feat: Add Exact Match metric (#6696)

* Add exact match metric

* Add release notes

* Cleanup comments in test_eval_exact_match.py

* Create separate preprocessing function; Add output_key parameter

* Update release note

---------

Co-authored-by: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Co-authored-by: Julian Risch <julian.risch@deepset.ai>

e2e/pipelines/test_eval_extractive_qa_pipeline.py

@@ -35,11 +35,7 @@ def test_extractive_qa_pipeline(tmp_path):
                         query="Who lives in Paris?",
                         score=0.7713339924812317,
                         data="Jean and I",
-                        document=Document(
+                        document=Document(content="My name is Jean and I live in Paris.", score=0.33144005810482535),
-                            id="6c90b78ad94e4e634e2a067b5fe2d26d4ce95405ec222cbaefaeb09ab4dce81e",
-                            content="My name is Jean and I live in Paris.",
-                            score=0.33144005810482535,
-                        ),
                         context=None,
                         document_offset=ExtractedAnswer.Span(start=11, end=21),
                         context_offset=None,
@@ -65,11 +61,7 @@ def test_extractive_qa_pipeline(tmp_path):
                         query="Who lives in Berlin?",
                         score=0.7047999501228333,
                         data="Mark and I",
-                        document=Document(
+                        document=Document(content="My name is Mark and I live in Berlin.", score=0.33144005810482535),
-                            id="10a183e965c2e107e20507c717f16559c58a8ba4bc7c577ea8dc32a8d6ca7a20",
-                            content="My name is Mark and I live in Berlin.",
-                            score=0.33144005810482535,
-                        ),
                         context=None,
                         document_offset=ExtractedAnswer.Span(start=11, end=21),
                         context_offset=None,
@@ -95,11 +87,7 @@ def test_extractive_qa_pipeline(tmp_path):
                         query="Who lives in Rome?",
                         score=0.7661304473876953,
                         data="Giorgio and I",
-                        document=Document(
+                        document=Document(content="My name is Giorgio and I live in Rome.", score=0.33144005810482535),
-                            id="fb0f1efe94b3c78aa1c4e5a17a5ef8270f70e89d36a3665c8362675e8a769a27",
-                            content="My name is Giorgio and I live in Rome.",
-                            score=0.33144005810482535,
-                        ),
                         context=None,
                         document_offset=ExtractedAnswer.Span(start=11, end=24),
                         context_offset=None,
@@ -127,10 +115,14 @@ def test_extractive_qa_pipeline(tmp_path):
     assert len(eval_result.outputs) == len(expected_outputs) == len(inputs)
     assert eval_result.runnable.to_dict() == qa_pipeline.to_dict()
 
-    metrics = eval_result.calculate_metrics(Metric.EM)
+    metrics_default = eval_result.calculate_metrics(Metric.EM, output_key="answers")
+    metrics_custom_parameters = eval_result.calculate_metrics(
+        Metric.EM, output_key="answers", ignore_case=True, ignore_punctuation=True, ignore_numbers=True
+    )
     # Save metric results to json
-    metrics.save(tmp_path / "exact_match_score.json")
+    metrics_default.save(tmp_path / "exact_match_score.json")
 
-    assert metrics["exact_match"] == 1.0
+    assert metrics_default["exact_match"] == 1.0
+    assert metrics_custom_parameters["exact_match"] == 1.0
     with open(tmp_path / "exact_match_score.json", "r") as f:
-        assert metrics == json.load(f)
+        assert metrics_default == json.load(f)

e2e/pipelines/test_eval_rag_pipelines.py

@@ -7,7 +7,7 @@ from haystack.components.embedders import SentenceTransformersDocumentEmbedder,
 from haystack.components.generators import HuggingFaceLocalGenerator
 from haystack.components.retrievers.in_memory import InMemoryBM25Retriever, InMemoryEmbeddingRetriever
 from haystack.components.writers import DocumentWriter
-from haystack.dataclasses import Document
+from haystack.dataclasses import Document, GeneratedAnswer
 from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack.evaluation.eval import eval
 from haystack.evaluation.metrics import Metric
@@ -59,9 +59,54 @@ def test_bm25_rag_pipeline(tmp_path):
     ]
 
