docs: Update Blob column references to ObjectRef column

Replaced references to "Blob column" and "blob column" in docstrings and comments with "ObjectRef column". Included links to the official BigQuery ObjectRef documentation where applicable.

Updates:
- `bigframes/session/__init__.py`: updated `from_glob_path` and `read_gbq_object_table`
- `bigframes/ml/llm.py`: updated `predict` docstring and class-level `note` section
- `bigframes/operations/semantics.py` and `bigframes/operations/ai.py`: updated internal comments
- `bigframes/dataframe.py`: updated docstring for `_get_display_df_and_blob_cols`

Co-authored-by: tswast <247555+tswast@users.noreply.github.com>

Author: google-labs-jules[bot]

bigframes/dataframe.py

@@ -790,7 +790,7 @@ 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:

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>.

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
 

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
 

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: