Merge pull request #247191 from HeidiSteen/heidist-js

prmerger-automator[bot] · web-flow · commit 9676a8371767 · 2023-08-08T16:17:04.000Z
[azure search] Cross link to vector code samples
diff --git a/articles/search/samples-python.md b/articles/search/samples-python.md
@@ -9,7 +9,7 @@ ms.author: heidist
 ms.service: cognitive-search
 ms.custom: devx-track-dotnet, devx-track-python
 ms.topic: conceptual
-ms.date: 07/27/2023
+ms.date: 08/02/2023
 ---
 
 # Python samples for Azure Cognitive Search
@@ -56,6 +56,7 @@ A demo repo provides proof-of-concept source code for examples or scenarios show
 
 | Repository | Description |
 |------------|-------------|
+| [**azure-search-vector-python-sample.ipynb**](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-python/code/azure-search-vector-image-python-sample.ipynb) | Uses the latest beta release of the **azure.search.documents** library in the Azure SDK for Python to generate embeddings, create and load an index, and run several vector queries. For more vector search Python demos, see [cognitive-search-vector-pr/demo-python](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-python). |
 | [**ChatGPT + Enterprise data with Azure OpenAI and Cognitive Search**](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/README.md) | Python code showing how to use Cognitive Search with the large language models in Azure OpenAI. For background, see this Tech Community blog post: [Revolutionize your Enterprise Data with ChatGPT](https://techcommunity.microsoft.com/t5/ai-applied-ai-blog/revolutionize-your-enterprise-data-with-chatgpt-next-gen-apps-w/ba-p/3762087). |
 
 
diff --git a/articles/search/search-get-started-vector.md b/articles/search/search-get-started-vector.md
@@ -13,7 +13,7 @@ ms.date: 07/07/2023
 # Quickstart: Use preview REST APIs for vector search queries
 
 > [!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 [alpha SDKs](https://github.com/Azure/cognitive-search-vector-pr#readme).
+> 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).
 
 Get started with vector search in Azure Cognitive Search using the **2023-07-01-Preview** REST APIs that create, load, and query a search index. Search indexes now support vector fields in the fields collection. When querying the search index, you can build vector-only queries, or create hybrid queries that target vector fields *and* textual fields configured for filters, sorts, facets, and semantic ranking.
 
@@ -39,7 +39,7 @@ Sample data consists of text and vector descriptions of 108 Azure services, gene
 
 + Vector data (text embeddings) is used for vector search. Currently, Cognitive Search doesn't generate vectors for you. For this quickstart, vector data was generated previously and copied into the "Upload Documents" request and into the query requests.
 
-  For documents, we generated vector data using demo code that calls Azure OpenAI for the embeddings. Demo code is currently using alpha builds of the Azure SDKs and is available in [Python](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-python) and [C#](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-dotnet).
+  For documents, we generated vector data using demo code that calls Azure OpenAI for the embeddings. Samples are currently using beta versions of the Azure SDKs and are available in [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), and [JavaScript](https://github.com/Azure/cognitive-search-vector-pr/tree/main/demo-javascript).
 
   For queries, we used the "Create Query Embeddings" request that calls Azure OpenAI and outputs embeddings for a search string. If you want to formulate your own vector queries against the sample data of 108 Azure services, provide your Azure OpenAI connection information in the Postman collection variables. Your Azure OpenAI service must have a deployment of an embedding model that's identical to the one used to generate embeddings in your search corpus. For this quickstart, the following parameters were used: 
   
diff --git a/articles/search/vector-search-how-to-chunk-documents.md b/articles/search/vector-search-how-to-chunk-documents.md
@@ -13,7 +13,7 @@ ms.date: 06/29/2023
 # Chunking large documents for vector search solutions in Cognitive Search
 
 > [!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 [alpha SDKs](https://github.com/Azure/cognitive-search-vector-pr#readme).
+> 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).
 
 This article describes several approaches for chunking large documents so that you can generate embeddings for vector search. Chunking is only required if source documents are too large for the maximum input size imposed by models. 
 
diff --git a/articles/search/vector-search-how-to-create-index.md b/articles/search/vector-search-how-to-create-index.md
@@ -13,7 +13,7 @@ ms.date: 07/31/2023
 # Add vector fields to a search index
 
 > [!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 [alpha SDKs](https://github.com/Azure/cognitive-search-vector-pr#readme).
+> 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* within a [search index](search-what-is-an-index.md), using a *vector configuration* to create the embedding space. 
 
diff --git a/articles/search/vector-search-how-to-generate-embeddings.md b/articles/search/vector-search-how-to-generate-embeddings.md
@@ -13,7 +13,7 @@ ms.date: 07/10/2023
 # Create and use embeddings for search queries and documents
 
 > [!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 [alpha SDKs](https://github.com/Azure/cognitive-search-vector-pr#readme).
+> 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).
 