     expected_outputs = [
-        {"llm": {"replies": ["Jean"]}},
+        {
-        {"llm": {"replies": ["Mark"]}},
+            "answer_builder": {
-        {"llm": {"replies": ["Giorgio"]}},
+                "answers": [
+                    GeneratedAnswer(
+                        data="Jean",
+                        query="Who lives in Paris?",
+                        documents=[
+                            Document(content="My name is Jean and I live in Paris.", score=0.33144005810482535),
+                            Document(content="My name is Giorgio and I live in Rome.", score=-0.17938556566116537),
+                            Document(content="My name is Mark and I live in Berlin.", score=-0.17938556566116537),
+                        ],
+                        meta={},
+                    )
+                ]
+            }
+        },
+        {
+            "answer_builder": {
+                "answers": [
+                    GeneratedAnswer(
+                        data="Mark",
+                        query="Who lives in Berlin?",
+                        documents=[
+                            Document(content="My name is Mark and I live in Berlin.", score=0.33144005810482535),
+                            Document(content="My name is Giorgio and I live in Rome.", score=-0.17938556566116537),
+                            Document(content="My name is Jean and I live in Paris.", score=-0.17938556566116537),
+                        ],
+                        meta={},
+                    )
+                ]
+            }
+        },
+        {
+            "answer_builder": {
+                "answers": [
+                    GeneratedAnswer(
+                        data="Giorgio",
+                        query="Who lives in Rome?",
+                        documents=[
+                            Document(content="My name is Giorgio and I live in Rome.", score=0.33144005810482535),
+                            Document(content="My name is Mark and I live in Berlin.", score=-0.17938556566116537),
+                            Document(content="My name is Jean and I live in Paris.", score=-0.17938556566116537),
+                        ],
+                        meta={},
+                    )
+                ]
+            }
+        },
     ]
 
     eval_result = eval(rag_pipeline, inputs=inputs, expected_outputs=expected_outputs)
@@ -71,13 +116,17 @@ def test_bm25_rag_pipeline(tmp_path):
     assert len(eval_result.outputs) == len(expected_outputs) == len(inputs)
     assert eval_result.runnable.to_dict() == rag_pipeline.to_dict()
 
-    metrics = eval_result.calculate_metrics(Metric.EM)
+    metrics_default = eval_result.calculate_metrics(Metric.EM, output_key="answers")
+    metrics_custom_parameters = eval_result.calculate_metrics(
+        Metric.EM, output_key="answers", ignore_case=True, ignore_punctuation=True, ignore_numbers=True
+    )
     # Save metric results to json
-    metrics.save(tmp_path / "exact_match_score.json")
+    metrics_default.save(tmp_path / "exact_match_score.json")
 
-    assert metrics["exact_match"] == 1.0
+    assert metrics_default["exact_match"] == 1.0
+    assert metrics_custom_parameters["exact_match"] == 1.0
     with open(tmp_path / "exact_match_score.json", "r") as f:
-        assert metrics == json.load(f)
+        assert metrics_default == json.load(f)
 
 
 def test_embedding_retrieval_rag_pipeline(tmp_path):
@@ -142,9 +191,54 @@ def test_embedding_retrieval_rag_pipeline(tmp_path):
     ]
 
