Commit History
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[NOTICKET] feat(knowledge-extraction): subdomains + counts — v3 complete
Rifqi Hafizuddin +1: Claude Opus 5The last two DomainContext v3 fields, both deterministic. No LLM, no prompt change, no spend. subdomains Rolled up from the glossary entries' own `subdomain_tags`, never asked of the model. Each tag was already chosen once, per term, with that term's evidence in front of the model; a second document-level classification would be the same judgement made with less context and no way to check it. Ordered by tag frequency descending with an ALPHABETICAL TIEBREAK, which is load-bearing rather than tidy: on the reference document `production` and `equipment` both score 5, and the entries list them in differing order, so without the tiebreak two runs of one document would disagree. n_terms / n_formulas / n_rules Counts of the other three artifacts. Cheap orientation for a reviewer deciding whether a queue is worth opening, and the fastest way to spot a run that silently extracted nothing. Both are computed in `_finalise_domain`, called at the END of extract_all rathe…172d8dc -
[NOTICKET] feat(knowledge-extraction): span-guard the domain branch
Rifqi Hafizuddin +1: Claude Opus 5The domain branch was the pipeline's ONE unverifiable output: GUARDED_FIELDS ["summary"] was `()`, so `title` and `purpose` were unchecked model prose on nano, the tier whose failure mode is least detectable. Every other branch had to prove its claims against the source; this one did not. The fix is not a bigger model. It is asking a different question: v2 "write a short orientation brief" -> composed -> unprovable v3 "locate the document's own statement" -> quoted -> checkable A summary cannot be found in the source by construction. A quote can. So `purpose` becomes `purpose_verbatim`, prompts/summary.txt is rewritten from summarise to locate, and both text fields join GUARDED_FIELDS + _TRANSCRIBED. Also wires validation into `_run_summary`, which never called validate_entry - the guard table alone would have been inert. The haystack is the WHOLE document, unlike the other branches: this one is handed every chunk, so a quote may legitimately come from anywhere in it. …e2865ac -
[NOTICKET] feat(knowledge-extraction): derived outline + corroborated key parameters
Rifqi Hafizuddin +1: Claude Opus 5The two DETERMINISTIC halves of the DomainContext v3 reshape. No LLM, no spend, no prompt change - which is exactly why they ship ahead of the rest: the parts that need a prompt rewrite and an eval run are split off and still proposed. outline Derived from the artifact's heading_path, in reading order, de-duplicated. The blocker was upstream: adapter.py was dropping heading_path on the floor, so the internal Chunk never carried it - the same way it once dropped `latex`, and with the same consequence, a branch inventing prose about a document whose own structure was sitting unread. Fixed at the seam. It is the only field on this branch carrying the strong guarantee for free: verbatim source structure, nothing to hallucinate. On the BUMA fixture it yields 11 entries from 29 of 31 chunks, including the verbatim "Physical of Availability (PA)" the pipeline is required to surface rather than tidy. key_parameters Was list[str]; now list[KeyParameter{surface, term_id}],…6f5d619
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[NOTICKET] refactor(knowledge-extraction): BriefContext -> DomainContext
Rifqi Hafizuddin +1: Claude Opus 5The workstream diagram has always called this box "Domain Knowledge", while the model and its fields described a short orientation card. Rename the entity to match the intent before anything is persisted: once an expert has approved rows keyed on these ids, changing the `b_` prefix is an id migration that loses the approvals - the exact failure the deterministic-id scheme exists to prevent. BriefContext -> DomainContext brief_id -> domain_id (prefix b_ -> d_) ExtractionResult.brief -> .domain brief_context.json -> domain_context.json Deliberately unchanged: - `Branch = "summary"` and GUARDED_FIELDS["summary"]. The branch literal is the measurement key in CallUsage.branch / RejectedField.branch; renaming it would break comparability with the committed eval baseline. - prompts/summary.txt. Editing prompt text needs the eval path, and the rewrite belongs with the v3 schema change, not with a rename. No field, prompt or guarding change - the …7f47c61
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[NOTICKET] eval(knowledge): X20 measures net -1; close X23 on expert ruling; instrument X25
Rifqi HafizuddinThree things, all from the same session. X20 -- FIRST CONTROLLED A/B IN THIS PROJECT, AND IT SAYS X20 COSTS A RULE. Arms alternated inside one session so drift hit both equally. Arm B reverts X20's three edits and KEEPS X23's synthetic examples, so the only variable is X20 itself. Rule branch only: 15 calls per run, ~35s, five rounds each. X20 in 5/14, every one of 5 runs X20 reverted 6/14, every one of 5 runs Zero variance in both arms, and exactly one rule differs: GAINLOSS_QTY_ZERO_PLAN, 0% with X20 and 100% without. The cause is X20's own final clause. That gold rule is a genuine conditional whose CONDITION is written symbolically -- "Jika (Qty_P(M) = 0, Qty_A(M) > 0), maka:" -- and X20 ends with "a statement that is mostly symbols, = and numbers is an equation". The heuristic cannot tell a computation from a symbolic condition, so it nulls a real rule. X20 is also not buying what it was written to buy. GAINLOSS_UNITS, the rule it targets, fires 0% in BOTH arms …e6ed414
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[NOTICKET] feat(knowledge_extraction): record which guarantee formula_latex carries
