Bump version for release. (#1564 )

Remove onnxruntime<=1.20.1 Windows pin (#1551 )
Add text/markdown to Accept header (#1554 )
2026-02-20 11:40:57 -08:00 · 2026-02-16 15:05:37 -08:00 · 2026-02-13 11:53:01 -08:00 · 2026-02-13 10:45:39 -08:00 · 2026-01-08 15:15:22 -08:00 · 2026-01-07 16:38:45 -08:00
35 changed files with 2809 additions and 44 deletions
@@ -1,2 +1,5 @@
 packages/markitdown/tests/test_files/** linguist-vendored
 packages/markitdown-sample-plugin/tests/test_files/** linguist-vendored
+
+# Treat PDF files as binary to prevent line ending conversion
+*.pdf binary
@@ -5,7 +5,7 @@ jobs:
  pre-commit:
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
@@ -5,7 +5,7 @@ jobs:
  tests:
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
      - uses: actions/setup-python@v5
        with:
          python-version: |
@@ -52,6 +52,7 @@ coverage.xml
 .hypothesis/
 .pytest_cache/
 cover/
+.test-logs/

 # Translations
 *.mo
@@ -15,7 +15,7 @@

 MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. To this end, it is most comparable to [textract](https://github.com/deanmalmgren/textract), but with a focus on preserving important document structure and content as Markdown (including: headings, lists, tables, links, etc.) While the output is often reasonably presentable and human-friendly, it is meant to be consumed by text analysis tools -- and may not be the best option for high-fidelity document conversions for human consumption.

-At present, MarkItDown supports:
+MarkItDown currently supports the conversion from:

 - PDF
 - PowerPoint
@@ -164,14 +164,14 @@ result = md.convert("test.pdf")
 print(result.text_content)
 ```

-To use Large Language Models for image descriptions, provide `llm_client` and `llm_model`:
+To use Large Language Models for image descriptions (currently only for pptx and image files), provide `llm_client` and `llm_model`:

 ```python
 from markitdown import MarkItDown
 from openai import OpenAI

 client = OpenAI()
-md = MarkItDown(llm_client=client, llm_model="gpt-4o")
+md = MarkItDown(llm_client=client, llm_model="gpt-4o", llm_prompt="optional custom prompt")
 result = md.convert("example.jpg")
 print(result.text_content)
 ```
@@ -199,7 +199,7 @@ contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additio

 ### How to Contribute

-You can help by looking at issues or helping review PRs. Any issue or PR is welcome, but we have also marked some as 'open for contribution' and 'open for reviewing' to help facilitate community contributions. These are ofcourse just suggestions and you are welcome to contribute in any way you like.
+You can help by looking at issues or helping review PRs. Any issue or PR is welcome, but we have also marked some as 'open for contribution' and 'open for reviewing' to help facilitate community contributions. These are of course just suggestions and you are welcome to contribute in any way you like.

 <div align="center">

@@ -3,8 +3,10 @@ FROM python:3.13-slim-bullseye
 ENV DEBIAN_FRONTEND=noninteractive
 ENV EXIFTOOL_PATH=/usr/bin/exiftool
 ENV FFMPEG_PATH=/usr/bin/ffmpeg
+ENV MARKITDOWN_ENABLE_PLUGINS=True

 # Runtime dependency
+# NOTE: Add any additional MarkItDown plugins here
 RUN apt-get update && apt-get install -y --no-install-recommends \
    ffmpeg \
    exiftool
@@ -54,7 +54,7 @@ Once mounted, all files under data will be accessible under `/workdir` in the co

 It is recommended to use the Docker image when running the MCP server for Claude Desktop.

-Follow [these instrutions](https://modelcontextprotocol.io/quickstart/user#for-claude-desktop-users) to access Claude's `claude_desktop_config.json` file.
+Follow [these instructions](https://modelcontextprotocol.io/quickstart/user#for-claude-desktop-users) to access Claude's `claude_desktop_config.json` file.

 Edit it to include the following JSON entry:

@@ -102,7 +102,7 @@ To debug the MCP server you can use the `mcpinspector` tool.
 npx @modelcontextprotocol/inspector
 ```

-You can then connect to the insepctor through the specified host and port (e.g., `http://localhost:5173/`).
+You can then connect to the inspector through the specified host and port (e.g., `http://localhost:5173/`).

 If using STDIO:
 * select `STDIO` as the transport type,
@@ -127,8 +127,7 @@ Finally:

 ## Security Considerations

-The server does not support authentication, and runs with the privileges if the user running it. For this reason, when running in SSE or Streamable HTTP mode, it is recommended to run the server bound to `localhost` (default).
-
+The server does not support authentication, and runs with the privileges of the user running it. For this reason, when running in SSE or Streamable HTTP mode, it is recommended to run the server bound to `localhost` (default).

 ## Trademarks

@@ -1,5 +1,6 @@
 import contextlib
 import sys
+import os
 from collections.abc import AsyncIterator
 from mcp.server.fastmcp import FastMCP
 from starlette.applications import Starlette
@@ -19,7 +20,15 @@ mcp = FastMCP("markitdown")
@mcp.tool()
 async def convert_to_markdown(uri: str) -> str:
    """Convert a resource described by an http:, https:, file: or data: URI to markdown"""
-    return MarkItDown().convert_uri(uri).markdown
+    return MarkItDown(enable_plugins=check_plugins_enabled()).convert_uri(uri).markdown
+
+
+def check_plugins_enabled() -> bool:
+    return os.getenv("MARKITDOWN_ENABLE_PLUGINS", "false").strip().lower() in (
+        "true",
+        "1",
+        "yes",
+    )


 def create_starlette_app(mcp_server: Server, *, debug: bool = False) -> Starlette:
@@ -35,24 +35,25 @@ dependencies = [
 [project.optional-dependencies]
 all = [
  "python-pptx",
-  "mammoth",
+  "mammoth~=1.11.0",
  "pandas",
  "openpyxl",
  "xlrd",
  "lxml",
-  "pdfminer.six",
+  "pdfminer.six>=20251230",
+  "pdfplumber>=0.11.9",
  "olefile",
  "pydub",
  "SpeechRecognition",
  "youtube-transcript-api~=1.0.0",
  "azure-ai-documentintelligence",
-  "azure-identity"
+  "azure-identity",
 ]
 pptx = ["python-pptx"]
-docx = ["mammoth", "lxml"]
+docx = ["mammoth~=1.11.0", "lxml"]
 xlsx = ["pandas", "openpyxl"]
 xls = ["pandas", "xlrd"]
-pdf = ["pdfminer.six"]
+pdf = ["pdfminer.six>=20251230", "pdfplumber>=0.11.9"]
 outlook = ["olefile"]
 audio-transcription = ["pydub", "SpeechRecognition"]
 youtube-transcription = ["youtube-transcript-api"]
@@ -1,4 +1,4 @@
 # SPDX-FileCopyrightText: 2024-present Adam Fourney <adamfo@microsoft.com>
 #
 # SPDX-License-Identifier: MIT
-__version__ = "0.1.2"
+__version__ = "0.1.5"
@@ -69,7 +69,7 @@ class DocumentConverter:
        data = file_stream.read(100) # ... peek at the first 100 bytes, etc.
        file_stream.seek(cur_pos)    # Reset the position to the original position

-        Prameters:
+        Parameters:
        - file_stream: The file-like object to convert. Must support seek(), tell(), and read() methods.
        - stream_info: The StreamInfo object containing metadata about the file (mimetype, extension, charset, set)
        - kwargs: Additional keyword arguments for the converter.
@@ -90,7 +90,7 @@ class DocumentConverter:
        """
        Convert a document to Markdown text.