     expected_outputs = [
-        {"llm": {"replies": ["Jean"]}},
+        {
-        {"llm": {"replies": ["Mark"]}},
+            "answer_builder": {
-        {"llm": {"replies": ["Giorgio"]}},
+                "answers": [
+                    GeneratedAnswer(
+                        data="Jean",
+                        query="Who lives in Paris?",
+                        documents=[
+                            Document(content="My name is Jean and I live in Paris.", score=0.33144005810482535),
+                            Document(content="My name is Giorgio and I live in Rome.", score=-0.17938556566116537),
+                            Document(content="My name is Mark and I live in Berlin.", score=-0.17938556566116537),
+                        ],
+                        meta={},
+                    )
+                ]
+            }
+        },
+        {
+            "answer_builder": {
+                "answers": [
+                    GeneratedAnswer(
+                        data="Mark",
+                        query="Who lives in Berlin?",
+                        documents=[
+                            Document(content="My name is Mark and I live in Berlin.", score=0.33144005810482535),
+                            Document(content="My name is Giorgio and I live in Rome.", score=-0.17938556566116537),
+                            Document(content="My name is Jean and I live in Paris.", score=-0.17938556566116537),
+                        ],
+                        meta={},
+                    )
+                ]
+            }
+        },
+        {
+            "answer_builder": {
+                "answers": [
+                    GeneratedAnswer(
+                        data="Giorgio",
+                        query="Who lives in Rome?",
+                        documents=[
+                            Document(content="My name is Giorgio and I live in Rome.", score=0.33144005810482535),
+                            Document(content="My name is Mark and I live in Berlin.", score=-0.17938556566116537),
+                            Document(content="My name is Jean and I live in Paris.", score=-0.17938556566116537),
+                        ],
+                        meta={},
+                    )
+                ]
+            }
+        },
     ]
 
     eval_result = eval(rag_pipeline, inputs=inputs, expected_outputs=expected_outputs)
@@ -154,10 +248,14 @@ def test_embedding_retrieval_rag_pipeline(tmp_path):
     assert len(eval_result.outputs) == len(expected_outputs) == len(inputs)
     assert eval_result.runnable.to_dict() == rag_pipeline.to_dict()
 
-    metrics = eval_result.calculate_metrics(Metric.EM)
+    metrics_default = eval_result.calculate_metrics(Metric.EM, output_key="answers")
+    metrics_custom_parameters = eval_result.calculate_metrics(
+        Metric.EM, output_key="answers", ignore_case=True, ignore_punctuation=True, ignore_numbers=True
+    )
     # Save metric results to json
-    metrics.save(tmp_path / "exact_match_score.json")
+    metrics_default.save(tmp_path / "exact_match_score.json")
 
-    assert metrics["exact_match"] == 1.0
+    assert metrics_default["exact_match"] == 1.0
+    assert metrics_custom_parameters["exact_match"] == 1.0
     with open(tmp_path / "exact_match_score.json", "r") as f:
-        assert metrics == json.load(f)
+        assert metrics_default == json.load(f)

haystack/evaluation/eval.py

@@ -1,7 +1,10 @@
 from typing import Any, Callable, Dict, List, Union
 
+import numpy as np
+
 from haystack import Pipeline
 from haystack.core.component import Component
+from haystack.evaluation.eval_utils import get_answers_from_output, preprocess_text
 from haystack.evaluation.metrics import Metric, MetricsResult
 
 
@@ -29,9 +32,15 @@ class EvaluationResult:
         self.outputs = outputs
         self.expected_outputs = expected_outputs
 
+        # Determine the type of the runnable
+        if str(type(runnable).__name__) == "Pipeline":
+            self.runnable_type = "pipeline"
+        else:
+            self.runnable_type = "component"
+
         # Mapping of metrics to their corresponding functions.
         # This should be kept in sync with the Metric enum
-        self._supported_metrics = {
+        self._supported_metrics: Dict[Metric, Callable[..., MetricsResult]] = {
             Metric.RECALL: self._calculate_recall,
             Metric.MRR: self._calculate_mrr,
             Metric.MAP: self._calculate_map,
@@ -65,8 +74,45 @@ class EvaluationResult:
     def _calculate_f1(self):
         return MetricsResult({"f1": None})
 
