chore: import upstream snapshot with attribution

haystack/components/evaluators/document_recall.py

@@ -0,0 +1,181 @@
+# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from enum import Enum
+from typing import Any
+
+from haystack import component, default_to_dict, logging
+from haystack.dataclasses import Document
+
+logger = logging.getLogger(__name__)
+
+
+class RecallMode(Enum):
+    """
+    Enum for the mode to use for calculating the recall score.
+    """
+
+    # Score is based on whether any document is retrieved.
+    SINGLE_HIT = "single_hit"
+    # Score is based on how many documents were retrieved.
+    MULTI_HIT = "multi_hit"
+
+    def __str__(self) -> str:
+        return self.value
+
+    @staticmethod
+    def from_str(string: str) -> "RecallMode":
+        """
+        Convert a string to a RecallMode enum.
+        """
+        enum_map = {e.value: e for e in RecallMode}
+        mode = enum_map.get(string)
+        if mode is None:
+            msg = f"Unknown recall mode '{string}'. Supported modes are: {list(enum_map.keys())}"
+            raise ValueError(msg)
+        return mode
+
+
+@component
+class DocumentRecallEvaluator:
+    """
+    Evaluator that calculates the Recall score for a list of documents.
+
+    Returns both a list of scores for each question and the average.
+    There can be multiple ground truth documents and multiple predicted documents as input.
+
+    Usage example:
+    ```python
+    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
+    ```
+    """
+
+    def __init__(
+        self, mode: str | RecallMode = RecallMode.SINGLE_HIT, document_comparison_field: str = "content"
+    ) -> None:
+        """
+        Create a DocumentRecallEvaluator component.
+
+        :param mode:
+            Mode to use for calculating the recall score.
+        :param document_comparison_field:
+            The Document field to use for comparison. Possible options:
+            - `"content"`: uses `doc.content`
+            - `"id"`: uses `doc.id`
+            - A `meta.` prefix followed by a key name: uses `doc.meta["<key>"]`
+              (e.g. `"meta.file_id"`, `"meta.page_number"`)
+              Nested keys are supported (e.g. `"meta.source.url"`).
+        """
+        if isinstance(mode, str):
+            mode = RecallMode.from_str(mode)
+
+        self.mode = mode
+        self.document_comparison_field = document_comparison_field
+
+    def _get_comparison_value(self, doc: Document) -> Any:
+        """
+        Extract the comparison value from a document based on the configured field.
+        """
+        if self.document_comparison_field == "content":
+            return doc.content
+        if self.document_comparison_field == "id":
+            return doc.id
+        if self.document_comparison_field.startswith("meta."):
+            parts = self.document_comparison_field[5:].split(".")
+            value = doc.meta
+            for part in parts:
+                if not isinstance(value, dict) or part not in value:
+                    return None
+                value = value[part]
+            return value
+        msg = (
+            f"Unsupported document_comparison_field: '{self.document_comparison_field}'. "
+            "Use 'content', 'id', or 'meta.<key>'."
+        )
+        raise ValueError(msg)
+
+    def _recall_single_hit(self, ground_truth_documents: list[Document], retrieved_documents: list[Document]) -> float:
+        unique_truths = {self._get_comparison_value(g) for g in ground_truth_documents}
+        unique_retrievals = {self._get_comparison_value(p) for p in retrieved_documents}
+        retrieved_ground_truths = unique_truths.intersection(unique_retrievals)
+
+        return float(len(retrieved_ground_truths) > 0)
+
+    def _recall_multi_hit(self, ground_truth_documents: list[Document], retrieved_documents: list[Document]) -> float:
+        unique_truths = {self._get_comparison_value(g) for g in ground_truth_documents}
+        unique_retrievals = {self._get_comparison_value(p) for p in retrieved_documents}
+        retrieved_ground_truths = unique_truths.intersection(unique_retrievals)
+
+        if not unique_truths or unique_truths <= {"", None}:
+            logger.warning(
+                "There are no ground truth documents or none of them contain a valid comparison value. "
+                "Score will be set to 0."
+            )
+            return 0.0
+
+        if not unique_retrievals or unique_retrievals <= {"", None}:
+            logger.warning(
+                "There are no retrieved documents or none of them contain a valid comparison value. "
+                "Score will be set to 0."
+            )
+            return 0.0
+
+        return len(retrieved_ground_truths) / len(unique_truths)
+
+    @component.output_types(score=float, individual_scores=list[float])
+    def run(
+        self, ground_truth_documents: list[list[Document]], retrieved_documents: list[list[Document]]
+    ) -> dict[str, Any]:
+        """
+        Run the DocumentRecallEvaluator on the given inputs.
+
+        `ground_truth_documents` and `retrieved_documents` must have the same length.
+
+        :param ground_truth_documents:
+            A list of expected documents for each question.
+        :param retrieved_documents:
+            A list of retrieved documents for each question.
+        A dictionary with the following outputs:
+            - `score` - The average of calculated scores.
+            - `individual_scores` - A list of numbers from 0.0 to 1.0 that represents the proportion of matching
+                documents retrieved. If the mode is `single_hit`, the individual scores are 0 or 1.
+        """
+        if len(ground_truth_documents) != len(retrieved_documents):
+            msg = "The length of ground_truth_documents and retrieved_documents must be the same."
+            raise ValueError(msg)
+
+        if self.mode == RecallMode.SINGLE_HIT:
+            mode_function = self._recall_single_hit
+        elif self.mode == RecallMode.MULTI_HIT:
+            mode_function = self._recall_multi_hit
+
+        scores = [mode_function(gt, ret) for gt, ret in zip(ground_truth_documents, retrieved_documents, strict=True)]
+
+        return {"score": sum(scores) / len(retrieved_documents), "individual_scores": scores}
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Serializes the component to a dictionary.
+
+        :returns:
+            Dictionary with serialized data.
+        """
+        return default_to_dict(self, mode=str(self.mode), document_comparison_field=self.document_comparison_field)