docs/src/content/example-posts/pdf-to-markdown.md
In this tutorial, we'll build a simple app that converts PDF files to Markdown and saves them to a local directory.
You declare the transformation logic with native Python without worrying about changes.
Think: target_state = transformation(source_state)
When your source data is updated, or your processing logic is changed (for example, switching parsers or tweaking conversion settings), CocoIndex performs smart incremental processing that only reprocesses the minimum. And it keeps your Markdown files always up to date in production.
Install CocoIndex and dependencies:
pip install 'cocoindex>=1.0.0' docling
Create a new directory for your project:
mkdir pdf-to-markdown
cd pdf-to-markdown
Create a pdf_files/ directory and add your PDF files:
mkdir pdf_files
You can download sample PDF files from the git repo.
Create a .env file to configure the database path:
echo "COCOINDEX_DB=./cocoindex.db" > .env
Define a CocoIndex App — the top-level runnable unit in CocoIndex.
import pathlib
import cocoindex as coco
from cocoindex.connectors import localfs
from cocoindex.resources.file import PatternFilePathMatcher
from docling.datamodel.accelerator_options import AcceleratorDevice, AcceleratorOptions
from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.document_converter import DocumentConverter, PdfFormatOption
In the main function, we walk through each file in the source directory and process it.
@coco.fn
async def app_main(sourcedir: pathlib.Path, outdir: pathlib.Path) -> None:
files = localfs.walk_dir(
sourcedir,
recursive=True,
path_matcher=PatternFilePathMatcher(included_patterns=["**/*.pdf"]),
)
await coco.mount_each(process_file, files.items(), outdir)
For each file, coco.mount_each() mounts a processing component. It's up to
you to pick the process granularity, for example it can be at directory level,
file level, or page level.
In this example, because we want to independently convert each file to Markdown, it is the most natural to pick it at the file level.
For a file, we use Docling to convert it to Markdown. The converter follows Docling's explicit accelerator configuration pattern and is pinned to CPU for portability across local machines. The Docling accelerator docs were checked on 2026-05-31; Docling documents CPU as the mode that works everywhere, while MPS/CUDA/XPU depend on compatible hardware and PyTorch builds.
_pipeline_options = PdfPipelineOptions(
accelerator_options=AcceleratorOptions(device=AcceleratorDevice.CPU)
)
_converter = DocumentConverter(
format_options={
InputFormat.PDF: PdfFormatOption(pipeline_options=_pipeline_options)
}
)
@coco.fn(memo=True)
def process_file(
file: localfs.File,
outdir: pathlib.Path,
) -> None:
markdown = _converter.convert(
file.file_path.resolve()
).document.export_to_markdown()
outname = file.file_path.path.stem + ".md"
localfs.declare_file(outdir / outname, markdown, create_parent_dirs=True)
We use @coco.fn with memo=True to create a memoized function that processes each file.
app = coco.App(
"PdfToMarkdown",
app_main,
sourcedir=pathlib.Path("./pdf_files"),
outdir=pathlib.Path("./out"),
)
Run the pipeline:
cocoindex update main.py
CocoIndex will:
out/ directorypdf_files/ to Markdown in out/Check the output:
ls out/
# example.md (one .md file for each input PDF)
The power of CocoIndex is incremental processing. Try these:
Add a new file:
Add a new PDF to pdf_files/, then run:
cocoindex update main.py
Only the new file is processed.
Modify a file:
Replace a PDF in pdf_files/ with an updated version, then run:
cocoindex update main.py
Only the changed file is reprocessed.
Delete a file:
rm pdf_files/example.pdf
cocoindex update main.py
The corresponding Markdown file is automatically removed.