diff --git a/letta/schemas/passage.py b/letta/schemas/passage.py
index aeb8662d..327a882d 100644
--- a/letta/schemas/passage.py
+++ b/letta/schemas/passage.py
@@ -56,20 +56,33 @@ class Passage(PassageBase):
 
     created_at: datetime = Field(default_factory=get_utc_time, description="The creation date of the passage.")
 
-    @field_validator("embedding")
+    @field_validator("embedding", mode="before")
     @classmethod
-    def pad_embeddings(cls, embedding: List[float]) -> List[float]:
+    def pad_embeddings(cls, embedding: List[float], info) -> List[float]:
         """Pad embeddings to `MAX_EMBEDDING_SIZE`. This is necessary to ensure all stored embeddings are the same size."""
-        # Only do this if using pgvector
-        from letta.helpers.tpuf_client import should_use_tpuf
+        if embedding is None:
+            return embedding
 
-        if not should_use_tpuf() or settings.environment != "PRODUCTION":
-            import numpy as np
+        # Check if this is an archival memory passage (has archive_id) or file passage (has file_id)
+        data = info.data if hasattr(info, "data") else {}
+        is_archival = data.get("archive_id") is not None
+        is_file = data.get("file_id") is not None
+
+        # Pad if using pgvector
+        if settings.letta_pg_uri_no_default:
+            # For archival memory: always pad
+            # For file passages: only pad if NOT using turbopuffer
+            from letta.helpers.tpuf_client import should_use_tpuf
+
+            should_pad = is_archival or (is_file and not should_use_tpuf())
+
+            if should_pad:
+                import numpy as np
 
-            if embedding and len(embedding) != MAX_EMBEDDING_DIM:
                 np_embedding = np.array(embedding)
-                padded_embedding = np.pad(np_embedding, (0, MAX_EMBEDDING_DIM - np_embedding.shape[0]), mode="constant")
-                return padded_embedding.tolist()
+                if np_embedding.shape[0] != MAX_EMBEDDING_DIM:
+                    padded_embedding = np.pad(np_embedding, (0, MAX_EMBEDDING_DIM - np_embedding.shape[0]), mode="constant")
+                    return padded_embedding.tolist()
 
         return embedding