Addressed blocking issues

HeidiSteen · HeidiSteen · commit 4183a43d08c5 · 2023-08-10T20:31:51.000-07:00
diff --git a/articles/search/vector-search-how-to-create-index.md b/articles/search/vector-search-how-to-create-index.md
@@ -206,27 +206,21 @@ In the following REST API example, "title" and "content" contain textual content
 
 ### [**.NET**](#tab/dotnet-add-field)
 
-Under construction.
++ Use the [**Azure.Search.Documents 11.5.0-beta.4**](https://www.nuget.org/packages/Azure.Search.Documents/11.5.0-beta.4) package for vector scenarios. 
 
-Use the [**Azure.Search.Documents 11.5.0-beta.4**](https://www.nuget.org/packages/Azure.Search.Documents/11.5.0-beta.4) package for vector scenarios. 
-
-See the [cognitive-search-vector-pr](https://github.com/Azure/cognitive-search-vector-pr) GitHub repository for .NET code samples.
++ See the [cognitive-search-vector-pr](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-dotnet) GitHub repository for .NET code samples.
 
 ### [**Python**](#tab/python-add-field)
 
-Under construction.
-
-Use the [**Azure.Search.Documents 11.4.0b8**](https://pypi.org/project/azure-search-documents/11.4.0b8/) package for vector scenarios. 
++ Use the [**Azure.Search.Documents 11.4.0b8**](https://pypi.org/project/azure-search-documents/11.4.0b8/) package for vector scenarios. 
 
-See the [cognitive-search-vector-pr](https://github.com/Azure/cognitive-search-vector-pr) GitHub repository for Python code samples.
++ See the [cognitive-search-vector-pr](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-python) GitHub repository for Python code samples.
 
 ### [**JavaScript**](#tab/js-add-field)
 
-Under construction.
-
-Use the [**@azure/search-documents 12.0.0-beta.2**](https://www.npmjs.com/package/@azure/search-documents/v/12.0.0-beta.2) package for vector scenarios.
++ Use the [**@azure/search-documents 12.0.0-beta.2**](https://www.npmjs.com/package/@azure/search-documents/v/12.0.0-beta.2) package for vector scenarios.  
 
-See the [cognitive-search-vector-pr](https://github.com/Azure/cognitive-search-vector-pr) GitHub repository for JavaScript code samples.
++ See the [cognitive-search-vector-pr](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-javascript) GitHub repository for JavaScript code samples.
 
 ---
 
diff --git a/articles/search/vector-search-how-to-query.md b/articles/search/vector-search-how-to-query.md
@@ -15,9 +15,9 @@ ms.date: 08/10/2023
 > [!IMPORTANT]
 > Vector search is in public preview under [supplemental terms of use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). It's available through the Azure portal, preview REST API, and [beta client libraries](https://github.com/Azure/cognitive-search-vector-pr#readme).
 
-In Azure Cognitive Search, if you added vector fields to a search index, this article explains how to query those fields. It also explains how to combine vector queries with full text search and semantic search for hybrid query combination scenarios.
+In Azure Cognitive Search, if you added vector fields to a search index, this article explains how to query those fields. It also explains how to combine vector queries with full text search and semantic search for *hybrid query* combination scenarios.
 
-Query execution in Cognitive Search doesn't include vector conversion of the input string. Encoding (text-to-vector) of the query string requires that you pass the text to an embedding model for vectorization. You would then pass the output of the call to the embedding model to the search engine for similarity search over vector fields.
+Cognitive Search doesn't provide built-in vectorization of the input string. Encoding (text-to-vector) of the query string requires that you pass the string to an embedding model for vectorization. You would then pass the output of the call to the embedding model to the search engine for similarity search over vector fields.
 
 All results are returned in plain text, including vectors. If you use Search Explorer in the Azure portal to query an index that contains vectors, the numeric vectors are returned in plain text. Because numeric vectors aren't useful in search results, choose other fields in the index as a proxy for the vector match. For example, if an index has "descriptionVector" and "descriptionText" fields, the query can match on "descriptionVector" but the search result shows "descriptionText". Use the `select` parameter to specify only human-readable fields in the results.
 
@@ -45,7 +45,9 @@ You can also send an empty query (`search=*`) against the index. If the vector f
 
 To query a vector field, the query itself must be a vector. To convert a text query string provided by a user into a vector representation, your application must call an embedding library that provides this capability. Use the same embedding library that you used to generate embeddings in the source documents.
 