-    def _calculate_em(self):
+    def _calculate_em(
-        return MetricsResult({"exact_match": 1.0})
+        self, output_key: str, regexes_to_ignore=None, ignore_case=False, ignore_punctuation=False, ignore_numbers=False
+    ) -> MetricsResult:
+        """
+        Calculates the Exact Match (EM) score between two lists of predictions and labels.
+        Exact Match (EM) score measures the percentage of samples where the predicted text exactly matches the
+          corresponding ground truth label.
+
+        :param output_key: The key of the output to use for comparison.
+        :param regexes_to_ignore (list, optional): A list of regular expressions. If provided, it removes substrings
+            matching these regular expressions from both predictions and labels before comparison. Defaults to None.
+        :param ignore_case (bool, optional): If True, performs case-insensitive comparison. Defaults to False.
+        :param ignore_punctuation (bool, optional): If True, removes punctuation from both predictions and labels before
+            comparison. Defaults to False.
+        :param ignore_numbers (bool, optional): If True, removes numerical digits from both predictions and labels
+            before comparison. Defaults to False.
+        :return: A MetricsResult object containing the calculated Exact Match (EM) score.
+        """
+
+        predictions = get_answers_from_output(
+            outputs=self.outputs, output_key=output_key, runnable_type=self.runnable_type
+        )
+        labels = get_answers_from_output(
+            outputs=self.expected_outputs, output_key=output_key, runnable_type=self.runnable_type
+        )
+
+        if len(predictions) != len(labels):
+            raise ValueError("The number of predictions and labels must be the same.")
+        if len(predictions) == len(labels) == 0:
+            # Return Exact Match as 0 for no inputs
+            return MetricsResult({"exact_match": 0.0})
+
+        predictions = preprocess_text(predictions, regexes_to_ignore, ignore_case, ignore_punctuation, ignore_numbers)
+        labels = preprocess_text(labels, regexes_to_ignore, ignore_case, ignore_punctuation, ignore_numbers)
+
+        score_list = np.array(predictions) == np.array(labels)
+        exact_match_score = np.mean(score_list)
+
+        return MetricsResult({"exact_match": exact_match_score})
 
     def _calculate_sas(self):
         return MetricsResult({"exact_match": None})

haystack/evaluation/eval_utils.py

@@ -0,0 +1,65 @@
+import re
+import string
+from typing import Any, Dict, List
+
+
+def preprocess_text(
+    texts: List[str], regexes_to_ignore=None, ignore_case=False, ignore_punctuation=False, ignore_numbers=False
+) -> List[str]:
+    """
+    Preprocess the outputs of the runnable to remove unwanted characters.
+
+    :param regexes_to_ignore (list, optional): A list of regular expressions. If provided, it removes substrings
+        matching these regular expressions from the text. Defaults to None.
+    :param ignore_case (bool, optional): If True, converts all characters to lowercase. Defaults to False.
+    :param ignore_punctuation (bool, optional): If True, removes punctuation from the text. Defaults to False.
+    :param ignore_numbers (bool, optional): If True, removes numerical digits from the text. Defaults to False.
+    :return: A list of preprocessed strings.
+    """
+    if regexes_to_ignore:
+        combined_regex = "|".join(regexes_to_ignore)
+        texts = [re.sub(combined_regex, "", text, flags=re.IGNORECASE) for text in texts]
+
+    if ignore_case:
+        texts = [text.lower() for text in texts]
+
+    if ignore_punctuation:
+        translator = str.maketrans("", "", string.punctuation)
+        texts = [text.translate(translator) for text in texts]
+
+    if ignore_numbers:
+        translator = str.maketrans("", "", string.digits)
+        texts = [text.translate(translator) for text in texts]
+
+    return texts
+
+
+def get_answers_from_output(outputs: List[Dict[str, Any]], output_key: str, runnable_type: str) -> List[str]:
+    """
+    Extracts the answers from the output of a pipeline or component.
+
+    :param outputs: The outputs of the runnable.
+    :return: List of answers from the runnable output.
+    """
+    answers = []
+    if runnable_type == "pipeline":
+        # Iterate over output from each Pipeline run
+        for output in outputs:
+            # Iterate over output of component in each Pipeline run
+            for component_output in output.values():
+                # Only extract answers based on key
+                for key in component_output.keys():
+                    if output_key in key:
+                        for generated_answer in component_output[output_key]:
+                            if generated_answer.data:
+                                answers.append(generated_answer.data)
+    else:
+        # Iterate over output from each Component run
+        for output in outputs:
+            # Only extract answers based on key
+            for key in output.keys():
+                if output_key in key:
+                    for generated_answer in output[output_key]:
+                        if generated_answer.data:
+                            answers.append(generated_answer.data)
+    return answers

