feat: GenAI SDK client(multimodal) - Implement create_from_gemini_request_jsonl in Datasets and AsyncDatasets classes.

agentplatform/_genai/datasets.py

@@ -1112,6 +1112,109 @@ def create_from_bigframes(
             multimodal_dataset=multimodal_dataset, config=config
         )
 
+    def create_from_gemini_request_jsonl(
+        self,
+        *,
+        gcs_uri: str,
+        multimodal_dataset: Optional[types.MultimodalDatasetOrDict] = None,
+        target_table_id: Optional[str] = None,
+        config: Optional[types.CreateMultimodalDatasetConfigOrDict] = None,
+    ) -> types.MultimodalDataset:
+        """Creates a multimodal dataset from a JSONL file stored on GCS.
+
+        The JSONL file should contain instances of Gemini
+        `GenerateContentRequest` on each line. The data will be stored in a
+        BigQuery table with a single column called "requests". The
+        request_column_name in the dataset metadata will be set to "requests".
+
+        Args:
+            gcs_uri (str):
+                The Google Cloud Storage URI of the JSONL file to import.
+                For example, 'gs://my-bucket/path/to/data.jsonl'
+            multimodal_dataset:
+                Optional. A representation of a multimodal dataset.
+            target_table_id (str):
+                Optional. The BigQuery table id where the dataframe will be
+                uploaded. The table id can be in the format of "dataset.table"
+                or "project.dataset.table". Note that the BigQuery
+                dataset must already exist and be in the same location as the
+                multimodal dataset. If not provided, a generated table id will
+                be created in the `vertex_datasets` dataset (e.g.
+                `project.vertex_datasets_us_central1.multimodal_dataset_4cbf7ffd`).
+            config:
+                Optional. A configuration for creating the multimodal dataset. If not
+                provided, the default configuration will be used.
+
+        Returns:
+            The created multimodal dataset.
+        """
+        bigquery = _datasets_utils._try_import_bigquery()
+
+        if isinstance(multimodal_dataset, dict):
+            multimodal_dataset = types.MultimodalDataset(**multimodal_dataset)
+        elif not multimodal_dataset:
+            multimodal_dataset = types.MultimodalDataset()
+
+        if not gcs_uri.startswith("gs://"):
+            raise ValueError(
+                "Invalid GCS URI format. Expected: gs://bucket-name/object-path"
+            )
+
+        project = self._api_client.project
+        location = self._api_client.location
+        credentials = self._api_client._credentials
+
+        if target_table_id:
+            target_table_id = _datasets_utils._normalize_and_validate_table_id(
+                table_id=target_table_id,
+                project=project,
+                location=location,
+                credentials=credentials,
+            )
+        else:
+            dataset_id = _datasets_utils._create_default_bigquery_dataset_if_not_exists(
+                project=project, location=location, credentials=credentials
+            )
+            target_table_id = _datasets_utils._generate_target_table_id(dataset_id)
+
+        request_column_name = "requests"
+
+        # Setup LoadJobConfig to load the JSONL file as a CSV directly from GCS.
+        # We use an unused character (unit separator \x1f) as the field delimiter
+        # and an empty string as the quote character. This forces BigQuery to
+        # treat each line (a valid JSON string) as a single CSV row.
+        job_config = bigquery.LoadJobConfig(
+            source_format=bigquery.SourceFormat.CSV,
+            field_delimiter="\x1f",
+            quote_character="",
+            schema=[bigquery.SchemaField(request_column_name, "JSON")],
+            write_disposition=bigquery.WriteDisposition.WRITE_TRUNCATE,
+        )
+
+        client = bigquery.Client(project=project, credentials=credentials)
+        load_job = client.load_table_from_uri(
+            gcs_uri,
+            target_table_id,
+            job_config=job_config,
+        )
+        load_job.result()
+
+        multimodal_dataset = multimodal_dataset.model_copy(deep=True)
+        metadata = multimodal_dataset.metadata or types.SchemaTablesDatasetMetadata()
+
+        read_config = (
+            metadata.gemini_request_read_config or types.GeminiRequestReadConfig()
+        )
+        read_config.assembled_request_column_name = request_column_name
+        metadata.gemini_request_read_config = read_config
+
+        multimodal_dataset.metadata = metadata
+        multimodal_dataset.set_bigquery_uri(f"bq://{target_table_id}")
+
+        return self.create_from_bigquery(
+            multimodal_dataset=multimodal_dataset, config=config
+        )
+
     def update_multimodal_dataset(
         self,
         *,
@@ -2400,6 +2503,111 @@ async def create_from_bigframes(
             multimodal_dataset=multimodal_dataset, config=config
         )
 
