Merge pull request #248012 from HeidiSteen/heidist-vectors

minor vector edits

Author: prmerger-automator[bot]

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

@@ -15,13 +15,17 @@ 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.
-> + Add one or more vector configurations to the index schema. 
+> + Add one or more vector configurations. 
 > + Load the index with vector data [as a separate step](#load-vector-data-for-indexing), after the index schema is defined.
 
+Code samples in the [cognitive-search-vector-pr](https://github.com/Azure/cognitive-search-vector-pr) repository demonstrate end-to-end workflows that include schema definition, vectorization, indexing, and queries.
+
 ## Prerequisites
 
 + Azure Cognitive Search, in any region and on any tier. Most existing services support vector search. For a small subset of services created prior to January 2019, an index containing vector fields fails on creation. In this situation, a new service must be created.
@@ -36,7 +40,7 @@ Prior to indexing, assemble a document payload that includes fields of vector an
 
 Make sure your documents:
 
-1. Provide a field or a metadata property that uniquely identifies each document. All search indexes require a document key. To satisfy document key requirements, your documents must have one field or property that is unique in the index. This field must be mapped to type `Edm.String` and `key=true` in the search index. 
+1. Provide a field or a metadata property that uniquely identifies each document. All search indexes require a document key. To satisfy document key requirements, a source document must have one field or property that can uniquely identify it in the index. This source field must be mapped to an index field of type `Edm.String` and `key=true` in the search index. 
 
 1. Provide vector data (an array of single-precision floating point numbers) in source fields.
 
@@ -103,7 +107,7 @@ Although you can add a field to an index, there's no portal (Import data wizard)
 
 ### [**REST API**](#tab/rest-add-field)
 
-Use the **2023-07-01-Prevew** REST API for vector scenarios. If you're updating an existing index to include vector fields, make sure the `allowIndexDowntime` query parameter is set to `true`.
+Use the **2023-07-01-Preview** REST API for vector scenarios. If you're updating an existing index to include vector fields, make sure the `allowIndexDowntime` query parameter is set to `true`.
 
 In the following REST API example, "title" and "content" contain textual content used in full text search and semantic search, while "titleVector" and "contentVector" contain vector data.
 

articles/search/vector-search-how-to-query.md

@@ -15,22 +15,32 @@ 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:
 
-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.
+> [!div class="checklist"]
+> + [Query vector fields](#query-syntax-for-vector-search).
+> + [Combine vector, full text search, and semantic search in a hybrid query](#query-syntax-for-hybrid-search).
+> + [Query multiple vector fields at once](#query-syntax-for-vector-query-over-multiple-fields).
+> + [Run multiple vector queries in parallel](#query-syntax-for-multiple-vector-queries).
 
-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.
+Code samples in the [cognitive-search-vector-pr](https://github.com/Azure/cognitive-search-vector-pr) repository demonstrate end-to-end workflows that include schema definition, vectorization, indexing, and queries.
 
 ## Prerequisites
 
 + Azure Cognitive Search, in any region and on any tier. Most existing services support vector search. For a small subset of services created prior to January 2019, an index containing vector fields will fail on creation. In this situation, a new service must be created.
 
 + A search index containing vector fields. See [Add vector fields to a search index](vector-search-how-to-query.md).
 
-+ Use REST API version 2023-07-01-preview, the [beta client libraries](https://github.com/Azure/cognitive-search-vector-pr/tree/main), or Search Explorer in the Azure portal.
++ Use REST API version **2023-07-01-Preview**, the [beta client libraries](https://github.com/Azure/cognitive-search-vector-pr/tree/main), or Search Explorer in the Azure portal.
 
 + (Optional) If you want to also use [semantic search (preview)](semantic-search-overview.md) and vector search together, your search service must be Basic tier or higher, with [semantic search enabled](semantic-search-overview.md#enable-semantic-search).
 
+## Limitations
+
+Cognitive Search doesn't provide built-in vectorization of the query 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.
+
 ## Check your index for vector fields
 
 If you aren't sure whether your search index already has vector fields, look for:
@@ -114,7 +124,7 @@ 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 +148,7 @@ 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)
 
@@ -198,7 +208,7 @@ api-key: {{admin-api-key}}
 
 ## Query syntax for vector query over multiple fields
 
-You can set the "vectors.fields" property to multiple vector fields. For example, the Postman collection has vector fields named "titleVector" and "contentVector". Your vector query executes over both the "titleVector" and "contentVector" fields, which must have the same embedding space since they share the same query vector.
+You can set the "vectors.fields" property to multiple vector fields. For example, the Postman collection has vector fields named "titleVector" and "contentVector". A single vector query executes over both the "titleVector" and "contentVector" fields, which must have the same embedding space since they share the same query vector.
 
 ```http
 POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version={{api-version}}
@@ -296,4 +306,4 @@ Multiple sets are created if the query targets multiple vector fields, or if the
 
 ## Next steps
 
-As a next step, we recommend reviewing the demo code for [Python](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-python), or [C#](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-dotnet).
+As a next step, we recommend reviewing the demo code for [Python](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-python), [C#](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-dotnet) or [JavaScript](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-javascript).

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.