Fix SDK tests

nucliadb/nucliadb/ingest/service/writer.py

@@ -231,7 +231,7 @@ async def _create_kb_hosted(self, request: KnowledgeBoxNew) -> str:
         await self.proc.create_kb(
             request.slug,
             request.config,
-            parse_model_metadata_from_request(request),
+            parse_model_metadata_from_json(request.learning_config),
             forceuuid=kbid,
             release_channel=release_channel,
         )
@@ -778,31 +778,11 @@ def parse_model_metadata_from_learning_config(
     return model
 
 
-def parse_model_metadata_from_request(
-    request: KnowledgeBoxNew,
+def parse_model_metadata_from_json(
+    data: str,
 ) -> SemanticModelMetadata:
-    model = SemanticModelMetadata()
-    model.similarity_function = request.similarity
-    if request.HasField("vector_dimension"):
-        model.vector_dimension = request.vector_dimension
-    else:
-        logger.warning(
-            "Vector dimension not set. Will be detected automatically on the first vector set."
-        )
-
-    if len(request.matryoshka_dimensions) > 0:
-        if model.vector_dimension not in request.matryoshka_dimensions:
-            logger.warning(
-                "Vector dimensions is inconsistent with matryoshka dimensions! Ignoring them",
-                extra={
-                    "kbid": request.forceuuid,
-                    "kbslug": request.slug,
-                },
-            )
-        else:
-            model.matryoshka_dimensions.extend(request.matryoshka_dimensions)
-
-    return model
+    config = json.loads(data)
+    return SemanticModelMetadata(**config)
 
 
 def get_release_channel(request: KnowledgeBoxNew) -> utils_pb2.ReleaseChannel.ValueType:

nucliadb_sdk/nucliadb_sdk/tests/fixtures.py

@@ -136,7 +136,7 @@ async def init_fixture(
 ):
     sdk = nucliadb_sdk.NucliaDB(region=nucliadb_sdk.Region.ON_PREM, url=nucliadb.url)
     slug = uuid.uuid4().hex
-    kb_obj = sdk.create_knowledge_box(slug=slug)
+    kb_obj = sdk.create_knowledge_box(slug=slug,learning_configuration={"similarity_function":"COSINE","vector_dimension":768})
     kbid = kb_obj.uuid
 
     import_resp = requests.get(dataset_location)