fix(LongTermMemory): enforce stored context budgets before publication

plugins/KnowledgeEngine/LongTermMemory/README.md

@@ -652,4 +652,4 @@ Existing historical summary records without lineage cannot be retroactively trac
 Document deletion invalidates dependent summaries and pending drafts for every affected memory scope before removing imported records. Deleting a knowledge base clears only that collection's pending drafts. Preview generation and collection deletion share the same collection lock, so an in-flight model preview cannot save source text after deletion completes; a preview for a removed KB is rejected before calling the model. Each model call holding that lock is capped at 30 seconds; queued callers and storage operations still follow their normal API lifecycle.
 
 
-A reviewed summary must also fit the bounded recall record budget before publication; oversized summaries are rejected without writing an episode or hiding source facts. Reduce the consolidation candidate count and preview again. This is a character/serialization bound, not an exact token estimate.
+A reviewed summary must also fit the bounded recall record budget before publication; oversized summaries are rejected before activation, with source facts retained. If Host-added metadata takes a staged summary over budget, the staged record is removed. Reduce the consolidation candidate count and preview again. This is a character/serialization bound, not an exact token estimate.

plugins/KnowledgeEngine/LongTermMemory/store/consolidation.py

@@ -220,6 +220,10 @@ async def apply_summary(store, collection_id: str, embedding_model_uuid: str, us
     written = await store.get_episode_by_id(collection_id, episode_id, user_key)
     if written is None or any(written["metadata"].get(key) != value for key, value in meta.items()):
         raise RuntimeError("Consolidation draft could not be verified; source memories were retained")
+    stored_record = {"id": episode_id, "metadata": written["metadata"], "document": written["content"], "score": None, "distance": 0.0}
+    if not select_memories([stored_record], "", top_k=1):
+        await store.delete_episode_by_id(collection_id, episode_id, user_key)
+        raise ValueError("Stored summary exceeds the recall budget; source memories were retained")
     await store._write_json(proposal_key(collection_id, user_key), {}, collection_id)
     summary = await store.update_episode_status(
         collection_id, embedding_model_uuid, episode_id, user_key, store.EPISODE_STATUS_ACTIVE

plugins/KnowledgeEngine/LongTermMemory/tests/store/test_semantic_consolidation.py

@@ -362,3 +362,20 @@ async def test_summary_too_large_for_recall_is_not_published():
         await store.apply_consolidation("kb", "emb", "scope", proposal_id=draft["proposal_id"])
     assert set(plugin.records) == {"one", "two"}
     assert all(item["metadata"]["status"] == "active" for item in plugin.records.values())
+
+
+@pytest.mark.asyncio
+async def test_host_metadata_cannot_make_published_summary_exceed_budget():
+    class VerboseHostPlugin(SemanticPlugin):
+        async def vector_upsert(self, *args, **kwargs):
+            kwargs["metadata"] = [{**metadata, "_backend_annotation": "x" * 8000} for metadata in kwargs["metadata"]]
+            await super().vector_upsert(*args, **kwargs)
+
+    plugin = VerboseHostPlugin()
+    add_source(plugin, "source", "A fact that must remain searchable")
+    store = MemoryStore(plugin)
+    draft = await preview(store)
+    with pytest.raises(ValueError, match="Stored summary exceeds"):
+        await store.apply_consolidation("kb", "emb", "scope", proposal_id=draft["proposal_id"])
+    assert set(plugin.records) == {"source"}
+    assert plugin.records["source"]["metadata"]["status"] == "active"