Parsing DOCX, PPTX, and XLSX into clean text for LLM ingestion

Office documents are the default file format inside most organizations — proposals in DOCX, decks in PPTX, financial models in XLSX. Any ingestion pipeline that indexes “everything the business has” eventually has to read all three, and each one lives in a different library with a different API, different failure modes, and its own maintenance burden.

Three formats, three libraries, three sets of edge cases

A typical Python stack ends up with python-docx for Word, python-pptx for PowerPoint, and openpyxl or pandas for Excel. Each is reasonably good at what it does, but together they create real overhead:

• Three APIs to learn and maintain. Extracting “the text” means writing and testing three separate code paths, each with its own object model (paragraphs and runs; slides and shapes; sheets, rows, and cells).
• Version drift. DOCX/PPTX/XLSX are all OOXML under the hood, but library versions lag behind Office’s own format changes — a document saved from a newer Office build can trip up an older library version in ways that are hard to reproduce locally.
• Inconsistent handling of the “weird” content. Text inside a table cell, inside a text box on a slide, inside a merged Excel range, in headers and footers, in speaker notes — each library has different (and differently documented) support for these, so “extract the text” quietly means different things depending on which file type hit your pipeline.
• No shared error handling. A corrupt DOCX raises a different exception than a corrupt XLSX, so your pipeline’s retry and logging logic has to special-case each library’s failure modes.

None of this is hard, exactly. It’s just three times the surface area for a problem that’s conceptually the same each time: turn a document into text.

One call, regardless of which Office format it is

txtfetch runs all three formats through Apache Tika, which normalizes DOCX, PPTX, and XLSX (along with their legacy .doc/.ppt/.xls counterparts and ODF equivalents) into the same extraction path. You don’t branch on file type — you send the file, and the response is identical in shape regardless of what came in.

A Word proposal:

curl -X POST https://api.txtfetch.com/v1/extract \
  -H "Authorization: Bearer $TXTFETCH_KEY" \
  -F file=@proposal.docx

A slide deck:

curl -X POST https://api.txtfetch.com/v1/extract \
  -H "Authorization: Bearer $TXTFETCH_KEY" \
  -F file=@quarterly-review.pptx

A spreadsheet, fetched straight from wherever it’s hosted instead of downloaded first:

curl -X POST "https://api.txtfetch.com/v1/extract?url=https://example.com/models/forecast.xlsx" \
  -H "Authorization: Bearer $TXTFETCH_KEY"

All three return the same JSON shape:

{
  "status": "success",
  "extracted_text": "Q3 Forecast\nRegion: North America\nRevenue: 4.2M..."
}

Table cells, slide text and notes, and sheet contents all come back as part of extracted_text — flattened into the same linear text your chunker already expects from a PDF or an HTML page, so the rest of your ingestion code doesn’t need a special branch for “this one came from Excel.”

Fewer parsers, fewer things to patch

Dropping three format-specific libraries for one HTTP call means one thing to version, one error shape ({"status": "error", "error": "..."}) to handle, and one place to fix if a document category starts behaving oddly — instead of tracking python-docx, python-pptx, and openpyxl release notes for compatibility breaks. For a document-heavy ingestion pipeline that has to treat “read anything from the business” as a requirement, that’s the whole point: less code that exists purely to keep up with file formats.

Want to point it at your own DOCX/PPTX/XLSX archive? Get in touch and we’ll set you up with an API key.