-        Prameters:
+        Parameters:
        - file_stream: The file-like object to convert. Must support seek(), tell(), and read() methods.
        - stream_info: The StreamInfo object containing metadata about the file (mimetype, extension, charset, set)
        - kwargs: Additional keyword arguments for the converter.
@@ -107,6 +107,13 @@ class MarkItDown:
        requests_session = kwargs.get("requests_session")
        if requests_session is None:
            self._requests_session = requests.Session()
+            # Signal that we prefer markdown over HTML, etc. if the server supports it.
+            # e.g., https://blog.cloudflare.com/markdown-for-agents/
+            self._requests_session.headers.update(
+                {
+                    "Accept": "text/markdown, text/html;q=0.9, text/plain;q=0.8, */*;q=0.1"
+                }
+            )
        else:
            self._requests_session = requests_session

@@ -115,6 +122,7 @@ class MarkItDown:
        # TODO - remove these (see enable_builtins)
        self._llm_client: Any = None
        self._llm_model: Union[str | None] = None
+        self._llm_prompt: Union[str | None] = None
        self._exiftool_path: Union[str | None] = None
        self._style_map: Union[str | None] = None

@@ -139,6 +147,7 @@ class MarkItDown:
            # TODO: Move these into converter constructors
            self._llm_client = kwargs.get("llm_client")
            self._llm_model = kwargs.get("llm_model")
+            self._llm_prompt = kwargs.get("llm_prompt")
            self._exiftool_path = kwargs.get("exiftool_path")
            self._style_map = kwargs.get("style_map")

@@ -559,6 +568,9 @@ class MarkItDown:
                if "llm_model" not in _kwargs and self._llm_model is not None:
                    _kwargs["llm_model"] = self._llm_model

+                if "llm_prompt" not in _kwargs and self._llm_prompt is not None:
+                    _kwargs["llm_prompt"] = self._llm_prompt
+
                if "style_map" not in _kwargs and self._style_map is not None:
                    _kwargs["style_map"] = self._style_map

@@ -84,6 +84,9 @@ def _get_mime_type_prefixes(types: List[DocumentIntelligenceFileType]) -> List[s
            prefixes.append(
                "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
            )
+        elif type_ == DocumentIntelligenceFileType.HTML:
+            prefixes.append("text/html")
+            prefixes.append("application/xhtml+xml")
        elif type_ == DocumentIntelligenceFileType.PDF:
            prefixes.append("application/pdf")
            prefixes.append("application/x-pdf")
@@ -119,6 +122,8 @@ def _get_file_extensions(types: List[DocumentIntelligenceFileType]) -> List[str]
            extensions.append(".bmp")
        elif type_ == DocumentIntelligenceFileType.TIFF:
            extensions.append(".tiff")
+        elif type_ == DocumentIntelligenceFileType.HTML:
+            extensions.append(".html")
    return extensions


@@ -1,4 +1,6 @@
 import sys
+import io
+from warnings import warn

 from typing import BinaryIO, Any

@@ -13,6 +15,7 @@ from .._exceptions import MissingDependencyException, MISSING_DEPENDENCY_MESSAGE
 _dependency_exc_info = None
 try:
    import mammoth
+
 except ImportError:
    # Preserve the error and stack trace for later
    _dependency_exc_info = sys.exc_info()
@@ -1,7 +1,11 @@
 import json
-import subprocess
 import locale
-from typing import BinaryIO, Any, Union
+import subprocess
+from typing import Any, BinaryIO, Union
+
+
+def _parse_version(version: str) -> tuple:
+    return tuple(map(int, (version.split("."))))


 def exiftool_metadata(
@@ -13,6 +17,24 @@ def exiftool_metadata(
    if not exiftool_path:
        return {}

+    # Verify exiftool version
+    try:
+        version_output = subprocess.run(
+            [exiftool_path, "-ver"],
+            capture_output=True,
+            text=True,
+            check=True,
+        ).stdout.strip()
+        version = _parse_version(version_output)
+        min_version = (12, 24)
+        if version < min_version:
+            raise RuntimeError(
+                f"ExifTool version {version_output} is vulnerable to CVE-2021-22204. "
+                "Please upgrade to version 12.24 or later."
+            )
+    except (subprocess.CalledProcessError, ValueError) as e:
+        raise RuntimeError("Failed to verify ExifTool version.") from e
+
    # Run exiftool
    cur_pos = file_stream.tell()
    try:
@@ -92,9 +92,11 @@ class _CustomMarkdownify(markdownify.MarkdownConverter):
        """Same as usual converter, but removes data URIs"""

        alt = el.attrs.get("alt", None) or ""
-        src = el.attrs.get("src", None) or ""
+        src = el.attrs.get("src", None) or el.attrs.get("data-src", None) or ""
        title = el.attrs.get("title", None) or ""
        title_part = ' "%s"' % title.replace('"', r"\"") if title else ""
+        # Remove all line breaks from alt
+        alt = alt.replace("\n", " ")
        if (
            convert_as_inline
            and el.parent.name not in self.options["keep_inline_images_in"]
@@ -107,5 +109,18 @@ class _CustomMarkdownify(markdownify.MarkdownConverter):

        return "![%s](%s%s)" % (alt, src, title_part)

+    def convert_input(
+        self,
+        el: Any,
+        text: str,
+        convert_as_inline: Optional[bool] = False,
+        **kwargs,
+    ) -> str:
+        """Convert checkboxes to Markdown [x]/[ ] syntax."""
+
+        if el.get("type") == "checkbox":
+            return "[x] " if el.has_attr("checked") else "[ ] "
+        return ""
+
    def convert_soup(self, soup: Any) -> str:
        return super().convert_soup(soup)  # type: ignore
@@ -1,22 +1,69 @@
 import sys
 import io
-
+import re
 from typing import BinaryIO, Any

-
 from .._base_converter import DocumentConverter, DocumentConverterResult
 from .._stream_info import StreamInfo
 from .._exceptions import MissingDependencyException, MISSING_DEPENDENCY_MESSAGE

+# Pattern for MasterFormat-style partial numbering (e.g., ".1", ".2", ".10")
+PARTIAL_NUMBERING_PATTERN = re.compile(r"^\.\d+$")

-# Try loading optional (but in this case, required) dependencies
-# Save reporting of any exceptions for later
+
+def _merge_partial_numbering_lines(text: str) -> str:
+    """
+    Post-process extracted text to merge MasterFormat-style partial numbering
+    with the following text line.
+
+    MasterFormat documents use partial numbering like:
+        .1  The intent of this Request for Proposal...
+        .2  Available information relative to...
+
+    Some PDF extractors split these into separate lines:
+        .1
+        The intent of this Request for Proposal...
+
+    This function merges them back together.
+    """
+    lines = text.split("\n")
+    result_lines: list[str] = []
+    i = 0
+
+    while i < len(lines):
+        line = lines[i]
+        stripped = line.strip()
+
+        # Check if this line is ONLY a partial numbering
+        if PARTIAL_NUMBERING_PATTERN.match(stripped):
+            # Look for the next non-empty line to merge with
+            j = i + 1
+            while j < len(lines) and not lines[j].strip():
+                j += 1
+
+            if j < len(lines):
+                # Merge the partial numbering with the next line
+                next_line = lines[j].strip()
+                result_lines.append(f"{stripped} {next_line}")
+                i = j + 1  # Skip past the merged line
+            else:
+                # No next line to merge with, keep as is
+                result_lines.append(line)
+                i += 1
+        else:
+            result_lines.append(line)
+            i += 1
+
+    return "\n".join(result_lines)
+
+
+# Load dependencies
 _dependency_exc_info = None
 try:
    import pdfminer
    import pdfminer.high_level
+    import pdfplumber
 except ImportError:
-    # Preserve the error and stack trace for later
    _dependency_exc_info = sys.exc_info()


@@ -28,16 +75,435 @@ ACCEPTED_MIME_TYPE_PREFIXES = [
 ACCEPTED_FILE_EXTENSIONS = [".pdf"]


