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- Updated the planning exercise retrieval process to implement a multistage approach, ranking the entire visible library deterministically against the expectation profile. - Removed the previous profile OR pool mechanism, simplifying the retrieval logic and ensuring full-text search is only used as a scoring signal. - Adjusted the `compose_retrieval_phase` function to accommodate the new full library ranking strategy. - Incremented version to 0.8.177 and updated changelog to reflect these changes in planning exercise capabilities.
110 lines
3.4 KiB
Python
110 lines
3.4 KiB
Python
"""Tests Planungs-Retrieval Phase A (Voll-Library-Ranking)."""
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from planning_exercise_profiles import ExerciseMatchProfile, PlanningTargetProfile
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from planning_exercise_retrieval import (
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fetch_all_visible_exercise_rows,
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rank_visible_library_hits,
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)
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def test_fetch_all_visible_has_no_profile_or_pool_filter():
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captured = {}
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class _Cur:
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def execute(self, sql, params):
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captured["sql"] = sql
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captured["params"] = list(params)
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def fetchall(self):
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return []
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fetch_all_visible_exercise_rows(
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_Cur(),
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vis_sql="(e.visibility = 'official' OR (e.visibility = 'private' AND e.created_by = %s))",
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vis_params=[42],
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query="Kime Partner",
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exercise_kind_any=["simple"],
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)
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sql = captured["sql"].lower()
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assert "exercise_skills" not in sql
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assert "@@ plainto_tsquery" not in sql
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assert " exists " not in sql.replace("primary_focus_name", "")
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params = captured["params"]
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assert params[0] == "Kime Partner"
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assert params[1] == 42
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assert params[2] == "archived"
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assert params[3] == "simple"
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assert params[-1] == 8000
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def test_rank_visible_library_prefers_profile_over_untagged_pool_miss():
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"""Übung ohne Pool-Tags, aber hoher Profil-Match, muss vor schwachem Treffer ranken."""
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target = PlanningTargetProfile(
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focus_area_ids={10: 1.0},
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skill_weights={5: 1.0},
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sources=["framework_catalog"],
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)
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rows = [
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{"id": 1, "title": "Schwach", "summary": "", "primary_focus_name": "X", "ft_rank": 0.9},
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{"id": 2, "title": "Stark", "summary": "", "primary_focus_name": "Karate", "ft_rank": 0.0},
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]
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profiles = {
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1: ExerciseMatchProfile(exercise_id=1, focus_area_ids={99: 1.0}),
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2: ExerciseMatchProfile(exercise_id=2, focus_area_ids={10: 1.0}, skill_weights={5: 1.0}),
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}
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class _Cur:
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def execute(self, sql, params=None):
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return None
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def fetchall(self):
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return []
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def _fake_load(cur, exercise_ids, *, batch=400):
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_ = (cur, batch)
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return {eid: profiles[eid] for eid in exercise_ids if eid in profiles}
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def _fake_skills(cur, exercise_ids, *, batch=400):
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_ = (cur, batch)
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return {1: {99}, 2: {5, 10}}
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import planning_exercise_retrieval as mod
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orig_profiles = mod._load_match_profiles_chunked
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orig_skills = mod._load_skill_sets_chunked
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try:
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mod._load_match_profiles_chunked = _fake_load
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mod._load_skill_sets_chunked = _fake_skills
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hits, _ = rank_visible_library_hits(
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_Cur(),
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rows,
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query="",
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intent="suggest_next",
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intent_weights={
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"fulltext": 0.08,
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"progression": 0.28,
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"skill": 0.12,
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"plan": 0.10,
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"profile": 0.25,
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"repeat_unit": -0.30,
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"repeat_group": -0.15,
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},
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target=target,
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pack={
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"planned_exercise_ids": [],
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"group_recent_exercise_ids": [],
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"progression_successor_ids": [],
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"anchor_skill_ids": [],
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"anchor_exercise_id": None,
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"progression_edge_notes": {},
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},
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)
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finally:
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mod._load_match_profiles_chunked = orig_profiles
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mod._load_skill_sets_chunked = orig_skills
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assert hits[0]["id"] == 2
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assert hits[0]["score"] > hits[1]["score"]
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