Lars
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- Made `unit_id` and `group_id` optional in `PlanningExerciseSuggestRequest` to support client context without a saved unit. - Refactored `_load_group_recent_exercise_ids` to handle cases where `exclude_unit_id` is optional. - Introduced `build_client_planning_context_pack` for improved context handling in client-free searches. - Updated `suggest_planning_exercises` to utilize the new client context pack when `unit_id` is not provided. - Incremented version to 0.8.172 and updated changelog to reflect these enhancements in the planning AI capabilities.
705 lines
25 KiB
Python
705 lines
25 KiB
Python
"""
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Planungs-KI P0: Kontext-Pack + Hybrid-Retrieval für Übungssuche in der Trainingsplanung.
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Siehe .claude/docs/working/PLANNING_EXERCISE_SUGGEST_CONTEXT.md
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"""
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from __future__ import annotations
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import re
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from typing import Any, Dict, List, Optional, Sequence, Set, Tuple
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from fastapi import HTTPException
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from pydantic import BaseModel, Field
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from tenant_context import TenantContext, library_content_visibility_sql
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from planning_exercise_profiles import (
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load_exercise_match_profiles_bulk,
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score_exercise_against_target,
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)
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from planning_exercise_llm_rank import try_llm_rerank_planning_hits
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from planning_exercise_target_pipeline import (
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build_planning_target_with_query_pipeline,
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compose_retrieval_phase,
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)
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# Planungs-Berechtigung + Sektionen (bestehende Implementierung)
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from routers.training_planning import (
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_assert_training_unit_permission,
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_fetch_sections,
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_has_planning_role,
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)
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INTENT_SUGGEST_NEXT = "suggest_next"
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INTENT_PROGRESSION_NEXT = "progression_next"
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INTENT_DEEPEN_EXERCISE = "deepen_exercise"
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INTENT_CONTINUE_PLAN = "continue_plan_goal"
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INTENT_FREE_SEARCH = "free_search"
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VALID_INTENTS = {
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INTENT_SUGGEST_NEXT,
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INTENT_PROGRESSION_NEXT,
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INTENT_DEEPEN_EXERCISE,
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INTENT_CONTINUE_PLAN,
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INTENT_FREE_SEARCH,
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}
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_CANDIDATE_POOL_LIMIT = 400
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_LLM_RERANK_PRE_LIMIT = 32
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class PlanningExerciseSuggestRequest(BaseModel):
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unit_id: Optional[int] = Field(default=None, ge=1)
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group_id: Optional[int] = Field(default=None, ge=1)
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section_order_index: Optional[int] = Field(default=None, ge=0)
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phase_order_index: Optional[int] = Field(default=None, ge=0)
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parallel_stream_order_index: Optional[int] = Field(default=None, ge=0)
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anchor_exercise_id: Optional[int] = Field(default=None, ge=1)
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progression_graph_id: Optional[int] = Field(default=None, ge=1)
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query: Optional[str] = ""
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intent_hint: Optional[str] = None
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planned_exercise_ids: Optional[List[int]] = None
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include_llm_intent: bool = True
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include_llm_rank: bool = False
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limit: int = Field(default=20, ge=1, le=50)
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exercise_kind_any: Optional[List[str]] = None
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def resolve_planning_exercise_intent(query: Optional[str], intent_hint: Optional[str]) -> str:
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hint = (intent_hint or "").strip().lower()
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if hint in VALID_INTENTS:
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return hint
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q = (query or "").strip().lower()
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if not q:
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return INTENT_SUGGEST_NEXT
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if any(w in q for w in ("nächste", "naechste", "vorschlag", "vorschlagen", "empfehl")):