+    async def create_from_gemini_request_jsonl(
+        self,
+        *,
+        gcs_uri: str,
+        multimodal_dataset: Optional[types.MultimodalDatasetOrDict] = None,
+        target_table_id: Optional[str] = None,
+        config: Optional[types.CreateMultimodalDatasetConfigOrDict] = None,
+    ) -> types.MultimodalDataset:
+        """Creates a multimodal dataset from a JSONL file stored on GCS.
+
+        The JSONL file should contain instances of Gemini
+        `GenerateContentRequest` on each line. The data will be stored in a
+        BigQuery table with a single column called "requests". The
+        request_column_name in the dataset metadata will be set to "requests".
+
+        Args:
+            gcs_uri (str):
+                The Google Cloud Storage URI of the JSONL file to import.
+                For example, 'gs://my-bucket/path/to/data.jsonl'
+            multimodal_dataset:
+                Optional. A representation of a multimodal dataset.
+            target_table_id (str):
+                Optional. The BigQuery table id where the dataframe will be
+                uploaded. The table id can be in the format of "dataset.table"
+                or "project.dataset.table". Note that the BigQuery
+                dataset must already exist and be in the same location as the
+                multimodal dataset. If not provided, a generated table id will
+                be created in the `vertex_datasets` dataset (e.g.
+                `project.vertex_datasets_us_central1.multimodal_dataset_4cbf7ffd`).
+            config:
+                Optional. A configuration for creating the multimodal dataset. If not
+                provided, the default configuration will be used.
+
+        Returns:
+            The created multimodal dataset.
+        """
+        bigquery = _datasets_utils._try_import_bigquery()
+
+        if isinstance(multimodal_dataset, dict):
+            multimodal_dataset = types.MultimodalDataset(**multimodal_dataset)
+        elif not multimodal_dataset:
+            multimodal_dataset = types.MultimodalDataset()
+
+        if not gcs_uri.startswith("gs://"):
+            raise ValueError(
+                "Invalid GCS URI format. Expected: gs://bucket-name/object-path"
+            )
+
+        project = self._api_client.project
+        location = self._api_client.location
+        credentials = self._api_client._credentials
+
+        if target_table_id:
+            target_table_id = (
+                await _datasets_utils._normalize_and_validate_table_id_async(
+                    table_id=target_table_id,
+                    project=project,
+                    location=location,
+                    credentials=credentials,
+                )
+            )
+        else:
+            dataset_id = await _datasets_utils._create_default_bigquery_dataset_if_not_exists_async(
+                project=project, location=location, credentials=credentials
+            )
+            target_table_id = _datasets_utils._generate_target_table_id(dataset_id)
+
+        request_column_name = "requests"
+
+        # Setup LoadJobConfig to load the JSONL file as a CSV directly from GCS.
+        # We use an unused character (unit separator \x1f) as the field delimiter
+        # and an empty string as the quote character. This forces BigQuery to
+        # treat each line (a valid JSON string) as a single CSV row.
+        job_config = bigquery.LoadJobConfig(
+            source_format=bigquery.SourceFormat.CSV,
+            field_delimiter="\x1f",
+            quote_character="",
+            schema=[bigquery.SchemaField(request_column_name, "JSON")],
+            write_disposition=bigquery.WriteDisposition.WRITE_TRUNCATE,
+        )
+
+        client = bigquery.Client(project=project, credentials=credentials)
+        load_job = client.load_table_from_uri(
+            gcs_uri,
+            target_table_id,
+            job_config=job_config,
+        )
+        await asyncio.to_thread(load_job.result)
+
+        multimodal_dataset = multimodal_dataset.model_copy(deep=True)
+        metadata = multimodal_dataset.metadata or types.SchemaTablesDatasetMetadata()
+
+        read_config = (
+            metadata.gemini_request_read_config or types.GeminiRequestReadConfig()
+        )
+        read_config.assembled_request_column_name = request_column_name
+        metadata.gemini_request_read_config = read_config
+
+        multimodal_dataset.metadata = metadata
+        multimodal_dataset.set_bigquery_uri(f"bq://{target_table_id}")
+
+        return await self.create_from_bigquery(
+            multimodal_dataset=multimodal_dataset, config=config
+        )
+
     async def update_multimodal_dataset(
         self,
         *,

tests/unit/agentplatform/genai/replays/test_create_multimodal_datasets.py

@@ -295,6 +295,50 @@ def test_create_dataset_from_bigframes_preserves_other_metadata(client, is_repla
     )
 
