feat: Add CSV splitter with side-by-side BFS detection

haystack/components/converters/csv.py

@@ -2,10 +2,13 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
+import csv
 import io
 import os
+import warnings
+from collections import deque
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Deque, Dict, List, Optional, Tuple, Union
 
 from haystack import Document, component, logging
 from haystack.components.converters.utils import get_bytestream_from_source, normalize_metadata
@@ -102,3 +105,231 @@ def run(
             documents.append(document)
 
         return {"documents": documents}
+
+
+@component
+class CSVDocumentSplitter:
+    """
+    A component to split a CSV document into multiple CSV documents
+
+    It is based on:
+    1) Consecutive empty rows (the "classic" approach).
+    2) Optionally detecting side-by-side tables (via a BFS search for non-empty cells).
+
+    Attributes:
+        split_threshold (int): The number of consecutive empty rows required to split the CSV. Default is 2.
+        delimiter (str): The delimiter used in the CSV. Default is ','.
+        quotechar (str): The quote character used in the CSV. Default is '"'.
+        trim_empty_rows (bool): Whether to trim leading/trailing empty rows in each block. Default is True.
+        skip_errors (bool): Whether to skip documents with parsing errors. Default is True.
+        detect_side_tables (bool): Whether to detect tables that sit side-by-side in different columns.
+                                   If True, a more expensive BFS-based approach is used.
+    """
+
+    def __init__(
+        self,
+        split_threshold: int = 2,
+        delimiter: str = ",",
+        quotechar: str = '"',
+        trim_empty_rows: bool = True,
+        skip_errors: bool = True,
+        detect_side_tables: bool = False,
+    ) -> None:
+        if split_threshold < 1:
+            raise ValueError("split_threshold must be at least 1")
+        self.split_threshold = split_threshold
+        self.delimiter = delimiter
+        self.quotechar = quotechar
+        self.trim_empty_rows = trim_empty_rows
+        self.skip_errors = skip_errors
+        self.detect_side_tables = detect_side_tables
+
+    def _split_csv_content(self, content: str) -> List[str]:
+        """Splits CSV content into blocks of CSV text."""
+        try:
+            reader = csv.reader(
+                io.StringIO(content), delimiter=self.delimiter, quotechar=self.quotechar, skipinitialspace=True
+            )
+            rows = list(reader)
+        except csv.Error as e:
+            raise ValueError(f"CSV parsing error: {str(e)}") from e
+
+        if self.detect_side_tables:
+            blocks_2d = self._split_rows_side_by_side(rows)
+        else:
+            blocks_2d = self._split_rows_into_blocks(rows)
+
+        return [self._block_to_csv(b) for b in blocks_2d]
+
+    def _split_rows_into_blocks(self, rows: List[List[str]]) -> List[List[List[str]]]:
+        """
+        "Classic" approach: identifies table blocks separated by consecutive empty rows.
+
+        Controlled by `split_threshold`.
+        """
+        blocks: List[List[List[str]]] = []
+        current_block: List[List[str]] = []
+        empty_count: int = 0
+
+        for row in rows:
+            # row is considered empty if all cells are blank
+            if all(cell.strip() == "" for cell in row):
+                empty_count += 1
+            else:
+                if empty_count >= self.split_threshold and current_block:
+                    blocks.append(current_block)
+                    current_block = []
+                empty_count = 0
+                current_block.append(row)
+
+        if current_block:
+            blocks.append(current_block)
+
+        return self._clean_blocks(blocks)
+
+    def _split_rows_side_by_side(self, rows: List[List[str]]) -> List[List[List[str]]]:
+        """
+        BFS to detect *all* distinct regions of non-empty cells, including side-by-side tables.
+
+        Each connected group of cells is output as a separate block.
+        By "connected," we mean non-empty cells that touch horizontally or vertically.
+        """
+        if not rows:
+            return []
+
+        # 1) Normalize row lengths so we have a 2D grid
+        max_cols = max(len(row) for row in rows)
+        for row in rows:
+            while len(row) < max_cols:
+                row.append("")
+
+        R, C = len(rows), max_cols
+        visited = [[False] * C for _ in range(R)]
+        directions = [(1, 0), (-1, 0), (0, 1), (0, -1)]
+
+        blocks = []
+
+        def bfs(start_r: int, start_c: int) -> List[Tuple[int, int]]:
+            """Collect all connected (non-empty) cells using BFS starting from (start_r, start_c)."""
