rag-pipeline-dotnet

To build a RAG pipeline in .NET, this Skill guides you through the setup, ingestion, indexing, retrieval, and generation phases using Microsoft Semantic Kernel. You configure chat completion and text embedding services, then ingest documents into a vector collection for querying.

Yes, this Skill supports deploying a retrieval-augmented generation pipeline in an air-gapped environment. It incorporates critical considerations for data classification and federal compliance to ensure secure, isolated operation without external network dependencies.

This Semantic Kernel RAG implementation addresses federal compliance by incorporating critical considerations for FedRAMP, NIST AI RMF, and data classification. It is designed for enterprise and federal environments requiring strict adherence to security standards.

The best way to create a document Q&A system in C# is using this Skill's end-to-end RAG implementation. It leverages Microsoft Semantic Kernel to ingest documents into a vector collection, enabling secure question answering directly from your indexed knowledge base.

You configure vector search in .NET by setting up Semantic Kernel with text embedding, then ingesting documents into a specific collection like 'policies'. This Skill guides the embedding pipeline and indexing process to enable efficient vector search across your knowledge base.