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feat: GenAI SDK client(multimodal) - Accept an explicit bigquery_uri parameter in create_from_bigquery #6604

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feat: GenAI SDK client(multimodal) - Accept an explicit bigquery_uri parameter in create_from_bigquery #6604

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72 changes: 72 additions & 0 deletions tests/unit/vertexai/genai/replays/test_create_multimodal_datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,6 +115,40 @@ def test_create_dataset_from_bigquery(client):
)


@pytest.mark.usefixtures("mock_generate_multimodal_dataset_display_name")
def test_create_dataset_from_bigquery_with_uri(client):
dataset = client.datasets.create_from_bigquery(
bigquery_uri=f"bq://{BIGQUERY_TABLE_NAME}",
)
assert isinstance(dataset, types.MultimodalDataset)
assert dataset.metadata.input_config.bigquery_source.uri == (
f"bq://{BIGQUERY_TABLE_NAME}"
)


def test_create_dataset_from_bigquery_preserves_other_metadata(client):
dataset = client.datasets.create_from_bigquery(
bigquery_uri=f"bq://{BIGQUERY_TABLE_NAME}",
multimodal_dataset={
"display_name": "test-from-bigquery-uri",
"metadata": {
"gemini_request_read_config": {
"assembled_request_column_name": "test_column"
}
},
},
)
assert isinstance(dataset, types.MultimodalDataset)
assert dataset.display_name == "test-from-bigquery-uri"
assert (
dataset.metadata.gemini_request_read_config.assembled_request_column_name
== "test_column"
)
assert dataset.metadata.input_config.bigquery_source.uri == (
f"bq://{BIGQUERY_TABLE_NAME}"
)


@pytest.mark.usefixtures("mock_generate_multimodal_dataset_display_name")
def test_create_dataset_from_bigquery_no_display_name(client):
dataset = client.datasets.create_from_bigquery(
Expand Down Expand Up @@ -254,6 +288,44 @@ async def test_create_dataset_from_bigquery_async(client):
)


@pytest.mark.asyncio
@pytest.mark.usefixtures("mock_generate_multimodal_dataset_display_name")
async def test_create_dataset_from_bigquery_with_uri_async(client):
dataset = await client.aio.datasets.create_from_bigquery(
bigquery_uri=f"bq://{BIGQUERY_TABLE_NAME}",
)
assert isinstance(dataset, types.MultimodalDataset)
assert dataset.metadata.input_config.bigquery_source.uri == (
f"bq://{BIGQUERY_TABLE_NAME}"
)


@pytest.mark.asyncio
async def test_create_dataset_from_bigquery_preserves_other_metadata_async(
client,
):
dataset = await client.aio.datasets.create_from_bigquery(
bigquery_uri=f"bq://{BIGQUERY_TABLE_NAME}",
multimodal_dataset={
"display_name": "test-from-bigquery-uri",
"metadata": {
"gemini_request_read_config": {
"assembled_request_column_name": "test_column"
}
},
},
)
assert isinstance(dataset, types.MultimodalDataset)
assert dataset.display_name == "test-from-bigquery-uri"
assert (
dataset.metadata.gemini_request_read_config.assembled_request_column_name
== "test_column"
)
assert dataset.metadata.input_config.bigquery_source.uri == (
f"bq://{BIGQUERY_TABLE_NAME}"
)


@pytest.mark.asyncio
@pytest.mark.usefixtures("mock_generate_multimodal_dataset_display_name")
async def test_create_dataset_from_bigquery_no_display_name_async(client):
Expand Down
29 changes: 19 additions & 10 deletions vertexai/_genai/_datasets_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@

import google.auth.credentials
from vertexai._genai.types import common
from pydantic import BaseModel
from google.genai import _common


METADATA_SCHEMA_URI = (
Expand All @@ -31,18 +31,27 @@
_DEFAULT_BQ_DATASET_PREFIX = "vertex_datasets"
_DEFAULT_BQ_TABLE_PREFIX = "multimodal_dataset"

T = TypeVar("T", bound=BaseModel)
T = TypeVar("T", bound=_common.BaseModel)


def create_from_response(model_type: Type[T], response: dict[str, Any]) -> T:
def create_from_response(
model_type: Type[T],
response: dict[str, Any],
config: Any | None = None,
) -> T:
"""Creates a model from a response."""
model_field_names = model_type.model_fields.keys()
filtered_response = {}
for key, value in response.items():
snake_key = common.camel_to_snake(key)
if snake_key in model_field_names:
filtered_response[snake_key] = value
return model_type(**filtered_response)
kwargs = (
{
"config": {
"response_schema": getattr(config, "response_schema", None),
"response_json_schema": getattr(config, "response_json_schema", None),
"include_all_fields": getattr(config, "include_all_fields", None),
}
}
if config
else {}
)
return model_type._from_response(response=response, kwargs=kwargs)


def validate_multimodal_dataset_bigquery_uri(
Expand Down
54 changes: 42 additions & 12 deletions vertexai/_genai/datasets.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -924,23 +924,34 @@ def _wait_for_operation(
def create_from_bigquery(
self,
*,
multimodal_dataset: types.MultimodalDatasetOrDict,
bigquery_uri: Optional[str] = None,
multimodal_dataset: Optional[types.MultimodalDatasetOrDict] = None,
config: Optional[types.CreateMultimodalDatasetConfigOrDict] = None,
) -> types.MultimodalDataset:
"""Creates a multimodal dataset from a BigQuery table.