+def _to_markdown_table(table: list[list[str]], include_separator: bool = True) -> str:
+    """Convert a 2D list (rows/columns) into a nicely aligned Markdown table.
+
+    Args:
+        table: 2D list of cell values
+        include_separator: If True, include header separator row (standard markdown).
+                          If False, output simple pipe-separated rows.
+    """
+    if not table:
+        return ""
+
+    # Normalize None → ""
+    table = [[cell if cell is not None else "" for cell in row] for row in table]
+
+    # Filter out empty rows
+    table = [row for row in table if any(cell.strip() for cell in row)]
+
+    if not table:
+        return ""
+
+    # Column widths
+    col_widths = [max(len(str(cell)) for cell in col) for col in zip(*table)]
+
+    def fmt_row(row: list[str]) -> str:
+        return (
+            "|"
+            + "|".join(str(cell).ljust(width) for cell, width in zip(row, col_widths))
+            + "|"
+        )
+
+    if include_separator:
+        header, *rows = table
+        md = [fmt_row(header)]
+        md.append("|" + "|".join("-" * w for w in col_widths) + "|")
+        for row in rows:
+            md.append(fmt_row(row))
+    else:
+        md = [fmt_row(row) for row in table]
+
+    return "\n".join(md)
+
+
+def _extract_form_content_from_words(page: Any) -> str | None:
+    """
+    Extract form-style content from a PDF page by analyzing word positions.
+    This handles borderless forms/tables where words are aligned in columns.
+
+    Returns markdown with proper table formatting:
+    - Tables have pipe-separated columns with header separator rows
+    - Non-table content is rendered as plain text
+
+    Returns None if the page doesn't appear to be a form-style document,
+    indicating that pdfminer should be used instead for better text spacing.
+    """
+    words = page.extract_words(keep_blank_chars=True, x_tolerance=3, y_tolerance=3)
+    if not words:
+        return None
+
+    # Group words by their Y position (rows)
+    y_tolerance = 5
+    rows_by_y: dict[float, list[dict]] = {}
+    for word in words:
+        y_key = round(word["top"] / y_tolerance) * y_tolerance
+        if y_key not in rows_by_y:
+            rows_by_y[y_key] = []
+        rows_by_y[y_key].append(word)
+
+    # Sort rows by Y position
+    sorted_y_keys = sorted(rows_by_y.keys())
+    page_width = page.width if hasattr(page, "width") else 612
+
+    # First pass: analyze each row
+    row_info: list[dict] = []
+    for y_key in sorted_y_keys:
+        row_words = sorted(rows_by_y[y_key], key=lambda w: w["x0"])
+        if not row_words:
+            continue
+
+        first_x0 = row_words[0]["x0"]
+        last_x1 = row_words[-1]["x1"]
+        line_width = last_x1 - first_x0
+        combined_text = " ".join(w["text"] for w in row_words)
+
+        # Count distinct x-position groups (columns)
+        x_positions = [w["x0"] for w in row_words]
+        x_groups: list[float] = []
+        for x in sorted(x_positions):
+            if not x_groups or x - x_groups[-1] > 50:
+                x_groups.append(x)
+
+        # Determine row type
+        is_paragraph = line_width > page_width * 0.55 and len(combined_text) > 60
+
+        # Check for MasterFormat-style partial numbering (e.g., ".1", ".2")
+        # These should be treated as list items, not table rows
+        has_partial_numbering = False
+        if row_words:
+            first_word = row_words[0]["text"].strip()
+            if PARTIAL_NUMBERING_PATTERN.match(first_word):
+                has_partial_numbering = True
+
+        row_info.append(
+            {
+                "y_key": y_key,
+                "words": row_words,
+                "text": combined_text,
+                "x_groups": x_groups,
+                "is_paragraph": is_paragraph,
+                "num_columns": len(x_groups),
+                "has_partial_numbering": has_partial_numbering,
+            }
+        )
+
+    # Collect ALL x-positions from rows with 3+ columns (table-like rows)
+    # This gives us the global column structure
+    all_table_x_positions: list[float] = []
+    for info in row_info:
+        if info["num_columns"] >= 3 and not info["is_paragraph"]:
+            all_table_x_positions.extend(info["x_groups"])
+
+    if not all_table_x_positions:
+        return None
+
+    # Compute adaptive column clustering tolerance based on gap analysis
+    all_table_x_positions.sort()
+
+    # Calculate gaps between consecutive x-positions
+    gaps = []
+    for i in range(len(all_table_x_positions) - 1):
+        gap = all_table_x_positions[i + 1] - all_table_x_positions[i]
+        if gap > 5:  # Only significant gaps
+            gaps.append(gap)
+
+    # Determine optimal tolerance using statistical analysis
+    if gaps and len(gaps) >= 3:
+        # Use 70th percentile of gaps as threshold (balances precision/recall)
+        sorted_gaps = sorted(gaps)
+        percentile_70_idx = int(len(sorted_gaps) * 0.70)
+        adaptive_tolerance = sorted_gaps[percentile_70_idx]
+
+        # Clamp tolerance to reasonable range [25, 50]
+        adaptive_tolerance = max(25, min(50, adaptive_tolerance))
+    else:
+        # Fallback to conservative value
+        adaptive_tolerance = 35
+
+    # Compute global column boundaries using adaptive tolerance
+    global_columns: list[float] = []
+    for x in all_table_x_positions:
+        if not global_columns or x - global_columns[-1] > adaptive_tolerance:
+            global_columns.append(x)
+
+    # Adaptive max column check based on page characteristics
+    # Calculate average column width
+    if len(global_columns) > 1:
+        content_width = global_columns[-1] - global_columns[0]
+        avg_col_width = content_width / len(global_columns)
+
+        # Forms with very narrow columns (< 30px) are likely dense text
+        if avg_col_width < 30:
+            return None
+
+        # Compute adaptive max based on columns per inch
+        # Typical forms have 3-8 columns per inch
+        columns_per_inch = len(global_columns) / (content_width / 72)
+
+        # If density is too high (> 10 cols/inch), likely not a form
+        if columns_per_inch > 10:
+            return None
+
+        # Adaptive max: allow more columns for wider pages
+        # Standard letter is 612pt wide, so scale accordingly
+        adaptive_max_columns = int(20 * (page_width / 612))
+        adaptive_max_columns = max(15, adaptive_max_columns)  # At least 15
+
+        if len(global_columns) > adaptive_max_columns:
+            return None
+    else:
+        # Single column, not a form
+        return None
+
+    # Now classify each row as table row or not
+    # A row is a table row if it has words that align with 2+ of the global columns
+    for info in row_info:
+        if info["is_paragraph"]:
+            info["is_table_row"] = False
+            continue
+
+        # Rows with partial numbering (e.g., ".1", ".2") are list items, not table rows
+        if info["has_partial_numbering"]:
+            info["is_table_row"] = False
+            continue
+
+        # Count how many global columns this row's words align with
+        aligned_columns: set[int] = set()
+        for word in info["words"]:
+            word_x = word["x0"]
+            for col_idx, col_x in enumerate(global_columns):
+                if abs(word_x - col_x) < 40:
+                    aligned_columns.add(col_idx)
+                    break
+
+        # If row uses 2+ of the established columns, it's a table row
+        info["is_table_row"] = len(aligned_columns) >= 2
+
+    # Find table regions (consecutive table rows)
+    table_regions: list[tuple[int, int]] = []  # (start_idx, end_idx)
+    i = 0
+    while i < len(row_info):
+        if row_info[i]["is_table_row"]:
+            start_idx = i
+            while i < len(row_info) and row_info[i]["is_table_row"]:
+                i += 1
+            end_idx = i
+            table_regions.append((start_idx, end_idx))
+        else:
+            i += 1
+
+    # Check if enough rows are table rows (at least 20%)
+    total_table_rows = sum(end - start for start, end in table_regions)
+    if len(row_info) > 0 and total_table_rows / len(row_info) < 0.2:
+        return None
+
+    # Build output - collect table data first, then format with proper column widths
+    result_lines: list[str] = []
+    num_cols = len(global_columns)
+
+    # Helper function to extract cells from a row
+    def extract_cells(info: dict) -> list[str]:
+        cells: list[str] = ["" for _ in range(num_cols)]
+        for word in info["words"]:
+            word_x = word["x0"]
+            # Find the correct column using boundary ranges
+            assigned_col = num_cols - 1  # Default to last column
+            for col_idx in range(num_cols - 1):
+                col_end = global_columns[col_idx + 1]
+                if word_x < col_end - 20:
+                    assigned_col = col_idx
+                    break
+            if cells[assigned_col]:
+                cells[assigned_col] += " " + word["text"]
+            else:
+                cells[assigned_col] = word["text"]
+        return cells
+
+    # Process rows, collecting table data for proper formatting
+    idx = 0
+    while idx < len(row_info):
+        info = row_info[idx]
+
+        # Check if this row starts a table region
+        table_region = None
+        for start, end in table_regions:
+            if idx == start:
+                table_region = (start, end)
+                break
+
+        if table_region:
+            start, end = table_region
+            # Collect all rows in this table
+            table_data: list[list[str]] = []
+            for table_idx in range(start, end):
+                cells = extract_cells(row_info[table_idx])
+                table_data.append(cells)
+
+            # Calculate column widths for this table
+            if table_data:
+                col_widths = [
+                    max(len(row[col]) for row in table_data) for col in range(num_cols)
+                ]
+                # Ensure minimum width of 3 for separator dashes
+                col_widths = [max(w, 3) for w in col_widths]
+
+                # Format header row
+                header = table_data[0]
+                header_str = (
+                    "| "
+                    + " | ".join(
+                        cell.ljust(col_widths[i]) for i, cell in enumerate(header)
+                    )
+                    + " |"
+                )
+                result_lines.append(header_str)
+
+                # Format separator row
+                separator = (
+                    "| "
+                    + " | ".join("-" * col_widths[i] for i in range(num_cols))
+                    + " |"
+                )
+                result_lines.append(separator)
+
+                # Format data rows
+                for row in table_data[1:]:
+                    row_str = (
+                        "| "
+                        + " | ".join(
+                            cell.ljust(col_widths[i]) for i, cell in enumerate(row)
+                        )
+                        + " |"
+                    )
+                    result_lines.append(row_str)
+
+            idx = end  # Skip to end of table region
+        else:
+            # Check if we're inside a table region (not at start)
+            in_table = False
+            for start, end in table_regions:
+                if start < idx < end:
+                    in_table = True
+                    break
+
+            if not in_table:
+                # Non-table content
+                result_lines.append(info["text"])
+            idx += 1
+
+    return "\n".join(result_lines)
+
+
+def _extract_tables_from_words(page: Any) -> list[list[list[str]]]:
+    """
+    Extract tables from a PDF page by analyzing word positions.
+    This handles borderless tables where words are aligned in columns.
+
+    This function is designed for structured tabular data (like invoices),
+    not for multi-column text layouts in scientific documents.
+    """
+    words = page.extract_words(keep_blank_chars=True, x_tolerance=3, y_tolerance=3)
+    if not words:
+        return []
+
+    # Group words by their Y position (rows)
+    y_tolerance = 5
+    rows_by_y: dict[float, list[dict]] = {}
+    for word in words:
+        y_key = round(word["top"] / y_tolerance) * y_tolerance
+        if y_key not in rows_by_y:
+            rows_by_y[y_key] = []
+        rows_by_y[y_key].append(word)
+
+    # Sort rows by Y position
+    sorted_y_keys = sorted(rows_by_y.keys())
+
+    # Find potential column boundaries by analyzing x positions across all rows
+    all_x_positions = []
+    for words_in_row in rows_by_y.values():
+        for word in words_in_row:
+            all_x_positions.append(word["x0"])
+
+    if not all_x_positions:
+        return []
+
+    # Cluster x positions to find column starts
+    all_x_positions.sort()
+    x_tolerance_col = 20
+    column_starts: list[float] = []
+    for x in all_x_positions:
+        if not column_starts or x - column_starts[-1] > x_tolerance_col:
+            column_starts.append(x)
+
+    # Need at least 3 columns but not too many (likely text layout, not table)
+    if len(column_starts) < 3 or len(column_starts) > 10:
+        return []
+
+    # Find rows that span multiple columns (potential table rows)
+    table_rows = []
+    for y_key in sorted_y_keys:
+        words_in_row = sorted(rows_by_y[y_key], key=lambda w: w["x0"])
+
+        # Assign words to columns
+        row_data = [""] * len(column_starts)
+        for word in words_in_row:
+            # Find the closest column
+            best_col = 0
+            min_dist = float("inf")
+            for i, col_x in enumerate(column_starts):
+                dist = abs(word["x0"] - col_x)
+                if dist < min_dist:
+                    min_dist = dist
+                    best_col = i
+
+            if row_data[best_col]:
+                row_data[best_col] += " " + word["text"]
+            else:
+                row_data[best_col] = word["text"]
+
+        # Only include rows that have content in multiple columns
+        non_empty = sum(1 for cell in row_data if cell.strip())
+        if non_empty >= 2:
+            table_rows.append(row_data)
+
+    # Validate table quality - tables should have:
+    # 1. Enough rows (at least 3 including header)
+    # 2. Short cell content (tables have concise data, not paragraphs)
+    # 3. Consistent structure across rows
+    if len(table_rows) < 3:
+        return []
+
+    # Check if cells contain short, structured data (not long text)
+    long_cell_count = 0
+    total_cell_count = 0
+    for row in table_rows:
+        for cell in row:
+            if cell.strip():
+                total_cell_count += 1
+                # If cell has more than 30 chars, it's likely prose text
+                if len(cell.strip()) > 30:
+                    long_cell_count += 1
+
+    # If more than 30% of cells are long, this is probably not a table
+    if total_cell_count > 0 and long_cell_count / total_cell_count > 0.3:
+        return []
+
+    return [table_rows]
+
+
 class PdfConverter(DocumentConverter):
    """
-    Converts PDFs to Markdown. Most style information is ignored, so the results are essentially plain-text.
+    Converts PDFs to Markdown.
+    Supports extracting tables into aligned Markdown format (via pdfplumber).
+    Falls back to pdfminer if pdfplumber is missing or fails.
    """

