
Most common position in a pipeline	On its own or in an evaluation pipeline. To be used after a separate pipeline that has generated the inputs for the Evaluator.
Mandatory run variables	"ground_truth_documents": A list containing another list of ground truth documents. This accounts for one list of ground truth documents per question. "retrieved_documents": A list containing another list of retrieved documents. This accounts for one list of retrieved documents per question.
Output variables	A dictionary containing: - `score`: A number from 0.0 to 1.0 that represents the mean reciprocal rank - `individual_scores`: A list of the individual reciprocal ranks ranging from 0.0 to 1.0 for each input pair of a list of retrieved documents and a list of ground truth documents
API reference	Evaluators
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/evaluators/document_mrr.py

Overview

You can use the DocumentMRREvaluator component to evaluate documents retrieved by a Haystack pipeline, such as a RAG pipeline, against ground truth labels. A higher mean reciprocal rank is better and indicates that relevant documents appear at an earlier position in the list of retrieved documents.

To initialize a DocumentMRREvaluator, there are no parameters required.

Usage

On its own

Below is an example where we use a DocumentMRREvaluator component to evaluate documents retrieved for two queries. For the first query, there is one ground truth document and one retrieved document. For the second query, there are two ground truth documents and three retrieved documents.

from haystack import Document
from haystack.components.evaluators import DocumentMRREvaluator

evaluator = DocumentMRREvaluator()
result = evaluator.run(
    ground_truth_documents=[
        [Document(content="France")],
        [Document(content="9th century"), Document(content="9th")],
    ],
    retrieved_documents=[
        [Document(content="France")],
        [Document(content="9th century"), Document(content="10th century"), Document(content="9th")],
    ],
)
print(result["individual_scores"])
# [1.0, 1.0]
print(result["score"])
# 1.0

In a pipeline

Below is an example where we use a DocumentRecallEvaluator and a DocumentMRREvaluator in a pipeline to evaluate two answers and compare them to ground truth answers. Running a pipeline instead of the individual components simplifies calculating more than one metric.

from haystack import Document, Pipeline
from haystack.components.evaluators import DocumentMRREvaluator, DocumentRecallEvaluator

pipeline = Pipeline()
mrr_evaluator = DocumentMRREvaluator()
recall_evaluator = DocumentRecallEvaluator()
pipeline.add_component("mrr_evaluator", mrr_evaluator)
pipeline.add_component("recall_evaluator", recall_evaluator)

ground_truth_documents=[
    [Document(content="France")],
    [Document(content="9th century"), Document(content="9th")],
]
retrieved_documents=[
    [Document(content="France")],
    [Document(content="9th century"), Document(content="10th century"), Document(content="9th")],
]

result = pipeline.run(
		{
			"mrr_evaluator": {"ground_truth_documents": ground_truth_documents,
	    "retrieved_documents": retrieved_documents},
	    "recall_evaluator": {"ground_truth_documents": ground_truth_documents,
	    "retrieved_documents": retrieved_documents}
    }
)

for evaluator in result:
    print(result[evaluator]["individual_scores"])
# [1.0, 1.0]
# [1.0, 1.0]
for evaluator in result:
    print(result[evaluator]["score"])
# 1.0
# 1.0