modify _preprocess

haystack/components/readers/extractive.py

@@ -205,7 +205,7 @@ def _flatten_documents(
         return flattened_queries, flattened_documents, query_ids
 
     def _preprocess(  # pylint: disable=too-many-positional-arguments
-        self, queries: List[str], documents: List[Document], max_seq_length: int, query_ids: List[int], stride: int
+        self, *, queries: List[str], documents: List[Document], max_seq_length: int, query_ids: List[int], stride: int
     ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", List["Encoding"], List[int], List[int]]:
         """
         Splits and tokenizes Documents and preserves structures by returning mappings to query and Document IDs.
@@ -600,7 +600,11 @@ def run(  # pylint: disable=too-many-positional-arguments
             queries, nested_documents
         )
         input_ids, attention_mask, sequence_ids, encodings, query_ids, document_ids = self._preprocess(
-            flattened_queries, flattened_documents, max_seq_length, query_ids, stride
+            queries=flattened_queries,
+            documents=flattened_documents,
+            max_seq_length=max_seq_length,
+            query_ids=query_ids,
+            stride=stride,
         )
 
         num_batches = math.ceil(input_ids.shape[0] / max_batch_size) if max_batch_size else 1

test/components/readers/test_extractive.py

@@ -321,7 +321,7 @@ def test_flatten_documents(mock_reader: ExtractiveReader):
 
 def test_preprocess(mock_reader: ExtractiveReader):
     _, _, seq_ids, _, query_ids, doc_ids = mock_reader._preprocess(
-        example_queries * 3, example_documents[0], 384, [1, 1, 1], 0
+        queries=example_queries * 3, documents=example_documents[0], max_seq_length=384, query_ids=[1, 1, 1], stride=0
     )
     expected_seq_ids = torch.full((3, 384), -1, dtype=torch.int)
     expected_seq_ids[:, :16] = 0
@@ -333,7 +333,11 @@ def test_preprocess(mock_reader: ExtractiveReader):
 
 def test_preprocess_splitting(mock_reader: ExtractiveReader):
     _, _, seq_ids, _, query_ids, doc_ids = mock_reader._preprocess(
-        example_queries * 4, example_documents[0] + [Document(content="a" * 64)], 96, [1, 1, 1, 1], 0
+        queries=example_queries * 4,
+        documents=example_documents[0] + [Document(content="a" * 64)],
+        max_seq_length=96,
+        query_ids=[1, 1, 1, 1],
+        stride=0,
     )
     assert seq_ids.shape[0] == 5
     assert query_ids == [1, 1, 1, 1, 1]