    def accepts(
        self,
        file_stream: BinaryIO,
        stream_info: StreamInfo,
-        **kwargs: Any,  # Options to pass to the converter
+        **kwargs: Any,
    ) -> bool:
        mimetype = (stream_info.mimetype or "").lower()
        extension = (stream_info.extension or "").lower()
@@ -55,9 +521,8 @@ class PdfConverter(DocumentConverter):
        self,
        file_stream: BinaryIO,
        stream_info: StreamInfo,
-        **kwargs: Any,  # Options to pass to the converter
+        **kwargs: Any,
    ) -> DocumentConverterResult:
-        # Check the dependencies
        if _dependency_exc_info is not None:
            raise MissingDependencyException(
                MISSING_DEPENDENCY_MESSAGE.format(
@@ -65,13 +530,58 @@ class PdfConverter(DocumentConverter):
                    extension=".pdf",
                    feature="pdf",
                )
-            ) from _dependency_exc_info[
-                1
-            ].with_traceback(  # type: ignore[union-attr]
+            ) from _dependency_exc_info[1].with_traceback(
                _dependency_exc_info[2]
-            )
+            )  # type: ignore[union-attr]

-        assert isinstance(file_stream, io.IOBase)  # for mypy
-        return DocumentConverterResult(
-            markdown=pdfminer.high_level.extract_text(file_stream),
-        )
+        assert isinstance(file_stream, io.IOBase)
+
+        markdown_chunks: list[str] = []
+
+        # Read file stream into BytesIO for compatibility with pdfplumber
+        pdf_bytes = io.BytesIO(file_stream.read())
+
+        try:
+            # Track how many pages are form-style vs plain text
+            form_pages = 0
+            plain_pages = 0
+
+            with pdfplumber.open(pdf_bytes) as pdf:
+                for page in pdf.pages:
+                    # Try form-style word position extraction
+                    page_content = _extract_form_content_from_words(page)
+
+                    # If extraction returns None, this page is not form-style
+                    if page_content is None:
+                        plain_pages += 1
+                        # Extract text using pdfplumber's basic extraction for this page
+                        text = page.extract_text()
+                        if text and text.strip():
+                            markdown_chunks.append(text.strip())
+                    else:
+                        form_pages += 1
+                        if page_content.strip():
+                            markdown_chunks.append(page_content)
+
+            # If most pages are plain text, use pdfminer for better text handling
+            if plain_pages > form_pages and plain_pages > 0:
+                pdf_bytes.seek(0)
+                markdown = pdfminer.high_level.extract_text(pdf_bytes)
+            else:
+                # Build markdown from chunks
+                markdown = "\n\n".join(markdown_chunks).strip()
+
+        except Exception:
+            # Fallback if pdfplumber fails
+            pdf_bytes.seek(0)
+            markdown = pdfminer.high_level.extract_text(pdf_bytes)
+
+        # Fallback if still empty
+        if not markdown:
+            pdf_bytes.seek(0)
+            markdown = pdfminer.high_level.extract_text(pdf_bytes)
+
+        # Post-process to merge MasterFormat-style partial numbering with following text
+        markdown = _merge_partial_numbering_lines(markdown)
+
+        return DocumentConverterResult(markdown=markdown)
@@ -168,11 +168,23 @@ class PptxConverter(DocumentConverter):

                # Group Shapes
                if shape.shape_type == pptx.enum.shapes.MSO_SHAPE_TYPE.GROUP:
-                    sorted_shapes = sorted(shape.shapes, key=attrgetter("top", "left"))
+                    sorted_shapes = sorted(
+                        shape.shapes,
+                        key=lambda x: (
+                            float("-inf") if not x.top else x.top,
+                            float("-inf") if not x.left else x.left,
+                        ),
+                    )
                    for subshape in sorted_shapes:
                        get_shape_content(subshape, **kwargs)

-            sorted_shapes = sorted(slide.shapes, key=attrgetter("top", "left"))
+            sorted_shapes = sorted(
+                slide.shapes,
+                key=lambda x: (
+                    float("-inf") if not x.top else x.top,
+                    float("-inf") if not x.left else x.left,
+                ),
+            )
            for shape in sorted_shapes:
                get_shape_content(shape, **kwargs)

