Lars
8404a42b6c
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- Added support for club feature quota bypass based on portal roles and profile grants in the capabilities check. - Introduced new functions to handle quota bypass logic in club feature access and consumption. - Updated the FeatureUsageBadge component to reflect platform exemptions for features. - Incremented application version to 0.8.195 and database schema version to 20260606083 to reflect these changes. - Enhanced backend routers to include new logic for consuming club features during AI-related actions.
99 lines
3.6 KiB
Python
99 lines
3.6 KiB
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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from planning_exercise_path_builder import ProgressionPathSuggestRequest, suggest_progression_path
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from account_lifecycle import assert_min_account_state
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from capabilities import probe_capability
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from club_features import consume_club_feature, probe_club_feature_access, resolve_club_id_for_probe
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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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uses_ai = body.include_llm_intent or body.include_llm_rank
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club_id = resolve_club_id_for_probe(tenant) if uses_ai else None
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if uses_ai:
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assert_min_account_state(tenant, "active_member", endpoint="POST /planning/exercise-suggest")
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probe_capability(
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tenant,
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"planning.ai.suggest",
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action="planning_suggest",
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club_id=club_id,
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endpoint="POST /planning/exercise-suggest",
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)
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probe_club_feature_access(
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feature_id="ai_calls",
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action="planning_suggest",
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club_id=club_id,
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profile_id=tenant.profile_id,
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portal_role=tenant.global_role,
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endpoint="POST /planning/exercise-suggest",
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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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result = suggest_planning_exercises(cur, tenant=tenant, body=body)
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if uses_ai:
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consume_club_feature(
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feature_id="ai_calls",
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club_id=club_id,
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profile_id=tenant.profile_id,
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portal_role=tenant.global_role,
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action="planning_suggest",
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conn=conn,
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)
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return result
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@router.post("/progression-path-suggest")
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def post_progression_path_suggest(
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body: ProgressionPathSuggestRequest,
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tenant: TenantContext = Depends(get_tenant_context),
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):
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uses_ai = (
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body.include_llm_intent
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or body.include_llm_path_qa
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or body.include_ai_gap_fill
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)
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club_id = resolve_club_id_for_probe(tenant) if uses_ai else None
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if uses_ai:
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assert_min_account_state(
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tenant, "active_member", endpoint="POST /planning/progression-path-suggest"
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)
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probe_capability(
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tenant,
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"planning.ai.progression_path",
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action="progression_path_suggest",
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club_id=club_id,
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endpoint="POST /planning/progression-path-suggest",
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)
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probe_club_feature_access(
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feature_id="ai_calls",
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action="progression_path_suggest",
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club_id=club_id,
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profile_id=tenant.profile_id,
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portal_role=tenant.global_role,
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endpoint="POST /planning/progression-path-suggest",
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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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result = suggest_progression_path(cur, tenant=tenant, body=body)
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if uses_ai:
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consume_club_feature(
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feature_id="ai_calls",
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club_id=club_id,
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profile_id=tenant.profile_id,
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portal_role=tenant.global_role,
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action="progression_path_suggest",
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conn=conn,
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)
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return result
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