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Update README: Add note on vector storage with Dapper

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The README.md file has been updated to include a new note about how vectors are saved and retrieved using direct SQL queries with Dapper. Additionally, it provides a link to the master branch for those who prefer to use Entity Framework Core instead. This addition helps clarify the technologies used and offers options for different preferences.
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Marco Minerva
2024-10-31 15:25:06 +01:00
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@@ -6,6 +6,9 @@ A repository that showcases the native VECTOR type in Azure SQL Database to perf
The application is a Minimal API that exposes endpoints to load documents, generate embeddings and save them into the database as Vectors, and perform searches using Vector Search and RAG. Currently, only PDF files are supported. Embedding and Chat Completion are integrated with [Semantic Kernel](https://github.com/microsoft/semantic-kernel). The application is a Minimal API that exposes endpoints to load documents, generate embeddings and save them into the database as Vectors, and perform searches using Vector Search and RAG. Currently, only PDF files are supported. Embedding and Chat Completion are integrated with [Semantic Kernel](https://github.com/microsoft/semantic-kernel).
> [!NOTE]
> Vectors are saved and retrieved using direct SQL queries using [Dapper](https://github.com/DapperLib/Dapper). If you prefer to use Entity Framework Core, check out the [master branch](https://github.com/marcominerva/SqlDatabaseVectorSearch/tree/sql).
![SQL Database Vector Search](https://github.com/marcominerva/SqlDatabaseVectorSearch/blob/master/SqlDatabaseVectorSearch.png) ![SQL Database Vector Search](https://github.com/marcominerva/SqlDatabaseVectorSearch/blob/master/SqlDatabaseVectorSearch.png)
### Setup ### Setup