@@ -0,0 +1,26 @@
+import io
+from markitdown.converters._doc_intel_converter import (
+    DocumentIntelligenceConverter,
+    DocumentIntelligenceFileType,
+)
+from markitdown._stream_info import StreamInfo
+
+
+def _make_converter(file_types):
+    conv = DocumentIntelligenceConverter.__new__(DocumentIntelligenceConverter)
+    conv._file_types = file_types
+    return conv
+
+
+def test_docintel_accepts_html_extension():
+    conv = _make_converter([DocumentIntelligenceFileType.HTML])
+    stream_info = StreamInfo(mimetype=None, extension=".html")
+    assert conv.accepts(io.BytesIO(b""), stream_info)
+
+
+def test_docintel_accepts_html_mimetype():
+    conv = _make_converter([DocumentIntelligenceFileType.HTML])
+    stream_info = StreamInfo(mimetype="text/html", extension=None)
+    assert conv.accepts(io.BytesIO(b""), stream_info)
+    stream_info = StreamInfo(mimetype="application/xhtml+xml", extension=None)
+    assert conv.accepts(io.BytesIO(b""), stream_info)
@@ -0,0 +1,97 @@
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+TECHMART ELECTRONICS
+4567 Innovation Blvd
+San Francisco, CA 94103
+(415) 555-0199
+
+===================================
+
+Store #0342 - Downtown SF
+11/23/2024 14:32:18 PST
+TXN: TXN-98765-2024
+Cashier: Emily Rodriguez
+Register: POS-07
+
+-----------------------------------
+
+Wireless Noise-Cancelling
+Headphones - Premium Black
+AUDIO-5521 1 @ $349.99
+Member Discount $-50.00
+$299.99
+USB-C Hub 7-in-1 Adapter
+with HDMI & Ethernet
+ACC-8834 2 @ $79.99
+$159.98
+Portable SSD 2TB
+Thunderbolt 3 Compatible
+STOR-2241 1 @ $289.00
+Member Discount $-29.00
+$260.00
+Ergonomic Wireless Mouse
+Rechargeable Battery
+ACC-9012 1 @ $59.99
+$59.99
+Screen Cleaning Kit
+Professional Grade
+CARE-1156 3 @ $12.99
+$38.97
+HDMI 2.1 Cable 6ft
+8K Resolution Support
+CABLE-7789 2 @ $24.99
+Member Discount $-5.00
+$44.98
+-----------------------------------
+
+SUBTOTAL $863.91
+Member Discount (15%)-$84.00
+Sales Tax (8.5%) $66.23
+Rewards Applied -$25.00
+===================================
+TOTAL $821.14
+===================================
+
+PAYMENT METHOD
+Visa Card ending in 4782
+Auth: 847392
+Ref: REF-20241123-98765
+
+-----------------------------------
+
+REWARDS MEMBER
+Sarah Mitchell
+ID: TM-447821
+Points Earned: 821
+Total Points: 3,247
+Next Reward: $50 gift card
+at 5,000 pts (1,753 to go)
+
+-----------------------------------
+
+RETURN POLICY
+Returns within 30 days
+Receipt required
+Electronics must be unopened
+
+*TXN98765202411231432*
+
+Thank you for shopping!
+www.techmart.example.com
+
+===================================
+
@@ -0,0 +1,76 @@
+ZAVA AUTO REPAIR
+Certified Collision Repair
+123 Main Street, Redmond, WA 98052
+Phone: (425) 000-0000
+Preliminary Estimate (ID: EST-1008)
+| Customer Information |                     |     | Vehicle Information |                   |
+| -------------------- | ------------------- | --- | ------------------- | ----------------- |
+| Insured name         | Gabriel Diaz        |     | Year                | 2022              |
+| Claim #              | SF-1008             |     | Make                | Jeep              |
+| Policy #             | POL-2022-555        |     | Model               | Grand Cherokee    |
+| Phone                | (425) 111-1111      |     | Trim                | Limited           |
+| Email                | gabriel@contoso.com |     | VIN                 | 1C4RJFBG2NC123456 |
+|                      |                     |     | Color               | White             |
+|                      |                     |     | Odometer            | 9,800             |
+| Repair Order #       | RO-20221108         |     | Estimator           | Ellis Turner      |
+Estimate Totals
+|                  |     | Hours | Rate | Cost  |
+| ---------------- | --- | ----- | ---- | ----- |
+| Parts            |     |       |      | 2,100 |
+| Body Labor       |     | 2     | 150  | 300   |
+| Paint Labor      |     | 1.5   | 150  | 225   |
+| Mechanical Labor |     | -     | -    | -     |
+Supplies
+|               | Paint Supplies           |     |        | 60     |
+| ------------- | ------------------------ | --- | ------ | ------ |
+|               | Body Supplies            |     |        | 30     |
+| Other Charges |                          |     |        | 15     |
+| Subtotal      |                          |     |        | 2,730  |
+| Sales Tax     |                          |     | 10.20% | 278.46 |
+| GRAND TOTAL   |                          |     |        | 5,738  |
+| Note          | Minor rear bumper repair |     |        |        |
+This is a preliminary estimate for the visible damage of the vehicle. Additional damage / repairs / parts may be found
+after the vehicle has been disassembled and damaged parts have been removed. Suspension damages may be
+present, but can not be determined until an alignment on the vehicle has been done. Parts Prices may vary due to
+models and vehicle maker price updates. Please be advised if vehicle owner elects to have vehicle sent to service for
+any mechanical concerns, ALL service departments charge a vehicle diagnostic charge. If the mechanical concern is
+deemed not related to an insurance claim, vehicle owner will be reponsible for charges.
+
+ZAVA AUTO REPAIR
+Certified Collision Repair
+123 Main Street, Redmond, WA 98052
+Phone: (425) 000-0000
+Preliminary Estimate (ID: EST-1008)
+Customer Information Vehicle Information
+| Insured name   | Bruce Wayne                |     | Year      | 2025         |
+| -------------- | -------------------------- | --- | --------- | ------------ |
+| Claim #        |                            | 999 | Make      | Batman       |
+| Policy #       | IM-BATMAN                  |     | Model     | Batmobile    |
+| Phone          | (416) 555-1234             |     | Trim      | Limited      |
+| Email          | batman@wayneindustries.com |     | VIN       | XXX          |
+|                |                            |     | Color     | Black        |
+|                |                            |     | Odometer  | 1            |
+| Repair Order # | RO-20221108                |     | Estimator | Ellis Turner |
+Estimate Totals
+|                  |     | Hours | Rate | Cost   |
+| ---------------- | --- | ----- | ---- | ------ |
+| Parts            |     |       |      | 99,999 |
+| Body Labor       |     | 2     | 150  | 300    |
+| Paint Labor      |     | 1.5   | 150  | 225    |
+| Mechanical Labor |     | -     | -    | -      |
+Supplies
+|               | Paint Supplies           |     |        | 60        |
+| ------------- | ------------------------ | --- | ------ | --------- |
+|               | Body Supplies            |     |        | 30        |
+| Other Charges |                          |     |        | 15        |
+| Subtotal      |                          |     |        | 100,629   |
+| Sales Tax     |                          |     | 10.20% | 10264.158 |
+| GRAND TOTAL   |                          |     |        | 211,522   |
+| Note          | Minor rear bumper repair |     |        |           |
+
+This is a preliminary estimate for the visible damage of the vehicle. Additional damage / repairs / parts may be found
+after the vehicle has been disassembled and damaged parts have been removed. Suspension damages may be
+present, but can not be determined until an alignment on the vehicle has been done. Parts Prices may vary due to
+models and vehicle maker price updates. Please be advised if vehicle owner elects to have vehicle sent to service for
+any mechanical concerns, ALL service departments charge a vehicle diagnostic charge. If the mechanical concern is
+deemed not related to an insurance claim, vehicle owner will be reponsible for charges.
@@ -0,0 +1,44 @@
+INVENTORY RECONCILIATION REPORT
+Report ID: SPARSE-2024-INV-1234
+Warehouse: Distribution Center East
+Report Date: 2024-11-15
+Prepared By: Sarah Martinez
+| Product Code | Location | Expected | Actual | Variance | Status   |
+| ------------ | -------- | -------- | ------ | -------- | -------- |
+| SKU-8847     | A-12     | 450      |        |          |          |
+|              | B-07     |          | 289    | -23      |          |
+| SKU-9201     |          | 780      | 778    |          | OK       |
+|              | C-15     |          |        | +15      |          |
+| SKU-4563     | D-22     |          | 156    |          | CRITICAL |
+|              |          | 180      |        | -24      |          |
+| SKU-7728     | A-08     | 920      |        |          |          |
+|              |          |          | 935    | +15      | OK       |
+Variance Analysis:
+Summary Statistics:
+Total Variance Cost: $4,287.50
+Critical Items: 1
+Overall Accuracy: 97.2%
+Detailed Analysis by Category:
+The inventory reconciliation reveals several key findings. The primary variance driver is SKU-4563,
+which shows a -24 unit discrepancy requiring immediate investigation. Location B-07 handling of
+SKU-8847 also demonstrates significant variance. Cross-location verification protocols should be
+
+reviewed to prevent future discrepancies. The overall accuracy rate of 97.2% meets our target
+threshold, but critical items require expedited resolution to maintain operational efficiency.
+Extended Inventory Review:
+| Product Code | Category    | Unit Cost | Total Value | Last Audit | Notes      |
+| ------------ | ----------- | --------- | ----------- | ---------- | ---------- |
+| SKU-8847     | Electronics | $45.00    | $13,005.00  | 2024-10-15 |            |
+| SKU-9201     | Hardware    | $32.50    | $25,285.00  | 2024-10-22 | Verified   |
+| SKU-4563     | Software    | $120.00   | $18,720.00  |            | Critical   |
+| SKU-7728     | Accessories | $15.75    | $14,726.25  | 2024-11-01 |            |
+| SKU-3345     | Electronics | $67.00    | $22,445.00  | 2024-10-18 |            |
+| SKU-5512     | Hardware    | $89.00    | $31,150.00  |            | Pending    |
