diff --git a/src/omop_emb/backends/base_backend.py b/src/omop_emb/backends/base_backend.py
index d72ca5e..d761048 100644
--- a/src/omop_emb/backends/base_backend.py
+++ b/src/omop_emb/backends/base_backend.py
@@ -586,6 +586,7 @@ def bulk_upsert_embeddings(
         model_name: str,
         metric_type: MetricType,
         batches: Iterable[Tuple[Sequence[ConceptEmbeddingRecord], ndarray]],
+        total_n_batches: Optional[int] = None,
     ) -> None:
         """Upsert embeddings in multiple batches, delegating to ``upsert_embeddings``.
 
@@ -597,8 +598,13 @@ def bulk_upsert_embeddings(
             Validated once per batch via ``upsert_embeddings``.
         batches : Iterable[tuple[Sequence[ConceptEmbeddingRecord], ndarray]]
             Iterable of ``(records, embeddings)`` pairs.
+        total_n_batches : Optional[int]
+            Total number of batches for the progress bar.
         """
-        for records, embeddings in batches:
+        import tqdm
+
+        pbar = tqdm.tqdm(batches, desc=f"Upserting embeddings into {model_name} ({metric_type.value})", total=total_n_batches)
+        for records, embeddings in pbar:
             self.upsert_embeddings(
                 model_name=model_name,
                 metric_type=metric_type,
diff --git a/src/omop_emb/backends/index_config.py b/src/omop_emb/backends/index_config.py
index f2e53f6..127fa88 100644
--- a/src/omop_emb/backends/index_config.py
+++ b/src/omop_emb/backends/index_config.py
@@ -6,7 +6,7 @@
 from enum import Enum
 from typing import Any, Callable, Mapping, Optional, Self, cast, get_type_hints
 
-from omop_emb.config import IndexType, MetricType
+from omop_emb.config import IndexType, MetricType, parse_index_type
 
 # ----------------------------------------------------------------------------
 # Resevered metadata keys
@@ -245,6 +245,7 @@ def index_config_from_index_type(index_type: IndexType, **kwargs: Any) -> IndexC
     ValueError
         If ``index_type`` has no registered ``IndexConfig`` subclass.
     """
+    index_type = parse_index_type(index_type)
     if index_type == IndexType.FLAT:
         return FlatIndexConfig()
     if index_type == IndexType.HNSW:
diff --git a/src/omop_emb/interface.py b/src/omop_emb/interface.py
index ee1bd06..66f7aee 100644
--- a/src/omop_emb/interface.py
+++ b/src/omop_emb/interface.py
@@ -556,12 +556,14 @@ def upsert_concept_embeddings(
     def bulk_upsert_concept_embeddings(
         self,
         batches: Iterable[Tuple[Sequence[ConceptEmbeddingRecord], ndarray]],
+        total_n_batches: Optional[int] = None,
     ) -> None:
         """Upsert from a lazy ``(records, embeddings)`` iterable."""
         self._backend.bulk_upsert_embeddings(
             model_name=self.canonical_model_name,
             metric_type=self._metric_type,
             batches=batches,
+            total_n_batches=total_n_batches,
         )
 
     def embed_and_upsert_concepts(
diff --git a/src/omop_emb/storage/faiss/faiss_cache.py b/src/omop_emb/storage/faiss/faiss_cache.py
index bd55dcb..0ac1b94 100644
--- a/src/omop_emb/storage/faiss/faiss_cache.py
+++ b/src/omop_emb/storage/faiss/faiss_cache.py
@@ -56,7 +56,7 @@
 
 from omop_emb.config import MetricType, IndexType, ProviderType
 from omop_emb.utils.embedding_utils import EmbeddingConceptFilter, NearestConceptMatch
-from omop_emb.backends.index_config import IndexConfig
+from omop_emb.backends.index_config import IndexConfig, index_config_from_index_type
 from omop_emb.backends.base_backend import EmbeddingBackend
 from omop_emb.model_registry.model_registry_types import EmbeddingModelRecord
 
@@ -191,11 +191,14 @@ def from_json(cls, text: str) -> "CacheMetadata":
             If the JSON is malformed or contains an unknown enum value.
         """
         d = json.loads(text)
+        if not "index_config" in d:
+            raise ValueError("Missing 'index_config' field in cache metadata JSON.")
+
         return cls(
             model_name=d.get("model_name", ""),
             dimensions=int(d.get("dimensions", 0)),
             metric_type=MetricType(d["metric_type"]),
-            index_config=IndexConfig.from_dict(d.get("index_config", {})),
+            index_config=index_config_from_index_type(**d["index_config"]),
             row_count=int(d.get("row_count", -1)),
             exported_at=d.get("exported_at", ""),
             model_updated_at=d.get("model_updated_at"),
@@ -834,6 +837,7 @@ def _batches():
             model_name=self._model_name,
             metric_type=metric_type,
             batches=_batches(),
+            total_n_batches=n//batch_size + (1 if n % batch_size else 0)
         )
         logger.info(
             "Imported %d vectors for '%s' (metric=%s) into backend.",