Contextual Recall

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

Overview

The contextual recall metric is a single-turn RAG metric that uses LLM-as-a-judge to assess whether your retriever has surfaced enough relevant context to produce an answer similar to the expected output.

Required Parameters

These are the parameters you must supply in your test case to run evaluations for contextual recall 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 recall metric first extracts distinct statements from the expected output using an LLM, then uses the same LLM to check how many of those statements are supported by the retrieved context nodes.

\text{Contextual Recall} = \frac{\text{Number of Attributable Statements}}{\text{Total Number of Statements}}

The final score is the proportion of attributable statements in expected output.

Create Locally

You can create the ContextualRecallMetric in deepeval as follows:

from deepeval.metrics import ContextualRecallMetric

metric = ContextualRecallMetric()

Here's a list of parameters you can configure when creating a ContextualRecallMetric:

thresholdnumberdefault: 0.5

A float to represent the minimum passing threshold.

modelstring | Objectdefault: gpt-4.1

A string specifying which of OpenAI's GPT models to use OR any custom LLM model of type DeepEvalBaseLLM.

include_reasonbooleandefault: true

A boolean to enable the inclusion a reason for its evaluation score.

async_modebooleandefault: true

A boolean to enable concurrent execution within the measure() method.

strict_modebooleandefault: false

A boolean to enforce a binary metric score: 0 for perfection, 1 otherwise.

verbose_modebooleandefault: false

A boolean to print the intermediate steps used to calculate the metric score.

evaluation_templateContextualRecallTemplatedefault: deepeval's template

An instance of ContextualRecallTemplate object, which allows you to override the default prompts used to compute the ContextualRecallMetric score.

Create Remotely

For users not using deepeval python, or want to run evals remotely on Confident AI, you can use the contextual recall metric by adding it to a single-turn metric collection. This will allow you to use contextual recall metric for:

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