Lars
207817376d
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- Implemented optional LLM-Rerank functionality in the planning exercise suggestion process, allowing for improved exercise ranking based on user-defined criteria. - Updated the `suggestPlanningExercises` API to accept `planned_exercise_ids` for client-side overrides, enhancing flexibility in exercise selection. - Enhanced the `ExercisePickerModal` to reflect LLM ranking status and support new planning context features. - Incremented application version to 0.8.170 and updated changelog to document the new features and improvements in the planning AI capabilities.
21 lines
737 B
Python
21 lines
737 B
Python
"""
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POST /api/planning/exercise-suggest — planungsgebundene Übungssuche (Hybrid + Profil + optional LLM-Rerank).
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"""
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from fastapi import APIRouter, Depends
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from db import get_db, get_cursor
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from tenant_context import TenantContext, get_tenant_context
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from planning_exercise_suggest import PlanningExerciseSuggestRequest, suggest_planning_exercises
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router = APIRouter(prefix="/api/planning", tags=["planning_exercise_suggest"])
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@router.post("/exercise-suggest")
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def post_planning_exercise_suggest(
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body: PlanningExerciseSuggestRequest,
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tenant: TenantContext = Depends(get_tenant_context),
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):
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with get_db() as conn:
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cur = get_cursor(conn)
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return suggest_planning_exercises(cur, tenant=tenant, body=body)
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