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Claude Opus 5.5
Faster long documents: Smart Humanize uses the fast engine at any length (FAST_MAX_WORDS default 0; a 22,000-word part took 13 min on the sentence-by-sentence engine, the fast engine reached 0% in about 4); report-guided runs at 0% stop when a round rewords fewer than 3 (or 5%) of the highlighted sentences left (5 of 12 minutes went on taking 80 to 72); wallet usual cost of long fast runs from live measurements; pricing table rows all on the fast engine
d14dbe0 | """Fast engine (fast.py) end to end on a Word file, with the models replaced by fakes: the detector flags sentences | |
| with AI stock words, the language model offers versions, one of which changes the meaning.""" | |
| from docx import Document | |
| import detector as D | |
| import fast as F | |
| import fluency as FL | |
| import groqai as G | |
| import humanizer as H | |
| import meaning as MEANING | |
| AI = ("Furthermore, the proposed controller plays a crucial role in reducing the harmonic distortion of the grid " | |
| "current from 4.8% to 1.9% [14].") | |
| AI2 = "These findings provide valuable insights for engineers seeking to design robust inverter control schemes." | |
| HUMAN = ("We built the test rig in the basement lab and ran it for six weeks before the first usable data came out, " | |
| "mostly because the sensors kept drifting.") | |
| def _ai(t): | |
| """Share of AI-sounding sentences in the text (a window score behaves like this).""" | |
| sents = [x for x in D.split_sentences(t) if x.strip()] or [t] | |
| bad = sum(any(w in x for w in ("crucial", "valuable", "Furthermore")) for x in sents) | |
| return 0.05 + 0.9 * bad / len(sents) | |
| def test_the_fast_engine_lowers_the_score_and_never_uses_an_unfaithful_version(tmp_path, monkeypatch): | |
| monkeypatch.setattr(D, "score_texts", lambda texts: [_ai(t) for t in texts]) | |
| monkeypatch.setattr(H, "_detector_scores", lambda texts: [_ai(t) for t in texts]) | |
| monkeypatch.setattr(FL, "acceptable", lambda pairs: [True] * len(pairs)) | |
| monkeypatch.setattr(MEANING, "similarities", lambda pairs: [0.95] * len(pairs)) | |
| seen = {} | |
| import json | |
| def request(gen, system, user, max_tokens, deadline=None): | |
| seen["faithful"] = "KEEP EVERY DETAIL" in system | |
| seen.setdefault("gens", set()).add(gen) | |
| items = [] | |
| for i, line in enumerate([x for x in user.splitlines() if "SENTENCE:" in x], 1): | |
| if "harmonic" in line: | |
| vs = ["The controller lowers the harmonic distortion of the grid current from 4.8% to 1.9% [14].", | |
| "The controller is the main reason the distortion vanished [14]."] | |
| elif "valuable" in line: | |
| vs = ["Engineers who design inverter control schemes can use these results."] | |
| else: | |
| vs = [] | |
| items.append({"id": i, "versions": vs}) | |
| return json.dumps({"items": items}) | |
| monkeypatch.setattr(F, "_request", request) | |
| monkeypatch.setattr(F, "GENERATORS", ["gemini:model-a", "groq:model-b"]) | |
| monkeypatch.setenv("GEMINI_API_KEY", "test-key") | |
| monkeypatch.setattr(G, "KEY", "test-key") | |
| monkeypatch.setattr(G, "verify", lambda pairs, **k: [True if "lowers" in b or "Engineers" in b | |
| else "claim changed" for _, b in pairs]) | |
| d = Document() | |
| d.add_paragraph(HUMAN + " " + AI + " " + AI2) | |
| d.add_paragraph(HUMAN) | |
| path = tmp_path / "paper.docx" | |
| d.save(str(path)) | |
| r = F.humanize_docx(str(path), budget=60) | |
| text = "\n".join(p.text for p in Document(r["path"]).paragraphs) | |
| assert r["before"]["pct"] > 0 and r["after"]["pct"] == 0 | |
| assert "lowers the harmonic distortion" in text and "[14]" in text and "4.8% to 1.9%" in text | |
| assert "vanished" not in text # rejected by the faithfulness check | |
| assert HUMAN in text # unflagged sentences are left alone | |
| assert seen["faithful"] is True # keep-every-detail prompt | |
| assert seen["gens"] == {"gemini:model-a", "groq:model-b"} # every model asked | |
| def test_a_rewrite_of_a_sentence_beginning_does_not_end_the_sentence_early(): | |
| assert F._fit("The model was trained on 12,500 images", "The model learned from 12,500 images.") \ | |
| == "The model learned from 12,500 images" | |
| assert F._fit("A full sentence.", "Another full sentence.") == "Another full sentence." | |
| def test_a_request_still_running_at_the_deadline_is_dropped_and_the_job_ends_on_time(monkeypatch): | |
| """Whatever the length of the document, the result comes back within the time budget: a slow model's answer is | |
| not waited for.""" | |
| import time | |
| import json | |
| def request(gen, system, user, max_tokens, deadline=None): | |
| if gen.startswith("slow"): | |
| time.sleep(3) # answers after the deadline | |
