
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 of a list of ground truth documents. This accounts for one list of ground truth documents per question. "retrieved_documents": A list of a 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 recall score over all inputs - `individual_scores`: A list of the individual recall scores ranging from 0.0 to 1.0 of each input pair of a list of retrieved documents and a list of ground truth documents. If the mode is set to single_hit, each individual score is either 0 or 1.
API reference	Evaluators
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/evaluators/document_recall.py

Overview

You can use the DocumentRecallEvaluator component to evaluate documents retrieved by a Haystack pipeline, such as a RAG Pipeline, against ground truth labels.

When initializing a DocumentRecallEvaluator, you can set the mode parameter to
RecallMode.SINGLE_HIT or RecallMode.MULTI_HIT. By default, RecallMode.SINGLE_HIT is used.

RecallMode.SINGLE_HIT means that any of the ground truth documents need to be retrieved to count as a correct retrieval with a recall score of 1. A single retrieved document can achieve the full score.

RecallMode.MULTI_HIT means that all of the ground truth documents need to be retrieved to count as a correct retrieval with a recall score of 1. The number of retrieved documents must be at least the number of ground truth documents to achieve the full score.

Usage

On its own

Below is an example where we use a DocumentRecallEvaluator 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 DocumentRecallEvaluator

evaluator = DocumentRecallEvaluator()
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