feat(logic): generator promptow do LLM + budzetowanie (LOG-29, LOG-30)
Pierwszy krok ustalonej kolejnosci: prompt z podgladem, uzyteczny od razu BEZ integracji API (ta przyjdzie w LOG-31). LOG-29 — app/prompt.py: - profil natal (ekran Interpretacje) i period (ekran Kalendarz), - prompt po polsku: zadanie -> dane horoskopu (pozycje, osie, Lots, aspekty, sekta, zodiak) -> wskazania z baz wg wagi -> instrukcje -> zastrzezenie, - twarde reguly: kazda teza musi cytowac konkretny sygnifikator, zakaz wychodzenia poza dostarczone dane, jawne wskazanie sprzecznosci, - deterministyczny: ten sam horoskop + budzet = ten sam prompt. LOG-30 — redukcja do budzetu (concise/medium/extensive): dedup -> grupowanie z licznikiem -> sortowanie wg punktacji sily (LOG-21) -> obciecie ogona (jednostka = CALE wskazanie) -> skracanie dlugich opisow. Statystyki zwracaja ile weszlo/pominieto i jaki byl prog — takze w tresci promptu, zeby model wiedzial, ze widzi wybor. POST /chart/prompt — zwraca sam prompt + statystyki, bez wolania modelu. Prezentacja: przycisk „Generuj prompt (AI)" na obu ekranach, wybor budzetu, pole z promptem + kopiowanie (z fallbackiem dla http bez secure context). WAZNE (znalezione przy tescie e2e): padnieta warstwa danych zabiera tylko wskazania — horoskop i OS CZASU zostaja, bo sa czysto obliczeniowe. Wczesniej blad bazy gubil cala osie czasu, czyniac prognoze okresowa bezuzyteczna. Zabezpieczone testem. Testy: 19 nowych, calosc 118 passed / 1 skipped. Zweryfikowane e2e w przegladarce: profil natal (3340 znakow) i period (15 zdarzen, 4728 znakow). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit was merged in pull request #9.
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"""Generator promptów (LOG-29) i budżetowanie (LOG-30).
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Testy nie wołają żadnego modelu — sprawdzają skład promptu i niezmienniki redukcji.
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"""
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import pytest
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from app.prompt import (
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BUDGETS,
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build_natal_prompt,
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build_period_prompt,
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natal_indications,
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period_indications,
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reduce_indications,
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)
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CHART = {
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"zodiac": "tropical",
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"house_system": "whole_sign",
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"sect": "day",
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"positions": [
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{"name": "Sun", "sign": "Taurus", "in_sign": "Tau 10°12'37\"", "house": 11, "direction": "D"},
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{"name": "Moon", "sign": "Aries", "in_sign": "Ari 7°48'40\"", "house": 10, "direction": "D"},
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],
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"angles": {"Asc": {"name": "Asc", "in_sign": "Can 16°00'54\""},
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"MC": {"name": "MC", "in_sign": "Pis 25°50'50\""}},
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"lots": [{"name": "Fortune", "in_sign": "Can 7°15'14\"", "house": 1,
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"formula": "Asc + Moon − Sun"}],
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"aspects": [{"obj1": "Sun", "obj2": "Moon", "aspect": "conjunction", "orb": 2.34, "as": "A"}],
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}
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def _report(n_samples=3, score=2.0):
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return {"objects": [{
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"object": "Sun", "sign": "Taurus", "house": 11,
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"facets": [{
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"type": "sign", "label": "w znaku Taurus", "score": score,
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"samples": [{"significator": f"[Su in [Tau {i}", "expanded": f"Sun in Taurus {i}",
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"effect": f"efekt numer {i}"} for i in range(n_samples)],
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}],
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}]}
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# ---------------------------------------------------------------- budżetowanie
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def test_budget_limits_prompt_size():
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# opisy MUSZĄ być różne — identyczne zlałoby grupowanie w jedną pozycję
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big = {"objects": [{
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"object": "Sun", "sign": "Taurus", "house": 11,
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"facets": [{"type": "sign", "label": "w znaku Taurus", "score": 1.0,
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"samples": [{"significator": f"[Su {i}", "expanded": f"Sun {i}",
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"effect": f"opis {i} " + "x" * 200} for i in range(500)]}],
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}]}
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out = build_natal_prompt(CHART, big, budget="concise")
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assert out["stats"]["chars"] <= BUDGETS["concise"] * 1.1 # z marginesem na sekcje stałe
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assert out["stats"]["omitted"] > 0
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def test_bigger_budget_includes_more():
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big = _report(n_samples=300, score=1.0)
