Merge branch 'heidist-docs' of https://github.com/HeidiSteen/azure-docs-pr into heidist-docs

Author: HeidiSteen

articles/search/vector-search-overview.md

@@ -87,7 +87,7 @@ Vector search is a method of information retrieval that aims to overcome the lim
 
 *Embeddings* are a specific type of vector representation created by machine learning models that capture the semantic meaning of text, or representations of other content such as images. Natural language machine learning models are trained on large amounts of data to identify patterns and relationships between words. During training, they learn to represent any input as a vector of real numbers in an intermediary step called the *encoder*. After training is complete, these language models can be modified so the intermediary vector representation becomes the model's output. The resulting embeddings are high-dimensional vectors, where words with similar meanings are closer together in the vector space, as explained in [this Azure OpenAI Service article](/azure/cognitive-services/openai/concepts/understand-embeddings). 
 
-The effectiveness of vector search in retrieving relevant information depends on the effectiveness of the embedding model in distilling the meaning of documents and queries into the resulting vector. The best models are well-trained on the types of data they're representing. You can evaluate existing models such as Azure OpenAI text-embedding-ada-002 or bring your own model that's trained directly on the problem space, or fine-tune a general-purpose mode. Azure Cognitive Search doesn't impose constraints on which model you choose, so pick the best one for your data. 
+The effectiveness of vector search in retrieving relevant information depends on the effectiveness of the embedding model in distilling the meaning of documents and queries into the resulting vector. The best models are well-trained on the types of data they're representing. You can evaluate existing models such as Azure OpenAI text-embedding-ada-002, bring your own model that's trained directly on the problem space, or fine-tune a general-purpose model. Azure Cognitive Search doesn't impose constraints on which model you choose, so pick the best one for your data. 
 
 In order to create effective embeddings for vector search, it's important to take input size limitations into account. Therefore, we recommend following the [guidelines for chunking data](vector-search-how-to-chunk-documents.md) before generating embeddings. This best practice ensures that the embeddings accurately capture the relevant information and enable more efficient vector search.