+| SKU-6678     | Software    | $200.00   | $42,000.00  | 2024-10-25 | High Value |
+| SKU-7789     | Accessories | $8.50     | $5,950.00   | 2024-11-05 |            |
+| SKU-2234     | Electronics | $125.00   | $35,000.00  |            |            |
+| SKU-1123     | Hardware    | $55.00    | $27,500.00  | 2024-10-30 | Verified   |
+Recommendations:
+1. Immediate review of SKU-4563 handling procedures. 2. Implement additional verification for critical
+items. 3. Schedule follow-up audit for high-value products (SKU-6678, SKU-2234).
+Approval:
@@ -0,0 +1,62 @@
+BOOKING ORDER
+Print Date 12/15/2024 14:30:22
+Page 1 of 1
+STARLIGHT CINEMAS
+Orders
+| Order / Rev: | 2024-12-5678   |     |     | Cinema:          |     | Downtown Multiplex |
+| ------------ | -------------- | --- | --- | ---------------- | --- | ------------------ |
+| Alt Order #: | SC-WINTER-2024 |     |     | Primary Contact: |     | Sarah Johnson      |
+Product Desc: Holiday Movie Marathon Package Location: NYC-01
+| Estimate:            | EST-456                 |     |     | Region: |     | NORTHEAST |
+| -------------------- | ----------------------- | --- | --- | ------- | --- | --------- |
+| Booking Dates:       | 12/20/2024 - 12/31/2024 |     |     |         |     |           |
+| Original Date / Rev: | 12/01/24 / 12/10/24     |     |     |         |     |           |
+| Order Type:          | Premium Package         |     |     |         |     |           |
+Booking Agency
+| Name:            | Premier Entertainment Group |     |     |                |     |           |
+| ---------------- | --------------------------- | --- | --- | -------------- | --- | --------- |
+|                  |                             |     |     | Billing Type:  |     | Net 30    |
+| Contact:         | Michael Chen                |     |     |                |     |           |
+|                  |                             |     |     | Payment Terms: |     | Corporate |
+| Billing Contact: | accounting@premierent.com   |     |     |                |     |           |
+|                  |                             |     |     | Commission:    |     | 10%       |
+555 Broadway Suite 1200
+New York, NY 10012
+Customer
+| Name:          | Universal Studios Distribution |     |     |     |     |     |
+| -------------- | ------------------------------ | --- | --- | --- | --- | --- |
+| Category:      | Film Distributor               |     |     |     |     |     |
+| Contact Email: | bookings@universalstudios.com  |     |     |     |     |     |
+| Customer ID:   | CUST-98765                     |     |     |     |     |     |
+| Revenue Code:  | FILM-PREMIUM                   |     |     |     |     |     |
+Booking Summary
+| Start Date | End Date | # Shows | Gross Amount | Net Amount |     |     |
+| ---------- | -------- | ------- | ------------ | ---------- | --- | --- |
+| 12/20/24   | 12/31/24 | 48      | $12,500.00   | $11,250.00 |     |     |
+Totals
+| Month         | # Shows | Gross Amount |     | Net Amount |     | Occupancy |
+| ------------- | ------- | ------------ | --- | ---------- | --- | --------- |
+| December 2024 | 48      | $12,500.00   |     | $11,250.00 |     | 85%       |
+| Totals        | 48      | $12,500.00   |     | $11,250.00 |     | 85%       |
+Account Representatives
+Representative Territory Region Start Date / End Date Commission %
+| Sarah Johnson | NYC Metro | NORTHEAST | 12/20/24 - 12/31/24 |     | 100% |     |
+| ------------- | --------- | --------- | ------------------- | --- | ---- | --- |
+Show Schedule Details
+Ln Screen Start End Movie Title Format Showtime Days Shows Rate Type Total
+1 SCR-1 12/20/24 12/25/24 Holiday Spectacular IMAX 3D 7:00 PM Daily 12 $250 PM $3,000
+(Runtime: 142 min); Holiday Season Premium
+2 SCR-2 12/20/24 12/31/24 Winter Wonderland Standard 4:30 PM Daily 24 $150 MT $3,600
+(Runtime: 98 min); Matinee Special
+3 SCR-1 12/26/24 12/31/24 New Year Mystery 4DX 9:30 PM Daily 12 $300 PM $3,600
+(Runtime: 116 min); Premium Experience
+Show Details
+| Show Screen | Date Range | Title | Showtime | Days Type | Rate | Revenue |
+| ----------- | ---------- | ----- | -------- | --------- | ---- | ------- |
+1 SCR-1 12/20-12/25 Holiday Spectacular 7:00 PM Daily PM $250 $3,000
+This booking order is subject to cinema availability and standard terms.
+2 SCR-2 12/20-12/31 Winter Wonderland 4:30 PM Daily MT $150 $3,600
+All showtimes are approximate and subject to change.
+3 SCR-1 12/26-12/31 New Year Mystery 9:30 PM Daily PM $300 $3,600
+| Total Revenue: |     |     |     |     |     | $12,500.00 |
+| -------------- | --- | --- | --- | --- | --- | ---------- |
@@ -0,0 +1,65 @@
+1
+
+Introduction
+
+Large language models (LLMs) are becoming a crucial building block in developing powerful agents
+that utilize LLMs for reasoning, tool usage, and adapting to new observations (Yao et al., 2022; Xi
+et al., 2023; Wang et al., 2023b) in many real-world tasks. Given the expanding tasks that could
+benefit from LLMs and the growing task complexity, an intuitive approach to scale up the power of
+agents is to use multiple agents that cooperate. Prior work suggests that multiple agents can help
+encourage divergent thinking (Liang et al., 2023), improve factuality and reasoning (Du et al., 2023),
+and provide validation (Wu et al., 2023). In light of the intuition and early evidence of promise, it is
+intriguing to ask the following question: how can we facilitate the development of LLM applications
+that could span a broad spectrum of domains and complexities based on the multi-agent approach?
+
+Our insight is to use multi-agent conversations to achieve it. There are at least three reasons con-
+firming its general feasibility and utility thanks to recent advances in LLMs: First, because chat-
+optimized LLMs (e.g., GPT-4) show the ability to incorporate feedback, LLM agents can cooperate
+through conversations with each other or human(s), e.g., a dialog where agents provide and seek rea-
+soning, observations, critiques, and validation. Second, because a single LLM can exhibit a broad
+range of capabilities (especially when configured with the correct prompt and inference settings),
+conversations between differently configured agents can help combine these broad LLM capabilities
+in a modular and complementary manner. Third, LLMs have demonstrated ability to solve complex
+tasks when the tasks are broken into simpler subtasks. Multi-agent conversations can enable this
+partitioning and integration in an intuitive manner. How can we leverage the above insights and
+support different applications with the common requirement of coordinating multiple agents, poten-
+tially backed by LLMs, humans, or tools exhibiting different capacities? We desire a multi-agent
+conversation framework with generic abstraction and effective implementation that has the flexibil-
+ity to satisfy different application needs. Achieving this requires addressing two critical questions:
+(1) How can we design individual agents that are capable, reusable, customizable, and effective in
+multi-agent collaboration? (2) How can we develop a straightforward, unified interface that can
+accommodate a wide range of agent conversation patterns? In practice, applications of varying
+complexities may need distinct sets of agents with specific capabilities, and may require different
+conversation patterns, such as single- or multi-turn dialogs, different human involvement modes, and
+static vs. dynamic conversation. Moreover, developers may prefer the flexibility to program agent
+interactions in natural language or code. Failing to adequately address these two questions would
+limit the framework’s scope of applicability and generality.
+While there is contemporaneous exploration of multi-agent approaches,3 we present AutoGen, a
+generalized multi-agent conversation framework (Figure 1), based on the following new concepts.
+1 Customizable and conversable agents. AutoGen uses a generic design of agents that can lever-
+age LLMs, human inputs, tools, or a combination of them. The result is that developers can
+easily and quickly create agents with different roles (e.g., agents to write code, execute code,
+wire in human feedback, validate outputs, etc.) by selecting and configuring a subset of built-in
+capabilities. The agent’s backend can also be readily extended to allow more custom behaviors.
+To make these agents suitable for multi-agent conversation, every agent is made conversable –
+they can receive, react, and respond to messages. When configured properly, an agent can hold
+multiple turns of conversations with other agents autonomously or solicit human inputs at cer-
+tain rounds, enabling human agency and automation. The conversable agent design leverages the
+strong capability of the most advanced LLMs in taking feedback and making progress via chat
+and also allows combining capabilities of LLMs in a modular fashion. (Section 2.1)
+
+2 Conversation programming. A fundamental insight of AutoGen is to simplify and unify com-
+plex LLM application workflows as multi-agent conversations. So AutoGen adopts a program-
+ming paradigm centered around these inter-agent conversations. We refer to this paradigm as
+conversation programming, which streamlines the development of intricate applications via two
+primary steps: (1) defining a set of conversable agents with specific capabilities and roles (as
+described above); (2) programming the interaction behavior between agents via conversation-
+centric computation and control. Both steps can be achieved via a fusion of natural and pro-
+gramming languages to build applications with a wide range of conversation patterns and agent
+behaviors. AutoGen provides ready-to-use implementations and also allows easy extension and
+experimentation for both steps. (Section 2.2)
+
+3We refer to Appendix A for a detailed discussion.
+
+2
+
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@@ -4,6 +4,7 @@ import os
 import re
 import shutil
 import pytest
+from unittest.mock import MagicMock