| return json.dumps({"items": [{"id": 1, "versions": ["Too late."]}]}) | |
| return json.dumps({"items": [{"id": 1, "versions": ["The controller lowers the distortion [14]."]}]}) | |
| monkeypatch.setattr(F, "_request", request) | |
| monkeypatch.setattr(F, "GENERATORS", ["groq:fast-model", "slow:model"]) | |
| monkeypatch.setitem(F._URLS, "slow", ("http://unused", "SLOW_KEY")) | |
| monkeypatch.setenv("SLOW_KEY", "k") | |
| monkeypatch.setattr(G, "KEY", "test-key") | |
| t = time.time() | |
| out = F._versions(["The controller reduces the distortion [14]."], ["The controller reduces the distortion [14]."], | |
| frozenset(), 2, 0, deadline=time.time() + 1.0) | |
| assert time.time() - t < 2.0 # did not wait for the slow model | |
| assert out == [["The controller lowers the distortion [14]."]] | |
| def test_an_unchecked_rewrite_is_never_used(tmp_path, monkeypatch): | |
| """When the faithfulness check cannot run (service busy, spending cap), sentences keep their wording.""" | |
| import json | |
| monkeypatch.setattr(D, "score_texts", lambda texts: [_ai(t) for t in texts]) | |
| monkeypatch.setattr(H, "_detector_scores", lambda texts: [_ai(t) for t in texts]) | |
| monkeypatch.setattr(FL, "acceptable", lambda pairs: [True] * len(pairs)) | |
| monkeypatch.setattr(MEANING, "similarities", lambda pairs: [0.95] * len(pairs)) | |
| monkeypatch.setattr(F, "_request", lambda gen, system, user, mt, deadline=None: json.dumps( | |
| {"items": [{"id": i, "versions": ["The controller lowers the harmonic distortion from 4.8% to 1.9% [14]."]} | |
| for i in range(1, 4)]})) | |
| monkeypatch.setattr(F, "GENERATORS", ["groq:model-b"]) | |
| monkeypatch.setattr(G, "KEY", "test-key") | |
| monkeypatch.setattr(G, "verify", lambda pairs, **k: [None] * len(pairs)) # the check could not run | |
| d = Document() | |
| d.add_paragraph(HUMAN + " " + AI + " " + AI2) | |
| path = tmp_path / "paper.docx" | |
| d.save(str(path)) | |
| r = F.humanize_docx(str(path), budget=60) | |
| text = "\n".join(p.text for p in Document(r["path"]).paragraphs) | |
| assert AI in text and AI2 in text # nothing unchecked went in | |
| def test_a_report_run_at_zero_stops_when_a_round_clears_too_few_highlighted_sentences(tmp_path, monkeypatch): | |
| """At 0% with only the report's sentences left, a round that rewords only one of ten clear of their original | |
| wording ends the run (each such round cost a minute on a thesis part and changed almost nothing).""" | |
| import json | |
| monkeypatch.setattr(D, "score_texts", lambda texts: [0.05] * len(texts)) # our detector: human already | |
| monkeypatch.setattr(H, "_detector_scores", lambda texts: [0.05] * len(texts)) | |
| monkeypatch.setattr(H, "_ctx_scores", lambda pairs, ctx, cur: [0.05] * len(pairs)) | |
| monkeypatch.setattr(FL, "acceptable", lambda pairs: [True] * len(pairs)) | |
| monkeypatch.setattr(MEANING, "similarities", lambda pairs: [0.95] * len(pairs)) | |
| monkeypatch.setattr(G, "verify", lambda pairs, **k: [True] * len(pairs)) | |
| rounds = [] | |
| def request(gen, system, user, max_tokens, deadline=None): | |
| rounds.append(1) | |
| lines = [x.split("SENTENCE:", 1)[1].strip() for x in user.splitlines() if "SENTENCE:" in x] | |
| items = [{"id": i, "versions": [("Quite unlike before, nobody here wrote anything resembling it." | |
| if i == 1 else "Indeed, " + t[0].lower() + t[1:])]} | |
| for i, t in enumerate(lines, 1)] | |
| return json.dumps({"items": items}) | |
| monkeypatch.setattr(F, "_request", request) | |
| monkeypatch.setattr(F, "GENERATORS", ["groq:model-b"]) | |
| monkeypatch.setattr(G, "KEY", "test-key") | |
| sents = ["Rainfall in the northern valley rose steadily over the past decade.", | |
| "Farmers responded by planting crops that tolerate wet soil better.", | |
| "Local markets now sell more rice than wheat during the monsoon months.", | |
| "Village councils built drainage channels along most of the main roads.", | |
| "School attendance dropped whenever the river burst its banks in spring.", | |
| "Doctors reported more cases of fever after each flood had passed.", | |
| "Young people increasingly moved to nearby towns looking for steady jobs.", | |
| "Older residents kept traditional houses raised on wooden stilts above ground.", | |
| "Fishing became a reliable source of income for many poor households.", | |
| "Government grants helped repair bridges damaged by the heavy storms."] | |
| d = Document() | |
| d.add_paragraph(" ".join(sents)) | |
| path = tmp_path / "paper.docx" | |
| d.save(str(path)) | |
| r = F.humanize_docx(str(path), budget=60, also=lambda t: t in sents, also_overlap=0.15) | |
| assert len(rounds) == 1 | |
| assert any("few highlighted sentences" in x for x in r["log"]) | |