8000 test: add tests for gemini structured output array and struct types by GarrettWu · Pull Request #1670 · googleapis/python-bigquery-dataframes · GitHub
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test: add tests for gemini structured output array and struct types #1670

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Merged
merged 4 commits into from
May 5, 2025
Merged

test: add tests for gemini structured output array and struct types #1670

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e9719bc
test: add tests for gemini structured output array and struct types
GarrettWu Apr 29, 2025
583660e
Merge remote-tracking branch 'github/main' into garrettwu-struct
GarrettWu May 2, 2025
d013b67
Merge remote-tracking branch 'github/main' into garrettwu-struct
GarrettWu May 3, 2025
5a729fa
fix test in python 3.9
GarrettWu May 5, 2025
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2 changes: 1 addition & 1 deletion bigframes/ml/llm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -617,7 +617,7 @@ def predict(
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>.
Supported types are int64, float64, bool and string. If None, output text result.
Supported types are int64, float64, bool, string, array<type> and struct<column type>. If None, output text result.
Returns:
bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values.
"""
Expand Down
18 changes: 0 additions & 18 deletions tests/system/small/ml/conftest.py
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Expand Up @@ -29,7 +29,6 @@
globals,
imported,
linear_model,
llm,
remote,
)

Expand Down Expand Up @@ -339,20 +338,3 @@ def imported_xgboost_model(
output={"predicted_label": "float64"},
model_path=imported_xgboost_array_model_path,
)


@pytest.fixture(scope="session")
def bqml_gemini_text_generator(bq_connection, session) -> llm.GeminiTextGenerator:
return llm.GeminiTextGenerator(
model_name="gemini-1.5-flash-002",
connection_name=bq_connection,
session=session,
)


@pytest.fixture(scope="session")
def bqml_claude3_text_generator(bq_connection, session) -> llm.Claude3TextGenerator:
return llm.Claude3TextGenerator(
connection_name=bq_connection,
session=session,
)
47 changes: 19 additions & 28 deletions tests/system/small/ml/test_llm.py
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Expand Up @@ -16,6 +16,7 @@
from unittest import mock

import pandas as pd
import pyarrow as pa
import pytest

import bigframes
Expand Down Expand Up @@ -253,22 +254,27 @@ def test_gemini_text_generator_predict_output_schema_success(
"int_output": "int64",
"float_output": "float64",
"str_output": "string",
"array_output": "array<int64>",
"struct_output": "struct<number int64>",
}
df = gemini_text_generator_model.predict(
llm_text_df, output_schema=output_schema
).to_pandas()
df = gemini_text_generator_model.predict(llm_text_df, output_schema=output_schema)
assert df["bool_output"].dtype == pd.BooleanDtype()
assert df["int_output"].dtype == pd.Int64Dtype()
assert df["float_output"].dtype == pd.Float64Dtype()
assert df["str_output"].dtype == pd.StringDtype(storage="pyarrow")
assert df["array_output"].dtype == pd.ArrowDtype(pa.list_(pa.int64()))
assert df["struct_output"].dtype == pd.ArrowDtype(
pa.struct([("number", pa.int64())])
)

pd_df = df.to_pandas()
utils.check_pandas_df_schema_and_index(
df,
pd_df,
columns=list(output_schema.keys()) + ["prompt", "full_response", "status"],
index=3,
col_exact=False,
)

assert df["bool_output"].dtype == pd.BooleanDtype()
assert df["int_output"].dtype == pd.Int64Dtype()
assert df["float_output"].dtype == pd.Float64Dtype()
assert df["str_output"].dtype == pd.StringDtype(storage="pyarrow")


# Overrides __eq__ function for comparing as mock.call parameter
class EqCmpAllDataFrame(bpd.DataFrame):
Expand Down Expand Up @@ -305,8 +311,7 @@ def test_text_generator_retry_success(
session,
model_class,
options,
bqml_gemini_text_generator: llm.GeminiTextGenerator,
bqml_claude3_text_generator: llm.Claude3TextGenerator,
bq_connection,
):
# Requests.
df0 = EqCmpAllDataFrame(
Expand Down Expand Up @@ -387,11 +392,7 @@ def test_text_generator_retry_success(
),
]

text_generator_model = (
bqml_gemini_text_generator
if (model_class == llm.GeminiTextGenerator)
else bqml_claude3_text_generator
)
text_generator_model = model_class(connection_name=bq_connection, session=session)
text_generator_model._bqml_model = mock_bqml_model

with mock.patch.object(core.BqmlModel, "generate_text_tvf", generate_text_tvf):
Expand Down Expand Up @@ -448,13 +449,7 @@ def test_text_generator_retry_success(
),
],
)
def test_text_generator_retry_no_progress(
session,
model_class,
options,
bqml_gemini_text_generator: llm.GeminiTextGenerator,
bqml_claude3_text_generator: llm.Claude3TextGenerator,
):
def test_text_generator_retry_no_progress(session, model_class, options, bq_connection):
# Requests.
df0 = EqCmpAllDataFrame(
{
Expand Down Expand Up @@ -514,11 +509,7 @@ def test_text_generator_retry_no_progress(
),
]

text_generator_model = (
bqml_gemini_text_generator
if (model_class == llm.GeminiTextGenerator)
else bqml_claude3_text_generator
)
text_generator_model = model_class(connection_name=bq_connection, session=session)
text_generator_model._bqml_model = mock_bqml_model

with mock.patch.object(core.BqmlModel, "generate_text_tvf", generate_text_tvf):
Expand Down
54 changes: 54 additions & 0 deletions tests/system/small/ml/test_multimodal_llm.py
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Expand Up @@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.

import pandas as pd
import pyarrow as pa
import pytest

import bigframes
Expand Down Expand Up @@ -68,3 +70,55 @@ def test_gemini_text_generator_multimodal_input(
index=2,
col_exact=False,
)


@pytest.mark.parametrize(
"model_name",
(
"gemini-1.5-pro-001",
# "gemini-1.5-pro-002",
"gemini-1.5-flash-001",
"gemini-1.5-flash-002",
"gemini-2.0-flash-exp",
"gemini-2.0-flash-001",
),
)
@pytest.mark.flaky(retries=2)
def test_gemini_text_generator_multimodal_structured_output(
images_mm_df: bpd.DataFrame, model_name, test_session, bq_connection
):
bigframes.options.experiments.blob = True

gemini_text_generator_model = llm.GeminiTextGenerator(
model_name=model_name, connection_name=bq_connection, session=test_session
)
output_schema = {
"bool_output": "bool",
"int_output": "int64",
"float_output": "float64",
"str_output": "string",
"array_output": "array<int64>",
"struct_output": "struct<number int64>",
}
df = gemini_text_generator_model.predict(
images_mm_df,
prompt=["Describe", images_mm_df["blob_col"]],
output_schema=output_schema,
)
assert df["bool_output"].dtype == pd.BooleanDtype()
assert df["int_output"].dtype == pd.Int64Dtype()
assert df["float_output"].dtype == pd.Float64Dtype()
assert df["str_output"].dtype == pd.StringDtype(storage="pyarrow")
assert df["array_output"].dtype == pd.ArrowDtype(pa.list_(pa.int64()))
assert df["struct_output"].dtype == pd.ArrowDtype(
pa.struct([("number", pa.int64())])
)

pd_df = df.to_pandas()
utils.check_pandas_df_schema_and_index(
pd_df,
columns=list(output_schema.keys())
+ ["blob_col", "prompt", "full_response", "status"],
index=2,
col_exact=False,
)
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