releasenotes/notes/add-exact-match-a7df21717238b771.yaml

@@ -0,0 +1,8 @@
+---
+features:
+  - |
+    Adds support for the Exact Match metric to `EvaluationResult.calculate_metrics(...)`:
+    ```python
+    from haystack.evaluation.metrics import Metric
+    exact_match_metric = eval_result.calculate_metrics(Metric.EM, output_key="answers")
+    ```

test/evaluation/test_eval_exact_match.py

@@ -0,0 +1,167 @@
+from haystack import Pipeline
+from haystack.dataclasses import GeneratedAnswer
+from haystack.evaluation.eval import EvaluationResult
+
+
+class TestExactMatch:
+    def create_evaluation_result(self, predictions, labels):
+        """
+        Creates an evaluation result of a RAG pipeline using the list of predictions and labels for testing the exact match.
+        """
+        runnable = Pipeline()
+        inputs = []
+        outputs = [
+            {"answer_builder": {"answers": [GeneratedAnswer(data=pred, query="", documents=[], meta={})]}}
+            for pred in predictions
+        ]
+        expected_outputs = [
+            {"answer_builder": {"answers": [GeneratedAnswer(data=label, query="", documents=[], meta={})]}}
+            for label in labels
+        ]
+        evaluation_result = EvaluationResult(runnable, inputs, outputs, expected_outputs)
+        return evaluation_result
+
+    def test_exact_match_empty_inputs(self):
+        """
+        Test exact match with empty inputs
+        """
+        runnable = Pipeline()
+        inputs = []
+        outputs = [
+            {"answer_builder": {"answers": []}},
+            {"answer_builder": {"answers": []}},
+            {"answer_builder": {"answers": []}},
+        ]
+        expected_outputs = [
+            {"answer_builder": {"answers": []}},
+            {"answer_builder": {"answers": []}},
+            {"answer_builder": {"answers": []}},
+        ]
+        evaluation_result = EvaluationResult(runnable, inputs, outputs, expected_outputs)
+        # Expecting 0% exact match for empty inputs
+        em_result = evaluation_result._calculate_em(output_key="answers")
+
+        assert em_result["exact_match"] == 0.0
+
+    def test_exact_match_same_inputs(self):
+        """
+        Test exact match with default parameters
+        """
+        predictions = ["OpenSource", "HaystackAI", "LLMs"]
+        labels = ["OpenSource", "HaystackAI", "LLMs"]
+        evaluation_result = self.create_evaluation_result(predictions, labels)
+        em_result = evaluation_result._calculate_em(output_key="answers")
+
+        assert em_result["exact_match"] == 1.0
+
+    def test_exact_match_single_word(self):
+        """
+        Test exact match with single-word inputs
+        """
+        predictions = ["OpenSource"]
+        labels = ["OpenSource"]
+
+        evaluation_result = self.create_evaluation_result(predictions, labels)
+        em_result = evaluation_result._calculate_em(output_key="answers")
+
+        assert em_result["exact_match"] == 1.0
+
+    def test_exact_match_negative_case(self):
+        """
+        Test exact match with deliberately mismatched predictions and labels
+        """
+        predictions = ["OpenSource", "HaystackAI", "LLMs"]
+        labels = ["Source", "HaystackAI", "LLMs"]
+
+        evaluation_result = self.create_evaluation_result(predictions, labels)
+        # Expecting EM to be 2/3 as 2 out of 3 items match
+        expected_em = 2 / 3
+        em_result = evaluation_result._calculate_em(output_key="answers")
+
+        assert em_result["exact_match"] == expected_em
