diff --git a/align_data/embeddings/embedding_utils.py b/align_data/embeddings/embedding_utils.py
index b0b1d16..0fc4ae2 100644
--- a/align_data/embeddings/embedding_utils.py
+++ b/align_data/embeddings/embedding_utils.py
@@ -94,7 +94,7 @@ def wrapper(*args, **kwargs):
 @handle_openai_errors
 def _single_batch_moderation_check(batch: List[str]) -> List[ModerationInfoType]:
     """Process a batch for moderation checks."""
-    return client.moderations.create(input=batch)["results"]
+    return client.moderations.create(input=batch).results
 
 
 def moderation_check(texts: List[str], max_batch_size: int = 4096, tokens_counter: Callable[[str], int] = len) -> List[ModerationInfoType]:
@@ -129,8 +129,8 @@ def moderation_check(texts: List[str], max_batch_size: int = 4096, tokens_counte
 @handle_openai_errors
 def _single_batch_compute_openai_embeddings(batch: List[str], **kwargs) -> List[List[float]]:
     """Compute embeddings for a batch."""
-    batch_data = client.embeddings.create(input=batch, engine=OPENAI_EMBEDDINGS_MODEL, **kwargs).data
-    return [d["embedding"] for d in batch_data]
+    batch_data = client.embeddings.create(input=batch, model=OPENAI_EMBEDDINGS_MODEL, **kwargs).data
+    return [d.embedding for d in batch_data]
 
 
 def _compute_openai_embeddings(
@@ -199,7 +199,7 @@ def get_embeddings_or_none_if_flagged(
     - Tuple[Optional[List[List[float]]], ModerationInfoListType]: Tuple containing the list of embeddings (or None if any text is flagged) and the moderation results.
     """
     moderation_results = moderation_check(texts)
-    if any(result["flagged"] for result in moderation_results):
+    if any(result.flagged for result in moderation_results):
         return None, moderation_results
 
     embeddings = get_embeddings_without_moderation(texts, source, **kwargs)
@@ -229,7 +229,7 @@ def get_embeddings(
 
     # Check all texts for moderation flags
     moderation_results = moderation_check(texts)
-    flags = [result["flagged"] for result in moderation_results]
+    flags = [result.flagged for result in moderation_results]
 
     non_flagged_texts = [text for text, flag in zip(texts, flags) if not flag]
     non_flagged_embeddings = get_embeddings_without_moderation(non_flagged_texts, source, **kwargs)
diff --git a/align_data/embeddings/pinecone/update_pinecone.py b/align_data/embeddings/pinecone/update_pinecone.py
index f1a06f3..75f64f6 100644
--- a/align_data/embeddings/pinecone/update_pinecone.py
+++ b/align_data/embeddings/pinecone/update_pinecone.py
@@ -112,11 +112,11 @@ def _make_pinecone_entry(self, article: Article) -> PineconeEntry | None:
             text_chunks = get_text_chunks(article, self.text_splitter)
             embeddings, moderation_results = get_embeddings(text_chunks, article.source)
 
-            if any(result["flagged"] for result in moderation_results):
+            if any(result.flagged for result in moderation_results):
                 flagged_text_chunks = [
                     f'Chunk {i}: "{text}"'
                     for i, (text, result) in enumerate(zip(text_chunks, moderation_results))
-                    if result["flagged"]
+                    if result.flagged
                 ]
                 logger.warning(
                     f"OpenAI moderation flagged text chunks for the following article: {article.id}"
@@ -137,12 +137,12 @@ def _make_pinecone_entry(self, article: Article) -> PineconeEntry | None:
                 confidence=article.confidence,
             )
         except (
-            ValueError,
-            TypeError,
-            AttributeError,
+            # ValueError,
+            # TypeError,
+            # AttributeError,
             ValidationError,
-            MissingFieldsError,
-            MissingEmbeddingModelError,
+            # MissingFieldsError,
+            # MissingEmbeddingModelError,
         ) as e:
             logger.warning(e)
             article.append_comment(f"Error encountered while processing this article: {e}")