Repository: deepset-ai/haystack

Issue:
Implement function to calculate Exact Match metric
As specified in proposal #5794 we need to implement a function to calculate the Exact Match metric.

Ideally the function should be part of the private interface and called only through the `calculate_metrics` function (see #6063). `_calculate_em()` could be a nice name.

For more detailed information check out the original proposal.

Comments:

Patch:
diff --git a/haystack/components/evaluators/__init__.py b/haystack/components/evaluators/__init__.py
new file mode 100644
index 00000000..9550a5f4
--- /dev/null
+++ b/haystack/components/evaluators/__init__.py
@@ -0,0 +1,3 @@
+from .answer_exact_match import AnswerExactMatchEvaluator
+
+__all__ = ["AnswerExactMatchEvaluator"]
diff --git a/haystack/components/evaluators/answer_exact_match.py b/haystack/components/evaluators/answer_exact_match.py
new file mode 100644
index 00000000..4927f4e1
--- /dev/null
+++ b/haystack/components/evaluators/answer_exact_match.py
@@ -0,0 +1,59 @@
+from typing import Dict, List
+
+from haystack.core.component import component
+
+
+@component
+class AnswerExactMatchEvaluator:
+    """
+    Evaluator that checks if the predicted answers matches any of the ground truth answers exactly.
+    The result is a number from 0.0 to 1.0, it represents the proportion of questions where any predicted answer
+    matched one of the ground truth answers.
+    Each question can have multiple ground truth answers and multiple predicted answers.
+
+    Usage example:
+    ```python
+    from haystack.components.evaluators import AnswerExactMatchEvaluator
+
+    evaluator = AnswerExactMatchEvaluator()
+    result = evaluator.run(
+        questions=["What is the capital of Germany?", "What is the capital of France?"],
+        ground_truth_answers=[["Berlin"], ["Paris"]],
+        predicted_answers=[["Berlin"], ["Paris"]],
+    )
+    print(result["result"])
+    # 1.0
+    ```
+    """
+
+    @component.output_types(result=float)
+    def run(
+        self, questions: List[str], ground_truth_answers: List[List[str]], predicted_answers: List[List[str]]
+    ) -> Dict[str, float]:
+        """
+        Run the AnswerExactMatchEvaluator on the given inputs.
+        All lists must have the same length.
+
+        :param questions:
+            A list of questions.
+        :param ground_truth_answers:
+            A list of expected answers for each question.
+        :param predicted_answers:
+            A list of predicted answers for each question.
+        :returns:
+            A dictionary with the following outputs:
+            - `result` - A number from 0.0 to 1.0 that represents the proportion of questions where any predicted
+                         answer matched one of the ground truth answers.
+        """
+        if not len(questions) == len(ground_truth_answers) == len(predicted_answers):
+            raise ValueError("The length of questions, ground_truth_answers, and predicted_answers must be the same.")
+
+        matches = 0
+        for truths, extracted in zip(ground_truth_answers, predicted_answers):
+            if set(truths) & set(extracted):
+                matches += 1
+
+        # The proportion of questions where any predicted answer matched one of the ground truth answers
+        result = matches / len(questions)
+
+        return {"result": result}
diff --git a/releasenotes/notes/exact-match-evaluator-197bb87b65e19d0c.yaml b/releasenotes/notes/exact-match-evaluator-197bb87b65e19d0c.yaml
new file mode 100644
index 00000000..ad380617
--- /dev/null
+++ b/releasenotes/notes/exact-match-evaluator-197bb87b65e19d0c.yaml
@@ -0,0 +1,6 @@
+---
+features:
+  - |
+    Add `AnswerExactMatchEvaluator`, a Component that can be used to calculate the Exact Match metric
+    given a list of questions, a list of expected answers for each question and the list of predicted
+    answers for each question.


Test patch:
diff --git a/test/components/evaluators/test_answer_exact_match.py b/test/components/evaluators/test_answer_exact_match.py
new file mode 100644
index 00000000..c179c74a
--- /dev/null
+++ b/test/components/evaluators/test_answer_exact_match.py
@@ -0,0 +1,61 @@
+import pytest
+
+from haystack.components.evaluators import AnswerExactMatchEvaluator
+
+
+def test_run_with_all_matching():
+    evaluator = AnswerExactMatchEvaluator()
+    result = evaluator.run(
+        questions=["What is the capital of Germany?", "What is the capital of France?"],
+        ground_truth_answers=[["Berlin"], ["Paris"]],
+        predicted_answers=[["Berlin"], ["Paris"]],
+    )
+
+    assert result["result"] == 1.0
+
+
+def test_run_with_no_matching():
+    evaluator = AnswerExactMatchEvaluator()
+    result = evaluator.run(
+        questions=["What is the capital of Germany?", "What is the capital of France?"],
+        ground_truth_answers=[["Berlin"], ["Paris"]],
+        predicted_answers=[["Paris"], ["London"]],
+    )
+
+    assert result["result"] == 0.0
+
+
+def test_run_with_partial_matching():
+    evaluator = AnswerExactMatchEvaluator()
+    result = evaluator.run(
+        questions=["What is the capital of Germany?", "What is the capital of France?"],
+        ground_truth_answers=[["Berlin"], ["Paris"]],
+        predicted_answers=[["Berlin"], ["London"]],
+    )
+
+    assert result["result"] == 0.5
+
+
+def test_run_with_different_lengths():
+    evaluator = AnswerExactMatchEvaluator()
+
+    with pytest.raises(ValueError):
+        evaluator.run(
+            questions=["What is the capital of Germany?"],
+            ground_truth_answers=[["Berlin"], ["Paris"]],
+            predicted_answers=[["Berlin"], ["London"]],
+        )
+
+    with pytest.raises(ValueError):
+        evaluator.run(
+            questions=["What is the capital of Germany?", "What is the capital of France?"],
+            ground_truth_answers=[["Berlin"]],
+            predicted_answers=[["Berlin"], ["London"]],
+        )
+
+    with pytest.raises(ValueError):
+        evaluator.run(
+            questions=["What is the capital of Germany?", "What is the capital of France?"],
+            ground_truth_answers=[["Berlin"], ["Paris"]],
+            predicted_answers=[["Berlin"]],
+        )