 
+@pytest.mark.usefixtures("mock_bigquery_client", "mock_import_bigframes")
+def test_create_from_gemini_request_jsonl(client, is_replay_mode):
+    if is_replay_mode:
+        gcs_uri = "gs://test-bucket/test-blob.jsonl"
+    else:
+        gcs_uri = (
+            "gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl"
+        )
+
+    dataset = client.datasets.create_from_gemini_request_jsonl(
+        gcs_uri=gcs_uri,
+        target_table_id=BIGQUERY_TABLE_NAME,
+        multimodal_dataset={
+            "display_name": "test-from-gemini-jsonl",
+        },
+    )
+    assert dataset.display_name == "test-from-gemini-jsonl"
+    assert (
+        dataset.metadata.gemini_request_read_config.assembled_request_column_name
+        == "requests"
+    )
+    if not is_replay_mode:
+        bigquery_client = bigquery.Client(
+            project=client._api_client.project,
+            location=client._api_client.location,
+            credentials=client._api_client._credentials,
+        )
+        rows = bigquery_client.list_rows(
+            dataset.metadata.input_config.bigquery_source.uri[5:]
+        )
+        df = rows.to_dataframe()
+        assert len(df) > 0
+        assert "requests" in df.columns
+        assert "contents" in str(df["requests"].iloc[0])
+
+
+def test_create_from_gemini_request_jsonl_raises_invalid_gcs_uri(client):
+    with pytest.raises(
+        ValueError,
+        match="Invalid GCS URI format. Expected: gs://bucket-name/object-path",
+    ):
+        client.datasets.create_from_gemini_request_jsonl(gcs_uri="invalid_uri")
+
+
 pytestmark = pytest_helper.setup(
     file=__file__,
     globals_for_file=globals(),
@@ -549,3 +593,51 @@ async def test_create_dataset_from_bigframes_preserves_other_metadata_async(
     assert dataset.metadata.input_config.bigquery_source.uri == (
         f"bq://{BIGQUERY_TABLE_NAME}"
     )
+
+
+@pytest.mark.asyncio
+@pytest.mark.usefixtures("mock_bigquery_client", "mock_import_bigframes")
+async def test_create_from_gemini_request_jsonl_async(client, is_replay_mode):
+    if is_replay_mode:
+        gcs_uri = "gs://test-bucket/test-blob-async.jsonl"
+    else:
+        gcs_uri = (
+            "gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl"
+        )
+
+    dataset = await client.aio.datasets.create_from_gemini_request_jsonl(
+        gcs_uri=gcs_uri,
+        target_table_id=BIGQUERY_TABLE_NAME,
+        multimodal_dataset={
+            "display_name": "test-from-gemini-jsonl-async",
+        },
+    )
+    assert dataset.display_name == "test-from-gemini-jsonl-async"
+    assert (
+        dataset.metadata.gemini_request_read_config.assembled_request_column_name
+        == "requests"
+    )
+    if not is_replay_mode:
+        bigquery_client = bigquery.Client(
+            project=client._api_client.project,
+            location=client._api_client.location,
+            credentials=client._api_client._credentials,
+        )
+        rows = bigquery_client.list_rows(
+            dataset.metadata.input_config.bigquery_source.uri[5:]
+        )
+        df = rows.to_dataframe()
+        assert len(df) > 0
+        assert "requests" in df.columns
+        assert "contents" in str(df["requests"].iloc[0])
+
+
+@pytest.mark.asyncio
+async def test_create_from_gemini_request_jsonl_raises_invalid_gcs_uri_async(client):
+    with pytest.raises(
+        ValueError,
+        match="Invalid GCS URI format. Expected: gs://bucket-name/object-path",
+    ):
+        await client.aio.datasets.create_from_gemini_request_jsonl(
+            gcs_uri="invalid_uri"
+        )