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Fix typo.

Author: AnnaMHuff

articles/search/vector-store.md

@@ -31,8 +31,7 @@ In Azure AI Search, there are two patterns for working with search results.
 
 + Generative search. Language models formulate a response to the user's query using data from Azure AI Search. This pattern includes an orchestration layer to coordinate prompts and maintain context. In this pattern, search results are fed into prompt flows, received by chat models like GPT and Text-Davinci. This approach is based on [**Retrieval augmented generation (RAG)**](retrieval-augmented-generation-overview.md) architecture, where the search index provides the grounding data.
 
-+ Classic search using a search bar, query input string, and rendered results. The search engine accepts and executes the vector query, formulates a response, and you render those results in a client app. In Azure AI Search, results are returned in a flattened row set, and you can choose which fields to include search results. Since there
-s no chat model, it's expected that you would populate the vector store (search index) with nonvector content that's human readable in your response. Although the search engine matches on vectors, you should use nonvector values to populate the search results. [**Vector queries**](vector-search-how-to-query.md) and [**hybrid queries**](hybrid-search-how-to-query.md) cover the types of query requests you can formulate for classic search scenarios.
++ Classic search using a search bar, query input string, and rendered results. The search engine accepts and executes the vector query, formulates a response, and you render those results in a client app. In Azure AI Search, results are returned in a flattened row set, and you can choose which fields to include search results. Since there's no chat model, it's expected that you would populate the vector store (search index) with nonvector content that's human readable in your response. Although the search engine matches on vectors, you should use nonvector values to populate the search results. [**Vector queries**](vector-search-how-to-query.md) and [**hybrid queries**](hybrid-search-how-to-query.md) cover the types of query requests you can formulate for classic search scenarios.
 
 Your index schema should reflect your primary use case. The following section highlights the differences in field composition for solutions built for generative AI or classic search.