Turn Contextual Relevancy

MLLM-as-a-judge

Multi-turn

Chatbot

Multimodal

The turn contextual relevancy metric is a conversational metric that evaluates whether the retrieval context contains relevant information to address the user's input throughout a conversation.

Required Arguments

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

• turns

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 TurnContextualRelevancyMetric() 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 TurnContextualRelevancyMetric

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 = TurnContextualRelevancyMetric(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 TurnContextualRelevancyMetric:

• [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-4.1'.
• [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 TurnContextualRelevancyMetric on a single test case as a standalone, one-off execution.

...

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

caution

This is great for debugging or if you wish to build your own evaluation pipeline, but you will NOT get the benefits (testing reports, Confident AI platform) and all the optimizations (speed, caching, computation) the evaluate() function or deepeval test run offers.

How Is It Calculated?

The TurnContextualRelevancyMetric score is calculated according to the following equation:

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

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

1. Extracts statements from each retrieval context node
2. Evaluates each statement to determine if it is relevant to the user's input
3. Calculates the interaction score as the ratio of relevant statements to total statements

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

The final score is the average of all relevancy scores across the conversation. This measures whether your retrieval system is returning contextually relevant information for each turn.