Embeddings are numerical representations of data that capture semantic meaning for AI and machine learning systems. They convert data such as text, images, or audio into numerical vectors, allowing AI systems to measure similarity, understand context, and power applications such as semantic search, recommendation systems, clustering, and Retrieval-Augmented Generation (RAG).
🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
A ChatGPT integration library for .NET, supporting both OpenAI and Azure OpenAI Service
Face recognition and analytics library based on deep neural networks and ONNX runtime
eShopLite is a set of reference .NET applications implementing an eCommerce site with features like Semantic Search, MCP, Reasoning models and more.
VectorRAG.Net is a .NET-native high-performance vector database library for semantic search and RAG (Retrieval-Augmented Generation). Core search is based on Random Hyperplane LSH candidate generation with exact rerank by dot/cosine.
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.
C# library for approximate nearest neighbors search using Hierarchical Navigable Small World graphs
Semantic search in Unity!
C# SDK for the Hugging Face API -- inference, embeddings, and model hub
C# library for fast embeddings projection using Uniform Manifold Approximation and Projection
A lightweight implementation of Kernel Memory as a Service
.NET library for local embedding generation using ONNX and Microsoft.Extensions.AI
🧠✂️ SemanticSlicer — A smart text chunker for LLM-ready documents.
C# SDK based on official OpenAI OpenAPI specification
eShopLite - Semantic Search is a reference .NET application implementing an eCommerce site with Search features using Keyword Search and Semantic Search.
An example that shows how to use Semantic Kernel and Kernel Memory to work with embeddings in a .NET application using SQL Server as Vector Database.
Published: 2026-10-19
Example how to implement a question & answer flow using semantic search with OpenAI - by using C# & Semantic Kernel