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small = build_natal_prompt(CHART, big, budget="concise")["stats"]
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large = build_natal_prompt(CHART, big, budget="extensive")["stats"]
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assert large["included"] > small["included"]
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assert large["omitted"] < small["omitted"]
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def test_grouping_merges_identical_effects():
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items = [
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{"context": "Sun — w znaku", "sort_key": ("Sun", "a"), "score": 1.0,
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"significator": "Sun in Taurus", "effect": "ten sam opis"},
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{"context": "Sun — w znaku", "sort_key": ("Sun", "a"), "score": 1.0,
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"significator": "Sun in Taurus (inny zapis)", "effect": "Ten Sam Opis "},
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]
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chosen, stats = reduce_indications(items, 10_000)
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assert len(chosen) == 1 # zlane w jedno
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assert chosen[0]["count"] == 2 # z licznikiem wystąpień
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assert stats["after_grouping"] == 1
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assert stats["deduplicated"] == 1
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def test_sorted_by_score_desc():
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items = [
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{"context": f"c{i}", "sort_key": (f"c{i}", ""), "score": float(i),
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"significator": f"s{i}", "effect": f"e{i}"} for i in range(5)
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]
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chosen, _ = reduce_indications(items, 10_000)
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scores = [c["score"] for c in chosen]
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assert scores == sorted(scores, reverse=True)
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def test_weakest_are_dropped_first():
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items = [
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{"context": f"c{i}", "sort_key": (f"c{i}", ""), "score": float(i),
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"significator": f"s{i}", "effect": "opis " * 20} for i in range(20)
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]
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chosen, stats = reduce_indications(items, 400)
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assert stats["omitted"] > 0
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# to, co weszło, ma wagę nie niższą niż to, co odpadło
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assert stats["min_score_included"] >= stats["max_score_omitted"]
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def test_long_effects_are_shortened():
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items = [{"context": "c", "sort_key": ("c", ""), "score": 1.0,
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"significator": "s", "effect": "x" * 1000}]
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chosen, stats = reduce_indications(items, 10_000)
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assert stats["shortened"] == 1
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assert "skrócono" in chosen[0]["effect"]
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def test_never_truncates_mid_indication():
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items = [{"context": "c", "sort_key": ("c", ""), "score": 1.0,
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"significator": "s", "effect": "opis " * 50} for _ in range(10)]
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chosen, _ = reduce_indications(items, 200)
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assert chosen # zawsze co najmniej jedno całe
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for c in chosen:
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assert c["effect"].endswith(("opis", "[…skrócono]")) # nie urwane w pół słowa
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def test_unknown_budget_rejected():
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with pytest.raises(ValueError):
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build_natal_prompt(CHART, _report(), budget="gigantyczny")
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# ----------------------------------------------------------------- skład promptu
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def test_natal_prompt_contains_required_sections():
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p = build_natal_prompt(CHART, _report(), moment_label="1984-04-30 09:20 UTC")["prompt"]
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for section in ["# ZADANIE", "# DANE HOROSKOPU", "# WSKAZANIA Z BAZ",
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"# JAK MA WYGLĄDAĆ ODPOWIEDŹ", "# ZASTRZEŻENIE"]:
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assert section in p
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assert "HOROSKOPU\nURODZENIOWEGO" in p or "URODZENIOWEGO" in p
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def test_natal_prompt_carries_chart_data():
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p = build_natal_prompt(CHART, _report(), moment_label="1984-04-30 09:20 UTC")["prompt"]
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assert "Tau 10°12'37\"" in p and "dom 11" in p # pozycja + dom
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assert "Can 16°00'54\"" in p # Asc
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assert "Fortune" in p # Lots
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assert "Sun conjunction Moon" in p or "conjunction" in p
