docs: Rename Blob column references to ObjectRef column (#2535)

tswast · google-labs-jules[bot] · web-flow · commit 44e0ffd947e9 · 2026-03-26T13:57:33.000-07:00
Updates documentation and internal comments to use the term "ObjectRef column" instead of "Blob column", as per the official BigQuery documentation. Links to the documentation are included in user-facing docstrings. --- *PR created automatically by Jules for task [15739234298342142432](https://jules.google.com/task/15739234298342142432) started by @tswast* Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com> Co-authored-by: tswast <247555+tswast@users.noreply.github.com>
diff --git a/bigframes/dataframe.py b/bigframes/dataframe.py
@@ -817,7 +817,7 @@ def __repr__(self) -> str:
         )
 
     def _get_display_df_and_blob_cols(self) -> tuple[DataFrame, list[str]]:
-        """Process blob columns for display."""
+        """Process ObjectRef columns for display."""
         df = self
         blob_cols = []
         if bigframes.options.display.blob_display:
diff --git a/bigframes/ml/llm.py b/bigframes/ml/llm.py
@@ -286,7 +286,7 @@ class MultimodalEmbeddingGenerator(base.RetriableRemotePredictor):
     """Multimodal embedding generator LLM model.
 
     .. note::
-        BigFrames Blob is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the
+        BigFrames ObjectRef is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the
         Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is"
         and might have limited support. For more information, see the launch stage descriptions
         (https://cloud.google.com/products#product-launch-stages).
@@ -374,7 +374,7 @@ def predict(
         Args:
             X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series):
                 Input DataFrame or Series, can contain one or more columns. If multiple columns are in the DataFrame, it must contain a "content" column for prediction.
-                The content column must be of string type or BigFrames Blob of image or video.
+                The content column must be of string type or BigFrames `ObjectRef <https://docs.cloud.google.com/bigquery/docs/objectref-columns>`_ of image or video.
 
             max_retries (int, default 0):
                 Max number of retries if the prediction for any rows failed. Each try needs to make progress (i.e. has successfully predicted rows) to continue the retry.
@@ -668,13 +668,13 @@ def predict(
 
             prompt (Iterable of str or bigframes.series.Series, or None, default None):
                 .. note::
-                    BigFrames Blob is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the
+                    BigFrames ObjectRef is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the
                     Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is"
                     and might have limited support. For more information, see the launch stage descriptions
                     (https://cloud.google.com/products#product-launch-stages).
 
                 Construct a prompt struct column for prediction based on the input. The input must be an Iterable that can take string literals,
-                such as "summarize", string column(s) of X, such as X["str_col"], or blob column(s) of X, such as X["blob_col"].
+                such as "summarize", string column(s) of X, such as X["str_col"], or `ObjectRef column(s) <https://docs.cloud.google.com/bigquery/docs/objectref-columns>`_ of X, such as X["objectref_col"].
                 It creates a struct column of the items of the iterable, and use the concatenated result as the input prompt. No-op if set to None.
             output_schema (Mapping[str, str] or None, default None):
                 The schema used to generate structured output as a bigframes DataFrame. The schema is a string key-value pair of <column_name>:<type>.
diff --git a/bigframes/operations/ai.py b/bigframes/operations/ai.py
@@ -203,7 +203,7 @@ def map(
         has_blob_column = False
         for column in columns:
             if df[column].dtype == dtypes.OBJ_REF_DTYPE:
-                # Don't cast blob columns to string
+                # Don't cast ObjectRef columns to string
                 has_blob_column = True
                 continue
 
diff --git a/bigframes/operations/semantics.py b/bigframes/operations/semantics.py
@@ -382,7 +382,7 @@ def filter(self, instruction: str, model, ground_with_google_search: bool = Fals
         has_blob_column = False
         for column in columns:
             if df[column].dtype == dtypes.OBJ_REF_DTYPE:
-                # Don't cast blob columns to string
+                # Don't cast ObjectRef columns to string
                 has_blob_column = True
                 continue
 
@@ -501,7 +501,7 @@ def map(
         has_blob_column = False
         for column in columns:
             if df[column].dtype == dtypes.OBJ_REF_DTYPE:
-                # Don't cast blob columns to string
+                # Don't cast ObjectRef columns to string
                 has_blob_column = True
                 continue
 
diff --git a/bigframes/session/__init__.py b/bigframes/session/__init__.py
@@ -2234,12 +2234,12 @@ def _create_temp_table(
     def from_glob_path(
         self, path: str, *, connection: Optional[str] = None, name: Optional[str] = None
     ) -> dataframe.DataFrame:
-        r"""Create a BigFrames DataFrame that contains a BigFrames Blob column from a global wildcard path.
+        r"""Create a BigFrames DataFrame that contains a BigFrames `ObjectRef column <https://docs.cloud.google.com/bigquery/docs/objectref-columns>`_ from a global wildcard path.
         This operation creates a temporary BQ Object Table under the hood and requires bigquery.connections.delegate permission or BigQuery Connection Admin role.
         If you have an existing BQ Object Table, use read_gbq_object_table().
 
         .. note::
-            BigFrames Blob is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the
+            BigFrames ObjectRef is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the
             Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is"
             and might have limited support. For more information, see the launch stage descriptions
             (https://cloud.google.com/products#product-launch-stages).
@@ -2252,7 +2252,7 @@ def from_glob_path(
                 If None, use default connection in session context. BigQuery DataFrame will try to create the connection and attach
                 permission if the connection isn't fully set up.
             name (str):
-                The column name of the Blob column.
+                The column name of the ObjectRef column.
         Returns:
             bigframes.pandas.DataFrame:
                 Result BigFrames DataFrame.
@@ -2295,18 +2295,18 @@ def _create_bq_connection(
     def read_gbq_object_table(
         self, object_table: str, *, name: Optional[str] = None
     ) -> dataframe.DataFrame:
-        """Read an existing object table to create a BigFrames Blob DataFrame. Use the connection of the object table for the connection of the blob.
+        """Read an existing object table to create a BigFrames `ObjectRef <https://docs.cloud.google.com/bigquery/docs/objectref-columns>`_ DataFrame. Use the connection of the object table for the connection of the ObjectRef.
         This function dosen't retrieve the object table data. If you want to read the data, use read_gbq() instead.
 
         .. note::
-            BigFrames Blob is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the
+            BigFrames ObjectRef is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the
             Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is"
             and might have limited support. For more information, see the launch stage descriptions
             (https://cloud.google.com/products#product-launch-stages).
 
         Args:
             object_table (str): name of the object table of form <PROJECT_ID>.<DATASET_ID>.<TABLE_ID>.
-            name (str or None): the returned blob column name.
+            name (str or None): the returned ObjectRef column name.
 
         Returns:
             bigframes.pandas.DataFrame:

Original file line number	Diff line number	Diff line change
`@@ -817,7 +817,7 @@ def __repr__(self) -> str:`
`817`	`817`	`)`
`818`	`818`
`819`	`819`	`def _get_display_df_and_blob_cols(self) -> tuple[DataFrame, list[str]]:`
`820`		`- """Process blob columns for display."""`
	`820`	`+ """Process ObjectRef columns for display."""`
`821`	`821`	`df = self`
`822`	`822`	`blob_cols = []`
`823`	`823`	`if bigframes.options.display.blob_display:`