ValueError: attempt to get argmin of an empty sequence when converting PDF with no headings

[rafikoham]

When attempting to convert a PDF document that doesn't contain any detectable headings using document_to_markdown, a ValueError is raised:

ValueError: attempt to get argmin of an empty sequence

This error occurs within the add_heading_level_metadata function in raglite document_to_markdown.py. It's triggered when the heading_font_sizes array is empty, indicating that no heading font sizes could be determined from the PDF.

I guess it's trying to find the minimum value in an empty NumPy array (heading_font_sizes). This suggests that the PDF file that i have may not have any detectable headings or their font sizes are not being recognized correctly.

Here's the pdf file :

groovy.pdf

------------- Full error --------------------------------------------------

---

ValueError Traceback (most recent call last)
in <cell line: 0>()
1 doc_path=Path(file_path)
----> 2 doc=document_to_markdown(doc_path)
3 doc

4 frames
/usr/local/lib/python3.11/dist-packages/raglite/_markdown.py in document_to_markdown(doc_path)
202 # Parse the PDF with pdftext and convert it to Markdown.
203 pages = dictionary_output(doc_path, sort=True, keep_chars=False)
--> 204 doc = "\n\n".join(parsed_pdf_to_markdown(pages))
205 else:
206 try:

/usr/local/lib/python3.11/dist-packages/raglite/_markdown.py in parsed_pdf_to_markdown(pages)
184
185 # Add heading level metadata.
--> 186 pages = add_heading_level_metadata(pages)
187 # Add emphasis metadata.
188 pages = add_emphasis_metadata(pages)

/usr/local/lib/python3.11/dist-packages/raglite/_markdown.py in add_heading_level_metadata(pages)
75 idx = 6
76 else:
---> 77 idx = np.argmin(np.abs(heading_font_sizes - span_font_size)) # type: ignore[assignment]
78 span["md"]["heading_level"] = idx + 1
79 heading_level[idx] += len(span["text"])

/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py in argmin(a, axis, out, keepdims)
1323 """
1324 kwds = {'keepdims': keepdims} if keepdims is not np._NoValue else {}
-> 1325 return _wrapfunc(a, 'argmin', axis=axis, out=out, **kwds)
1326
1327

/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
57
58 try:
---> 59 return bound(*args, **kwds)
60 except TypeError:
61 # A TypeError occurs if the object does have such a method in its

ValueError: attempt to get argmin of an empty sequence

[rafikoham]

The error occurs because the heading_font_sizes array, which is derived from the clustering of font sizes larger than the mode, is empty. This could happen if the document doesn't have any font sizes larger than the mode font size, or if the clustering process fails due to insufficient data.

[rafikoham]

I've tried to fix this and it worked by doing the following :

• Added validation for empty PDFs and PDFs without extractable text
• Added a fallback to extract raw text if markdown conversion fails
• Made font size handling more robust by:
  - Checking for empty heading font sizes
  - Treating text as paragraphs when no headings are found
  - Made mdformat optional in case it fails

 """Convert any document to Markdown."""

import re
from copy import deepcopy
from pathlib import Path
from typing import Any

import mdformat
import numpy as np
from pdftext.extraction import dictionary_output
from sklearn.cluster import KMeans


def parsed_pdf_to_markdown(pages: list[dict[str, Any]]) -> list[str]:  # noqa: C901, PLR0915
    """Convert a PDF parsed with pdftext to Markdown."""


    def add_heading_level_metadata(pages: list[dict[str, Any]]) -> list[dict[str, Any]]:  # noqa: C901
        """Add heading level metadata to a PDF parsed with pdftext."""

        def extract_font_size(span: dict[str, Any]) -> float:
            """Extract the font size from a text span."""
            font_size: float = 1.0
            if span["font"]["size"] > 1:  # A value of 1 appears to mean "unknown" in pdftext.
                font_size = span["font"]["size"]
            elif digit_sequences := re.findall(r"\d+", span["font"]["name"] or ""):
                font_size = float(digit_sequences[-1])
            elif "\n" not in span["text"]:  # Occasionally a span can contain a newline character.
                if round(span["rotation"]) in (0.0, 180.0, -180.0):
                    font_size = span["bbox"][3] - span["bbox"][1]
                elif round(span["rotation"]) in (90.0, -90.0, 270.0, -270.0):
                    font_size = span["bbox"][2] - span["bbox"][0]
            return font_size

