Enhance Gap Fill Offer Handling and Progression Path Logic

- Updated `suggest_progression_path` to ensure unique gap fill offers are collected and added based on their IDs, improving the relevance of suggestions.
- Refined the logic for setting `slot_status` and handling `gap_offer` and `proposal_key` in steps, enhancing clarity in progression path management.
- Improved the `collectGapOffersFromApiResponse` function to consolidate gap offers from various sources, ensuring comprehensive offer retrieval.
- Enhanced the handling of unfilled slots in `applyMatchStepsToSlots`, ensuring proper assignment of proposals and gap offers.
- Added tests to validate the new logic for gap fill offers and slot assignments, ensuring robustness in path suggestion features.

backend/planning_exercise_path_builder.py

@@ -2107,7 +2107,7 @@ def suggest_progression_path(
             max_steps=max_steps,
         )
         if body.include_ai_gap_fill:
-            seen_offer_ids = {o.get("offer_id") for o in gap_fill_offers}
+            seen_offer_ids = {o.get("offer_id") for o in gap_fill_offers if o.get("offer_id")}
             for step in steps:
                 if step.get("exercise_id") is not None:
                     continue
@@ -2115,6 +2115,12 @@ def suggest_progression_path(
                     major_idx = int(step["roadmap_major_step_index"])
                 except (TypeError, ValueError, KeyError):
                     continue
+                if step.get("gap_offer") and step.get("proposal_key"):
+                    oid = step["gap_offer"].get("offer_id")
+                    if oid and oid not in seen_offer_ids:
+                        gap_fill_offers.append(dict(step["gap_offer"]))
+                        seen_offer_ids.add(oid)
+                    continue
                 stage_spec = next(
                     (
                         s
@@ -2123,15 +2129,19 @@ def suggest_progression_path(
                     ),
                     None,
                 )
-                if stage_spec is None:
-                    continue
+                learning_goal = (
+                    (stage_spec.learning_goal if stage_spec else None)
+                    or step.get("roadmap_learning_goal")
+                    or step.get("title")
+                    or ""
+                ).strip()
                 spec = {
                     "source": "roadmap_unfilled",
                     "insert_after_index": max(major_idx - 1, -1),
                     "roadmap_major_step_index": major_idx,
                     "phase": (step.get("roadmap_phase") or "vertiefung").strip().lower(),
-                    "title_hint": (stage_spec.learning_goal or step.get("title") or f"Slot {major_idx + 1}")[:120],
-                    "sketch": (stage_spec.learning_goal or "").strip(),
+                    "title_hint": (learning_goal or f"Slot {major_idx + 1}")[:120],
+                    "sketch": learning_goal,
                     "rationale": f"Slot {major_idx + 1} — keine passende Bibliotheks-Übung; KI-Entwurf für diese Stufe.",
                 }
                 offer = build_gap_fill_offer(
@@ -2148,7 +2158,10 @@ def suggest_progression_path(
                         semantic_brief=semantic_brief,
                     ),
                 )
-                if offer.get("offer_id") not in seen_offer_ids:
+                step["gap_offer"] = offer
+                step["proposal_key"] = offer.get("offer_id")
+                step["slot_status"] = "unfilled"
+                if offer.get("offer_id") and offer.get("offer_id") not in seen_offer_ids:
                     gap_fill_offers.append(offer)
                     seen_offer_ids.add(offer.get("offer_id"))
 

frontend/src/utils/progressionGraphDraft.js

@@ -214,10 +214,21 @@ const GAP_OFFER_SOURCE_PRIORITY = {
 }
 
 export function collectGapOffersFromApiResponse(res) {
-  const top = Array.isArray(res?.gap_fill_offers) ? res.gap_fill_offers : []
-  if (top.length) return top
-  const qa = res?.path_qa || {}
-  return Array.isArray(qa?.gap_fill_offers) ? qa.gap_fill_offers : []
+  const out = []
+  const seen = new Set()
+  const add = (offer) => {
+    if (!offer || typeof offer !== 'object') return
+    const id = offer.offer_id || `${offer.source}-${offer.roadmap_major_step_index}`
+    if (seen.has(id)) return
+    seen.add(id)
+    out.push(offer)
+  }
+  for (const offer of res?.gap_fill_offers || []) add(offer)
+  for (const offer of res?.path_qa?.gap_fill_offers || []) add(offer)
+  for (const step of res?.steps || []) {
+    if (step?.gap_offer) add(step.gap_offer)
+  }
+  return out
 }
 
 /** Maximal ein Angebot pro Slot — Roadmap-Lücken vor Brücken/QS. */
@@ -805,13 +816,25 @@ export function applyMatchStepsToSlots(draft, apiSteps) {
 
     const isProposal = Boolean(step.is_ai_proposal) || step.exercise_id == null
     const hasAiPayload = Boolean(step.ai_suggestion) || Boolean(step.proposal_key)
-    if (isProposal && !hasAiPayload) {
-      const wasLibrary =
-        nextSlots[idx].primary?.kind === 'library' && nextSlots[idx].primary.exerciseId != null
-      const mustClear = step.slot_status === 'unfilled' || step.slot_status === 'stripped'
-      if (!wasLibrary || mustClear) {
+    const isUnfilledSlot =
+      step.slot_status === 'unfilled' ||
+      step.slot_status === 'stripped' ||
+      step.roadmap_match_source === 'unfilled' ||
+      Boolean(step.gap_offer)
+    if (isProposal && !hasAiPayload && isUnfilledSlot) {
+      const offer = step.gap_offer || {}
+      nextSlots[idx].primary = proposalSlotExercise({
+        title:
+          offer.title_hint ||
+          step.roadmap_learning_goal ||
+          step.title ||
+          nextSlots[idx].learning_goal ||
+          `Slot ${idx + 1}`,
+        proposalKey: offer.offer_id || step.proposal_key || `roadmap-unfilled-${idx}`,
+        aiSuggestion: offer.ai_suggestion || null,
+      })
+    } else if (isProposal && !hasAiPayload) {
       nextSlots[idx].primary = emptySlotExercise()
-      }
     } else if (isProposal) {
       nextSlots[idx].primary = proposalSlotExercise({
         title: step.title || nextSlots[idx].learning_goal,