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return INTENT_SUGGEST_NEXT
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if "vertief" in q:
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return INTENT_DEEPEN_EXERCISE
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if "progression" in q or "graph" in q or "pfad" in q:
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return INTENT_PROGRESSION_NEXT
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if "aufbau" in q or "planung" in q or "bisher" in q:
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return INTENT_CONTINUE_PLAN
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return INTENT_FREE_SEARCH
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def _intent_weights(intent: str) -> Dict[str, float]:
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base = {
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"fulltext": 0.18,
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"progression": 0.18,
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"skill": 0.12,
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"plan": 0.08,
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"profile": 0.22,
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"repeat_unit": -0.30,
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"repeat_group": -0.15,
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}
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if intent == INTENT_SUGGEST_NEXT:
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return {
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**base,
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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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"fulltext": 0.08,
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}
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if intent == INTENT_PROGRESSION_NEXT:
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return {**base, "progression": 0.42, "fulltext": 0.12, "skill": 0.10, "profile": 0.20}
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if intent == INTENT_DEEPEN_EXERCISE:
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return {**base, "skill": 0.15, "profile": 0.35, "fulltext": 0.15, "progression": 0.10}
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if intent == INTENT_CONTINUE_PLAN:
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return {**base, "plan": 0.12, "skill": 0.10, "profile": 0.30, "fulltext": 0.10, "progression": 0.08}
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if intent == INTENT_FREE_SEARCH:
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return {**base, "fulltext": 0.45, "progression": 0.08, "skill": 0.08, "profile": 0.15}
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return base
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def _collect_planned_exercise_ids(sections: Sequence[Dict[str, Any]]) -> List[int]:
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out: List[int] = []
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seen: Set[int] = set()
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for sec in sorted(sections, key=lambda s: int(s.get("order_index") or 0)):
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items = sec.get("items") or []
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for it in sorted(items, key=lambda x: int(x.get("order_index") or 0)):
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if str(it.get("item_type") or "").strip().lower() == "note":
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continue
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raw = it.get("exercise_id")
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if raw is None:
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continue
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try:
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eid = int(raw)
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except (TypeError, ValueError):
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continue
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if eid < 1 or eid in seen:
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continue
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seen.add(eid)
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out.append(eid)
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return out
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def _resolve_anchor_from_plan(
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planned_ids: Sequence[int],
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anchor_exercise_id: Optional[int],
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) -> Optional[int]:
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if anchor_exercise_id and int(anchor_exercise_id) > 0:
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return int(anchor_exercise_id)
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if planned_ids:
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return int(planned_ids[-1])
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return None
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def _load_exercise_titles(cur, exercise_ids: Sequence[int]) -> Dict[int, str]:
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if not exercise_ids:
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return {}
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ids = list(dict.fromkeys(int(x) for x in exercise_ids if int(x) > 0))
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ph = ",".join(["%s"] * len(ids))
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cur.execute(
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f"SELECT id, title FROM exercises WHERE id IN ({ph})",
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ids,
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)
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return {int(r["id"]): str(r["title"] or "").strip() for r in cur.fetchall()}
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def _load_skill_ids_for_exercise(cur, exercise_id: Optional[int]) -> Set[int]:
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if not exercise_id:
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return set()
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cur.execute(
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"SELECT skill_id FROM exercise_skills WHERE exercise_id = %s",
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(int(exercise_id),),
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)
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return {int(r["skill_id"]) for r in cur.fetchall() if r.get("skill_id")}
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def _load_progression_successors(
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cur,
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graph_id: Optional[int],
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from_exercise_id: Optional[int],
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) -> Tuple[Set[int], Dict[int, str]]:
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if not graph_id or not from_exercise_id:
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return set(), {}
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cur.execute(
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"""
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SELECT to_exercise_id, notes
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FROM exercise_progression_edges
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WHERE graph_id = %s AND from_exercise_id = %s
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AND LOWER(TRIM(edge_type)) = 'next_exercise'
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""",
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(int(graph_id), int(from_exercise_id)),
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)
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ids: Set[int] = set()
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notes: Dict[int, str] = {}
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for row in cur.fetchall():
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tid = int(row["to_exercise_id"])
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ids.add(tid)
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n = (row.get("notes") or "").strip()
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if n:
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notes[tid] = n
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return ids, notes
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def _load_group_recent_exercise_ids(
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cur,
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group_id: Optional[int],
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exclude_unit_id: Optional[int] = None,
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limit: int = 40,
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) -> Set[int]:
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if not group_id:
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return set()
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if exclude_unit_id is not None:
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cur.execute(
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"""
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SELECT tusi.exercise_id AS eid
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FROM training_units tu
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INNER JOIN training_unit_sections tus ON tus.training_unit_id = tu.id
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INNER JOIN training_unit_section_items tusi ON tusi.section_id = tus.id
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WHERE tu.group_id = %s
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AND tu.id <> %s
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AND tusi.exercise_id IS NOT NULL
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AND COALESCE(tu.status, '') <> 'cancelled'
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ORDER BY tu.planned_date DESC NULLS LAST, tu.id DESC, tusi.order_index DESC
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LIMIT 200
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""",
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(int(group_id), int(exclude_unit_id)),
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)
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else:
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cur.execute(
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"""
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SELECT tusi.exercise_id AS eid
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FROM training_units tu
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INNER JOIN training_unit_sections tus ON tus.training_unit_id = tu.id
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INNER JOIN training_unit_section_items tusi ON tusi.section_id = tus.id
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WHERE tu.group_id = %s
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AND tusi.exercise_id IS NOT NULL
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AND COALESCE(tu.status, '') <> 'cancelled'
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ORDER BY tu.planned_date DESC NULLS LAST, tu.id DESC, tusi.order_index DESC
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LIMIT 200
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""",
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(int(group_id),),
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)
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out: Set[int] = set()
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for r in cur.fetchall():
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if r.get("eid") is None:
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continue
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out.add(int(r["eid"]))
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if len(out) >= limit:
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break