        # Copy the pages.
        pages = deepcopy(pages)
        # Extract an array of all font sizes used by the text spans.
        font_sizes = np.asarray(
            [
                extract_font_size(span)
                for page in pages
                for block in page["blocks"]
                for line in block["lines"]
                for span in line["spans"]
            ]
        )
        font_sizes = np.round(font_sizes * 2) / 2
        unique_font_sizes, counts = np.unique(font_sizes, return_counts=True)
        # Determine the paragraph font size as the mode font size.
        tiny = unique_font_sizes < min(5, np.max(unique_font_sizes))
        counts[tiny] = -counts[tiny]
        mode = np.argmax(counts)
        counts[tiny] = -counts[tiny]
        mode_font_size = unique_font_sizes[mode]
        # Determine (at most) 6 heading font sizes by clustering font sizes larger than the mode.
        heading_font_sizes = unique_font_sizes[mode + 1 :]
        if len(heading_font_sizes) > 0:
            heading_counts = counts[mode + 1 :]
            kmeans = KMeans(n_clusters=min(6, len(heading_font_sizes)), random_state=42)
            kmeans.fit(heading_font_sizes[:, np.newaxis], sample_weight=heading_counts)
            heading_font_sizes = np.sort(np.ravel(kmeans.cluster_centers_))[::-1]
        # Add heading level information to the text spans and lines.
        for page in pages:
            for block in page["blocks"]:
                for line in block["lines"]:
                    if "md" not in line:
                        line["md"] = {}
                    heading_level = np.zeros(8)  # 0-5: <h1>-<h6>, 6: <p>, 7: <small>
                    for span in line["spans"]:
                        if "md" not in span:
                            span["md"] = {}
                        span_font_size = extract_font_size(span)
                        if span_font_size < mode_font_size:
                            idx = 7
                        elif span_font_size == mode_font_size:
                            idx = 6
                        else:
                            # Handle case when there are no heading font sizes
                            if len(heading_font_sizes) == 0:
                                idx = 6  # Treat as normal paragraph text
                            else:
                                idx = np.argmin(np.abs(heading_font_sizes - span_font_size))
                        span["md"]["heading_level"] = idx + 1
                        heading_level[idx] += len(span["text"])
                    line["md"]["heading_level"] = np.argmax(heading_level) + 1
        return pages

    def add_emphasis_metadata(pages: list[dict[str, Any]]) -> list[dict[str, Any]]:
        """Add emphasis metadata such as bold and italic to a PDF parsed with pdftext."""
        # Copy the pages.
        pages = deepcopy(pages)
        # Add emphasis metadata to the text spans.
        for page in pages:
            for block in page["blocks"]:
                for line in block["lines"]:
                    if "md" not in line:
                        line["md"] = {}
                    for span in line["spans"]:
                        if "md" not in span:
                            span["md"] = {}
                        span["md"]["bold"] = span["font"]["weight"] > 500  # noqa: PLR2004
                        span["md"]["italic"] = "ital" in (span["font"]["name"] or "").lower()
                    line["md"]["bold"] = all(
                        span["md"]["bold"] for span in line["spans"] if span["text"].strip()
                    )
                    line["md"]["italic"] = all(
                        span["md"]["italic"] for span in line["spans"] if span["text"].strip()
                    )
        return pages

    def strip_page_numbers(pages: list[dict[str, Any]]) -> list[dict[str, Any]]:
        """Strip page numbers from a PDF parsed with pdftext."""
        # Copy the pages.
        pages = deepcopy(pages)
        # Remove lines that only contain a page number.
        for page in pages:
            for block in page["blocks"]:
                block["lines"] = [
                    line
                    for line in block["lines"]
                    if not re.match(
                        r"^\s*[#0]*\d+\s*$", "".join(span["text"] for span in line["spans"])
                    )
                ]
        return pages

