feat: Add DocumentLanguageClassifier 2.0

e2e/preview/pipelines/test_preprocessing_pipeline.py

@@ -0,0 +1,84 @@
+import json
+
+from haystack.preview import Pipeline
+from haystack.preview.components.embedders import SentenceTransformersDocumentEmbedder
+from haystack.preview.components.file_converters import TextFileToDocument
+from haystack.preview.components.preprocessors import TextDocumentSplitter, DocumentCleaner, DocumentLanguageClassifier
+from haystack.preview.components.routers import FileTypeRouter
+from haystack.preview.components.writers import DocumentWriter
+from haystack.preview.document_stores import MemoryDocumentStore
+
+
+def test_preprocessing_pipeline(tmp_path):
+    # Create the pipeline and its components
+    document_store = MemoryDocumentStore()
+    preprocessing_pipeline = Pipeline()
+    preprocessing_pipeline.add_component(instance=FileTypeRouter(mime_types=["text/plain"]), name="file_type_router")
+    preprocessing_pipeline.add_component(instance=TextFileToDocument(), name="text_file_converter")
+    preprocessing_pipeline.add_component(instance=DocumentLanguageClassifier(), name="language_classifier")
+    preprocessing_pipeline.add_component(instance=DocumentCleaner(), name="cleaner")
+    preprocessing_pipeline.add_component(
+        instance=TextDocumentSplitter(split_by="sentence", split_length=1), name="splitter"
+    )
+    preprocessing_pipeline.add_component(
+        instance=SentenceTransformersDocumentEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
+        name="embedder",
+    )
+    preprocessing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name="writer")
+    preprocessing_pipeline.connect("file_type_router.text/plain", "text_file_converter.paths")
+    preprocessing_pipeline.connect("text_file_converter.documents", "language_classifier.documents")
+    preprocessing_pipeline.connect("language_classifier.en", "cleaner.documents")
+    preprocessing_pipeline.connect("cleaner.documents", "splitter.documents")
+    preprocessing_pipeline.connect("splitter.documents", "embedder.documents")
+    preprocessing_pipeline.connect("embedder.documents", "writer.documents")
+
+    # Draw the pipeline
+    preprocessing_pipeline.draw(tmp_path / "test_preprocessing_pipeline.png")
+
+    # Serialize the pipeline to JSON
+    with open(tmp_path / "test_preprocessing_pipeline.json", "w") as f:
+        print(json.dumps(preprocessing_pipeline.to_dict(), indent=4))
+        json.dump(preprocessing_pipeline.to_dict(), f)
+
+    # Load the pipeline back
+    with open(tmp_path / "test_preprocessing_pipeline.json", "r") as f:
+        preprocessing_pipeline = Pipeline.from_dict(json.load(f))
+
+    # Write a txt file
+    with open(tmp_path / "test_file_english.txt", "w") as f:
+        f.write(
+            "This is an english sentence. There is more to it. It's a long text."
+            "Spans multiple lines."
+            ""
+            "Even contains empty lines.  And extra whitespaces."
+        )
+
+    # Write a txt file
+    with open(tmp_path / "test_file_german.txt", "w") as f:
+        f.write("Ein deutscher Satz ohne Verb.")
+
+    # Add two txt files and one non-txt file
+    paths = [
+        tmp_path / "test_file_english.txt",
+        tmp_path / "test_file_german.txt",
+        tmp_path / "test_preprocessing_pipeline.json",
+    ]
+
+    result = preprocessing_pipeline.run({"file_type_router": {"sources": paths}})
+
+    assert result["writer"]["documents_written"] == 6
+    filled_document_store = preprocessing_pipeline.get_component("writer").document_store
+    assert filled_document_store.count_documents() == 6
+
+    # Check preprocessed texts and mime_types
+    stored_documents = filled_document_store.filter_documents()
+    expected_texts = [
+        "This is an english sentence.",
+        " There is more to it.",
+        " It's a long text.",
+        "Spans multiple lines.",
+        "Even contains empty lines.",
+        " And extra whitespaces.",
+    ]
+    assert expected_texts == [document.text for document in stored_documents]
+    assert all(document.mime_type == "text/plain" for document in stored_documents)

haystack/preview/components/preprocessors/__init__.py

@@ -1,5 +1,6 @@
 from haystack.preview.components.preprocessors.text_document_cleaner import DocumentCleaner
 from haystack.preview.components.preprocessors.text_document_splitter import TextDocumentSplitter
+from haystack.preview.components.preprocessors.document_language_classifier import DocumentLanguageClassifier
 from haystack.preview.components.preprocessors.text_language_classifier import TextLanguageClassifier
 
-__all__ = ["TextDocumentSplitter", "DocumentCleaner", "TextLanguageClassifier"]
+__all__ = ["TextDocumentSplitter", "DocumentCleaner", "TextLanguageClassifier", "DocumentLanguageClassifier"]