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return out
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def _section_title_for_index(sections: Sequence[Dict[str, Any]], section_order_index: Optional[int]) -> Optional[str]:
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if section_order_index is None:
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return None
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for sec in sections:
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if int(sec.get("order_index") or -1) == int(section_order_index):
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t = (sec.get("title") or "").strip()
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return t or None
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return None
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def _normalize_query(query: Optional[str]) -> str:
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return re.sub(r"\s+", " ", (query or "").strip())
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def _skill_jaccard(a: Set[int], b: Set[int]) -> float:
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if not a or not b:
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return 0.0
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inter = len(a & b)
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union = len(a | b)
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return inter / union if union else 0.0
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def _apply_client_planned_override(
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cur,
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pack: Dict[str, Any],
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body: PlanningExerciseSuggestRequest,
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) -> Dict[str, Any]:
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"""Client-Plan (ungespeichertes Formular) überschreibt DB-Stand."""
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if not body.planned_exercise_ids:
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return pack
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planned_ids: List[int] = []
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seen: Set[int] = set()
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for raw in body.planned_exercise_ids:
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try:
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eid = int(raw)
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except (TypeError, ValueError):
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continue
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if eid < 1 or eid in seen:
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continue
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seen.add(eid)
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planned_ids.append(eid)
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if not planned_ids:
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return pack
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pack["planned_exercise_ids"] = planned_ids
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if not body.anchor_exercise_id:
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anchor_id = _resolve_anchor_from_plan(planned_ids, None)
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pack["anchor_exercise_id"] = anchor_id
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if anchor_id:
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titles = _load_exercise_titles(cur, [anchor_id])
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pack["anchor_title"] = titles.get(anchor_id)
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pack["anchor_skill_ids"] = sorted(_load_skill_ids_for_exercise(cur, anchor_id))
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else:
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pack["anchor_title"] = None
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pack["anchor_skill_ids"] = []
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return pack
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def build_planning_exercise_context_pack(
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cur,
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*,
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tenant: TenantContext,
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body: PlanningExerciseSuggestRequest,
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) -> Dict[str, Any]:
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profile_id = tenant.profile_id
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role = tenant.global_role
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if not _has_planning_role(role):
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raise HTTPException(status_code=403, detail="Nur Trainer dürfen Planungs-Vorschläge abrufen")
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cur.execute(
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"""
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SELECT tu.*, tg.name AS group_name
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FROM training_units tu
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LEFT JOIN training_groups tg ON tg.id = tu.group_id
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WHERE tu.id = %s
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""",
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(body.unit_id,),
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)
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unit_row = cur.fetchone()
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if not unit_row:
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raise HTTPException(status_code=404, detail="Trainingseinheit nicht gefunden")
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unit = dict(unit_row)
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if unit.get("framework_slot_id"):
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if role not in ("admin", "superadmin"):
