chore: import upstream snapshot with attribution

haystack/components/routers/document_length_router.py

@@ -0,0 +1,76 @@
+# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+
+
+from haystack import component
+from haystack.dataclasses import Document
+
+
+@component
+class DocumentLengthRouter:
+    """
+    Categorizes documents based on the length of the `content` field and routes them to the appropriate output.
+
+    A common use case for DocumentLengthRouter is handling documents obtained from PDFs that contain non-text
+    content, such as scanned pages or images. This component can detect empty or low-content documents and route them to
+    components that perform OCR, generate captions, or compute image embeddings.
+
+    ### Usage example
+
+    ```python
+    from haystack.components.routers import DocumentLengthRouter
+    from haystack.dataclasses import Document
+
+    docs = [
+        Document(content="Short"),
+        Document(content="Long document "*20),
+    ]
+
+    router = DocumentLengthRouter(threshold=10)
+
+    result = router.run(documents=docs)
+    print(result)
+
+    # {
+    #     "short_documents": [Document(content="Short", ...)],
+    #     "long_documents": [Document(content="Long document ...", ...)],
+    # }
+    ```
+    """
+
+    def __init__(self, *, threshold: int = 10) -> None:
+        """
+        Initialize the DocumentLengthRouter component.
+
+        :param threshold:
+            The threshold for the number of characters in the document `content` field. Documents where `content` is
+            None or whose character count is less than or equal to the threshold will be routed to the `short_documents`
+            output. Otherwise, they will be routed to the `long_documents` output.
+            To route only documents with None content to `short_documents`, set the threshold to a negative number.
+        """
+        self.threshold = threshold
+
+    @component.output_types(short_documents=list[Document], long_documents=list[Document])
+    def run(self, documents: list[Document]) -> dict[str, list[Document]]:
+        """
+        Categorize input documents into groups based on the length of the `content` field.
+
+        :param documents:
+            A list of documents to be categorized.
+
+        :returns: A dictionary with the following keys:
+            - `short_documents`: A list of documents where `content` is None or the length of `content` is less than or
+               equal to the threshold.
+            - `long_documents`: A list of documents where the length of `content` is greater than the threshold.
+        """
+        short_documents = []
+        long_documents = []
+
+        for doc in documents:
+            if doc.content is None or len(doc.content) <= self.threshold:
+                short_documents.append(doc)
+            else:
+                long_documents.append(doc)
+
+        return {"short_documents": short_documents, "long_documents": long_documents}