chore: import upstream snapshot with attribution

haystack/components/converters/xlsx.py

@@ -0,0 +1,236 @@
+# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import io
+import os
+from pathlib import Path
+from typing import Any, Literal
+
+from haystack import Document, component, logging
+from haystack.components.converters.utils import get_bytestream_from_source, normalize_metadata
+from haystack.dataclasses import ByteStream
+from haystack.lazy_imports import LazyImport
+
+logger = logging.getLogger(__name__)
+
+with LazyImport("Run 'pip install pandas openpyxl'") as pandas_xlsx_import:
+    import openpyxl
+    import pandas as pd
+
+with LazyImport("Run 'pip install tabulate'") as tabulate_import:
+    from tabulate import tabulate  # noqa: F401 # the library is used but not directly referenced
+
+
+@component
+class XLSXToDocument:
+    """
+    Converts XLSX (Excel) files into Documents.
+
+    Supports reading data from specific sheets or all sheets in the Excel file. If all sheets are read, a Document is
+    created for each sheet. The content of the Document is the table which can be saved in CSV or Markdown format.
+
+    ### Usage example
+
+    ```python
+    from haystack.components.converters.xlsx import XLSXToDocument
+    from datetime import datetime
+
+    converter = XLSXToDocument()
+    results = converter.run(
+        sources=["test/test_files/xlsx/basic_tables_two_sheets.xlsx"], meta={"date_added": datetime.now().isoformat()}
+    )
+    documents = results["documents"]
+
+    print(documents[0].content)
+    # >> ",A,B\\n1,col_a,col_b\\n2,1.5,test\\n"
+    ```
+    """
+
+    def __init__(
+        self,
+        table_format: Literal["csv", "markdown"] = "csv",
+        sheet_name: str | int | list[str | int] | None = None,
+        read_excel_kwargs: dict[str, Any] | None = None,
+        table_format_kwargs: dict[str, Any] | None = None,
+        *,
+        link_format: Literal["markdown", "plain", "none"] = "none",
+        store_full_path: bool = False,
+    ) -> None:
+        """
+        Creates a XLSXToDocument component.
+
+        :param table_format: The format to convert the Excel file to.
+        :param sheet_name: The name of the sheet to read. If None, all sheets are read.
+        :param read_excel_kwargs: Additional arguments to pass to `pandas.read_excel`.
+            See https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html#pandas-read-excel
+        :param table_format_kwargs: Additional keyword arguments to pass to the table format function.
+            - If `table_format` is "csv", these arguments are passed to `pandas.DataFrame.to_csv`.
+              See https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html#pandas-dataframe-to-csv
+            - If `table_format` is "markdown", these arguments are passed to `pandas.DataFrame.to_markdown`.
+              See https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_markdown.html#pandas-dataframe-to-markdown
+        :param link_format: The format for link output. Possible options:
+            - `"markdown"`: `[text](url)`
+            - `"plain"`: `text (url)`
+            - `"none"`: Only the text is extracted, link addresses are ignored.
+        :param store_full_path:
+            If True, the full path of the file is stored in the metadata of the document.
+            If False, only the file name is stored.
+        """
+        pandas_xlsx_import.check()
+        self.table_format = table_format
+        if table_format not in ["csv", "markdown"]:
+            raise ValueError(f"Unsupported export format: {table_format}. Choose either 'csv' or 'markdown'.")
+        if link_format not in ("markdown", "plain", "none"):
+            msg = f"Unknown link format '{link_format}'. Supported formats are: 'markdown', 'plain', 'none'"
+            raise ValueError(msg)
+        if table_format == "markdown":
+            tabulate_import.check()
+        self.link_format = link_format
+        self.sheet_name = sheet_name
+        self.read_excel_kwargs = read_excel_kwargs or {}
+        self.table_format_kwargs = table_format_kwargs or {}
+        self.store_full_path = store_full_path
+
+    @component.output_types(documents=list[Document])
+    def run(
+        self, sources: list[str | Path | ByteStream], meta: dict[str, Any] | list[dict[str, Any]] | None = None
+    ) -> dict[str, list[Document]]:
+        """
+        Converts a XLSX file to a Document.
+
+        :param sources:
+            List of file paths or ByteStream objects.
+        :param meta:
+            Optional metadata to attach to the documents.
+            This value can be either a list of dictionaries or a single dictionary.
+            If it's a single dictionary, its content is added to the metadata of all produced documents.
+            If it's a list, the length of the list must match the number of sources, because the two lists will
+            be zipped.
+            If `sources` contains ByteStream objects, their `meta` will be added to the output documents.