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cur.execute(
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"""
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SELECT fp.created_by FROM training_framework_slots s
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JOIN training_framework_programs fp ON fp.id = s.framework_program_id
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WHERE s.id = %s
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""",
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(unit["framework_slot_id"],),
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)
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fr = cur.fetchone()
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cb = fr["created_by"] if fr else None
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if unit.get("created_by") != profile_id and cb != profile_id:
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raise HTTPException(status_code=403, detail="Keine Berechtigung")
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else:
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if not unit.get("group_id"):
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raise HTTPException(status_code=404, detail="Trainingseinheit nicht gefunden")
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_assert_training_unit_permission(cur, unit, profile_id, role)
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sections = _fetch_sections(cur, int(body.unit_id))
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planned_ids = _collect_planned_exercise_ids(sections)
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anchor_id = _resolve_anchor_from_plan(planned_ids, body.anchor_exercise_id)
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anchor_skills = _load_skill_ids_for_exercise(cur, anchor_id)
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progression_ids, progression_notes = _load_progression_successors(
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cur, body.progression_graph_id, anchor_id
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)
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group_recent = _load_group_recent_exercise_ids(cur, unit.get("group_id"), int(body.unit_id))
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titles = _load_exercise_titles(cur, [x for x in [anchor_id] if x])
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anchor_title = titles.get(anchor_id) if anchor_id else None
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return {
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"unit_id": int(body.unit_id),
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"unit": {
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"id": int(body.unit_id),
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"framework_slot_id": unit.get("framework_slot_id"),
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"origin_framework_slot_id": unit.get("origin_framework_slot_id"),
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},
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"unit_title": (unit.get("title") or unit.get("planned_focus") or "").strip() or None,
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"group_id": unit.get("group_id"),
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"group_name": (unit.get("group_name") or "").strip() or None,
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"section_order_index": body.section_order_index,
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"section_title": _section_title_for_index(sections, body.section_order_index),
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"planned_exercise_ids": planned_ids,
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"anchor_exercise_id": anchor_id,
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"anchor_title": anchor_title,
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"anchor_skill_ids": sorted(anchor_skills),
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"progression_graph_id": body.progression_graph_id,
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"progression_successor_ids": sorted(progression_ids),
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"progression_edge_notes": progression_notes,
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"group_recent_exercise_ids": sorted(group_recent),
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}
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def build_client_planning_context_pack(
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cur,
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*,
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tenant: TenantContext,
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body: PlanningExerciseSuggestRequest,
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) -> Dict[str, Any]:
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"""Freie / Client-Kontext-Suche ohne persistierte training_units.id (Formular, Rahmen-Slot)."""
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role = tenant.global_role
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if not _has_planning_role(role):
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raise HTTPException(status_code=403, detail="Nur Trainer dürfen Planungs-Vorschläge abrufen")
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planned_ids: List[int] = []
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if body.planned_exercise_ids:
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seen: Set[int] = set()
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for raw in body.planned_exercise_ids:
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try:
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eid = int(raw)
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except (TypeError, ValueError):
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continue
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if eid < 1 or eid in seen:
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continue
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seen.add(eid)
