History

Kjell Tore Guttormsen d7751c0b9a test(consume): hit@8 over six questions against a random-ranker baseline hit@8 = 5 of 6, every hit at rank 1, against a chance baseline of 1.35 of 6 over a denominator of 629 concepts per question. Wall time 0.51-0.56 s per question; spent 17 970 - 74 838 bytes against a 120 000 limit. Two things this measurement did NOT establish, both in the report: - BOTH known-negative controls FAILED. A question the bundle has no answer to still returns eight excerpts, because no natural Norwegian question is lexically disjoint from a 629-concept corpus under a four-character shared-prefix rule -- measured per token, the interrogative `hvor` reaches 40 concepts, `brukes` 83. So `no_lexical_match` works per concept and not as a whole-question gate: an empty excerpt list is evidence of absence, a full one is not evidence of presence. The fix is named (rarity weighting) and NOT built, because this step's fence freezes the instrument before it is measured. - The question texts were written during execution, after the ranker existed. The plan recorded the gold documents' SIZE profile -- its per-row baselines sum to 1.35 and the sizes used here reproduce that exactly, which is an independent check that this is the set the plan profiled -- but it recorded no question texts, and three of six gold documents could not be pinned uniquely from the sizes. Not a blind evaluation, and the report says so. The scorer is a tool, not a script in a document: `tools/okf_consume_measure.py` takes the gold set as an INPUT because it is tracked in a public repository and an answer key names a consumer's documents. hit_rank, both chance baselines and the document-size census are unit-tested; the corpus run is a measurement. Public-file rule, checked with a pattern DERIVED from the corpus's own 39 document names rather than hand-picked, and shown able to find first (67 hits on the bundle's own index): zero corpus document names in any tracked file in this repository. One leak was found and removed on the way -- a corpus concept name in a code comment and a hardcoded corpus path in a test. Suite run after git add: 1230 passed, mypy --strict clean on 27 files, ruff clean. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>		2026-09-07 09:37:14 +02:00
..
consume-bundle	test(consume): hit@8 over six questions against a random-ranker baseline	2026-09-07 09:37:14 +02:00
k2-office	test(fixtures): a synthetic K2 denominator for pptx, odt and rtf	2026-09-07 05:17:18 +02:00
k2-office-fasit.json	test(fixtures): a synthetic K2 denominator for pptx, odt and rtf	2026-09-07 05:17:18 +02:00
make_fixtures.py	test(extract): hand-built office fixtures with frozen extracted text	2026-09-02 14:14:27 +02:00
make_k2_office.py	test(fixtures): a synthetic K2 denominator for pptx, odt and rtf	2026-09-07 05:17:18 +02:00
no-styles-krav.docx	test(extract): hand-built office fixtures with frozen extracted text	2026-09-02 14:14:27 +02:00
no-text-layer.pdf	feat(extract): implement pdf behind the [extract] extra with pdfplumber	2026-08-21 20:22:39 +02:00
propose-golden-default.json	test(propose): pin the default artifact with a committed golden	2026-09-07 01:20:31 +02:00
README.md	test(fixtures): a synthetic K2 denominator for pptx, odt and rtf	2026-09-07 05:17:18 +02:00
two-line-krav.docx	test(extract): hand-built office fixtures with frozen extracted text	2026-09-02 14:14:27 +02:00
two-line-krav.pdf	feat(extract): implement pdf behind the [extract] extra with pdfplumber	2026-08-21 20:22:39 +02:00
two-line-krav.xlsx	test(extract): hand-built office fixtures with frozen extracted text	2026-09-02 14:14:27 +02:00

README.md

Test fixtures

The PDF fixtures

two-line-krav.pdf and no-text-layer.pdf are hand-written minimal PDFs, regenerated by make_fixtures.py in this directory:

python3 tests/fixtures/make_fixtures.py

They carry no library's output — the objects are laid out by hand and the xref offsets computed from the emitted bytes — so they are auditable byte for byte and reproducible from that one file.

Fixture	What it is for
`two-line-krav.pdf`	One heading plus one requirement row with label and value on the same line. That pairing is the property `pdfplumber` was chosen for.
`no-text-layer.pdf`	A structurally valid page with no text operators — the shape a scanned or image-only PDF presents. Must fail fast (`extractor_empty_pdf`), never persist as an empty concept.

The office fixtures

two-line-krav.docx, no-styles-krav.docx and two-line-krav.xlsx are hand-laid OOXML containers, regenerated by the same make_fixtures.py. Every part is written out by hand and zipped with a fixed date_time, so they are byte-reproducible and carry no converter's output.

That last point is the whole policy, not a preference. A .docx written by the converter and then read by the converter proves only that the converter agrees with itself, and would stay green through any conversion defect that is symmetric — which is most of them.

Fixture	What it is for
`two-line-krav.docx`	A heading plus one requirement row with label and value on the same line — the docx mirror of `two-line-krav.pdf`.
`no-styles-krav.docx`	The same document without `word/styles.xml`. A negative control: the body survives and the heading marker does not, which is what proves the styles part is load-bearing rather than decoration.
`two-line-krav.xlsx`	A sheet name that becomes a heading, plus a label/value pair on one row.

