reverted unrelated edits

Author: HeidiSteen

articles/search/search-what-is-an-index.md

@@ -186,7 +186,7 @@ You can get hands-on experience creating an index using almost any sample or wal
 But you'll also want to become familiar with methodologies for loading an index with data. Index definition and data import strategies are defined in tandem. The following articles provide more information about creating and loading an index.
 
 + [Create a search index](search-how-to-create-search-index.md)
-+ [Create a vector index](vector-search-how-to-create-index.md)
++ [Create a vector store](vector-search-how-to-create-index.md)
 + [Create an index alias](search-how-to-alias.md)
 + [Data import overview](search-what-is-data-import.md)
 + [Load an index](search-how-to-load-search-index.md)

articles/search/vector-search-how-to-chunk-documents.md

@@ -14,7 +14,7 @@ ms.date: 02/22/2024
 
 # Chunking large documents for vector search solutions in Azure AI Search
 
-Partitioning large documents into smaller chunks can help you stay under the maximum token input limits of embedding models. For example, the maximum length of input text for the [Azure OpenAI](/azure/ai-services/openai/how-to/embeddings) embedding models is 8,191 tokens. Given that each token is around four characters of text for common OpenAI models, this maximum limit is equivalent to around 6,000 words of text. If you're using these models to generate embeddings, it's critical that the input text stays under the limit. Partitioning your content into chunks ensures that your data can be processed by the embedding models used to populate vector indexes and text-to-vector query conversions. 
+Partitioning large documents into smaller chunks can help you stay under the maximum token input limits of embedding models. For example, the maximum length of input text for the [Azure OpenAI](/azure/ai-services/openai/how-to/embeddings) embedding models is 8,191 tokens. Given that each token is around four characters of text for common OpenAI models, this maximum limit is equivalent to around 6,000 words of text. If you're using these models to generate embeddings, it's critical that the input text stays under the limit. Partitioning your content into chunks ensures that your data can be processed by the embedding models used to populate vector stores and text-to-vector query conversions. 
 
 This article describes several approaches for data chunking. Chunking is only required if source documents are too large for the maximum input size imposed by models.