adding batch gemini predict sample (GoogleCloudPlatform#12621)

* feat(generativeai): Add batch-predict sample using Gemini model

Author: CadillacBurgess1

generative_ai/batch_predict/gemini_batch_predict.py

@@ -0,0 +1,86 @@
+# Copyright 2024 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
+#
+#    https://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.
+import os
+
+from vertexai.preview.batch_prediction import BatchPredictionJob
+
+PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
+
+
+def batch_predict_gemini_createjob(input_uri: str, output_uri: str) -> BatchPredictionJob:
+    """Perform batch text prediction using a Gemini AI model.
+    Args:
+        input_uri (str): URI of the input file in BigQuery table or Google Cloud Storage.
+            Example: "gs://[BUCKET]/[DATASET].jsonl" OR "bq://[PROJECT].[DATASET].[TABLE]"
+
+        output_uri (str): URI of the output folder,  in BigQuery table or Google Cloud Storage.
+            Example: "gs://[BUCKET]/[OUTPUT].jsonl" OR "bq://[PROJECT].[DATASET].[TABLE]"
+    Returns:
+        batch_prediction_job: The batch prediction job object containing details of the job.
+    """
+
+    # [START generativeaionvertexai_batch_predict_gemini_createjob]
+    import time
+    import vertexai
+
+    from vertexai.preview.batch_prediction import BatchPredictionJob
+
+    # TODO(developer): Update and un-comment below lines
+    # input_uri ="gs://[BUCKET]/[OUTPUT].jsonl" # Example
+    # output_uri ="gs://[BUCKET]"
+
+    # Initialize vertexai
+    vertexai.init(project=PROJECT_ID, location="us-central1")
+
+    # Submit a batch prediction job with Gemini model
+    batch_prediction_job = BatchPredictionJob.submit(
+        source_model="gemini-1.5-flash-001",
+        input_dataset=input_uri,
+        output_uri_prefix=output_uri
+    )
+
+    # Check job status
+    print(f"Job resouce name: {batch_prediction_job.resource_name}")
+    print(f"Model resource name with the job: {batch_prediction_job.model_name}")
+    print(f"Job state: {batch_prediction_job.state.name}")
+
+    # Refresh the job until complete
+    while not batch_prediction_job.has_ended:
+        time.sleep(5)
+        batch_prediction_job.refresh()
+
+    # Check if the job succeeds
+    if batch_prediction_job.has_succeeded:
+        print("Job succeeded!")
+    else:
+        print(f"Job failed: {batch_prediction_job.error}")
+
+    # Check the location of the output
+    print(f"Job output location: {batch_prediction_job.output_location}")
+
+    # Example response:
+    #  Job output location: gs://yourbucket/gen-ai-batch-prediction/prediction-model-year-month-day-hour:minute:second.12345
+
+    # https://storage.googleapis.com/cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl
+
+    return batch_prediction_job
+
+    # [END generativeaionvertexai_batch_predict_gemini_createjob]
+
+
+if __name__ == "__main__":
+    # TODO(developer): Update gsc bucket and file paths
+    GCS_BUCKET = "gs://yourbucket"
+    batch_predict_gemini_createjob(f"gs://{GCS_BUCKET}/batch_data/sample_input_file.jsonl",
+                                   f"gs://{GCS_BUCKET}/batch_preditions/sample_output/")

generative_ai/batch_predict/test_batch_predict.py

@@ -13,8 +13,11 @@
 # limitations under the License.
 from typing import Callable
 
+
 import batch_code_predict
 import batch_text_predict
+import gemini_batch_predict
+
 
 from google.cloud import storage
 from google.cloud.aiplatform import BatchPredictionJob
@@ -70,3 +73,12 @@ def test_batch_code_predict(output_folder: pytest.fixture()) -> None:
         )
     )
     assert OUTPUT_PATH in job.output_info.gcs_output_directory
+
+
+def test_batch_gemini_predict(output_folder: pytest.fixture()) -> None:
+    input_uri = f"gs://{INPUT_BUCKET}/batch/prompt_for_batch_gemini_predict.jsonl"
+    job = _main_test(
+        test_func=lambda: gemini_batch_predict.batch_predict_gemini_createjob(
+            input_uri, output_folder)
+    )
+    assert OUTPUT_PATH in job.output_location