    def convert_to_markdown(pages: list[dict[str, Any]]) -> list[str]:  # noqa: C901, PLR0912
        """Convert a list of pages to Markdown."""
        pages_md = []
        for page in pages:
            page_md = ""
            for block in page["blocks"]:
                block_text = ""
                for line in block["lines"]:
                    # Build the line text and style the spans.
                    line_text = ""
                    for span in line["spans"]:
                        if (
                            not line["md"]["bold"]
                            and not line["md"]["italic"]
                            and span["md"]["bold"]
                            and span["md"]["italic"]
                        ):
                            line_text += f"***{span['text']}***"
                        elif not line["md"]["bold"] and span["md"]["bold"]:
                            line_text += f"**{span['text']}**"
                        elif not line["md"]["italic"] and span["md"]["italic"]:
                            line_text += f"*{span['text']}*"
                        else:
                            line_text += span["text"]
                    # Add emphasis to the line (if it's not a heading or whitespace).
                    line_text = line_text.rstrip()
                    line_is_whitespace = not line_text.strip()
                    line_is_heading = line["md"]["heading_level"] <= 6  # noqa: PLR2004
                    if not line_is_heading and not line_is_whitespace:
                        if line["md"]["bold"] and line["md"]["italic"]:
                            line_text = f"***{line_text}***"
                        elif line["md"]["bold"]:
                            line_text = f"**{line_text}**"
                        elif line["md"]["italic"]:
                            line_text = f"*{line_text}*"
                    # Set the heading level.
                    if line_is_heading and not line_is_whitespace:
                        line_text = f"{'#' * line['md']['heading_level']} {line_text}"
                    line_text += "\n"
                    block_text += line_text
                block_text = block_text.rstrip() + "\n\n"
                page_md += block_text
            pages_md.append(page_md.strip())
        return pages_md

    def merge_split_headings(pages: list[str]) -> list[str]:
        """Merge headings that are split across lines."""

        def _merge_split_headings(match: re.Match[str]) -> str:
            atx_headings = [line.strip("# ").strip() for line in match.group().splitlines()]
            return f"{match.group(1)} {' '.join(atx_headings)}\n\n"

        pages_md = [
            re.sub(
                r"^(#+)[ \t]+[^\n]+\n+(?:^\1[ \t]+[^\n]+\n+)+",
                _merge_split_headings,
                page,
                flags=re.MULTILINE,
            )
            for page in pages
        ]
        return pages_md

    # Add heading level metadata.
    pages = add_heading_level_metadata(pages)
    # Add emphasis metadata.
    pages = add_emphasis_metadata(pages)
    # Strip page numbers.
    pages = strip_page_numbers(pages)
    # Convert the pages to Markdown.
    pages_md = convert_to_markdown(pages)
    # Merge headings that are split across lines.
    pages_md = merge_split_headings(pages_md)
    return pages_md


def document_to_markdown(doc_path: Path) -> str:
    """Convert any document to GitHub Flavored Markdown."""
    # Convert the file's content to GitHub Flavored Markdown.
    if doc_path.suffix == ".pdf":
        try:
            # Parse the PDF with pdftext and convert it to Markdown.
            pages = dictionary_output(doc_path, sort=True, keep_chars=False)
            
            # Validate pages
            if not pages:
                return "Error: PDF appears to be empty"
                
            # Check if pages have valid content
            has_content = False
            for page in pages:
                if page.get("blocks"):
                    for block in page["blocks"]:
                        if block.get("lines"):
                            for line in block["lines"]:
                                if line.get("spans"):
                                    for span in line["spans"]:
                                        if span.get("text", "").strip():
                                            has_content = True
                                            break
                                    if has_content:
                                        break
                            if has_content:
                                break
                    if has_content:
                        break
            
            if not has_content:
                return "Error: PDF contains no extractable text"
                
            try:
                doc = "\n\n".join(parsed_pdf_to_markdown(pages))
            except Exception as e:
                # If markdown conversion fails, return raw text
                text_content = []
                for page in pages:
                    for block in page.get("blocks", []):
                        for line in block.get("lines", []):
                            for span in line.get("spans", []):
                                if text := span.get("text", "").strip():
                                    text_content.append(text)
                doc = "\n\n".join(text_content)
                
        except Exception as e:
            return f"Error processing PDF: {str(e)}"
    else:
        try:
            # Use pandoc for everything else.
            import pypandoc
            doc = pypandoc.convert_file(doc_path, to="gfm")
        except ImportError as error:
            error_message = (
                "To convert files to Markdown with pandoc, please install the `pandoc` extra."
            )
            raise ImportError(error_message) from error
        except RuntimeError:
            # File format not supported, fall back to reading the text.
            doc = doc_path.read_text()
            
    # Improve Markdown quality.
    try:
        doc = mdformat.text(doc)
    except:
        pass  # If mdformat fails, return unformatted text
        
    return doc
`

[rafikoham]

Here are the modified blocks :

Handle case when there are no heading font sizes so I've put it to idx=6 and treat it like a normal paraghraph

Adding this block which parse the pdf into md with pdftext

And improve the quality with mdformat (pass otherwise)

[rafikoham]

I've forked the project and applied the modifications,

I can create a PR if you want.

https://github.com/rafikoham/raglite

Yours Faithfully,

Rafik Adonis Hammoutène