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planned_ids.append(eid)
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anchor_id = _resolve_anchor_from_plan(planned_ids, body.anchor_exercise_id)
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anchor_skills = _load_skill_ids_for_exercise(cur, anchor_id)
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progression_ids, progression_notes = _load_progression_successors(
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cur, body.progression_graph_id, anchor_id
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)
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group_id = body.group_id
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group_name = None
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if group_id:
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cur.execute("SELECT name FROM training_groups WHERE id = %s", (int(group_id),))
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gr = cur.fetchone()
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if gr:
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group_name = (gr.get("name") or "").strip() or None
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group_recent = _load_group_recent_exercise_ids(cur, group_id, exclude_unit_id=None)
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titles = _load_exercise_titles(cur, [x for x in [anchor_id] if x])
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anchor_title = titles.get(anchor_id) if anchor_id else None
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return {
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"unit_id": None,
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"unit": {
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"id": None,
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"framework_slot_id": None,
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"origin_framework_slot_id": None,
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},
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"unit_title": None,
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"group_id": group_id,
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"group_name": group_name,
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"section_order_index": body.section_order_index,
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"section_title": None,
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"planned_exercise_ids": planned_ids,
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"anchor_exercise_id": anchor_id,
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"anchor_title": anchor_title,
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"anchor_skill_ids": sorted(anchor_skills),
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"progression_graph_id": body.progression_graph_id,
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"progression_successor_ids": sorted(progression_ids),
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"progression_edge_notes": progression_notes,
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"group_recent_exercise_ids": sorted(group_recent),
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"context_mode": "client_free",
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}
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def suggest_planning_exercises(
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cur,
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*,
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tenant: TenantContext,
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body: PlanningExerciseSuggestRequest,
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) -> Dict[str, Any]:
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if body.unit_id:
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pack = build_planning_exercise_context_pack(cur, tenant=tenant, body=body)
|
|
else:
|
|
pack = build_client_planning_context_pack(cur, tenant=tenant, body=body)
|
|
pack = _apply_client_planned_override(cur, pack, body)
|
|
query = _normalize_query(body.query)
|
|
heuristic_intent = resolve_planning_exercise_intent(query, body.intent_hint)
|
|
|
|
pipeline_context = {
|
|
"unit_title": pack.get("unit_title"),
|
|
"group_name": pack.get("group_name"),
|
|
"section_title": pack.get("section_title"),
|
|
"planned_count": len(pack.get("planned_exercise_ids") or []),
|
|
"anchor_title": pack.get("anchor_title"),
|
|
"anchor_exercise_id": pack.get("anchor_exercise_id"),
|
|
"progression_graph_id": pack.get("progression_graph_id"),
|
|
}
|
|
target_profile, intent, scenario_kind, query_intent_summary = build_planning_target_with_query_pipeline(
|
|
cur,
|
|
unit=pack["unit"],
|
|
planned_exercise_ids=pack["planned_exercise_ids"],
|
|
anchor_exercise_id=pack.get("anchor_exercise_id"),
|
|
query=query,
|
|
heuristic_intent=heuristic_intent,
|
|
include_llm_intent=body.include_llm_intent,
|
|
context_summary=pipeline_context,
|
|
)
|
|
weights = _intent_weights(intent)
|
|
target_profile_summary = target_profile.to_summary_dict(cur)
|
|
query_intent_applied = bool(query_intent_summary.get("llm_applied"))
|
|
|
|
profile_id = tenant.profile_id
|
|
role = tenant.global_role
|
|
vis_sql, vis_params = library_content_visibility_sql(
|
|
alias="e",
|
|
profile_id=profile_id,
|
|
role=role,
|
|
effective_club_id=tenant.effective_club_id,
|
|
)
|
|
|
|
where = [vis_sql, "COALESCE(e.status, '') <> %s"]
|
|
params: List[Any] = []
|
|
if query:
|
|
ft_select = "ts_rank_cd(e.search_vector, plainto_tsquery('german', %s)) AS ft_rank"
|
|
params.append(query)
|
|
else:
|
|
ft_select = "0.0::float AS ft_rank"
|
|
|
|
params.extend(list(vis_params))
|
|
params.append("archived")
|
|
|
|
ek_filtered: List[str] = []
|
|
if body.exercise_kind_any:
|
|
for raw in body.exercise_kind_any:
|
|
s = str(raw or "").strip().lower()
|
|
if s in ("simple", "combination") and s not in ek_filtered:
|
|
ek_filtered.append(s)
|
|
if ek_filtered:
|
|
ph = ",".join(["%s"] * len(ek_filtered))
|
|
where.append(f"(LOWER(TRIM(COALESCE(e.exercise_kind::text,''))) IN ({ph}))")
|
|
params.extend(ek_filtered)
|
|
|
|
sql = f"""
|
|
SELECT e.id, e.title, e.summary,