+            queue: Deque[Tuple[int, int]] = deque()
+            queue.append((start_r, start_c))
+            visited[start_r][start_c] = True
+            connected_cells = [(start_r, start_c)]
+
+            while queue:
+                r0, c0 = queue.popleft()
+                for dr, dc in directions:
+                    rr, cc = r0 + dr, c0 + dc
+                    if 0 <= rr < R and 0 <= cc < C and not visited[rr][cc] and rows[rr][cc].strip() != "":
+                        visited[rr][cc] = True
+                        queue.append((rr, cc))
+                        connected_cells.append((rr, cc))
+            return connected_cells
+
+        # 2) Find connected components of non-empty cells
+        for r in range(R):
+            for c in range(C):
+                if not visited[r][c] and rows[r][c].strip() != "":
+                    cells = bfs(r, c)
+                    min_r = min(x[0] for x in cells)
+                    max_r = max(x[0] for x in cells)
+                    min_c = min(x[1] for x in cells)
+                    max_c = max(x[1] for x in cells)
+                    submatrix = []
+                    for rr in range(min_r, max_r + 1):
+                        row_slice = rows[rr][min_c : max_c + 1]
+                        submatrix.append(row_slice)
+                    blocks.append(submatrix)
+
+        cleaned_blocks = []
+        for b in blocks:
+            cb = self._clean_single_block_2d(b)
+            if cb:
+                cleaned_blocks.append(cb)
+
+        return cleaned_blocks
+
+    def _clean_blocks(self, blocks: List[List[List[str]]]) -> List[List[List[str]]]:
+        """
+        Existing row-based cleaning (trim leading/trailing empty rows).
+        """
+        cleaned = []
+        for block in blocks:
+            if not self.trim_empty_rows:
+                cleaned_block = block.copy()
+            else:
+                start, end = 0, len(block)
+                while start < end and all(cell.strip() == "" for cell in block[start]):
+                    start += 1
+                while end > start and all(cell.strip() == "" for cell in block[end - 1]):
+                    end -= 1
+                cleaned_block = block[start:end]
+
+            if cleaned_block:
+                cleaned.append(cleaned_block)
+        return cleaned
+
+    def _clean_single_block_2d(self, block_2d: List[List[str]]) -> List[List[str]]:
+        """
+        For a 2D block remove empty top/bottom rows, and empty left/right columns.
+        """
+        if not block_2d:
+            return []
+
+        if self.trim_empty_rows:
+            start_r, end_r = 0, len(block_2d)
+            while start_r < end_r and all(cell.strip() == "" for cell in block_2d[start_r]):
+                start_r += 1
+            while end_r > start_r and all(cell.strip() == "" for cell in block_2d[end_r - 1]):
+                end_r -= 1
+            block_2d = block_2d[start_r:end_r]
+
+        if not block_2d:
+            return []
+
+        min_col, max_col = 0, len(block_2d[0])
+        while min_col < max_col:
+            if all(row[min_col].strip() == "" for row in block_2d):
+                min_col += 1
+            else:
+                break
+        while max_col > min_col:
+            if all(row[max_col - 1].strip() == "" for row in block_2d):
+                max_col -= 1
+            else:
+                break
+
+        if min_col >= max_col:
+            return []
+
+        cleaned = [row[min_col:max_col] for row in block_2d]
+        return cleaned
+
+    def _block_to_csv(self, block: List[List[str]]) -> str:
+        """Converts a 2D block of rows into CSV text."""
+        output = io.StringIO()
+        writer = csv.writer(output, delimiter=self.delimiter, quotechar=self.quotechar, quoting=csv.QUOTE_MINIMAL)
+        writer.writerows(block)
+        return output.getvalue().strip()
+
+    @component.output_types(documents=List[Document])
+    def run(self, documents: List[Document]) -> Dict[str, List[Document]]:
+        """Processes documents to split CSV content."""
+        split_docs: List[Document] = []
+        for doc in documents:
+            if not doc.content:
+                continue
+            try:
+                blocks = self._split_csv_content(doc.content)
+            except Exception as e:
+                if not self.skip_errors:
+                    raise
+                warnings.warn(f"Skipping document {doc.id}: {str(e)}", UserWarning)
+                continue
+
+            for idx, csv_text in enumerate(blocks):
+                meta = doc.meta.copy()
+                meta["split_id"] = idx
+                split_docs.append(Document(content=csv_text, meta=meta))
+
+        return {"documents": split_docs}