 
 1. Select **Save** to save the settings. 
 
-1. Return to Visual Studio Code. 
+1. Return to Visual Studio Code.
+
+1. Select **Terminal** > **New Terminal** to get a command line.
+
+1. Type `git pull origin main` to pull down the changes from your forked main branch.
 
 1. Refresh your Static web app to see the Static web app's application settings. 
 
@@ -160,4 +164,4 @@ The following instructions create and configure the Azure Static Web App resourc
 
 ## Clean up
 
-If you no longer need Azure Cognitive Search or Azure Static Web Apps, remember to delete both resources in your subscription.
+If you no longer need Azure AI Search or Azure Static Web Apps, remember to delete both resources in your subscription.

search-website-functions-v4/bulk-upload/readme.md

@@ -1,4 +1,4 @@
-# Create Cognitive Search Index from CSV file
+# Create Azure AI Search Index from CSV file
 
 1. Change the following values in the `bulk_upload.py` file:
 

search-website-functions-v4/client/src/pages/Home/Home.js

@@ -20,7 +20,7 @@ export default function Home() {
     <main className="main main--home">
       <div className="row home-search">
         <img className="logo" src={logo} alt="Cognitive Search"></img>
-        <p className="poweredby lead">Powered by Azure Cognitive Search</p>
+        <p className="poweredby lead">Powered by Azure AI Search</p>
         <SearchBar postSearchHandler={navigateToSearchPage}></SearchBar>
       </div>
     </main>