|
|
(
|
|
SELECT fa.name FROM exercise_focus_areas efa
|
|
JOIN focus_areas fa ON fa.id = efa.focus_area_id
|
|
WHERE efa.exercise_id = e.id
|
|
ORDER BY efa.is_primary DESC NULLS LAST, fa.name ASC
|
|
LIMIT 1
|
|
) AS primary_focus_name,
|
|
{ft_select}
|
|
FROM exercises e
|
|
WHERE {' AND '.join(where)}
|
|
ORDER BY e.updated_at DESC, e.id DESC
|
|
LIMIT %s
|
|
"""
|
|
params.append(_CANDIDATE_POOL_LIMIT)
|
|
cur.execute(sql, params)
|
|
rows = cur.fetchall()
|
|
|
|
planned_set = set(pack["planned_exercise_ids"])
|
|
group_recent_set = set(pack["group_recent_exercise_ids"])
|
|
progression_set = set(pack["progression_successor_ids"])
|
|
anchor_skills = set(pack["anchor_skill_ids"])
|
|
anchor_id = pack.get("anchor_exercise_id")
|
|
progression_notes = pack.get("progression_edge_notes") or {}
|
|
last_planned_skills: Set[int] = set()
|
|
if pack["planned_exercise_ids"]:
|
|
last_planned_skills = _load_skill_ids_for_exercise(cur, pack["planned_exercise_ids"][-1])
|
|
|
|
# Skill-IDs + ExerciseMatchProfile pro Kandidat (Batch)
|
|
cand_ids = [int(r["id"]) for r in rows]
|
|
skills_by_ex: Dict[int, Set[int]] = {cid: set() for cid in cand_ids}
|
|
match_profiles = load_exercise_match_profiles_bulk(cur, cand_ids)
|
|
if cand_ids:
|
|
ph = ",".join(["%s"] * len(cand_ids))
|
|
cur.execute(
|
|
f"SELECT exercise_id, skill_id FROM exercise_skills WHERE exercise_id IN ({ph})",
|
|
cand_ids,
|
|
)
|
|
for r in cur.fetchall():
|
|
skills_by_ex.setdefault(int(r["exercise_id"]), set()).add(int(r["skill_id"]))
|
|
|
|
max_ft = 0.0
|
|
scored: List[Dict[str, Any]] = []
|
|
for row in rows:
|
|
eid = int(row["id"])
|
|
if anchor_id and eid == int(anchor_id):
|
|
continue
|
|
ft = float(row.get("ft_rank") or 0.0)
|
|
if ft > max_ft:
|
|
max_ft = ft
|
|
scored.append(
|
|
{
|
|
"row": row,
|
|
"eid": eid,
|
|
"ft": ft,
|
|
"skills": skills_by_ex.get(eid, set()),
|
|
}
|
|
)
|
|
|
|
hits: List[Dict[str, Any]] = []
|
|
for item in scored:
|
|
eid = item["eid"]
|
|
row = item["row"]
|
|
ft_norm = (item["ft"] / max_ft) if max_ft > 0 else 0.0
|
|
prog_hit = 1.0 if eid in progression_set else 0.0
|
|
skill_sim = _skill_jaccard(anchor_skills, item["skills"]) if anchor_skills else 0.0
|
|
plan_aff = 0.0
|
|
if last_planned_skills and item["skills"]:
|
|
plan_aff = _skill_jaccard(last_planned_skills, item["skills"])
|
|
repeat_unit = 1.0 if eid in planned_set else 0.0
|
|
repeat_group = 1.0 if eid in group_recent_set else 0.0
|
|
profile_score = 0.0
|
|
profile_reasons: List[str] = []
|
|
emp = match_profiles.get(eid)
|
|
if emp:
|
|
profile_score, profile_reasons = score_exercise_against_target(
|
|
emp, target_profile, intent=intent
|
|
)
|
|
|
|
score = (
|
|
weights["fulltext"] * ft_norm
|
|
+ weights["progression"] * prog_hit
|
|
+ weights["skill"] * skill_sim
|
|
+ weights["plan"] * plan_aff
|
|
+ weights["profile"] * profile_score
|
|
+ weights["repeat_unit"] * repeat_unit
|
|
+ weights["repeat_group"] * repeat_group
|
|
)
|
|
|
|
reasons: List[str] = []
|
|
if query and ft_norm >= 0.35:
|
|
reasons.append("Volltext-Treffer")
|
|
if prog_hit > 0:
|
|
note = progression_notes.get(eid)
|
|
reasons.append(
|
|
f"Nachfolger im Progressionsgraph{f': {note}' if note else ''}"
|
|
)
|
|
if skill_sim >= 0.2 and anchor_id:
|
|
reasons.append("Fähigkeiten passen zur Anker-Übung")
|
|
if plan_aff >= 0.25:
|
|
reasons.append("Schließt an Skills der letzten geplanten Übung an")
|
|
if repeat_unit > 0:
|
|
reasons.append("Bereits in dieser Einheit eingeplant")
|
|
if repeat_group > 0 and repeat_unit <= 0:
|
|
reasons.append("Kürzlich in der Gruppe verwendet")
|
|
for pr in profile_reasons:
|
|
if pr not in reasons:
|
|
reasons.append(pr)
|
|
|
|
if score <= 0 and not reasons and not query:
|
|
# Leere Query: trotzdem schwache Kandidaten mit Skill/Progression
|
|
if prog_hit or skill_sim or plan_aff or profile_score:
|
|
score = 0.05 + prog_hit * 0.3 + skill_sim * 0.2 + profile_score * 0.25
|
|
|
|
hits.append(
|
|
{
|
|
"id": eid,
|
|
"title": row.get("title"),
|
|
"summary": row.get("summary"),
|
|
"focus_area": row.get("primary_focus_name"),
|
|
"score": round(max(0.0, min(1.0, score)), 4),
|
|
"reasons": reasons,
|
|
}
|
|
)
|
|
|
|
hits.sort(key=lambda h: (-h["score"], h.get("title") or ""))
|
|
|
|
llm_applied = False
|
|
retrieval_phase = compose_retrieval_phase(query_intent=query_intent_applied, llm_rank=False)
|
|
if body.include_llm_rank:
|
|
pre_limit = max(int(body.limit), _LLM_RERANK_PRE_LIMIT)
|
|
pool_hits = hits[:pre_limit]
|
|
pool_hits, llm_applied = try_llm_rerank_planning_hits(
|
|
cur,
|
|
hits=pool_hits,
|
|
skills_by_ex=skills_by_ex,
|
|
query=query,
|
|
intent=intent,
|
|
context_summary={
|
|
"unit_title": pack.get("unit_title"),
|
|
"group_name": pack.get("group_name"),
|
|
"section_title": pack.get("section_title"),
|
|
"planned_count": len(planned_set),
|
|
"anchor_title": pack.get("anchor_title"),
|
|
"intent": intent,
|
|
},
|
|
target_profile_summary=target_profile_summary,
|
|
limit=int(body.limit),
|
|
)
|
|
if llm_applied:
|
|
retrieval_phase = compose_retrieval_phase(
|
|
query_intent=query_intent_applied,
|
|
llm_rank=True,
|
|
)
|
|
tail = hits[pre_limit:]
|
|
hits = pool_hits + tail
|
|
else:
|
|
hits = pool_hits[: int(body.limit)]
|
|
else:
|
|
hits = hits[: int(body.limit)]
|
|
|
|
hits = hits[: int(body.limit)]
|
|
|
|
context_summary = {
|
|
"unit_title": pack.get("unit_title"),
|
|
"group_name": pack.get("group_name"),
|
|
"section_title": pack.get("section_title"),
|
|
"planned_count": len(planned_set),
|
|
"anchor_title": pack.get("anchor_title"),
|
|
"anchor_exercise_id": pack.get("anchor_exercise_id"),
|
|
"progression_graph_id": pack.get("progression_graph_id"),
|
|
"context_mode": pack.get("context_mode") or ("unit" if pack.get("unit_id") else "client_free"),
|
|
}
|
|
|
|
return {
|
|
"context_summary": context_summary,
|
|
"target_profile_summary": target_profile_summary,
|
|
"scenario_kind": scenario_kind,
|
|
"query_intent_summary": query_intent_summary,
|
|
"retrieval_phase": retrieval_phase,
|
|
"llm_rank_applied": llm_applied,
|
|
"intent_resolved": intent,
|
|
"intent_heuristic": heuristic_intent,
|
|
"query_normalized": query or None,
|
|
"hits": hits,
|
|
}
|