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- Added support for the new planning exercise expectation profile slug in the AI prompt runtime. - Refactored SQL parameter handling in the planning exercise retrieval process to ensure correct binding for full-text search. - Updated the planning exercise suggestion logic to incorporate LLM expectation handling, improving the accuracy of exercise recommendations. - Introduced new functions to determine when to run the LLM expectation pipeline, enhancing the decision-making process for exercise suggestions. - Incremented version to 0.8.176 and updated changelog to reflect these enhancements in planning AI capabilities.
44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
"""Tests Planungs-Retrieval SQL-Parameter."""
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from planning_exercise_retrieval import fetch_retrieval_candidate_rows
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def test_fetch_retrieval_binds_query_before_visibility_params():
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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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{
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"id": 1,
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"title": "Test",
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"summary": "",
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"primary_focus_name": None,
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"ft_rank": 0.2,
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}
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]
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fetch_retrieval_candidate_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="nächste Übung planen",
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exercise_kind_any=None,
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target=__import__(
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"planning_exercise_profiles", fromlist=["PlanningTargetProfile"]
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).PlanningTargetProfile(),
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progression_successor_ids=set(),
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anchor_skill_ids={7},
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raw_pool_limit=10,
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)
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params = captured["params"]
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assert params[0] == "nächste Übung planen"
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assert params[1] == 42
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assert params[2] == "archived"
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assert params[-2] == "nächste Übung planen"
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assert params[-1] == 10
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