File cleanup for samples browser

Author: HeidiSteen

Quickstart/README.md

@@ -8,7 +8,7 @@ products:
   - azure-search
 ---
 
-# Python quickstart for Azure Search
+# Python sample for an Azure Search quickstart
 
 This sample is a Jupyter Python3 .ipynb file used in [Quickstart: Create and query an Azure Search index using a Jupyter Python notebook](https://docs.microsoft.com/azure/search/search-get-started-python). It demonstrates a connection to Azure Search, creating and loading an index, and query execution.
 
@@ -24,7 +24,7 @@ This sample is a Jupyter Python3 .ipynb file used in [Quickstart: Create and que
 
 ## Prerequisites
 
-- [Anaconda 3.x](https://www.anaconda.com/distribution/#download-section), providing Python 3.x and Jupyter Notebooks
+- [Anaconda 3.x](https://www.anaconda.com/distribution/#download-section) providing Python 3.x and Jupyter Notebooks
 - [Azure Search service](https://docs.microsoft.com/en-us/azure/search/search-create-service-portal)
 
 ## Setup
@@ -33,7 +33,7 @@ This sample is a Jupyter Python3 .ipynb file used in [Quickstart: Create and que
 2. Extract contents if the download is a zip file. Make sure the files are read-write.
 
 ## Running the sample
-1. Start Jupyter Notebook. On Windows with Anaconda3, you can use Anaconda Navigator to launch a notebook from the Start menu.
+1. On the Windows Start menu, select Anaconda3, and then select Jupyter Notebook.
 1. Open the azure-search-quickstart.ipynb file in Jupyter Notebook
 1. Replace <YOUR-SERVICE-NAME> and <YOUR-ADMIN-API-KEY> with the service and api-key details of your Azure Search service
 1. Run each step individually

README.md

@@ -2,10 +2,10 @@
 
 This repository contains Python sample code used in Azure Search quickstarts, tutorials, and examples. You can use the shared (free) Azure Search service to run any sample in this repository. Samples are in individual folders in this repository.
 
-## Python Quickstart - Azure Search
+## Quickstart - Azure Search
 
 This sample is a Jupyter Python3 .ipynb file used in [Quickstart: Create and query an Azure Search index using a Jupyter Python notebook](https://docs.microsoft.com/azure/search/search-get-started-python). Use this sample to learn service connections, index creation, data ingestion, and basic queries.
 
-## Tutorial - AI-enrichments in an Azure Search indexing pipeline
+## Tutorial - Add AI enrichments to an indexing pipeline
 
 This sample is also a Jupyter Python3 .ipynb file. It's used in the [Python Tutorial: Call Cognitive Services APIs in an Azure 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-Jupyter-Notebook/CONTRIBUTING.md renamed to Tutorial-AI-Enrichment/CONTRIBUTING.md

@@ -26,7 +26,7 @@ This sample demonstrates AI enrichment by building a cognitive search indexing p
 
 ## Prerequisites
 
-- [Anaconda 3.x](https://www.anaconda.com/distribution/#download-section), providing Python 3.x and Jupyter Notebooks
+- [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 Search service](https://docs.microsoft.com/en-us/azure/search/search-create-service-portal)
@@ -37,10 +37,10 @@ This sample demonstrates AI enrichment by building a cognitive search indexing p
 2. Extract contents if the download is a zip file. Make sure the files are read-write.
 
 ## Running the sample
-1. Start Jupyter Notebook. On Windows with Anaconda3, you can use Anaconda Navigator to launch a notebook from the Start menu.
+1. On the Windows Start menu, select Anaconda3, and then select Jupyter Notebook.
 2. Open the PythonTutorial-AzureSearch-AIEnrichment.ipynb file in Jupyter Notebook.
 3. Replace <YOUR-SERVICE-NAME> and <YOUR-ADMIN-API-KEY> with the service and api-key details of your Azure Search service.
 4. Replace <YOUR-BLOB-RESOURCE-CONNECTION-STRING> with a connection string to an Azure Blob storage resource that you created, and to which you uploaded [content files](https://github.com/Azure-Samples/azure-search-sample-data/tree/master/mixedContent) of various file types.
-5. Run each step individually
+5. Run each step individually.
 
 By sequentially executing each step, you can verify the printed response status or response output appears before continuing to the next step. The step that creates the indexer, in particular, may take a few minutes to complete. See the tutorial for more details.