Two things were measured while building these, and both are the same shape — structurally valid input, silently reduced output, exit code 0 and no warning:

Without word/styles.xml the docx extracts as flat prose with no heading. A fixture lacking that part would pin the body and pin nothing about structure, while looking exactly as convincing. Structure is the half the segment proposer reads.
With inline strings (t="inlineStr") rather than a shared string table, the xlsx extracts with the sheet name intact and every cell value gone. The fixture therefore uses a dimension element and a shared string table.

The K2 office fixture set (`k2-office/`)

krav-presentasjon.pptx, krav-tekstdokument.odt and krav-rikt-tekstformat.rtf are the synthetic denominator for the three office rows the corpus has none of. docs/2026-09-04-k2-pptx-odt-rtf.md measured that denominator at zero — K2/trinn1 holds 43 files and not one is a pptx, an odt or an rtf — so those rows were unmeasured in the sense of never having met a document at all. Regenerated by make_k2_office.py in this directory:

python3 tests/fixtures/make_k2_office.py

One document, three containers. All three carry the same authored content — a title, an intro, a 20-row label/value table, a caption and a 4x4 grid — so the only variable between the three measurements is the container and the reader that opens it. The counts are hand-counted once, in k2-office-fasit.json, and shared: 56 cells, 20 pairs, 59 distinct strings.

The generator and the fasit live one level up, and that is not tidiness. Door B walks its drop directory recursively, so anything parked inside k2-office/ would enter the run and N would stop being 3.

Same policy as the office fixtures above, for the same reason: every part is hand-laid and no converter wrote any of them. The commissioning order offered pandoc as a generator option; a file written by the converter and then read by the converter would prove only that the converter agrees with itself.

Two things were measured while building this set, both against the vendored pandoc 3.9, and both are the house shape — structurally plausible input, silently wrong output, exit code 0 and no warning:

RTF cell paragraphs need \pard\intbl. Without it, consecutive \trowd…\row rows are read as each row NESTED inside the previous one: five label/value rows came back as five levels of nested table, 2076 characters where 117 were expected.
The \uN? unicode escape loses the character after it. Measured directly: A\u248?BC reads back as AoC (ring letter present, B gone) and A\u248?xBC reads back as AoBC. The ? is taken as the control word's delimiter and \uc1 then skips a real character. The fixture writes \uN ? with an explicit space, which round-trips. This is the form Word emits, so it is a converter finding rather than a fixture quirk — recorded in docs/2026-09-07-k2-pptx-odt-rtf-fixtures.md, not worked around anywhere in src/.

Three synthetic documents in one house style are not a corpus. The rows stay unmeasured in extract._EVIDENCE and tests/test_k2_office_fixtures.py asserts that they do.

The proposer's default-profile golden

propose-golden-default.json is the artifact tools/okf_propose_segments.py produces for OUTLINE_DOCUMENT (defined in tests/test_propose_segments.py) with no flags at all, generated at commit 798f64a with --proposed-at 2026-09-03T00:00:00Z. The timestamp is an explicit argument because the artifact carries it verbatim; a wall-clock default would make the golden unreproducible by construction.

Why the fixture is OUTLINE_DOCUMENT and not DOCUMENT. The golden exists to go red if any later rule is accidentally defaulted ON. DOCUMENT was measured to contain zero bare-integer lines, so a golden over it would stay byte-identical through exactly the regression it was named to catch -- a trap written down but unable to fire. OUTLINE_DOCUMENT carries a bare-integer ascending run of three, which today's rules do not match (measured: bare 1 / 1. / 1) yield 0 candidates), so the golden pins that absence and breaks the moment it stops being true.

It transitively pins observed_extractor_version (src/llm_ingestion_okf/segmentation.py): the field is written into every artifact, so a converter or extractor bump turns this golden red. That red is legitimate -- read the diff and decide, exactly as for the frozen PDF literal below. Regenerate only after that decision, never to make a red go away.

Why the expected office text is frozen as a literal

The same reason as the PDF text below, with one addition: the literals are pinned to a named converter version. _pandoc.py refuses any binary but the vendored 3.9, and tests/test_extract.py asserts that version beside the literals. A frozen literal without a named converter pins nothing — it says "these bytes" without saying what produced them.

Why the expected PDF text is frozen as a literal

tests/test_extract.py asserts the extracted text of two-line-krav.pdf as an exact string. That is deliberate, and it is the mechanism behind a promise this library makes everywhere else:

Extraction is deterministic within a parser version. Measured 2026-08-21 across five configurations, two runs each, compared byte for byte (docs/2026-08-21-g2-pdf-extraction-measurement.md).
Extraction is not guaranteed stable across parser versions. pdfplumber pins pdfminer.six==20260107 exactly, and pdfminer.six ships date-stamped releases with no stability contract. So the real pin on extracted text is a transitive one, and it is exact.

The consequence is worth stating plainly: any golden fixture built on extracted PDF text is pinned to an exact parser version, and a parser upgrade is a fixture migration, not a routine bump. The frozen literal is what makes that upgrade break something visible instead of drifting silently. If it goes red after a dependency change, the correct response is to read the diff and decide, not to re-record the expectation.

The version range that carries this lives in pyproject.toml's [project.optional-dependencies] extract, with the same reasoning at the declaration site.

What these fixtures do not cover

Structured table recovery. Measured on real Vegnormalene, only 45 of 196 detected table objects are clean enough to hand to render_table unchanged; two independent parsers return the same wrong shape, because the breakage is in the documents' ruling geometry rather than in either library. PDFs enter this library as prose, and structured tables are out of scope until that is decided separately.