Args:
bigquery_uri:
Optional. The BigQuery URI of the table to create the dataset from.
e.g. "bq://project.dataset.table".
multimodal_dataset:
Required. A representation of a multimodal dataset.
Optional. A representation of a multimodal dataset.
config:
Optional. A configuration for creating the multimodal dataset. If not
provided, the default configuration will be used.

Returns:
A types.MultimodalDataset object representing a multimodal dataset.
"""
if isinstance(multimodal_dataset, dict):
if multimodal_dataset is None:
multimodal_dataset = types.MultimodalDataset()
elif isinstance(multimodal_dataset, dict):
multimodal_dataset = types.MultimodalDataset(**multimodal_dataset)

if bigquery_uri:
multimodal_dataset = multimodal_dataset.model_copy(deep=True)
multimodal_dataset.set_bigquery_uri(bigquery_uri)

_datasets_utils.validate_multimodal_dataset_bigquery_uri(multimodal_dataset)

if isinstance(config, dict):
Expand All @@ -963,7 +974,9 @@ def create_from_bigquery(
operation=multimodal_dataset_operation,
timeout_seconds=config.timeout,
)
return _datasets_utils.create_from_response(types.MultimodalDataset, response)
return _datasets_utils.create_from_response(
types.MultimodalDataset, response, config
)

def create_from_pandas(
self,
Expand Down Expand Up @@ -1302,6 +1315,7 @@ def assess_tuning_resources(
return _datasets_utils.create_from_response(
types.TuningResourceUsageAssessmentResult,
response["tuningResourceUsageAssessmentResult"],
config,
)

def assess_tuning_validity(
Expand Down Expand Up @@ -1368,6 +1382,7 @@ def assess_tuning_validity(
return _datasets_utils.create_from_response(
types.TuningValidationAssessmentResult,
response["tuningValidationAssessmentResult"],
config,
)

def assess_batch_prediction_resources(
Expand Down Expand Up @@ -1430,7 +1445,7 @@ def assess_batch_prediction_resources(
)
result = response["batchPredictionResourceUsageAssessmentResult"]
return _datasets_utils.create_from_response(
types.BatchPredictionResourceUsageAssessmentResult, result
types.BatchPredictionResourceUsageAssessmentResult, result, config
)

def assess_batch_prediction_validity(
Expand Down Expand Up @@ -1493,7 +1508,7 @@ def assess_batch_prediction_validity(
)
result = response["batchPredictionValidationAssessmentResult"]
return _datasets_utils.create_from_response(
types.BatchPredictionValidationAssessmentResult, result
types.BatchPredictionValidationAssessmentResult, result, config
)


Expand Down Expand Up @@ -2192,23 +2207,34 @@ async def _wait_for_operation(
async def create_from_bigquery(
self,
*,
multimodal_dataset: types.MultimodalDatasetOrDict,
bigquery_uri: Optional[str] = None,
multimodal_dataset: Optional[types.MultimodalDatasetOrDict] = None,
config: Optional[types.CreateMultimodalDatasetConfigOrDict] = None,
) -> types.MultimodalDataset:
"""Creates a multimodal dataset from a BigQuery table.

Args:
bigquery_uri:
Optional. The BigQuery URI of the table to create the dataset from.
e.g. "bq://project.dataset.table".
multimodal_dataset:
Required. A representation of a multimodal dataset.
Optional. A representation of a multimodal dataset.
config:
Optional. A configuration for creating the multimodal dataset. If not
provided, the default configuration will be used.

Returns:
A types.MultimodalDataset object representing a multimodal dataset.
"""
if isinstance(multimodal_dataset, dict):
if multimodal_dataset is None:
multimodal_dataset = types.MultimodalDataset()
elif isinstance(multimodal_dataset, dict):
multimodal_dataset = types.MultimodalDataset(**multimodal_dataset)

if bigquery_uri:
multimodal_dataset = multimodal_dataset.model_copy(deep=True)
multimodal_dataset.set_bigquery_uri(bigquery_uri)

_datasets_utils.validate_multimodal_dataset_bigquery_uri(multimodal_dataset)

if isinstance(config, dict):
Expand All @@ -2231,7 +2257,9 @@ async def create_from_bigquery(
operation=multimodal_dataset_operation,
timeout_seconds=config.timeout,
)
return _datasets_utils.create_from_response(types.MultimodalDataset, response)
return _datasets_utils.create_from_response(
types.MultimodalDataset, response, config
)

async def create_from_pandas(
self,
Expand Down Expand Up @@ -2568,6 +2596,7 @@ async def assess_tuning_resources(
return _datasets_utils.create_from_response(
types.TuningResourceUsageAssessmentResult,
response["tuningResourceUsageAssessmentResult"],
config,
)

async def assess_tuning_validity(
Expand Down Expand Up @@ -2634,6 +2663,7 @@ async def assess_tuning_validity(
return _datasets_utils.create_from_response(
types.TuningValidationAssessmentResult,
response["tuningValidationAssessmentResult"],
config,
)

async def assess_batch_prediction_resources(
Expand Down Expand Up @@ -2696,7 +2726,7 @@ async def assess_batch_prediction_resources(
)
result = response["batchPredictionResourceUsageAssessmentResult"]
return _datasets_utils.create_from_response(
types.BatchPredictionResourceUsageAssessmentResult, result
types.BatchPredictionResourceUsageAssessmentResult, result, config
)

async def assess_batch_prediction_validity(
Expand Down Expand Up @@ -2759,5 +2789,5 @@ async def assess_batch_prediction_validity(
)
result = response["batchPredictionValidationAssessmentResult"]
return _datasets_utils.create_from_response(
types.BatchPredictionValidationAssessmentResult, result
types.BatchPredictionValidationAssessmentResult, result, config
)
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