diff --git a/src/classifai/indexers/main.py b/src/classifai/indexers/main.py
index c384170..56bd1d8 100644
--- a/src/classifai/indexers/main.py
+++ b/src/classifai/indexers/main.py
@@ -632,7 +632,7 @@ def search(self, query: VectorStoreSearchInput, n_results=10, batch_size=8) -> V
                     {
                         "query_id": np.repeat(query_ids_batch, n_results),
                         "query_text": np.repeat(query_text_batch, n_results),
-                        "rank": np.tile(np.arange(n_results), len(query_text_batch)),
+                        "rank": np.tile(np.arange(1, n_results + 1), len(query_text_batch)),
                         "score": scores.flatten(),
                     }
                 )
diff --git a/src/classifai/servers/pydantic_models.py b/src/classifai/servers/pydantic_models.py
index 26ecabd..a3a9dd7 100644
--- a/src/classifai/servers/pydantic_models.py
+++ b/src/classifai/servers/pydantic_models.py
@@ -112,6 +112,18 @@ def convert_dataframe_to_reverse_search_pydantic_response(df: pd.DataFrame, meta
     Returns:
         RevResultsResponseBody: Pydantic model containing the structured response.
     """
+    # identify metadata columns from the DataFrame by checking which columns are in the meta_data dictionary
+    hook_columns = (
+        set(df.columns)
+        .difference(meta_data.keys())
+        .difference(
+            {
+                "id",
+                "doc_id",
+                "doc_text",
+            }
+        )
+    )
     results_list = []
 
     # Group rows by `id`
@@ -127,12 +139,16 @@ def convert_dataframe_to_reverse_search_pydantic_response(df: pd.DataFrame, meta
             # Extract metadata columns dynamically
             metadata_values = {meta: row[meta] for meta in meta_data if meta in row}
 
+            # Find other values - added by hooks - any other per-row columns not in reserved/meta
+            other_values = {k: v for k, v in row.items() if k in hook_columns}
+
             # Create a RevResultEntry object
             response_entries.append(
                 RevResultEntry(
                     label=row["doc_id"],
                     description=row["doc_text"],
                     **metadata_values,  # Add metadata dynamically
+                    **other_values,  # Add any extra columns dynamically
                 )
             )
 
@@ -160,6 +176,22 @@ def convert_dataframe_to_pydantic_response(df: pd.DataFrame, meta_data: dict) ->
     Returns:
         ResultsResponseBody: Pydantic model containing the structured response.
     """
+    # identify metadata columns from the DataFrame by checking which columns are in the meta_data dictionary
+    hook_columns = (
+        set(df.columns)
+        .difference(meta_data.keys())
+        .difference(
+            {
+                "query_id",
+                "query_text",
+                "doc_id",
+                "doc_text",
+                "score",
+                "rank",
+            }
+        )
+    )
+
     # Group rows by `query_id`
     grouped = df.groupby("query_id")
 
@@ -174,6 +206,9 @@ def convert_dataframe_to_pydantic_response(df: pd.DataFrame, meta_data: dict) ->
             # Extract metadata columns dynamically
             metadata_values = {meta: row[meta] for meta in meta_data}
 
+            # Find other values - added by hooks - any other per-row columns not in reserved/meta
+            other_values = {k: v for k, v in row.items() if k in hook_columns}
+
             # Create a ResultEntry object
             response_entries.append(
                 ResultEntry(
@@ -182,6 +217,7 @@ def convert_dataframe_to_pydantic_response(df: pd.DataFrame, meta_data: dict) ->
                     score=row["score"],  # Assuming `score` is a column in the DataFrame
                     rank=row["rank"],  # Assuming `rank` is a column in the DataFrame
                     **metadata_values,  # Add metadata dynamically
+                    **other_values,  # Add any extra columns dynamically
                 )
             )