 Cognitive Search doesn't host vectorization models, so one of your challenges is creating embeddings for query inputs and outputs. You can use any embedding model, but this article assumes Azure OpenAI embeddings models. Demos in the [sample repository](https://github.com/Azure/cognitive-search-vector-pr/tree/main) tap the [similarity embedding models](/azure/ai-services/openai/concepts/models#embeddings-models) of Azure OpenAI.
 
diff --git a/articles/search/vector-search-how-to-query.md b/articles/search/vector-search-how-to-query.md
@@ -13,7 +13,7 @@ ms.date: 07/31/2023
 # Query vector data in a search index
 
 > [!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 [alpha SDKs](https://github.com/Azure/cognitive-search-vector-pr#readme).
+> 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.
 
@@ -27,7 +27,7 @@ All results are returned in plain text, including vectors. If you use Search Exp
 
 + 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 or Azure portal to query vector fields. You can also use alpha versions of the Azure SDKs. For more information, see [this readme](https://github.com/Azure/cognitive-search-vector-pr/blob/main/README.md).
++ Use REST API version 2023-07-01-preview or Azure portal to query vector fields. You can also use [beta client libraries](https://github.com/Azure/cognitive-search-vector-pr/tree/main).
 
 + (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).
 
@@ -176,8 +176,6 @@ api-key: {{admin-api-key}}
 
 You can issue a search request containing multiple query vectors using the "vectors" query parameter. The queries execute concurrently in the search index, each one looking for similarities in the target vector fields. The result set is a union of the documents that matched both vector queries. A common example of this query request is when using models such as [CLIP](https://openai.com/research/clip) for a multi-modal vector search where the same model can vectorize image and non-image content.
 
-You must use REST for this scenario. Currently, there isn't support for multiple vector queries in the alpha SDKs.
-
 + `vectors.value` property contains the vector query generated from the embedding model used to create image and text vectors in the search index. 
 + `vectors.fields` contains the image vectors and text vectors in the search index. This is the searchable data.
 + `vectors.k` is the number of nearest neighbor matches to include in results.
diff --git a/articles/search/vector-search-index-size.md b/articles/search/vector-search-index-size.md
@@ -13,7 +13,7 @@ ms.date: 07/07/2023
 # Vector index size limit
 
 > [!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 [alpha SDKs](https://github.com/Azure/cognitive-search-vector-pr#readme).
+> 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).
 
 When you index documents with vector fields, Azure Cognitive Search constructs internal vector indexes using the algorithm parameters that you specified for the field. Because Cognitive Search imposes limits on vector index size, it's important that you know how to retrieve metrics about the vector index size, and how to estimate the vector index size requirements for your use case.
 
diff --git a/articles/search/vector-search-overview.md b/articles/search/vector-search-overview.md
@@ -13,7 +13,7 @@ ms.date: 07/28/2023
 # Vector search within Azure Cognitive Search
 
 > [!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 [alpha SDKs](https://github.com/Azure/cognitive-search-vector-pr#readme).
+> 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).
 
 This article is a high-level introduction to vector search in Azure Cognitive Search. It also explains integration with other Azure services and covers [terms and concepts](#vector-search-concepts) related to vector search development.
 
@@ -37,7 +37,7 @@ You can index vector data as fields in documents alongside textual and other typ
 
 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.
 
-Vector search does not support customer-managed keys (CMK) at this time. This means you will not be able to add vector fields to a index with CMK enabled.
+Vector search does not support customer-managed keys (CMK) at this time. This means you will not be able to add vector fields to an index with CMK enabled.
 
 ## Availability and pricing
 
diff --git a/articles/search/vector-search-ranking.md b/articles/search/vector-search-ranking.md
@@ -13,7 +13,7 @@ ms.date: 07/14/2023
 # Vector query execution and scoring in Azure Cognitive Search
 
 > [!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 [alpha SDKs](https://github.com/Azure/cognitive-search-vector-pr#readme).
+> 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).
 
 This article is for developers who need a deeper understanding of vector query execution and ranking in Azure Cognitive Search.
 
diff --git a/articles/search/whats-new.md b/articles/search/whats-new.md
@@ -7,7 +7,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: overview
-ms.date: 08/01/2023
+ms.date: 08/02/2023
 ms.custom: references_regions
 ---
 
@@ -23,6 +23,8 @@ Learn about the latest updates to Azure Cognitive Search functionality, docs, an
 | Item&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | Type |  Description |
 |-----------------------------|------|--------------|
 | [**azure-search-vector-sample.js**](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-javascript/code/azure-search-vector-sample.js) | Sample | Uses Node.js and the **@azure/search-documents 12.0.0-beta.2** library in the Azure SDK for JavaScript to generate embeddings, create and load an index, and run several vector queries. |
+| [**DotNetVectorDemo**](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-dotnet/readme.md) | Sample | Uses the **Azure.Search.Documents 11.5.0-beta.3** library to generate embeddings, create and load an index, and run several vector queries. |
+| [**azure-search-vector-python-sample.ipynb**](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-python/code/azure-search-vector-image-python-sample.ipynb) | Sample | Uses the latest beta release of the **azure.search.documents** library in the Azure SDK for Python to generate embeddings, create and load an index, and run several vector queries. For more vector search Python demos, see [cognitive-search-vector-pr/demo-python](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-python). |
 
 ## June 2023
 
