diff --git a/src/retrievals/models/embedding_auto.py b/src/retrievals/models/embedding_auto.py
index 3daf5928..58ed9b05 100644
--- a/src/retrievals/models/embedding_auto.py
+++ b/src/retrievals/models/embedding_auto.py
@@ -263,7 +263,7 @@ def encode_from_loader(
                     embeddings = embeddings.cpu()
                 all_embeddings.append(embeddings)
         if convert_to_numpy:
-            all_embeddings = np.asarray([emb.numpy() for emb in all_embeddings])
+            all_embeddings = np.concatenate([emb.numpy() for emb in all_embeddings], axis=0)
         else:
             all_embeddings = torch.concat(all_embeddings)
         return all_embeddings
diff --git a/src/retrievals/models/retrieval_auto.py b/src/retrievals/models/retrieval_auto.py
index d258bf75..ae8d9ed4 100644
--- a/src/retrievals/models/retrieval_auto.py
+++ b/src/retrievals/models/retrieval_auto.py
@@ -1,5 +1,5 @@
 import logging
-from typing import Optional, Union
+from typing import Literal, Optional, Union
 
 import faiss
 import numpy as np
@@ -13,7 +13,7 @@
 
 
 class AutoModelForRetrieval(object):
-    def __init__(self, method: str = "cosine") -> None:
+    def __init__(self, method: Literal['cosine', 'knn'] = "cosine") -> None:
         super().__init__()
         self.method = method
 
@@ -25,9 +25,9 @@ def similarity_search(
         top_k: int = 1,
         batch_size: int = -1,
         convert_to_numpy: bool = True,
-        convert_to_pandas: bool = False,
         **kwargs,
     ):
+        self.top_k = top_k
         if passage_embed is None and index_path is None:
             logging.warning('Please provide passage_embed for knn/tensor search or index_path for faiss search')
             return
@@ -53,11 +53,20 @@ def similarity_search(
         else:
             raise ValueError(f"Only cosine and knn method are supported by similarity_search, while get {self.method}")
 
-        if not convert_to_pandas:
-            return dists, indices
+        return dists, indices
 
-        retrieval = dict({'query': [], 'passage': [], 'labels': []})
-        return pd.from_dict(retrieval)
+    def get_pandas_candidate(self, query_ids, passage_ids, dists, indices):
+        if isinstance(query_ids, pd.Series):
+            query_ids = query_ids.values
+        if isinstance(passage_ids, pd.Series):
+            passage_ids = passage_ids.values
+
+        retrieval = {
+            'query': np.repeat(query_ids, self.top_k),
+            'passage': passage_ids[indices.ravel()],
+            'scores': dists.ravel(),
+        }
+        return pd.DataFrame(retrieval)
 
 
 class EnsembleRetriever(object):