-Here's an example of a query string submitted to a deployment of an Azure OpenAI model:
+You can find multiple instances of query string conversion in the [cognitive-search-vector-pr](https://github.com/Azure/cognitive-search-vector-pr/) repository for each of the Azure SDKs.
+
+Here's a REST API example of a query string submitted to a deployment of an Azure OpenAI model:
 
 ```http
 POST https://{{openai-service-name}}.openai.azure.com/openai/deployments/{{openai-deployment-name}}/embeddings?api-version={{openai-api-version}}
@@ -102,7 +104,7 @@ Be sure to the **JSON view** and formulate the query in JSON. The search bar in
 
    :::image type="content" source="media/vector-search-how-to-query/select-json-view.png" alt-text="Screenshot of the index list." border="true":::
 
-1. By default, the search API is 2023-07-01-Preview. This is the correct API version for vector search.
+1. By default, the search API is **2023-07-01-Preview**. This is the correct API version for vector search.
 
 1. Paste in a JSON vector query, and then select **Search**. You can use the REST example as a template for your JSON query.
 
@@ -140,21 +142,21 @@ Notice that "select" returns textual fields from the index. Although the vector
 
 ### [**.NET**](#tab/dotnet-vector-query)
 
-Under construction.
++ Use the [**Azure.Search.Documents 11.5.0-beta.4**](https://www.nuget.org/packages/Azure.Search.Documents/11.5.0-beta.4) package for vector scenarios. 
 
-Use the [Azure.Search.Documents 11.5.0-beta.4](https://www.nuget.org/packages/Azure.Search.Documents/11.5.0-beta.4) package. For code samples, see the [cognitive-search-vector-pr] GitHub repository.
++ See the [cognitive-search-vector-pr](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-dotnet) GitHub repository for .NET code samples.
 
 ### [**Python**](#tab/python-vector-query)
 
-Under construction.
++ Use the [**Azure.Search.Documents 11.4.0b8**](https://pypi.org/project/azure-search-documents/11.4.0b8/) package for vector scenarios. 
 
-Use the [Azure.Search.Documents 11.4.0b8](https://pypi.org/project/azure-search-documents/11.4.0b8/) package. For code samples, see the [cognitive-search-vector-pr] GitHub repository.
++ See the [cognitive-search-vector-pr](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-python) GitHub repository for Python code samples.
 
 ### [**JavaScript**](#tab/js-vector-query)
 
-Under construction.
++ Use the [**@azure/search-documents 12.0.0-beta.2**](https://www.npmjs.com/package/@azure/search-documents/v/12.0.0-beta.2) package for vector scenarios.  
 
-Use the [@azure/search-documents 12.0.0-beta.2](https://www.npmjs.com/package/@azure/search-documents/v/12.0.0-beta.2) package. For code samples, see the [cognitive-search-vector-pr] GitHub repository.
++ See the [cognitive-search-vector-pr](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-javascript) GitHub repository for JavaScript code samples.
 
 ---
 
diff --git a/articles/search/vector-search-overview.md b/articles/search/vector-search-overview.md
@@ -27,17 +27,17 @@ We recommend this article for background, but if you'd rather get started, follo
 
 You could also begin with the [vector quickstart](search-get-started-vector.md) or the [code samples on GitHub](https://github.com/Azure/cognitive-search-vector-pr).
 
+Support for vector search is in public preview and available through the [**2023-07-01-Preview REST APIs**](/rest/api/searchservice/index-preview), Azure portal, and the more recent beta packages of the Azure SDKs for [.NET](https://www.nuget.org/packages/Azure.Search.Documents/11.5.0-beta.4), [Python](https://pypi.org/project/azure-search-documents/11.4.0b8/), and [JavaScript](https://www.npmjs.com/package/@azure/search-documents/v/12.0.0-beta.2).
+
 ## What's vector search in Cognitive Search?
 
 Vector search is a new capability for indexing, storing, and retrieving vector embeddings from a search index. You can use it to power similarity search, multi-modal search, recommendations engines, or applications implementing the [Retrieval Augmented Generation (RAG) architecture](https://arxiv.org/abs/2005.11401).
 
-Support for vector search is in public preview and available through the [**2023-07-01-Preview REST APIs**](/rest/api/searchservice/index-preview), Azure portal, and the more recent beta packages of the Azure SDKs for [.NET](https://www.nuget.org/packages/Azure.Search.Documents/11.5.0-beta.4), [Python](https://pypi.org/project/azure-search-documents/11.4.0b8/), and [JavaScript](https://www.npmjs.com/package/@azure/search-documents/v/12.0.0-beta.2).
-
 The following diagram shows the indexing and query workflows for vector search.
 
 :::image type="content" source="media/vector-search-overview/vector-search-architecture-diagram-2.png" alt-text="Architecture of vector search workflow." border="true" lightbox="media/vector-search-overview/vector-search-architecture-diagram-2.png":::
 
-On the indexing side, prepare and load source documents that contain embeddings. Cognitive Search doesn't generate embeddings, so your solution should include calls to Azure OpenAI or other models that can create a vector representation of your image, audio, text, and other content. Add a *vector field* to your index definition on Cognitive Search. Load the index with a documents payload that includes the embeddings. Your index is now ready to query.
+On the indexing side, prepare source documents that contain embeddings. Cognitive Search doesn't generate embeddings, so your solution should include calls to Azure OpenAI or other models that can create a vector representation of your image, audio, text, and other content. Add a *vector field* to your index definition on Cognitive Search. Load the index with a documents payload that includes the embeddings. Your index is now ready to query.
 
 On the query side, in your client application, collect the query input. Add a step that converts the input into a vector, and then send the vector query to your index on Cognitive Search for a similarity search. Cognitive Search returns documents with the requested `k` nearest neighbors (kNN) in the results.
 
