#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
"""
Example Airflow DAG for Google Gen AI Gemini Batch API and File API.
"""
from __future__ import annotations
import os
from datetime import datetime
try:
from airflow.sdk import task
except ImportError:
# Airflow 2 path
from airflow.decorators import task # type: ignore[attr-defined,no-redef]
try:
from airflow.sdk import TriggerRule
except ImportError:
# Compatibility for Airflow < 3.1
from airflow.utils.trigger_rule import TriggerRule # type: ignore[no-redef,attr-defined]
from pathlib import Path
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.gen_ai import (
GenAIGeminiCancelBatchJobOperator,
GenAIGeminiCreateBatchJobOperator,
GenAIGeminiCreateEmbeddingsBatchJobOperator,
GenAIGeminiDeleteBatchJobOperator,
GenAIGeminiDeleteFileOperator,
GenAIGeminiGetBatchJobOperator,
GenAIGeminiGetFileOperator,
GenAIGeminiListBatchJobsOperator,
GenAIGeminiListFilesOperator,
GenAIGeminiUploadFileOperator,
)
from airflow.providers.google.common.utils.get_secret import get_secret
from airflow.providers.standard.operators.bash import BashOperator
[docs]
ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
[docs]
PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
[docs]
DAG_ID = "gen_ai_gemini_batch_api"
[docs]
GEMINI_API_KEY = "api_key"
[docs]
INLINED_REQUESTS_FOR_BATCH_JOB = [
{"contents": [{"parts": [{"text": "Tell me a one-sentence joke."}], "role": "user"}]},
{"contents": [{"parts": [{"text": "Why is the sky blue?"}], "role": "user"}]},
]
[docs]
INLINED_REQUESTS_FOR_EMBEDDINGS_BATCH_JOB = {
"contents": [{"parts": [{"text": "Why is the sky blue?"}], "role": "user"}]
}
[docs]
GEMINI_XCOM_API_KEY = "{{ task_instance.xcom_pull('get_gemini_api_key') }}"
[docs]
LOCAL_FILE_NAME = "gemini_batch_requests.jsonl"
[docs]
LOCAL_EMBEDDINGS_FILE_NAME = "gemini_batch_embeddings_requests.jsonl"
[docs]
UPLOAD_FILE_PATH = str(Path(__file__).parent / "resources" / LOCAL_FILE_NAME)
[docs]
IS_COMPOSER = bool(os.environ.get("COMPOSER_ENVIRONMENT", ""))
[docs]
PATH_TO_SAVE_RESULTS = "gcs/data" if IS_COMPOSER else str(Path(__file__).parent / "resources")
[docs]
UPLOAD_EMBEDDINGS_FILE_PATH = str(Path(__file__).parent / "resources" / LOCAL_EMBEDDINGS_FILE_NAME)
[docs]
UPLOADED_FILE_NAME = (
"{{ task_instance.xcom_pull(task_ids='upload_file_for_batch_job_task', key='file_name') }}"
)
[docs]
UPLOADED_EMBEDDINGS_FILE_NAME = (
"{{ task_instance.xcom_pull(task_ids='upload_file_for_embeddings_batch_job_task', key='file_name') }}"
)
[docs]
BATCH_JOB_WITH_INLINED_REQUESTS_NAME = (
"{{ task_instance.xcom_pull(task_ids='create_batch_job_using_inlined_requests_task', key='job_name') }}"
)
[docs]
BATCH_JOB_WITH_FILE_NAME = (
"{{ task_instance.xcom_pull(task_ids='create_batch_job_using_file_task', key='job_name') }}"
)
[docs]
EMBEDDINGS_BATCH_JOB_WITH_INLINED_REQUESTS_NAME = "{{ task_instance.xcom_pull(task_ids='create_embeddings_job_using_inlined_requests_task', key='job_name') }}"
[docs]
EMBEDDINGS_BATCH_JOB_WITH_FILE_NAME = (
"{{ task_instance.xcom_pull(task_ids='create_embeddings_job_using_file_task', key='job_name') }}"
)
with DAG(
dag_id=DAG_ID,
description="Sample DAG with Gemini Batch API.",
schedule="@once",
start_date=datetime(2024, 1, 1),
catchup=False,
tags=["example", "gen_ai", "gemini_batch_api", "gemini_file_api"],
render_template_as_native_obj=True,
) as dag:
@task
[docs]
def get_gemini_api_key():
return get_secret(GEMINI_API_KEY)
get_gemini_api_key_task = get_gemini_api_key()
# [START how_to_cloud_gen_ai_files_api_upload_file_task]
upload_file = GenAIGeminiUploadFileOperator(
task_id="upload_file_for_batch_job_task",
project_id=PROJECT_ID,
location=REGION,
file_path=UPLOAD_FILE_PATH,
