Turn Contextual Recall

Q: When should I use TurnContextualRecallMetric instead of the single-turn version?

When retrieval feeds a multi-turn ConversationalTestCase and you want to confirm each turn retrieved everything needed for the expected_outcome. The single-turn ContextualRecallMetric checks one query/context pair only.

Q: Why does recall require expected_outcome when relevancy doesn't?

Recall is measured against a reference: it breaks the expected_outcome into statements and checks how many map to a node in the retrieval_context. Without it there's nothing to test completeness against, which is why relevancy (referenceless) doesn't need it.

Q: My bot missed a detail the user needed — does a low recall confirm it's a retriever problem?

Largely, yes. Low recall means the retrieval_context never contained the info the expected_outcome needed, so the generator never had it. Fix the retriever (chunking, embeddings, top-k) before blaming the LLM.

LLM-as-a-judge

Multi-turn

RAG

Chatbot

Multimodal

The turn contextual recall metric is a conversational metric that evaluates whether the retrieval context contains sufficient information to support the expected outcome throughout a conversation.

Required Arguments

To use the TurnContextualRecallMetric, you'll have to provide the following arguments when creating a ConversationalTestCase:

turns
expected_outcome

You must provide the role, content, and retrieval_context for evaluation to happen. Read the How Is It Calculated section below to learn more.

Usage

The TurnContextualRecallMetric() can be used for end-to-end multi-turn evaluation:

from deepeval import evaluate
from deepeval.test_case import Turn, ConversationalTestCase
from deepeval.metrics import TurnContextualRecallMetric

content = "We offer a 30-day full refund at no extra cost."
retrieval_context = [
    "All customers are eligible for a 30 day full refund at no extra cost."
]

convo_test_case = ConversationalTestCase(
    turns=[
        Turn(role="user", content="What if these shoes don't fit?"),
        Turn(role="assistant", content=content, retrieval_context=retrieval_context)
    ],
    expected_outcome="The chatbot must explain the store policies like refunds, discounts, ..etc.",
)

metric = TurnContextualRecallMetric(threshold=0.5)

# To run metric as a standalone
# metric.measure(convo_test_case)
# print(metric.score, metric.reason)

evaluate(test_cases=[convo_test_case], metrics=[metric])

There are SEVEN optional parameters when creating a TurnContextualRecallMetric:

[Optional] threshold: a float representing the minimum passing threshold, defaulted to 0.5.
[Optional] model: a string specifying which of OpenAI's GPT models to use, OR any custom LLM model of type DeepEvalBaseLLM. Defaulted to gpt-5.4.
[Optional] include_reason: a boolean which when set to True, will include a reason for its evaluation score. Defaulted to True.
[Optional] strict_mode: a boolean which when set to True, enforces a binary metric score: 1 for perfection, 0 otherwise. It also overrides the current threshold and sets it to 1. Defaulted to False.
[Optional] async_mode: a boolean which when set to True, enables concurrent execution within the measure() method. Defaulted to True.
[Optional] verbose_mode: a boolean which when set to True, prints the intermediate steps used to calculate said metric to the console, as outlined in the How Is It Calculated section. Defaulted to False.
[Optional] window_size: an integer which defines the size of the sliding window of turns used during evaluation. Defaulted to 10.

As a standalone

You can also run the TurnContextualRecallMetric on a single test case as a standalone, one-off execution.

...

metric.measure(convo_test_case)
print(metric.score, metric.reason)

How Is It Calculated?

The TurnContextualRecallMetric score is calculated according to the following equation:

\text{Turn Contextual Recall} = \frac{\sum \text{Turn Contextual Recall Scores}}{\text{Total Number of Assistant Turns}}

The TurnContextualRecallMetric first constructs a sliding windows of turns. For each window, it:

Breaks down the expected outcome into individual sentences or statements
Evaluates each sentence to determine if it can be attributed to any node in the retrieval context
Calculates the interaction score as the ratio of attributable sentences to total sentences

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

The final score is the average of all recall scores across the conversation. This measures whether your retrieval system is providing sufficient information to generate the expected responses.

FAQs

When should I use TurnContextualRecallMetric instead of the single-turn version?

When retrieval feeds a multi-turn ConversationalTestCase and you want to confirm each turn retrieved everything needed for the expected_outcome. The single-turn ContextualRecallMetric checks one query/context pair only.

Why does recall require expected_outcome when relevancy doesn't?

Recall is measured against a reference: it breaks the expected_outcome into statements and checks how many map to a node in the retrieval_context. Without it there's nothing to test completeness against, which is why relevancy (referenceless) doesn't need it.

How is recall different from relevancy and precision?

Recall measures completeness, relevancy measures signal vs noise, and precision measures ranking.

My bot missed a detail the user needed — does a low recall confirm it's a retriever problem?

Largely, yes. Low recall means the retrieval_context never contained the info the expected_outcome needed, so the generator never had it. Fix the retriever (chunking, embeddings, top-k) before blaming the LLM.