 from markitdown._uri_utils import parse_data_uri, file_uri_to_path

@@ -287,6 +288,47 @@ def test_input_as_strings() -> None:
    assert "# Test" in result.text_content


+def test_doc_rlink() -> None:
+    # Test for: CVE-2025-11849
+    markitdown = MarkItDown()
+
+    # Document with rlink
+    docx_file = os.path.join(TEST_FILES_DIR, "rlink.docx")
+
+    # Directory containing the target rlink file
+    rlink_tmp_dir = os.path.abspath(os.sep + "tmp")
+
+    # Ensure the tmp directory exists
+    if not os.path.exists(rlink_tmp_dir):
+        pytest.skip(f"Skipping rlink test; {rlink_tmp_dir} directory does not exist.")
+        return
+
+    rlink_file_path = os.path.join(rlink_tmp_dir, "test_rlink.txt")
+    rlink_content = "de658225-569e-4e3d-9ed2-cfb6abf927fc"
+    b64_prefix = (
+        "ZGU2NTgyMjUtNTY5ZS00ZTNkLTllZDItY2ZiNmFiZjk"  # base64 prefix of rlink_content
+    )
+
+    if os.path.exists(rlink_file_path):
+        with open(rlink_file_path, "r", encoding="utf-8") as f:
+            existing_content = f.read()
+            if existing_content != rlink_content:
+                raise ValueError(
+                    f"Existing {rlink_file_path} content does not match expected content."
+                )
+    else:
+        with open(rlink_file_path, "w", encoding="utf-8") as f:
+            f.write(rlink_content)
+
+    try:
+        result = markitdown.convert(docx_file, keep_data_uris=True).text_content
+        assert (
+            b64_prefix not in result
+        )  # Make sure the target file was NOT embedded in the output
+    finally:
+        os.remove(rlink_file_path)
+
+
@pytest.mark.skipif(
    skip_remote,
    reason="do not run tests that query external urls",
@@ -300,9 +342,9 @@ def test_markitdown_remote() -> None:
        assert test_string in result.text_content

    # Youtube
-    result = markitdown.convert(YOUTUBE_TEST_URL)
-    for test_string in YOUTUBE_TEST_STRINGS:
-        assert test_string in result.text_content
+    # result = markitdown.convert(YOUTUBE_TEST_URL)
+    # for test_string in YOUTUBE_TEST_STRINGS:
+    #    assert test_string in result.text_content


@pytest.mark.skipif(
@@ -370,6 +412,50 @@ def test_markitdown_exiftool() -> None:
        assert target in result.text_content


