Discover how Microsoft.Extensions.VectorData brings unified vector database access to .NET - one interface for semantic search across any vector store with built-in support for embeddings, filtering, and RAG patterns.
The MongoDB EF Core provider now supports Queryable Encryption and Vector Search. Learn how to encrypt sensitive data while querying it and build AI-powered semantic search applications directly with EF Core.
A practical introduction to modern AI for .NET developers.
Announcing the preview of open, modular data ingestion building blocks in .NET, empowering developers to build scalable AI pipelines with seamless integration, extensibility, and easy getting started experiences across the .NET ecosystem.
Preview 2 of the .NET AI Chat Web App template introduces support for .NET Aspire and Qdrant vector database integration, making it easier to create cloud-native AI-powered chat applications with custom data.
Learn how to get started managing embedding vectors for search and Retrieval Augmented Generation (RAG) scenarios using the Milvus vector database with .NET.