+
+    def test_exact_match_ignore_case(self):
+        """
+        Test exact match with ignoring case sensitivity
+        """
+        predictions = ["OpenSource", "HaystackAI", "LLMs"]
+        labels = ["opensource", "HAYSTACKAI", "llMs"]
+
+        evaluation_result = self.create_evaluation_result(predictions, labels)
+        # Exact match after case ignoring
+        em_result = evaluation_result._calculate_em(output_key="answers", ignore_case=True)
+
+        assert em_result["exact_match"] == 1.0
+
+    def test_exact_match_ignore_punctuation(self):
+        """
+        Test exact match with ignoring punctuation
+        """
+        predictions = ["OpenSource!", "Haystack.AI", "LLMs,"]
+        labels = ["OpenSource", "HaystackAI", "LLMs"]
+
+        evaluation_result = self.create_evaluation_result(predictions, labels)
+        # Exact match after ignoring punctuation
+        em_result = evaluation_result._calculate_em(output_key="answers", ignore_punctuation=True)
+
+        assert em_result["exact_match"] == 1.0
+
+    def test_exact_match_ignore_numbers(self):
+        """
+        Test exact match with ignoring numbers
+        """
+        predictions = ["OpenSource123", "HaystackAI", "LLMs456"]
+        labels = ["OpenSource", "HaystackAI", "LLMs"]
+
+        evaluation_result = self.create_evaluation_result(predictions, labels)
+        # Exact match after ignoring numbers
+        em_result = evaluation_result._calculate_em(output_key="answers", ignore_numbers=True)
+        assert em_result["exact_match"] == 1.0
+
+    def test_exact_match_regex_ignore(self):
+        """
+        Test exact match with ignoring specific regex patterns
+        """
+        predictions = ["Open123Source", "HaystackAI", "LLMs456"]
+        labels = ["OpenSource", "HaystackAI", "LLMs"]
+
+        evaluation_result = self.create_evaluation_result(predictions, labels)
+        # Ignore numeric patterns
+        regex_to_ignore = [r"\d+"]
+        em_result = evaluation_result._calculate_em(output_key="answers", regexes_to_ignore=regex_to_ignore)
+
+        assert em_result["exact_match"] == 1.0
+
+    def test_exact_match_multiple_ignore_regex(self):
+        """
+        Test exact match with multiple ignoring parameters
+        """
+        predictions = ["Open123!Source", "Haystack.AI", "LLMs456,"]
+        labels = ["OpenSource", "HaystackAI", "LLMs"]
+
+        evaluation_result = self.create_evaluation_result(predictions, labels)
+        # Ignore numeric patterns and punctuation using regex
+        regex_to_ignore = [r"\d+", r"\W+"]
+        em_result = evaluation_result._calculate_em(output_key="answers", regexes_to_ignore=regex_to_ignore)
+
+        assert em_result["exact_match"] == 1.0
+
+    def test_exact_match_multiple_ignore_combination(self):
+        """
+        Test exact match with multiple ignoring parameters combined
+        """
+        predictions = ["Open%123!$Source", "Haystack.AI##", "^^LLMs456,"]
+        labels = ["OpenSource", "HaystackAI", "LLMs"]
+
+        evaluation_result = self.create_evaluation_result(predictions, labels)
+        # Ignore only special characters using regex
+        regex_to_ignore = [r"[^\w\s\d]+"]
+        em_result = evaluation_result._calculate_em(
+            output_key="answers",
+            ignore_numbers=True,
+            ignore_punctuation=True,
+            ignore_case=True,
+            regexes_to_ignore=regex_to_ignore,
+        )
+
+        assert em_result["exact_match"] == 1.0

