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Enhance documentation and refactor request handling

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Updated README.md to clarify functionality and added a link to OpenAPI documentation in MainLayout.razor. Removed unnecessary methods from RequestExtensions.cs for simplification. Streamlined docs.md by removing detailed JSON response examples while retaining key information about response streaming.
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Marco Minerva
2025-02-25 15:17:06 +01:00
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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 allows 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).
This repository contains a Blazor Web App as well as a Minimal API that allows to programatically interact with embeddings and RAG.
### Web App
![SQL Database Vector Search Web App](https://github.com/marcominerva/SqlDatabaseVectorSearch/blob/master/docs/SqlDatabaseVectorSearch_API_.png)