Address verbatims feedback

Author: HeidiSteen

articles/search/vector-search-overview.md

@@ -9,12 +9,16 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 01/29/2024
+ms.date: 04/09/2024
 ---
 
 # Vectors in Azure AI Search
 
-Vector search is an approach in information retrieval that stores numeric representations of content for search scenarios. Because the content is numeric rather than plain text, the search engine matches on vectors that are the most similar to the query, with no requirement for matching on exact terms.
+Vector search is an approach in information retrieval that supports indexing and query execution over numeric representations of content. Because the content is numeric rather than plain text, matching is based on vectors that are most similar to the query vector, which lets you find matches across:
+
++ semantically similar content ("dog" and "canine", conceptually similar yet linguistically distinct)
++ multilingual content ("dog" in English and "hund" in German)
++ multiple content types ("dog" in plain text and a photograph of a dog in an image file)
 
 This article is a high-level introduction to vectors in Azure AI Search. It also explains integration with other Azure services and covers [terminology and concepts](#vector-search-concepts) related to vector search development.
 
@@ -27,6 +31,22 @@ 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/azure-search-vector-samples). 
 
+## What scenarios can vector search support?
+
+Scenarios for vector search include:
+
++ **Vector database**. Azure AI Search stores the data that you query over. Use it as a [pure vector store](vector-store.md) any time you need long-term memory or a knowledge base, or grounding data for [Retrieval Augmented Generation (RAG) architecture](https://aka.ms/what-is-rag), or any app that uses vectors.
+
++ **Similarity search**. Encode text using embedding models such as OpenAI embeddings or open source models such as SBERT, and retrieve documents with queries that are also encoded as vectors.
+
++ **Search across different content types (multimodal)**. Encode images and text using multimodal embeddings (for example, with [OpenAI CLIP](https://github.com/openai/CLIP) or [GPT-4 Turbo with Vision](/azure/ai-services/openai/whats-new#gpt-4-turbo-with-vision-now-available) in Azure OpenAI) and query an embedding space composed of vectors from both content types.
+
++ [**Hybrid search**](hybrid-search-overview.md). In Azure AI Search, hybrid search refers to vector and keyword query execution from the same request. Vector support is implemented at the field level, with an index containing both vector fields and searchable text fields. The queries execute in parallel and the results are merged into a single response. Optionally, add [semantic ranking](semantic-search-overview.md) for more accuracy with L2 reranking using the same language models that power Bing.
+
++ **Multilingual search**. Providing a search experience in the users own language is possible through embedding models and chat models trained in multiple languages. If you need more control over translation, you can supplement with the [multi-language capabilities](search-language-support.md) that Azure AI Search supports for nonvector content, in hybrid search scenarios.
+
++ **Filtered vector search**. A query request can include a vector query and a [filter expression](search-filters.md). Filters apply to text and numeric fields, and are useful for metadata filters, and including or excluding search results based on filter criteria. Although a vector field isn't filterable itself, you can set up a filterable text or numeric field. The search engine can process the filter before or after the vector query executes.
+
 ## How vector search works in Azure AI Search
 
 Vector support includes indexing, storing, and querying of vector embeddings from a search index.
@@ -59,22 +79,6 @@ Vector search is available in:
 > [!NOTE]
 > Some older search services created before January 1, 2019 are deployed on infrastructure that doesn't support vector workloads. If you try to add a vector field to a schema and get an error, it's a result of outdated services. In this situation, you must create a new search service to try out the vector feature.
 
-## What scenarios can vector search support?
-
-Scenarios for vector search include:
-
-+ **Vector database**. Azure AI Search stores the data that you query over. Use it as a [pure vector store](vector-store.md) any time you need long-term memory or a knowledge base, or grounding data for [Retrieval Augmented Generation (RAG) architecture](https://aka.ms/what-is-rag), or any app that uses vectors.
-
-+ **Similarity search**. Encode text using embedding models such as OpenAI embeddings or open source models such as SBERT, and retrieve documents with queries that are also encoded as vectors.
-
-+ **Search across different content types (multimodal)**. Encode images and text using multimodal embeddings (for example, with [OpenAI CLIP](https://github.com/openai/CLIP) or [GPT-4 Turbo with Vision](/azure/ai-services/openai/whats-new#gpt-4-turbo-with-vision-now-available) in Azure OpenAI) and query an embedding space composed of vectors from both content types.
-
-+ [**Hybrid search**](hybrid-search-overview.md). In Azure AI Search, hybrid search refers to vector and keyword query execution from the same request. Vector support is implemented at the field level, with an index containing both vector fields and searchable text fields. The queries execute in parallel and the results are merged into a single response. Optionally, add [semantic ranking](semantic-search-overview.md) for more accuracy with L2 reranking using the same language models that power Bing.
-
-+ **Multilingual search**. Providing a search experience in the users own language is possible through embedding models and chat models trained in multiple languages. If you need more control over translation, you can supplement with the [multi-language capabilities](search-language-support.md) that Azure AI Search supports for nonvector content, in hybrid search scenarios.
-
-+ **Filtered vector search**. A query request can include a vector query and a [filter expression](search-filters.md). Filters apply to text and numeric fields, and are useful for metadata filters, and including or excluding search results based on filter criteria. Although a vector field isn't filterable itself, you can set up a filterable text or numeric field. The search engine can process the filter before or after the vector query executes.
-
 ## Azure integration and related services
 
 Azure AI Search is deeply integrated across the Azure AI platform. The following table lists several that are useful in vector workloads.