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Contextual Relevancy

Contextual Relevancy is a single-turn metric used to evaluate a RAG retriever

Overview

The contextual relevancy metric is a single-turn RAG metric that uses LLM-as-a-judge to evaluate whether all retrieved context is relevant to the input query.

Required Parameters

These are the parameters you must supply in your test case to run evaluations for contextual relevancy metric:

inputstringRequired

The input query you supply to your RAG application.

expected_outputstringRequired

The expected output your RAG application has to generate for a given input.

retrieval_contextlist of stringRequired

The retrieved context your retriever outputs for a given input sorted by their rank.

How Is It Calculated?

The contextual relevancy metric first extracts independent statements from all retrieved context using an LLM, then uses the same LLM to determine how many of those statements are relevant to the input query.


Contextual Relevancy=Number of Relevant StatementsTotal Number of Statements\text{Contextual Relevancy} = \frac{\text{Number of Relevant Statements}}{\text{Total Number of Statements}}

The final score is the proportion of relevant statements in retrieval context.

Usage

To run the contextual relevancy metric on Confident AI, add it to a single-turn metric collection. This will allow you to use contextual relevancy metric for:

  • Single-turn E2E testing
  • Single-turn component-level testing
  • Online and offline evals for traces and spans

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