Testing ai_parse_document vs PyMuPDF for PDF extraction

[FreshBrewedData]

I’ve been experimenting with the Databricks AI functions and recently ran a small test extracting structured information from a PDF document.

My initial approach was to use ai_parse_document to extract the text from the PDF.

While the function appeared to work at first glance, I noticed some transcription inaccuracies when validating the output. For example, a name and an identifier in the document were returned incorrectly.

These were small errors, but when extracting names or other identifiers, even minor inaccuracies make the results unreliable for downstream processing.

To test a different approach, I switched to PyMuPDF to extract the text from the PDF. This produced clean and accurate text from the document.

Once I had reliable text, I used ai_query to extract the fields I was interested in. I wrote a prompt asking the model to extract four specific fields from the document text and return the results in JSON format.

This part worked very well and produced consistent results.

The workflow that ended up being reliable looked like this:

PDF
→ PyMuPDF (text extraction)
→ ai_query (field extraction)
→ JSON output

In my testing, ai_query performed well once the document text was accurate. However, ai_parse_document was not reliable enough for precise extraction of names and identifiers.

I’m curious if others have experimented with ai_parse_document and have found ways to improve accuracy when extracting text from PDFs for structured data workflows.

[Ale_Armillotta]

Hi @FreshBrewedData .

In my experience the difference comes from how the two approaches work internally.

ai_parse_document relies on a multimodal AI model to interpret the document and reconstruct its structure (paragraphs, tables, layout elements, etc.). Because it uses a generative model, small transcription inaccuracies can occasionally appear in the extracted text, which can be problematic when working with precise identifiers like names or IDs.

Libraries like PyMuPDF, on the other hand, perform deterministic parsing of the PDF text layer. If the document already contains embedded text (not just scanned images), the extracted content is usually identical to the original and therefore more reliable for downstream processing.

However, this approach only works when the PDF contains a real text layer. If the document is scanned and essentially stored as images, PyMuPDF will not be able to extract the text, while ai_parse_document can still work because it performs OCR and visual document understanding.

For this reason, a hybrid workflow often works well:

PDF → PyMuPDF (text extraction when text layer exists) → ai_query (structured field extraction) → JSON

In cases where the document is scanned or layout-heavy, using ai_parse_document directly can be more effective.

Curious to hear if others have tested similar pipelines or compared this with ai_parse_document on scanned PDFs or complex layouts.

[FreshBrewedData]

I didn’t know ai_parse_document works better with scanned images. Thanks for sharing!

[Ale_Armillotta]

It works also as OCR and this is something that PyMuPDF can’t do. So if you have a scanned document, ai_pars is the only solution. Of course it’s an LLM so i can do mistake