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.

Lightweight, In-memory, Semantic Search, Text Vector Database to embed in any .NET Application

Semantic search in Unity!

A lightweight implementation of Kernel Memory as a Service

Local Vector Database coded in c# supports Cosine Similarity, Jaccard Dissimilarity as well as Euclidean , Manhattan, ChebyShev and Canberra distances

⚡ Production-ready .NET Standard 2.1 RAG library with 🤖 multi-AI provider support, 🏢 enterprise vector storage, 📄 intelligent document processing, and 🗄️ multi-database query coordination. 🌍 Cross-platform compatible.

RAG/StructRAG using SQLite, C# and Ollama

Powerful general-purpose library to simplify the development of C# application relying on Large Langage Models.

HyperVectorDB – simple. powerful. A local vector database built in C#, engineered for effortless precision. Explore your data using Cosine, Jaccard, Euclidean, Manhattan, Chebyshev, and Canberra distances.

A lightweight, cross-platform .NET library for building RAG (Retrieval-Augmented Generation) pipelines with local embedding models and SQLite vector storage. Perfect for developers who need privacy-focused, offline-capable document search and AI-powered question answering without external API dependencies.

Retrieval Augmented Generation (RAG) using Azure Cognitive Search

Using Azure OpenAI to demo RAG pattern with Azure Search as storage

AI-powered RAG assistant, built using a Domain-Driven Design (DDD) approach, helping candidates respond effectively to recruiter AI systems.

Local-first .NET 9 RAG engine mirroring PyRagix with ONNX embeddings, FAISS/Lucene search, and Ollama generation.