Rifqi Hafizuddin +1: Claude Opus 5Three paths leave formula_latex populated and they are not equally strong: located in the source markup, no markup to check against, or an operator the source cannot express. Until now the output could not tell them apart, so a verified formula and an unverifiable one reached the expert as identical rows. That gap is not theoretical. MinerU's pipeline backend writes multiplication as a bare letter x, so every product formula from such an artifact is unverifiable -- including the Production formula this contract uses as its own worked example. Every artifact on Sofhia's machine is pipeline, because she has no GPU. FormulaEntry gains latex_verification: verified | unverified_no_markup | unverified_operator, null when there is no claim to characterise. Set by the span check, never by the model -- the model cannot know which guard ran, and a field it could write would be a claim rather than a record. _mark_latex is a never-throw seam like the rest of validation: an entry type without th…f81a227 -
[NOTICKET] fix(knowledge_extraction): formula_latex is checked against Chunk.latex
Rifqi Hafizuddin +1: Claude Opus 5The field was guarded in name only. It sat in GUARDED_FIELDS but not in _TRANSCRIBED, so its value was never searched in the source -- only its provenance.span was. A structurally wrong transcription with a valid prose span passed, which is exactly the shape X21 produced: a \frac that lost its division reads as a product and states something the document does not. Adding the field to _TRANSCRIBED was not the fix. `text` carries rendered prose and can never contain LaTeX, so that would have nulled every formula entry and presented as a model failure rather than a validation bug. The haystack was the problem, not the check. Chunk.latex is now the haystack. The parsing half has always emitted it; extraction's adapter dropped it on the floor, which is what left the claim unprovable. It now crosses the seam and is compared with whitespace removed ENTIRELY rather than collapsed. That is a different notion of equality, not a weaker one. In LaTeX, whitespace outside \text{} is not significa…9c8a6e4 -
[NOTICKET] feat(knowledge_extraction): output schema v2 — entity ids, links, 16 fields cut
Rifqi Hafizuddin +1: Claude Fable 5Harry's review of the output contract asked for four things: an id on every entity, a formula referenced rather than restated, rules related to formulas, and a smaller schema. All four are adopted. Two defects surfaced while adding the ids, and they matter more than the ids do. Neither existing identifier survived a re-run: `cluster_id` was a frequency-rank ordinal (`#c000` over a list sorted by mention count), so one changed count reshuffled every id after it, and `rule_id` was written by the model, so the same rule could return a different slug next run. The product is an expert working a review queue — if Mas Beta approves 40 entries and a prompt is retuned, unstable ids lose which 40. Approval continuity, not linking, is why these exist. New `ids.py` mints term/formula/rule/brief ids as sha256 over content: never positional, never model-supplied. New `link.py` resolves every reference after the branches run — deterministic, no LLM, no spend — and returns dangle counts rather than…49dfe4d
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[NOTICKET] fix(knowledge_extraction): refuse an artifact that will not name its backend
Rifqi Hafizuddin +1: Claude Opus 5MinerU's `pipeline` and `vlm` backends emit different text from the same PDF. The same equation arrives as a semantic `\times` from one and as a spaced literal `x` from the other, and word boundaries that survive in `\text{...}` under `vlm` are gone under `pipeline`. Two extraction results measured on different backends are therefore not comparable, and nothing downstream can tell them apart after the fact. This is not hypothetical: the two halves spent a week comparing numbers taken from two different parses and read the disagreement as a rendering bug. Each side was measuring correctly, on a different artifact. The provenance already existed and was walked around. `ParsedDocument` carries `parser_backend`, and the parsing half reads it from MinerU's own `_middle.json` rather than from config, precisely so the recorded backend is the one actually used. It never reached extraction because what crossed the seam was a bare `content_list.json`, stripped of its envelope. So the CLI now …4db5f14
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[NOTICKET] feat(knowledge_extraction): paid extraction stage + validate, diff, queue
Rifqi Hafizuddin +1: Claude Fable 5Completes every stage that does not depend on the seam. The pipeline now runs end to end: artifact -> filters -> cluster -> rank -> LLM -> validate -> diff -> frequency-sorted review queue. extract/ client on the nano __54n quad; structured output is PROBED not assumed (json_schema, falling back to json_object + validate-retry, recording which applied); cached tokens read from the API, never modelled. Four branches, one call per TERM CLUSTER. Prompts stay as files so the fixed prefix is byte-identical and caching keeps engaging. validate/ span check (whitespace-only normalisation, never repairs, unlocatable provenance rejects every guarded field), escalation, conflict detection by token overlap — explainable to the reviewer, and it never picks a winner. diff/ new / duplicate / conflicting against an explicitly supplied active glossary, so the duplicate and conflicting paths are reachable. The …ab5ea78 -
[NOTICKET] feat(knowledge_extraction): free stages — models, seam adapter, filters, cluster, ranking
Rifqi Hafizuddin +1: Claude Fable 5First slice of extraction v2, rebuilt in this repo (the kex prototype is not being ported). All stages here are free: zero API calls, CPU only. New module src/knowledge_extraction/ (named to avoid colliding with src/knowledge/, the existing OCR->pgvector service, which is untouched): - models.py pydantic v2 contracts. Encodes the three invariants: content fields Optional, subdomain_tags as an enum, provenance.span mandatory. Adds source_wording so the literal source wording survives instead of being normalised away. - adapter.py THE ONLY file that knows the seam's shape. Stages depend on an internal Chunk subset, so the artifact contract can settle either way without touching a stage. Verified against both live shapes: Sofhia's bare-list draft with its `kind` discriminator, and the document-level envelope proposed in the checklist.…024c30a