SqlDatabaseVectorSearch and azure-sql-db-vector-search
These are ecosystem siblings—one provides production-ready application architecture (Blazor Web App with Minimal API for RAG), while the other provides foundational code samples demonstrating the same underlying vector search capabilities in SQL Server/Azure SQL.
About SqlDatabaseVectorSearch
marcominerva/SqlDatabaseVectorSearch
A Blazor Web App and Minimal API for performing RAG (Retrieval Augmented Generation) and vector search using the native VECTOR type in Azure SQL Database and Azure OpenAI.
This application helps you build a system that can answer questions based on your own documents. You provide various files like PDFs or Word documents, and the system processes them to generate answers with clear citations. This is ideal for anyone who needs to quickly get answers from large amounts of internal or specialized information.
About azure-sql-db-vector-search
Azure-Samples/azure-sql-db-vector-search
Samples about using vector in SQL Server and Azure SQL
This helps developers integrate advanced AI search capabilities directly into applications that use Azure SQL Database or SQL Server. It shows how to store and query numerical representations of data (embeddings) to enable semantic search, which finds results based on meaning, not just keywords. Developers can use this to enhance their applications with AI features like intelligent search and content generation.
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