test/evaluation/test_eval_utils.py

@@ -0,0 +1,245 @@
+from haystack.dataclasses import GeneratedAnswer
+from haystack.evaluation.eval_utils import get_answers_from_output, preprocess_text
+
+
+class TestEvalUtils:
+    def test_extract_answers_from_pipeline_output(self):
+        """
+        Test that the function correctly extracts answers from the output of a pipeline.
+        """
+        outputs = [
+            {
+                "answer_builder": {
+                    "answers": [GeneratedAnswer(data="Jean", query="Who lives in Paris?", documents=[], meta={})]
+                }
+            },
+            {
+                "answer_builder": {
+                    "answers": [GeneratedAnswer(data="Mark", query="Who lives in Berlin?", documents=[], meta={})]
+                }
+            },
+            {
+                "answer_builder": {
+                    "answers": [GeneratedAnswer(data="Giorgio", query="Who lives in Rome?", documents=[], meta={})]
+                }
+            },
+        ]
+
+        runnable_type = "pipeline"
+        output_key = "answers"
+        expected_answers = ["Jean", "Mark", "Giorgio"]
+
+        assert get_answers_from_output(outputs, output_key, runnable_type) == expected_answers
+
+    def test_extract_answers_from_component_output(self):
+        """
+        Test that the function correctly extracts answers from the output of a component.
+        """
+        outputs = [
+            {"answers": [GeneratedAnswer(data="Jean", query="Who lives in Paris?", documents=[], meta={})]},
+            {"answers": [GeneratedAnswer(data="Mark", query="Who lives in Berlin?", documents=[], meta={})]},
+            {"answers": [GeneratedAnswer(data="Giorgio", query="Who lives in Rome?", documents=[], meta={})]},
+        ]
+        runnable_type = "component"
+        output_key = "answers"
+        expected_answers = ["Jean", "Mark", "Giorgio"]
+
+        assert get_answers_from_output(outputs, output_key, runnable_type) == expected_answers
+
+    def test_ignore_other_output_keys(self):
+        """
+        Test that the function only extracts answers and ignores other output keys.
+        """
+        outputs = [
+            {
+                "llm": {"replies": ["llm_reply_1"]},
+                "answer_builder": {
+                    "answers": [GeneratedAnswer(data="Jean", query="Who lives in Paris?", documents=[], meta={})]
+                },
+            },
+            {
+                "llm": {"replies": ["llm_reply_2"]},
+                "answer_builder": {
+                    "answers": [GeneratedAnswer(data="Mark", query="Who lives in Berlin?", documents=[], meta={})]
+                },
+            },
+            {
+                "llm": {"replies": ["llm_reply_3"]},
+                "answer_builder": {
+                    "answers": [GeneratedAnswer(data="Giorgio", query="Who lives in Rome?", documents=[], meta={})]
+                },
+            },
+        ]
+
+        runnable_type = "pipeline"
+        output_key = "answers"
+        expected_answers = ["Jean", "Mark", "Giorgio"]
+
+        assert get_answers_from_output(outputs, output_key, runnable_type) == expected_answers
+
+    def test_handle_empty_outputs(self):
+        """
+        Test that the function correctly handles empty outputs.
+        """
+        outputs = []
+        runnable_type = "pipeline"
+        output_key = "answers"
+        expected_answers = []
+
+        assert get_answers_from_output(outputs, output_key, runnable_type) == expected_answers
+
+    def test_handle_missing_keys(self):
+        """
+        Test that the function correctly handles outputs with missing keys.
+        """
+        outputs = [
+            {
+                "llm": {"replies": ["llm_reply_1"]},
+                "answer_builder": {
+                    "answers": [GeneratedAnswer(data="Jean", query="Who lives in Paris?", documents=[], meta={})]
+                },
+            },
+            {
+                "answer_builder": {
+                    "answers": [GeneratedAnswer(data="Mark", query="Who lives in Berlin?", documents=[], meta={})]
+                }
+            },
+        ]
+
+        runnable_type = "pipeline"
+        output_key = "answers"
+        expected_answers = ["Jean", "Mark"]
+
+        assert get_answers_from_output(outputs, output_key, runnable_type) == expected_answers
+
+    def test_handle_missing_values(self):
