chore: add blob snippets (#1596)

* chore: add blob snippets

* fix lint

* polish

* fix

Author: GarrettWu

notebooks/experimental/multimodal_dataframe.ipynb

@@ -140,7 +140,7 @@
         }
       ],
       "source": [
-        "# Create blob columns from wildcard path. .\n",
+        "# Create blob columns from wildcard path.\n",
         "df_image = bpd.from_glob_path(\n",
         "    \"gs://cloud-samples-data/bigquery/tutorials/cymbal-pets/images/*\", name=\"image\"\n",
         ")\n",

samples/snippets/multimodal_test.py

@@ -0,0 +1,116 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+def test_multimodal_dataframe() -> None:
+    # [START bigquery_dataframes_multimodal_dataframe_create]
+    import bigframes
+
+    # Flag to enable the feature
+    bigframes.options.experiments.blob = True
+
+    import bigframes.pandas as bpd
+
+    # Create blob columns from wildcard path.
+    df_image = bpd.from_glob_path(
+        "gs://cloud-samples-data/bigquery/tutorials/cymbal-pets/images/*", name="image"
+    )
+    # Other ways are: from string uri column
+    # df = bpd.DataFrame({"uri": ["gs://<my_bucket>/<my_file_0>", "gs://<my_bucket>/<my_file_1>"]})
+    # df["blob_col"] = df["uri"].str.to_blob()
+
+    # From an existing object table
+    # df = bpd.read_gbq_object_table("<my_object_table>", name="blob_col")
+
+    # Take only the 5 images to deal with. Preview the content of the Mutimodal DataFrame
+    df_image = df_image.head(5)
+    df_image
+    # [END bigquery_dataframes_multimodal_dataframe_create]
+
+    # [START bigquery_dataframes_multimodal_dataframe_merge]
+    # Combine unstructured data with structured data
+    df_image["author"] = ["alice", "bob", "bob", "alice", "bob"]  # type: ignore
+    df_image["content_type"] = df_image["image"].blob.content_type()
+    df_image["size"] = df_image["image"].blob.size()
+    df_image["updated"] = df_image["image"].blob.updated()
+    df_image
+
+    # Filter images and display, you can also display audio and video types
+    df_image[df_image["author"] == "alice"]["image"].blob.display()
+    # [END bigquery_dataframes_multimodal_dataframe_merge]
+
+    # [START bigquery_dataframes_multimodal_dataframe_image_transform]
+    df_image["blurred"] = df_image["image"].blob.image_blur(
+        (20, 20), dst="gs://bigframes_blob_test/image_blur_transformed/"
+    )
+    df_image["resized"] = df_image["image"].blob.image_resize(
+        (300, 200), dst="gs://bigframes_blob_test/image_resize_transformed/"
+    )
+    df_image["normalized"] = df_image["image"].blob.image_normalize(
+        alpha=50.0,
+        beta=150.0,
+        norm_type="minmax",
+        dst="gs://bigframes_blob_test/image_normalize_transformed/",
+    )
+
+    # You can also chain functions together
+    df_image["blur_resized"] = df_image["blurred"].blob.image_resize(
+        (300, 200), dst="gs://bigframes_blob_test/image_blur_resize_transformed/"
+    )
+    df_image
+    # [END bigquery_dataframes_multimodal_dataframe_image_transform]
+
+    # [START bigquery_dataframes_multimodal_dataframe_ai]
+    from bigframes.ml import llm
+
+    gemini = llm.GeminiTextGenerator(model_name="gemini-1.5-flash-002")
+
+    # Deal with first 2 images as example
+    df_image = df_image.head(2)
+
+    # Ask the same question on the images
+    df_image = df_image.head(2)
+    answer = gemini.predict(df_image, prompt=["what item is it?", df_image["image"]])
+    answer[["ml_generate_text_llm_result", "image"]]
+
+    # Ask different questions
+    df_image["question"] = [  # type: ignore
+        "what item is it?",
+        "what color is the picture?",
+    ]
+    answer_alt = gemini.predict(
+        df_image, prompt=[df_image["question"], df_image["image"]]
+    )
+    answer_alt[["ml_generate_text_llm_result", "image"]]
+
+    # Generate embeddings on images
+    embed_model = llm.MultimodalEmbeddingGenerator()
+    embeddings = embed_model.predict(df_image["image"])
+    embeddings
+    # [END bigquery_dataframes_multimodal_dataframe_ai]
+
+    # [START bigquery_dataframes_multimodal_dataframe_pdf_chunk]
+    # PDF chunking
+    df_pdf = bpd.from_glob_path(
+        "gs://cloud-samples-data/bigquery/tutorials/cymbal-pets/documents/*", name="pdf"
+    )
+    df_pdf["chunked"] = df_pdf["pdf"].blob.pdf_chunk()
+    chunked = df_pdf["chunked"].explode()
+    chunked
+    # [END bigquery_dataframes_multimodal_dataframe_pdf_chunk]
+    assert df_image is not None
+    assert answer is not None
+    assert answer_alt is not None
+    assert embeddings is not None
+    assert chunked is not None