haystack/preview/components/preprocessors/document_language_classifier.py

@@ -0,0 +1,92 @@
+import logging
+from typing import List, Dict, Any, Optional
+
+from haystack.preview import component, default_from_dict, default_to_dict, Document
+from haystack.preview.lazy_imports import LazyImport
+
+logger = logging.getLogger(__name__)
+
+with LazyImport("Run 'pip install langdetect'") as langdetect_import:
+    import langdetect
+
+
+@component
+class DocumentLanguageClassifier:
+    """
+    Routes documents onto different output connections depending on their language.
+    This is useful for routing documents to different models in a pipeline depending on their language.
+    The set of supported languages can be specified.
+    For routing texts based on their language use the related TextLanguageClassifier component.
+
+    Example usage in and indexing pipeline that writes only English language documents to a Store:
+
+    ```python
+    document_store = MemoryDocumentStore()
+    p = Pipeline()
+    p.add_component(instance=TextFileToDocument(), name="text_file_converter")
+    p.add_component(instance=DocumentLanguageClassifier(), name="language_classifier")
+    p.add_component(instance=TextDocumentSplitter(), name="splitter")
+    p.add_component(instance=DocumentWriter(document_store=document_store), name="writer")
+    p.connect("text_file_converter.documents", "language_classifier.documents")
+    p.connect("language_classifier.documents", "splitter.documents")
+    p.connect("splitter.documents", "writer.documents")
+    ```
+    """
+
+    def __init__(self, languages: Optional[List[str]] = None):
+        """
+        :param languages: A list of languages in ISO code, each corresponding to a different output connection (see [langdetect` documentation](https://github.com/Mimino666/langdetect#languages)). By default, only ["en"] is supported and Documents of any other language are routed to "unmatched".
+        """
+        langdetect_import.check()
+        if not languages:
+            languages = ["en"]
+        self.languages = languages
+        component.set_output_types(
+            self, unmatched=List[Document], **{language: List[Document] for language in languages}
+        )
+
+    def run(self, documents: List[Document]):
+        """
+        Run the DocumentLanguageClassifier. This method routes the documents to different edges based on their language.
+        If a Document's text does not match any of the languages specified at initialization, it is routed to
+        a connection named "unmatched".
+
+        :param documents: A list of documents to route to different edges.
+        """
+        if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
+            raise TypeError(
+                "DocumentLanguageClassifier expects a list of Document as input. In case you want to classify a text, please use the TextLanguageClassifier."
+            )
+
+        output: Dict[str, List[Document]] = {language: [] for language in self.languages}
+        output["unmatched"] = []
+
+        for document in documents:
+            detected_language = self.detect_language(document)
+            if detected_language in self.languages:
+                output[detected_language].append(document)
+            else:
+                output["unmatched"].append(document)
+
+        return output
+
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Serialize this component to a dictionary.
+        """
+        return default_to_dict(self, languages=self.languages)
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "DocumentLanguageClassifier":
+        """
+        Deserialize this component from a dictionary.
+        """
+        return default_from_dict(cls, data)
+
+    def detect_language(self, document: Document) -> Optional[str]:
+        try:
+            language = langdetect.detect(document.text)
+        except langdetect.LangDetectException:
+            logger.warning("Langdetect cannot detect the language of Document with id: %s", document.id)
+            language = None
+        return language

releasenotes/notes/document-language-classifier-1ec0b3c4d08989c0.yaml

@@ -0,0 +1,4 @@
+---
+preview:
+  - |
+    Added DocumentLanguageClassifier component so that Documents can be routed to different components based on the detected language for example during preprocessing.

test/preview/components/preprocessors/test_document_language_classifier.py

@@ -0,0 +1,69 @@
+import logging
+import pytest
+
+from haystack.preview import Document
+from haystack.preview.components.preprocessors import DocumentLanguageClassifier
+
+
+class TestDocumentLanguageClassifier:
+    @pytest.mark.unit
+    def test_init(self):
+        component = DocumentLanguageClassifier()
+        assert component.languages == ["en"]
+
+    @pytest.mark.unit
+    def test_to_dict(self):
+        component = DocumentLanguageClassifier()
+        data = component.to_dict()
+        assert data == {"type": "DocumentLanguageClassifier", "init_parameters": {"languages": ["en"]}}
+
+    @pytest.mark.unit
+    def test_to_dict_with_custom_init_parameters(self):
+        component = DocumentLanguageClassifier(languages=["en", "de"])
+        data = component.to_dict()
+        assert data == {"type": "DocumentLanguageClassifier", "init_parameters": {"languages": ["en", "de"]}}
+
+    @pytest.mark.unit
+    def test_from_dict(self):
+        data = {"type": "DocumentLanguageClassifier", "init_parameters": {"languages": ["en", "de"]}}
+        component = DocumentLanguageClassifier.from_dict(data)
+        assert component.languages == ["en", "de"]
+
+    @pytest.mark.unit
+    def test_non_document_input(self):
+        with pytest.raises(TypeError, match="DocumentLanguageClassifier expects a list of Document as input."):
+            classifier = DocumentLanguageClassifier()
+            classifier.run(documents="This is an english sentence.")
+
+    @pytest.mark.unit
+    def test_single_document(self):
+        with pytest.raises(TypeError, match="DocumentLanguageClassifier expects a list of Document as input."):
+            classifier = DocumentLanguageClassifier()
+            classifier.run(documents=Document(text="This is an english sentence."))
+
+    @pytest.mark.unit
+    def test_empty_list(self):
+        classifier = DocumentLanguageClassifier()
+        result = classifier.run(documents=[])
+        assert result == {"en": [], "unmatched": []}
+
+    @pytest.mark.unit
+    def test_detect_language(self):
+        classifier = DocumentLanguageClassifier()
+        detected_language = classifier.detect_language(Document(text="This is an english sentence."))
+        assert detected_language == "en"
+
+    @pytest.mark.unit
+    def test_route_to_en_and_unmatched(self):
+        classifier = DocumentLanguageClassifier()
+        english_document = Document(text="This is an english sentence.")
+        german_document = Document(text="Ein deutscher Satz ohne Verb.")
+        result = classifier.run(documents=[english_document, german_document])
+        assert result == {"en": [english_document], "unmatched": [german_document]}
+
+    @pytest.mark.unit
+    def test_warning_if_no_language_detected(self, caplog):
+        with caplog.at_level(logging.WARNING):
+            classifier = DocumentLanguageClassifier()
+            classifier.run(documents=[Document(text=".")])
+            assert "Langdetect cannot detect the language of Document with id" in caplog.text