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assert "sekta: dzienna" in p
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assert "1984-04-30 09:20 UTC" in p
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def test_prompt_demands_citing_significators():
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p = build_natal_prompt(CHART, _report())["prompt"]
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assert "Teza bez wskazania jest niedopuszczalna" in p
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assert "Nie zmyślaj" in p
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def test_prompt_has_disclaimer():
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p = build_natal_prompt(CHART, _report())["prompt"]
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assert "nie stanowi porady" in p
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def test_prompt_is_deterministic():
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a = build_natal_prompt(CHART, _report(n_samples=50), budget="medium")["prompt"]
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b = build_natal_prompt(CHART, _report(n_samples=50), budget="medium")["prompt"]
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assert a == b
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def test_omission_is_reported_in_prompt():
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out = build_natal_prompt(CHART, _report(n_samples=400, score=1.0), budget="concise")
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assert out["stats"]["omitted"] > 0
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assert "pominięto" in out["prompt"] # użytkownik/model wie, że coś odpadło
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def test_empty_report_still_builds_prompt():
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out = build_natal_prompt(CHART, {"objects": []})
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assert out["stats"]["included"] == 0
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assert "brak trafień" in out["prompt"]
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assert "# DANE HOROSKOPU" in out["prompt"]
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# ------------------------------------------------------------------- okresowy
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EVENTS = [
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{"technique": "profection", "significator": "Lord of Year: Mars", "start": "2026-04-30",
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"exact": "2026-04-30", "end": "2027-04-30", "interpretations_count": 2,
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"interpretations": [{"significator": "[Ma", "expanded": "Mars", "effect": "opis marsowy"}]},
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{"technique": "solar_arc", "significator": "Sun conj Saturn", "start": "2026-01-01",
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"exact": "2026-06-15", "end": "2026-12-31", "interpretations_count": 1,
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"interpretations": [{"significator": "[Su [conj [Sa", "expanded": "Sun conjunction Saturn",
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"effect": "opis saturniczny"}]},
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]
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def test_period_prompt_has_dates_and_timeline():
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out = build_period_prompt(CHART, EVENTS, "2026-01-01", "2027-01-01")
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p = out["prompt"]
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assert "2026-01-01 — 2027-01-01" in p
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assert "# OŚ CZASU" in p
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assert "profection" in p and "solar_arc" in p
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assert "2026-06-15" in p # data dokładna zdarzenia
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assert "CHRONOLOGICZNIE" in p
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assert out["stats"]["events"] == 2
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assert out["profile"] == "period"
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def test_period_prompt_keeps_timeline_without_interpretations():
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"""Padnięta warstwa danych zabiera wskazania, ale NIE oś czasu — ta jest czysto
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obliczeniowa i bez niej prognoza okresowa jest bezużyteczna."""
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bare = [{k: v for k, v in ev.items()
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if k not in ("interpretations", "interpretations_count")} for ev in EVENTS]
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out = build_period_prompt(CHART, bare, "2026-01-01", "2027-01-01")
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assert out["stats"]["events"] == 2
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assert "profection" in out["prompt"] and "2026-06-15" in out["prompt"]
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assert out["stats"]["included"] == 0 # brak wskazań, ale oś czasu jest
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def test_period_indications_weight_by_hit_count():
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items = period_indications(EVENTS)
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assert [i["score"] for i in items] == [2.0, 1.0]
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def test_natal_indications_flatten_facets():
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items = natal_indications(_report(n_samples=4))
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assert len(items) == 4
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assert all(i["context"].startswith("Sun —") for i in items)
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assert all(i["score"] == 2.0 for i in items)
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