Contextual Precision

The contextual precision metric measures your RAG pipeline's retriever by evaluating whether nodes in your retrieval_context that are relevant to the given input are ranked higher than irrelevant ones. deepeval's contextual precision metric is a self-explaining LLM-Eval, meaning it outputs a reason for its metric score.

info

The ContextualPrecisionMetric focuses on evaluating the re-ranker of your RAG pipeline's retriever by assessing the ranking order of the text chunks in the retrieval_context.

Required Arguments

To use the ContextualPrecisionMetric, you'll have to provide the following arguments when creating an LLMTestCase:

• input
• actual_output
• expected_output
• retrieval_context

Example

from deepeval import evaluate
from deepeval.metrics import ContextualPrecisionMetric
from deepeval.test_case import LLMTestCase

# Replace this with the actual output from your LLM application
actual_output = "We offer a 30-day full refund at no extra cost."

# Replace this with the expected output from your RAG generator
expected_output = "You are eligible for a 30 day full refund at no extra cost."

# Replace this with the actual retrieved context from your RAG pipeline
retrieval_context = ["All customers are eligible for a 30 day full refund at no extra cost."]

metric = ContextualPrecisionMetric(
    threshold=0.7,
    model="gpt-4",
    include_reason=True
)
test_case = LLMTestCase(
    input="What if these shoes don't fit?",
    actual_output=actual_output,
    expected_output=expected_output,
    retrieval_context=retrieval_context
)

metric.measure(test_case)
print(metric.score)
print(metric.reason)

# or evaluate test cases in bulk
evaluate([test_case], [metric])

There are SEVEN optional parameters when creating a ContextualPrecisionMetric:

• [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-4o'.
• [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] evaluation_template: a class of type ContextualPrecisionTemplate, which allows you to override the default prompt templates used to compute the ContextualPrecisionMetric score. You can learn what the default prompts looks like here, and should read the How Is It Calculated section below to understand how you can tailor it to your needs. Defaulted to deepeval's ContextualPrecisionTemplate.

How Is It Calculated?

The ContextualPrecisionMetric score is calculated according to the following equation:

$$\text{Contextual Precision} = \frac{1}{\text{Number of Relevant Nodes}} \sum_{k=1}^{n} \left( \frac{\text{Number of Relevant Nodes Up to Position } k}{k} \times r_{k} \right)$$

info

• k is the (i+1)^th node in the retrieval_context
• n is the length of the retrieval_context
• r_k is the binary relevance for the k^th node in the retrieval_context. r_k = 1 for nodes that are relevant, 0 if not.

The ContextualPrecisionMetric first uses an LLM to determine for each node in the retrieval_context whether it is relevant to the input based on information in the expected_output, before calculating the weighted cumulative precision as the contextual precision score. The weighted cumulative precision (WCP) is used because it:

• Emphasizes on Top Results: WCP places a stronger emphasis on the relevance of top-ranked results. This emphasis is important because LLMs tend to give more attention to earlier nodes in the retrieval_context (which may cause downstream hallucination if nodes are ranked incorrectly).
• Rewards Relevant Ordering: WCP can handle varying degrees of relevance (e.g., "highly relevant", "somewhat relevant", "not relevant"). This is in contrast to metrics like precision, which treats all retrieved nodes as equally important.

A higher contextual precision score represents a greater ability of the retrieval system to correctly rank relevant nodes higher in the retrieval_context.