fix: prevent P2 from owning Qdrant lifecycle

harness/tht/adapters/factory.py

@@ -38,6 +38,7 @@ def build_vector_store(cfg: Config, *, require_write: bool = False) -> VectorSto
                 workspace_id=cfg._workspace_id,
                 workspace_revision=cfg._workspace_revision,
                 expected_dimension=cfg.embeddings.dim if cfg.embeddings is not None else None,
+                collection_lifecycle=resource.collection_lifecycle,
             )
         case other:  # pragma: no cover - Pydantic's discriminator rejects this first.
             raise ConfigError(f"Adapter vector non supportato: {other}")

harness/tht/adapters/vector/qdrant.py

@@ -55,6 +55,7 @@ class QdrantVectorStore:
         workspace_id: str,
         workspace_revision: str | None = None,
         expected_dimension: int | None = None,
+        collection_lifecycle: str = "create_if_missing",
         request: Callable[..., object] | None = None,
         connect_timeout: float = 2.0,
         read_timeout: float = 10.0,
@@ -64,6 +65,9 @@ class QdrantVectorStore:
         self._workspace_id = workspace_id
         self._workspace_revision = workspace_revision
         self._expected_dimension = expected_dimension
+        if collection_lifecycle not in ("create_if_missing", "require_existing"):
+            raise ValueError("Unsupported Qdrant collection lifecycle")
+        self._collection_lifecycle = collection_lifecycle
         self._request = request or requests.request
         self._timeout = (connect_timeout, read_timeout)
 
@@ -306,6 +310,8 @@ class QdrantVectorStore:
         if response is None:
             if not strict:
                 raise VectorStoreError("Qdrant collection is missing")
+            if self._collection_lifecycle == "require_existing":
+                raise VectorStoreError("Qdrant collection configuration mismatch (semantic_index_incompatible)")
             self._call(
                 "PUT",
                 f"/collections/{self._collection}",
@@ -328,11 +334,17 @@ class QdrantVectorStore:
             self._expected_dimension is not None
             and (size != self._expected_dimension or distance != "Cosine")
         ):
-            raise VectorStoreError("Qdrant collection configuration mismatch")
+            raise VectorStoreError("Qdrant collection configuration mismatch (semantic_index_incompatible)")
+        payload_schema = result.get("payload_schema")
+        if not isinstance(payload_schema, dict):
+            raise VectorStoreError("Qdrant returned malformed collection response")
         for field_name in _KEYWORD_INDEXES:
-            if field_name not in result.get("payload_schema", {}):
+            field = payload_schema.get(field_name)
+            if not isinstance(field, dict) or field.get("data_type") != "keyword":
                 if not strict:
                     raise VectorStoreError("Qdrant collection payload indexes mismatch")
+                if self._collection_lifecycle == "require_existing":
+                    raise VectorStoreError("Qdrant collection configuration mismatch (semantic_index_incompatible)")
                 self._call(
                     "PUT",
                     f"/collections/{self._collection}/index",

harness/tht/config.py

@@ -222,6 +222,9 @@ class QdrantConfig(BaseModel):
     type: Literal["qdrant"]
     base_url: str
     collection: str = Field(min_length=1)
+    # Internal runtime policy. Registry-rendered configs must not create or alter
+    # the workspace-owned semantic collection; legacy configs retain compatibility.
+    collection_lifecycle: Literal["create_if_missing", "require_existing"] = "create_if_missing"
 
 
 VectorResourceConfig = Annotated[
@@ -547,6 +550,25 @@ def load_config(path: Path) -> Config:
     _validate_internal_embedding_contract(expanded, path)
     _validate_internal_vector_contract(expanded, path)
     translated, used_legacy = translate_legacy_config(expanded)
+    vectors = translated.get("vectors")
+    resource_vector = expanded.get("resources", {}).get("vector") if isinstance(
+        expanded.get("resources"), dict
+    ) else None
+    if (
+        isinstance(vectors, dict)
+        and vectors.get("type") == "qdrant"
+        and isinstance(resource_vector, dict)
+        and "collection_lifecycle" in resource_vector
+    ):
+        vectors["collection_lifecycle"] = resource_vector["collection_lifecycle"]
+    # runtime_identity is the registry marker.  The lifecycle is an internal
+    # runtime policy, never a descriptor-controlled option.
+    if (
+        isinstance(translated.get("runtime_identity"), dict)
+        and isinstance(vectors, dict)
+        and vectors.get("type") == "qdrant"
+    ):
+        vectors["collection_lifecycle"] = "require_existing"
     _populate_legacy_views(translated)
     try:
         cfg = Config.model_validate(translated)
@@ -662,7 +684,7 @@ def _validate_internal_vector_contract(raw: dict[str, Any], path: Path) -> None:
     engine = vector.get("engine")
     base_url = vector.get("base_url")
     collection = vector.get("collection")
-    allowed = {"engine", "base_url", "collection"}
+    allowed = {"engine", "base_url", "collection", "collection_lifecycle"}
     unexpected = sorted(set(vector) - allowed)
     if unexpected:
         raise ConfigError(