Rag.NET.Evaluation 1.0.0

dotnet add package Rag.NET.Evaluation --version 1.0.0
                    
NuGet\Install-Package Rag.NET.Evaluation -Version 1.0.0
                    
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="Rag.NET.Evaluation" Version="1.0.0" />
                    
For projects that support PackageReference, copy this XML node into the project file to reference the package.
<PackageVersion Include="Rag.NET.Evaluation" Version="1.0.0" />
                    
Directory.Packages.props
<PackageReference Include="Rag.NET.Evaluation" />
                    
Project file
For projects that support Central Package Management (CPM), copy this XML node into the solution Directory.Packages.props file to version the package.
paket add Rag.NET.Evaluation --version 1.0.0
                    
#r "nuget: Rag.NET.Evaluation, 1.0.0"
                    
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package.
#:package Rag.NET.Evaluation@1.0.0
                    
#:package directive can be used in C# file-based apps starting in .NET 10 preview 4. Copy this into a .cs file before any lines of code to reference the package.
#addin nuget:?package=Rag.NET.Evaluation&version=1.0.0
                    
Install as a Cake Addin
#tool nuget:?package=Rag.NET.Evaluation&version=1.0.0
                    
Install as a Cake Tool

Rag.NET.Evaluation

Quality measurement for Rag.NET pipelines: embedding-distance and LLM-judge evaluators, A/B comparison with confidence intervals, synthetic dataset generation, and the shadow pipeline that captures production traffic for offline comparison.

Install

dotnet add package Rag.NET.Evaluation

Setup

Evaluators are constructed directly from the same Microsoft.Extensions.AI clients your pipeline uses:

using Rag.NET.Evaluation;

var evaluator = new EmbeddingDistanceEvaluator(embeddingGenerator);
// or, criterion-scored by a model:
var judge = new LlmJudgeEvaluator(chatClient);

Example

Score predicted answers against references — and make the score a regression gate in CI:

using Rag.NET.Evaluation;

var samples = new[]
{
    new EvaluationSample(
        Question:        "What is Retrieval-Augmented Generation?",
        PredictedAnswer: response.Answer,
        ReferenceAnswer: "RAG combines a retrieval system with a language model to " +
                         "generate answers grounded in retrieved documents."),
};

var result = await evaluator.EvaluateAsync(samples);
Console.WriteLine($"Mean score: {result.MeanScore:F4}");

if (result.MeanScore < 0.85)
    throw new InvalidOperationException("RAG quality regression");

UseShadow<TCandidate>() mirrors a sample of production questions through a candidate pipeline and captures both answers for later A/B comparison; RAGAS-style metrics live in Rag.NET.Evaluation.Ragas.

Full guide

Product Compatible and additional computed target framework versions.
.NET net10.0 is compatible.  net10.0-android was computed.  net10.0-browser was computed.  net10.0-ios was computed.  net10.0-maccatalyst was computed.  net10.0-macos was computed.  net10.0-tvos was computed.  net10.0-windows was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.

NuGet packages (1)

Showing the top 1 NuGet packages that depend on Rag.NET.Evaluation:

Package Downloads
Rag.NET.Evaluation.Ragas

RAGAS-style evaluation metrics for Rag.NET pipelines

GitHub repositories

This package is not used by any popular GitHub repositories.

Version Downloads Last Updated
1.0.0 0 9/15/2026
0.1.0 114 8/11/2026