edits

Author: HeidiSteen

articles/search/vector-search-how-to-create-index.md

@@ -15,7 +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, vector data is indexed as *vector fields* in a [search index](search-what-is-an-index.md), using a *vector configuration* to specify the embedding space. Follow these steps to index vector data:
+In Azure Cognitive Search, vector data is indexed as *vector fields* in a [search index](search-what-is-an-index.md), using a *vector configuration* to specify the embedding space. 
+
+Follow these steps to index vector data:
 
 > [!div class="checklist"]
 > + Add one or more vector fields to the index schema.

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 search* scenarios.
 
-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.
+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 query string to an embedding model for vectorization. You would then pass the response 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.
 
@@ -114,7 +114,7 @@ Be sure to the **JSON view** and formulate the query in JSON. The search bar in
 
 In this vector query, which is shortened for brevity, the "value" contains the vectorized text of the query input. The "fields" property specifies which vector fields are searched. The "k" property specifies the number of nearest neighbors to return as top hits.
 
-The sample vector query for this article is: `"what Azure services support full text search"`. The query targets the "contentVector" field.
+In the following example, the vector is a representation of this query string: `"what Azure services support full text search"`. The query request targets the "contentVector" field. The actual vector has 1536 embeddings. It's trimmed in this example for readability.
 
 ```http
 POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version={{api-version}}
@@ -138,7 +138,7 @@ api-key: {{admin-api-key}}
 
 The response includes 5 matches, and each result provides a search score, title, content, and category. In a similarity search, the response always includes "k" matches, even if the similarity is weak. For indexes that have fewer than "k" documents, only those number of documents will be returned.
 
-Notice that "select" returns textual fields from the index. Although the vector field is "retrievable" in this example, its content isn't usable as a search result.
+Notice that "select" returns textual fields from the index. Although the vector field is "retrievable" in this example, its content isn't usable as a search result, so it's not included in the results.
 
 ### [**.NET**](#tab/dotnet-vector-query)
 

articles/search/vector-search-overview.md

@@ -37,15 +37,15 @@ 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 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 transform image, audio, text, and other content into vector representations. Add a *vector field* to your index definition on Cognitive Search. Load the index with a documents payload that includes the vectors. 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.
 
-You can index vector data as fields in documents alongside textual and other types of content. Vector queries can be issued independently or in combination with other query types, including term queries (hybrid search) and filters in the same search request.
+You can index vector data as fields in documents alongside alphanumeric content. Vector queries can be issued singly or in combination with other query types, including term queries (hybrid search) and filters and semantic re-ranking in the same search request.
 
 ## Limitations
 
-Azure Cognitive Search doesn't generate vector embeddings for your content. You need to provide the embeddings yourself by using a service such as Azure OpenAI. See [How to generate embeddings](./vector-search-how-to-generate-embeddings.md) to learn more.
+Azure Cognitive Search doesn't generate vector embeddings for your content. You need to provide the embeddings yourself by using a solution like Azure OpenAI. See [How to generate embeddings](vector-search-how-to-generate-embeddings.md) to learn more.
 
 Vector search doesn't support customer-managed keys (CMK) at this time. This means you won't be able to add vector fields to an index with CMK enabled.