+        :returns:
+            A dictionary with the following keys:
+            - `documents`: Created documents
+        """
+        documents = []
+
+        meta_list = normalize_metadata(meta, sources_count=len(sources))
+
+        for source, metadata in zip(sources, meta_list, strict=True):
+            try:
+                bytestream = get_bytestream_from_source(source)
+            except Exception as e:
+                logger.warning("Could not read {source}. Skipping it. Error: {error}", source=source, error=e)
+                continue
+
+            try:
+                tables, tables_metadata = self._extract_tables(bytestream)
+            except Exception as e:
+                logger.warning(
+                    "Could not read {source} and convert it to a Document, skipping. Error: {error}",
+                    source=source,
+                    error=e,
+                )
+                continue
+
+            # Loop over tables and create a Document for each table
+            for table, excel_metadata in zip(tables, tables_metadata, strict=True):
+                merged_metadata = {**bytestream.meta, **metadata, **excel_metadata}
+
+                if not self.store_full_path and "file_path" in bytestream.meta:
+                    file_path = bytestream.meta["file_path"]
+                    merged_metadata["file_path"] = os.path.basename(file_path)
+
+                document = Document(content=table, meta=merged_metadata)
+                documents.append(document)
+
+        return {"documents": documents}
+
+    @staticmethod
+    def _generate_excel_column_names(n_cols: int) -> list[str]:
+        result = []
+        for i in range(n_cols):
+            col_name = ""
+            num = i
+            while num >= 0:
+                col_name = chr(num % 26 + 65) + col_name
+                num = num // 26 - 1
+            result.append(col_name)
+        return result
+
+    def _extract_tables(self, bytestream: ByteStream) -> tuple[list[str], list[dict]]:
+        """
+        Extract tables from an Excel file.
+        """
+        file_bytes = io.BytesIO(bytestream.data)
+        resolved_read_excel_kwargs = {
+            **self.read_excel_kwargs,
+            "sheet_name": self.sheet_name,
+            "header": None,  # Don't assign any pandas column labels
+            "engine": "openpyxl",  # Use openpyxl as the engine to read the Excel file
+        }
+        sheet_to_dataframe = pd.read_excel(io=file_bytes, **resolved_read_excel_kwargs)
+        if isinstance(sheet_to_dataframe, pd.DataFrame):
+            sheet_to_dataframe = {self.sheet_name: sheet_to_dataframe}
+
+        # If link extraction is enabled, load the workbook with openpyxl to read hyperlinks
+        hyperlinks_by_sheet: dict[str | int | None, dict[tuple[int, int], str]] = {}
+        if self.link_format != "none":
+            file_bytes.seek(0)
+            wb = openpyxl.load_workbook(file_bytes, data_only=True)
+            for sheet_key in sheet_to_dataframe:
+                if isinstance(sheet_key, int):
+                    ws = wb.worksheets[sheet_key]
+                elif sheet_key is None:
+                    ws = wb.active
+                else:
+                    ws = wb[sheet_key]
+                cell_links: dict[tuple[int, int], str] = {}
+                for row in ws.iter_rows():
+                    for cell in row:
+                        if cell.hyperlink and cell.hyperlink.target:
+                            # Convert to 0-based indices to match DataFrame positions
+                            cell_links[(cell.row - 1, cell.column - 1)] = cell.hyperlink.target
+                hyperlinks_by_sheet[sheet_key] = cell_links
+            wb.close()
+
+        updated_sheet_to_dataframe = {}
+        for key in sheet_to_dataframe:
+            df = sheet_to_dataframe[key]
+            # Row starts at 1 in Excel
+            df.index = df.index + 1
+            # Excel column names are Alphabet Characters
+            header = self._generate_excel_column_names(df.shape[1])
+            df.columns = header
+
+            # Apply hyperlinks to cell values
+            if key in hyperlinks_by_sheet:
+                for (row_idx, col_idx), url in hyperlinks_by_sheet[key].items():
+                    if row_idx < len(df) and col_idx < len(df.columns):
+                        cell_value = df.iat[row_idx, col_idx]
+                        text = str(cell_value) if pd.notna(cell_value) else ""
+                        if self.link_format == "markdown":
+                            df.iat[row_idx, col_idx] = f"[{text}]({url})"
+                        else:
+                            df.iat[row_idx, col_idx] = f"{text} ({url})"
+
+            updated_sheet_to_dataframe[key] = df
+
+        tables = []
+        metadata = []
+        for key, value in updated_sheet_to_dataframe.items():
+            if self.table_format == "csv":
+                resolved_kwargs = {"index": True, "header": True, "lineterminator": "\n", **self.table_format_kwargs}
+                tables.append(value.to_csv(**resolved_kwargs))
+            else:
+                resolved_kwargs = {
+                    "index": True,
+                    "headers": value.columns,
+                    "tablefmt": "pipe",
+                    **self.table_format_kwargs,
+                }
+                # to_markdown uses tabulate
+                tables.append(value.to_markdown(**resolved_kwargs))
+            # add sheet_name to metadata
+            metadata.append({"xlsx": {"sheet_name": key}})
+        return tables, metadata