Last updated on February 17, 2026 by Editorial Team
Author(s): Nagraj
Originally published on Towards AI.
A complete guide to implementing retrieval-enhanced generation using .NET, LM Studio embeddings, and local vector storage – no cloud required.
Has it never occurred to you that ChatGPT could answer questions related to your company’s documents without sending data to the cloud?
The article provides an in-depth guide on building a retrieval-augmented generation (RAG) system using .NET and a local vector database. It outlines the essential programming and implementation techniques required to create embeddings, perform document chunking, and enable semantic searches. The guide emphasizes building a fully operational system locally, ensuring data privacy and cost efficiency, while integrating advanced features like semantic kernels to enhance AI-powered applications.
Read the entire blog for free on Medium.
Published via Towards AI
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