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Enhance application structure and functionality

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- Updated README.md for clarity on application features.
- Added using directives and improved service configuration in Program.cs.
- Enhanced error handling and status code management in Program.cs.
- Changed application URL port in launchSettings.json.
- Added package references for Blazor Bootstrap and other libraries.
- Created new HTML structure in App.razor and implemented routing in Routes.razor.
- Updated MainLayout.razor for Blazor Bootstrap layout and sidebar navigation.
- Developed new components: Counter.razor, Documents.razor, Error.razor, Home.razor, and Weather.razor.
- Added utility classes: RequestExtensions.cs and StreamExtensions.cs.
- Updated app.css for custom styles and added favicon.png.
- Created functions.js for local time conversion utility.
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Marco Minerva
2025-02-14 16:58:51 +01:00
parent 5a507e972c
commit 09f15a9cb7
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README.md
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# SQL Database Vector Search Sample
A repository that showcases the native VECTOR type in Azure SQL Database to perform embeddings and RAG with Azure OpenAI.
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, PDF, DOCX, TXT and MD files are supported. Vectors are saved and retrieved with Entity Framework Core using the [EFCore.SqlServer.VectorSearch](https://github.com/efcore/EfCore.SqlServer.VectorSearch) library. 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, PDF, DOCX, TXT and MD files are supported. Vectors are saved and retrieved with Entity Framework Core using the [EFCore.SqlServer.VectorSearch](https://github.com/efcore/EfCore.SqlServer.VectorSearch) library. Embedding and Chat Completion are integrated with [Semantic Kernel](https://github.com/microsoft/semantic-kernel).
> [!NOTE]
> If you prefer to use straight SQL, check out the [sql branch](https://github.com/marcominerva/SqlDatabaseVectorSearch/tree/sql).