+        """
+        Test that the function correctly handles outputs with missing values.
+        """
+        outputs = [
+            {"answer_builder": {"answers": []}},
+            {
+                "answer_builder": {
+                    "answers": [GeneratedAnswer(data="Mark", query="Who lives in Berlin?", documents=[], meta={})]
+                }
+            },
+        ]
+        runnable_type = "pipeline"
+        output_key = "answers"
+        expected_answers = ["Mark"]
+
+        assert get_answers_from_output(outputs, output_key, runnable_type) == expected_answers
+
+    def test_preprocess_text_default_parameters(self):
+        """
+        Test preprocess_text with default parameters.
+        There should be no changes to the input text.
+        """
+        texts = ["Test, Output-1!", "Test, Output-2!"]
+        expected_output = ["Test, Output-1!", "Test, Output-2!"]
+        actual_output = preprocess_text(texts)
+
+        assert actual_output == expected_output
+
+    def test_preprocess_text_ignore_case(self):
+        """
+        Test preprocess_text with ignore_case=True.
+
+        """
+        texts = ["Test, Output-1!"]
+        expected_output = ["test, output-1!"]
+
+        actual_output = preprocess_text(texts, ignore_case=True)
+
+        assert actual_output == expected_output
+
+    def test_preprocess_text_ignore_punctuation(self):
+        """
+        Test preprocess_text with ignore_punctuation=True.
+        """
+        texts = ["Test, Output-1!"]
+        expected_output = ["Test Output1"]
+
+        actual_output = preprocess_text(texts, ignore_punctuation=True)
+
+        assert actual_output == expected_output
+
+    # Preprocess text with ignore_numbers=True.
+    def test_preprocess_text_ignore_numbers(self):
+        """
+        Test preprocess_text with ignore_numbers=True. It should be able to remove numbers from the input.
+        """
+        texts = ["Test, Output-1!"]
+        expected_output = ["Test, Output-!"]
+
+        actual_output = preprocess_text(texts, ignore_numbers=True)
+
+        assert actual_output == expected_output
+
+    def test_preprocess_text_regexes_to_ignore(self):
+        """
+        Test preprocess_text with a list of regex patterns to ignore.
+        """
+        texts = ["Test, Output-1!"]
+        expected_output = ["Test Output"]
+
+        # Use regex patterns to remove digits and non-alphanumeric characters
+        actual_output = preprocess_text(texts, regexes_to_ignore=[r"\d", r"[^\w\s]"])
+
+        assert actual_output == expected_output
+
+    def test_preprocess_text_empty_list(self):
+        """
+        Test preprocess_text with empty list of texts.
+        """
+        texts = []
+        expected_output = []
+
+        actual_output = preprocess_text(texts)
+
+        assert actual_output == expected_output
+
+    def test_preprocess_text_all_ignore_parameters(self):
+        """
+        Test preprocess_text with all ignore parameters set to True.
+        """
+        texts = ["Test, Output-1!"]
+        expected_output = ["test output"]
+
+        actual_output = preprocess_text(texts, ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
+
+        assert actual_output == expected_output
+
+    def test_preprocess_text_regexes_to_ignore_empty_string(self):
+        """
+        Test preprocess_text with regexes_to_ignore=[""].
+        """
+        texts = ["Test, Output-1!"]
+        expected_output = ["Test, Output-1!"]
+
+        actual_output = preprocess_text(texts, regexes_to_ignore=[""])
+
+        assert actual_output == expected_output
+
+    # Preprocess text with regexes_to_ignore=[".*"].
+    def test_preprocess_text_regexes_to_ignore_dot_star(self):
+        """
+        Test preprocess_text with regexes_to_ignore=[".*"].
+        """
+        texts = ["Test, Output-1!"]
+        expected_output = [""]
+
+        actual_output = preprocess_text(texts, regexes_to_ignore=[".*"])
+
+        assert actual_output == expected_output
+
+    def test_preprocess_text_regexes_to_ignore_same_substring(self):
+        """
+        Test preprocess_text with regexes_to_ignore where all the regex patterns match the same substring.
+        """
+        texts = ["Test, Output-1!"]
+        expected_output = ["Test, Output-!"]
+
+        actual_output = preprocess_text(texts, regexes_to_ignore=[r"\d", r"\d"])
+
+        assert actual_output == expected_output