A Practical Guide to Implementing Local RAG with .NET and Vector Database
Have you ever wondered if ChatGPT could answer queries about your company’s documents without sending data to the cloud? Well, now it’s possible with a Retrieval-Augmented Generation (RAG) system built using .NET and local vector databases. This article provides a comprehensive guide on how to create such a system, focusing on data privacy, cost efficiency, and advanced features like Semantic Kernel for AI-driven applications.
The key to building a successful RAG system lies in creating embeddings, performing document chunking, and enabling semantic searches. By following the programming and implementation techniques outlined in this guide, you can set up a fully operational system locally, ensuring that your data remains secure and within your control.
One of the advantages of implementing a local RAG system is the ability to integrate advanced features like Semantic Kernel, which enhances the capabilities of AI-driven applications. By harnessing the power of LM Studio embeddings and local vector storage, you can create a robust system that can effectively answer queries and provide valuable insights without relying on cloud services.
For a more in-depth look at how to build a local RAG system using .NET and vector databases, you can read the full blog for free on Medium.
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