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‎README.md

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This example shows how to use Azure OpenAI from Azure SQL database to get the vector embeddings of any choosen text, and then calculate the [cosine similarity](https://learn.microsoft.com/en-us/azure/storage/common/storage-sas-overview) against the Wikipedia articles (for which vector embeddings have been already calculated,) to find the articles that covers topics that are close - or similar - to the provided text.
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For an introduction on text and code embeddings, check out this OpenAI article: [Introducing text and code embeddings](https://openai.com/blog/introducing-text-and-code-embeddings).
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Azure SQL database can be used to significatly speed up vectors operations using column store indexes, so that search can have sub-seconds performances even on large datasets.
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![](_assets/cosine-similarity-search-result.png)

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