+def test_markitdown_llm_parameters() -> None:
+    """Test that LLM parameters are correctly passed to the client."""
+    mock_client = MagicMock()
+    mock_response = MagicMock()
+    mock_response.choices = [
+        MagicMock(
+            message=MagicMock(
+                content="Test caption with red circle and blue square 5bda1dd6"
+            )
+        )
+    ]
+    mock_client.chat.completions.create.return_value = mock_response
+
+    test_prompt = "You are a professional test prompt."
+    markitdown = MarkItDown(
+        llm_client=mock_client, llm_model="gpt-4o", llm_prompt=test_prompt
+    )
+
+    # Test image file
+    markitdown.convert(os.path.join(TEST_FILES_DIR, "test_llm.jpg"))
+
+    # Verify the prompt was passed to the OpenAI API
+    assert mock_client.chat.completions.create.called
+    call_args = mock_client.chat.completions.create.call_args
+    messages = call_args[1]["messages"]
+    assert len(messages) == 1
+    assert messages[0]["content"][0]["text"] == test_prompt
+
+    # Reset the mock for the next test
+    mock_client.chat.completions.create.reset_mock()
+
+    # TODO: may only use one test after the llm caption method duplicate has been removed:
+    # https://github.com/microsoft/markitdown/pull/1254
+    # Test PPTX file
+    markitdown.convert(os.path.join(TEST_FILES_DIR, "test.pptx"))
+
+    # Verify the prompt was passed to the OpenAI API for PPTX images too
+    assert mock_client.chat.completions.create.called
+    call_args = mock_client.chat.completions.create.call_args
+    messages = call_args[1]["messages"]
+    assert len(messages) == 1
+    assert messages[0]["content"][0]["text"] == test_prompt
+
+
@pytest.mark.skipif(
    skip_llm,
    reason="do not run llm tests without a key",
@@ -407,7 +493,9 @@ if __name__ == "__main__":
        test_markitdown_remote,
        test_speech_transcription,
        test_exceptions,
+        test_doc_rlink,
        test_markitdown_exiftool,
+        test_markitdown_llm_parameters,
        test_markitdown_llm,
    ]:
        print(f"Running {test.__name__}...", end="")
@@ -0,0 +1,171 @@
+#!/usr/bin/env python3 -m pytest
+"""Tests for MasterFormat-style partial numbering in PDF conversion."""
+
+import os
+import re
+import pytest
+
+from markitdown import MarkItDown
+from markitdown.converters._pdf_converter import PARTIAL_NUMBERING_PATTERN
+
+TEST_FILES_DIR = os.path.join(os.path.dirname(__file__), "test_files")
+
+
+class TestMasterFormatPartialNumbering:
+    """Test handling of MasterFormat-style partial numbering (.1, .2, etc.)."""
+
+    def test_partial_numbering_pattern_regex(self):
+        """Test that the partial numbering regex pattern correctly matches."""
+
+        # Should match partial numbering patterns
+        assert PARTIAL_NUMBERING_PATTERN.match(".1") is not None
+        assert PARTIAL_NUMBERING_PATTERN.match(".2") is not None
+        assert PARTIAL_NUMBERING_PATTERN.match(".10") is not None
+        assert PARTIAL_NUMBERING_PATTERN.match(".99") is not None
+
+        # Should NOT match other patterns
+        assert PARTIAL_NUMBERING_PATTERN.match("1.") is None
+        assert PARTIAL_NUMBERING_PATTERN.match("1.2") is None
+        assert PARTIAL_NUMBERING_PATTERN.match(".1.2") is None
+        assert PARTIAL_NUMBERING_PATTERN.match("text") is None
+        assert PARTIAL_NUMBERING_PATTERN.match(".a") is None
+        assert PARTIAL_NUMBERING_PATTERN.match("") is None
+
+    def test_masterformat_partial_numbering_not_split(self):
+        """Test that MasterFormat partial numbering stays with associated text.
+
+        MasterFormat documents use partial numbering like:
+            .1  The intent of this Request for Proposal...
+            .2  Available information relative to...
+
+        These should NOT be split into separate table columns, but kept
+        as coherent text lines with the number followed by its description.
+        """
+        pdf_path = os.path.join(TEST_FILES_DIR, "masterformat_partial_numbering.pdf")
+
+        markitdown = MarkItDown()
+        result = markitdown.convert(pdf_path)
+        text_content = result.text_content
+
+        # Partial numberings should NOT appear isolated on their own lines
+        # If they're isolated, it means the parser incorrectly split them from their text
+        lines = text_content.split("\n")
+        isolated_numberings = []
+        for line in lines:
+            stripped = line.strip()
+            # Check if line contains ONLY a partial numbering (with possible whitespace/pipes)
+            cleaned = stripped.replace("|", "").strip()
+            if cleaned in [".1", ".2", ".3", ".4", ".5", ".6", ".7", ".8", ".9", ".10"]:
+                isolated_numberings.append(stripped)
+
+        assert len(isolated_numberings) == 0, (
+            f"Partial numberings should not be isolated from their text. "
+            f"Found isolated: {isolated_numberings}"
+        )
+
+        # Verify that partial numberings appear WITH following text on the same line
+        # Look for patterns like ".1 The intent" or ".1  Some text"
+        partial_with_text = re.findall(r"\.\d+\s+\w+", text_content)
+        assert (
+            len(partial_with_text) > 0
+        ), "Expected to find partial numberings followed by text on the same line"
+
+    def test_masterformat_content_preserved(self):
+        """Test that MasterFormat document content is fully preserved."""
+        pdf_path = os.path.join(TEST_FILES_DIR, "masterformat_partial_numbering.pdf")
+
+        markitdown = MarkItDown()
+        result = markitdown.convert(pdf_path)
+        text_content = result.text_content
+
+        # Verify key content from the MasterFormat document is preserved
+        expected_content = [
+            "RFP for Construction Management Services",
+            "Section 00 00 43",
+            "Instructions to Respondents",
+            "Ken Sargent House",
+            "INTENT",
+            "Request for Proposal",
+            "KEN SARGENT HOUSE",
+            "GRANDE PRAIRIE, ALBERTA",
+            "Section 00 00 45",
+        ]
+
+        for content in expected_content:
+            assert (
+                content in text_content
+            ), f"Expected content '{content}' not found in extracted text"
+
+        # Verify partial numbering is followed by text on the same line
+        # .1 should be followed by "The intent" on the same line
+        assert re.search(
+            r"\.1\s+The intent", text_content
+        ), "Partial numbering .1 should be followed by 'The intent' text"
+
+        # .2 should be followed by "Available information" on the same line
+        assert re.search(
+            r"\.2\s+Available information", text_content
+        ), "Partial numbering .2 should be followed by 'Available information' text"
+
+        # Ensure text content is not empty and has reasonable length
+        assert (
+            len(text_content.strip()) > 100
+        ), "MasterFormat document should have substantial text content"
+
+    def test_merge_partial_numbering_with_empty_lines_between(self):
+        """Test that partial numberings merge correctly even with empty lines between.
+
+        When PDF extractors produce output like:
+            .1
+
+            The intent of this Request...
+
+        The merge logic should still combine them properly.
+        """
+        pdf_path = os.path.join(TEST_FILES_DIR, "masterformat_partial_numbering.pdf")
+
+        markitdown = MarkItDown()
+        result = markitdown.convert(pdf_path)
+        text_content = result.text_content
+
+        # The merged result should have .1 and .2 followed by text
+        # Check that we don't have patterns like ".1\n\nThe intent" (unmerged)
+        lines = text_content.split("\n")
+
+        for i, line in enumerate(lines):
+            stripped = line.strip()
+            # If we find an isolated partial numbering, the merge failed
+            if stripped in [".1", ".2", ".3", ".4", ".5", ".6", ".7", ".8"]:
+                # Check if next non-empty line exists and wasn't merged
+                for j in range(i + 1, min(i + 3, len(lines))):
+                    if lines[j].strip():
+                        pytest.fail(
+                            f"Partial numbering '{stripped}' on line {i} was not "
+                            f"merged with following text '{lines[j].strip()[:30]}...'"
+                        )
+                        break
+
+    def test_multiple_partial_numberings_all_merged(self):
+        """Test that all partial numberings in a document are properly merged."""
+        pdf_path = os.path.join(TEST_FILES_DIR, "masterformat_partial_numbering.pdf")
+
+        markitdown = MarkItDown()
+        result = markitdown.convert(pdf_path)
+        text_content = result.text_content
+
+        # Count occurrences of merged partial numberings (number followed by text)
+        merged_count = len(re.findall(r"\.\d+\s+[A-Za-z]", text_content))
+
+        # Count isolated partial numberings (number alone on a line)
+        isolated_count = 0
+        for line in text_content.split("\n"):
+            stripped = line.strip()
+            if re.match(r"^\.\d+$", stripped):
+                isolated_count += 1
+
+        assert (
+            merged_count >= 2
+        ), f"Expected at least 2 merged partial numberings, found {merged_count}"
+        assert (
+            isolated_count == 0
+        ), f"Found {isolated_count} isolated partial numberings that weren't merged"
Author	SHA1	Message	Date
afourney	4a5340f93b	Bump version for release. (#1564 )	2026-02-20 11:40:57 -08:00
Bas Nijholt	6b0fd15e60	Remove onnxruntime<=1.20.1 Windows pin (#1551 )	2026-02-16 15:05:37 -08:00
afourney	2b6ec9f315	Add text/markdown to Accept header (#1554 )	2026-02-13 11:53:01 -08:00
lesyk	c83de14a9c	[MS] Extend table support for wide tables (#1552 ) * feat: enhance PDF table extraction to support complex forms and add new test cases * feat: enhance PDF table extraction with adaptive column clustering and add comprehensive test cases * fix: correct formatting and improve assertions in PDF table tests	2026-02-13 10:45:39 -08:00
lesyk	7fdaefb724	Fix: PDF parsing doesn't support partially numbered lists (#1525 ) * Fix: PDF parsing doesn't support partially numbered lists * Refactor: Move import of PARTIAL_NUMBERING_PATTERN to the top of the test file * Refactor: Improve assertion formatting in partial numbering tests	2026-01-08 15:15:22 -08:00
lesyk	251dddcf0c	[MS] Update PDF table extraction to support aligned Markdown (#1499 ) * Added PDF table extraction feature with aligned Markdown (#1419) * Add PDF test files and enhance extraction tests - Added a medical report scan PDF for testing scanned PDF handling. - Included a retail purchase receipt PDF to validate receipt extraction functionality. - Introduced a multipage invoice PDF to test extraction of complex invoice structures. - Added a borderless table PDF for testing inventory reconciliation report extraction. - Implemented comprehensive tests for PDF table extraction, ensuring proper structure and data integrity. - Enhanced existing tests to validate the order and presence of extracted content across various PDF types. * fix: update dependencies for PDF processing and improve table extraction logic * Bumped version of pdfminer.six --------- Authored-by: Ashok <ashh010101@gmail.com>	2026-01-07 16:38:45 -08:00
afourney	dde250a456	Bump versions of mammoth and pdfminer.six (#1492 ) * Updated pyproject to require a minimum version of pdfminer.six to ensure CVE-2025-64512 is patched.	2025-12-01 10:11:24 -08:00
afourney	3d4fe3cdcc	Upgrade mammoth to 1.11.0 (#1452 )	2025-10-20 16:07:39 -07:00
afourney	447c047731	Test if mammoth resolves rlinks. (#1451 )	2025-10-20 15:54:05 -07:00
Meirna	8a9d8f1593	feat: add checkbox support to Markdown converter (#1208 ) This change introduces functionality to convert HTML checkbox input elements (<input type=checkbox>) into Markdown checkbox syntax ([ ] or [x]). Co-authored-by: Meirna Kamal <meirna.kamal@vodafone.com>	2025-08-26 15:30:47 -07:00
Richard Ye	17365654c9	Handle PPTX shapes where position is None (#1161 ) * Handle shapes where position is None * Fixed recursion error, and place no-coord shapes at front	2025-08-26 15:28:17 -07:00
Yuzhong Zhang	59eb60f8cb	fix docx parse error(\n in alt) (#1163 )	2025-08-26 15:20:17 -07:00
Dmitry	459d462f29	docs: correct minor typos (#1173 )	2025-08-26 15:15:23 -07:00
Noah Zhu	c3f6cb356c	Adding support for data-src Attribute (#1226 ) * supportfordata-src	2025-08-26 15:11:53 -07:00
Ebrahim Tayabali	0c4d3945a0	Update README.md (#1191 ) Fix: Subtle spelling mistake fixed.	2025-08-26 15:07:27 -07:00
Utkarsh kumar	f8b60b5403	Update README.md (#1350 ) ISSUE #1339	2025-08-26 15:02:56 -07:00
[W]DOS_	16ca285d30	Update README.md (#1335 ) Fix typo in README.md	2025-08-26 14:55:58 -07:00
Stefan Rink	b81a387616	fix: correctly pass custom llm prompt parameter (#1319 ) * fix: correctly pass custom llm prompt parameter	2025-08-26 14:51:10 -07:00
safen0s	ea1a3dfb60	Add HTML support to DocumentIntelligenceConverter (#1352 )	2025-08-26 14:34:43 -07:00
dependabot[bot]	b6e5da8874	Bump actions/checkout from 4 to 5 (#1394 ) Bumps [actions/checkout](https://github.com/actions/checkout) from 4 to 5.	2025-08-26 14:27:38 -07:00
t3tra	fb1ad24833	Ensure safe ExifTool usage: require >= 12.24 (#1399 ) * feat: add version verification for ExifTool to ensure security compliance * fix: improve ExifTool version verification ---------	2025-08-26 14:25:13 -07:00
JonahDelman	1178c2e211	Fixed documentation typos in _base_converter.py (#1393 )	2025-08-26 14:23:10 -07:00
afourney	9278119bb3	Resolved an issue with linked images in docx [mammoth] (#1405 )	2025-08-26 14:20:29 -07:00
onefloid	da7bcea527	docs: rephrase sentence (#1278 )	2025-06-03 21:09:25 -07:00
afourney	3bfb821c09	Have the MarkItDown MCP server read MARKITDOWN_ENABLE_PLUGINS from ENV (#1273 ) * Have the MarkItdown MCP server read MARKITDOWN_ENABLE_PLUGINS from os.environ * Update the Dockerfile to enable plugins. No puglins are installed by default.	2025-06-03 09:35:33 -07:00
Tomasz Kalinowski	62b72284fe	pin onnxruntime on Windows (#1274 ) closes #1266	2025-05-28 13:13:51 -07:00