pypdf

Added as the PDF text and table extraction dependency for linked datasheets, complementing page parsing so PDF fields can fill gaps with their own source attribution.

What worked

Declaring and wiring the dependency was simple alongside the HTML parser.

Added as a pinned dependency to compute the true page count instead of relying on embedded page markers, with the old marker count kept as fallback. A small probe verified a generated multi-page file and fallback behavior, and the suite passed.

What worked

Simple install and small API change made page counting trustworthy for the unattributed-page check.

Integrated as an optional PDF text and table source with plaintext fallback when unavailable, requiring no accounts or keys. The record shows seeded data and tests passing but no live manufacturer document fetch to confirm extraction quality.

What worked

Optional-use design kept the refresh job runnable without adding a required dependency or paid service.

What got in the way

Live PDF parsing behavior was not demonstrated in the record, so accuracy on varied layouts remains unobserved.

Used inside the sidecar to read printed page labels from the document catalog so citations can prefer printed numbers over file order.

What worked

Provided access to the underlying page-label tree without adding a heavy dependency.

What got in the way

Label tree traversal and style handling needed defensive code because documented examples were sparse for the exact case.

Installed the PDF library and switched page counting from fragile marker counting to renderer-based counting, then covered the behavior with new unit tests and the full suite. Install and API use were straightforward.

What worked

Small focused API for page counts worked first try and removed the dropped-page failure mode.

Integrated as the text-only fallback when primary PDF table extraction yields nothing. Lightweight API made the fallback path easy to implement and test.

What worked

Simple fallback role reduced risk from varied datasheet formats.

Used for free local screening of digital pages so only table-like and scan pages are sent to the paid API. Text extraction was sufficient for triage and fallback handling.

What worked

Lightweight, fast local text check that supported cost control without extra services.

Pinned pypdf 5.4.0 to cut statement pages out of a mixed packet for a second analysis and to build PDFs in tests. The library was absent until the virtualenv dependency install. After that install, the full suite passed with no reported mismatch in the page API.

What worked

One pinned release covered page extraction for the temporary analysis object and synthetic PDFs for tests.

Used it to pull per-page text from downloaded datasheet PDFs, so model-supplied quotes and page numbers could be checked deterministically. It read a hand-generated two-page test PDF correctly in the tests. I didn't test real scanned or complex manufacturer PDFs.

What worked

Simple per-page text extraction, a pure-Python install, and correct results on the synthetic PDF.

What got in the way

It can't help with scanned PDFs that have no text layer, so those have to go to manual review.

Used it to pull text from the address service's PDF manual so I could find the request parameters. A system-wide pip install was blocked by the externally-managed environment rule, so I installed it in a virtualenv. After that, extraction worked well.

I installed the library in a virtual environment and used it to stamp a prefixed page label on a test PDF, then read that label back. The first result was only the prefix. After checking the writer method signature, docstring, and style enum, I set a decimal style and the full folio came back.

What worked

With a numbering style set, writing and reading a prefix-plus-number page label worked, which is the folio form manuals use. The virtual-environment install completed and the corrected check passed.

What got in the way

The signature shows a start default of 0 while the docstring says start must be at least 1. Leaving style unset does not error; the label is emitted as the prefix alone, so the numeric part was missing until a style was passed explicitly.

Used pypdf to pull per-page text from datasheet PDFs so the pipeline could check that quoted excerpts appear on the stated page. It worked in tests with a hand-built PDF that has real text content. Real datasheets weren't tried, and reading order in tables may not match how a model quotes them.

What worked

Simple per-page text extraction with no extra system dependencies.

What got in the way

I had to hand-craft a test PDF, because blank PDFs have no extractable text. Table text order is a known risk for strict quote matching.

Pinned pypdf 6.19.0 and used it to build a minimal PDF, append a certificate page, and pass the bytes into the timestamp step. Page construction and the later incremental update worked together on the first complete prototype, and the version attribute printed cleanly.

What worked

Blank pages and a small content-stream PDF loaded and wrote without errors. Appended pages remained usable input for the signer, and the same pin continued to work through the automated suite.

Used pypdf in strict mode to check that the PDF from the hand-written renderer was valid and that its text could be extracted. After installing it in a virtual environment, it read the page and text correctly.

What worked

Strict parsing plus text extraction gave a quick, independent check of the PDF.

What got in the way

It wasn't preinstalled, and the system Python refused a global install, so I needed a venv.

Added as a lightweight dependency to read each PDF page's text layer. I used it to pick which pages look like tables and to check that every number the layout service returns appears in the page's own text. Tests built small PDFs and ran them through real pypdf extraction.

What worked

Installed quickly with pure Python and no system dependencies. Per-page text extraction was easy to call and its output was predictable enough to test against. I moved from an older pin to the current release to avoid known DoS issues in older versions.

What got in the way

Unicode minus signs came through as literal characters, which needed a moment's thought, though it worked fine in the end.

Used to pull per-page text for choosing which pages to send and checking extracted figures against them, to build sub-PDFs of selected pages, and to read generated test PDFs. Installed cleanly and worked on the first try.

What worked

Simple page-level text extraction and page subsetting with a pure-Python install.

What got in the way

Extracted text can run adjacent columns together, so the verification check can flag correct figures. That's a known limitation of text-layer extraction, not a crash.

Installed the pinned release into the existing virtual environment and used the reader to pull embedded page text from synthetic PDFs. That text layer is what decides whether a page looks like a table, a scan, or prose. An early failed run was a bad fixture that passed raw bytes into a text encoder, not a library failure. After the fixture was valid, text extraction supported the page-gate tests and the suite passed.

What worked

The install was a single pinned package command, and the reader API was small: open bytes and extract page text, including a non-strict open. That was enough to tell a text page from an empty one.

Installed a pinned version into the project venv and used it for an independent page count to cross-check what the OCR service reported. Install and tests went smoothly.

What worked

Pinned install worked first try; simple API for page counting.

Used pypdf to turn downloaded datasheet PDFs into text, capped at a set number of pages. It installed cleanly. The tests used a small hand-written PDF, and text extraction worked on it. I did not test it on real manufacturer datasheets.

What worked

Simple API with a pure-Python install. It accepted a minimal hand-written PDF in the tests.

What got in the way

It cannot easily create PDFs that contain text, so I had to write the test fixture by hand.

Installed pypdf to pull text out of a terms-of-service PDF so I could search it for storage and licensing clauses. Extraction worked well enough to read the relevant sections once it was installed.

What worked

A few lines with PdfReader extracted readable text from every page.

What got in the way

The first install failed because the system Python is externally managed. It needed a user install with the flag that overrides that protection.

I installed pypdf 5.1.0 and used it to build small PDFs in tests and to copy selected pages into the temporary file sent for table extraction. A mismatch between the expected page count and the file raises instead of dropping pages. The install and the tests that exercised extraction succeeded.

What worked

An exact pin installed on the first try in the existing virtual environment. Generated files and page copying behaved consistently under the test suite, including subset pages used for statement lines.

I installed pypdf 5.4.0 and used it to copy selected pages out of a mixed PDF so only those pages were sent for table extraction. The pinned install finished quietly, and the tests that cover sliced packets passed with no library errors.

What worked

A single pinned install was enough. Page slicing behaved consistently with the packet page numbers the reader stores on each line.

Pinned and installed pypdf so statement page ranges could be copied out of a mixed packet before table extraction. The quiet install succeeded on the first try, and the following test run passed, including the page-slicing checks.

What worked

The pinned install was prompt-free and the library was usable immediately. Slicing the selected pages behaved as the tests expected.