gemini_api_key=GEMINI_XCOM_API_KEY,
)
# [END how_to_cloud_gen_ai_files_api_upload_file_task]
upload_embeddings_file = GenAIGeminiUploadFileOperator(
task_id="upload_file_for_embeddings_batch_job_task",
project_id=PROJECT_ID,
location=REGION,
file_path=UPLOAD_EMBEDDINGS_FILE_PATH,
gemini_api_key=GEMINI_XCOM_API_KEY,
)
# [START how_to_cloud_gen_ai_files_api_get_file_task]
get_file = GenAIGeminiGetFileOperator(
task_id="get_file_for_batch_job_task",
project_id=PROJECT_ID,
location=REGION,
file_name=UPLOADED_FILE_NAME,
gemini_api_key=GEMINI_XCOM_API_KEY,
)
# [END how_to_cloud_gen_ai_files_api_get_file_task]
# [START how_to_cloud_gen_ai_files_api_list_files_task]
list_files = GenAIGeminiListFilesOperator(
task_id="list_files_files_api_task",
project_id=PROJECT_ID,
location=REGION,
gemini_api_key=GEMINI_XCOM_API_KEY,
)
# [END how_to_cloud_gen_ai_files_api_list_files_task]
# [START how_to_cloud_gen_ai_files_api_delete_file_task]
delete_file = GenAIGeminiDeleteFileOperator(
task_id="delete_file_for_batch_job_task",
project_id=PROJECT_ID,
location=REGION,
file_name=UPLOADED_FILE_NAME,
gemini_api_key=GEMINI_XCOM_API_KEY,
trigger_rule=TriggerRule.ALL_DONE,
)
# [END how_to_cloud_gen_ai_files_api_delete_file_task]
delete_embeddings_file = GenAIGeminiDeleteFileOperator(
task_id="delete_file_for_embeddings_batch_job_task",
project_id=PROJECT_ID,
location=REGION,
file_name=UPLOADED_EMBEDDINGS_FILE_NAME,
gemini_api_key=GEMINI_XCOM_API_KEY,
trigger_rule=TriggerRule.ALL_DONE,
)
# [START how_to_cloud_gen_ai_batch_api_create_batch_job_with_inlined_requests_task]
create_batch_job_using_inlined_requests = GenAIGeminiCreateBatchJobOperator(
task_id="create_batch_job_using_inlined_requests_task",
project_id=PROJECT_ID,
location=REGION,
model="gemini-3-pro-preview",
gemini_api_key=GEMINI_XCOM_API_KEY,
create_batch_job_config={
"display_name": "inlined-requests-batch-job",
},
input_source=INLINED_REQUESTS_FOR_BATCH_JOB,
wait_until_complete=True,
retrieve_result=True,
)
# [END how_to_cloud_gen_ai_batch_api_create_batch_job_with_inlined_requests_task]
# [START how_to_cloud_gen_ai_batch_api_create_batch_job_with_inlined_requests_deferrable_task]
create_batch_job_using_inlined_requests_deferrable = GenAIGeminiCreateBatchJobOperator(
task_id="create_batch_job_using_inlined_requests_deferrable_task",
project_id=PROJECT_ID,
location=REGION,
model="gemini-3-pro-preview",
gemini_api_key=GEMINI_XCOM_API_KEY,
create_batch_job_config={
"display_name": "deferrable-inlined-requests-batch-job",
},
input_source=INLINED_REQUESTS_FOR_BATCH_JOB,
retrieve_result=True,
deferrable=True,
)
# [END how_to_cloud_gen_ai_batch_api_create_batch_job_with_inlined_requests_deferrable_task]
# [START how_to_cloud_gen_ai_batch_api_create_batch_job_with_file_task]
create_batch_job_using_file = GenAIGeminiCreateBatchJobOperator(
task_id="create_batch_job_using_file_task",
project_id=PROJECT_ID,
location=REGION,
model="gemini-3-pro-preview",
gemini_api_key=GEMINI_XCOM_API_KEY,
create_batch_job_config={
"display_name": "file-upload-batch-job",
},
input_source=UPLOADED_FILE_NAME,
wait_until_complete=True,
retrieve_result=True,
results_folder=PATH_TO_SAVE_RESULTS,
)
# [END how_to_cloud_gen_ai_batch_api_create_batch_job_with_file_task]
# [START how_to_cloud_gen_ai_batch_api_create_embeddings_with_inlined_requests_task]
create_embeddings_job_using_inlined_requests = GenAIGeminiCreateEmbeddingsBatchJobOperator(
task_id="create_embeddings_job_using_inlined_requests_task",
project_id=PROJECT_ID,
location=REGION,
model="gemini-embedding-001",
wait_until_complete=False,
gemini_api_key=GEMINI_XCOM_API_KEY,
create_embeddings_config={
"display_name": "inlined-requests-embeddings-job",
},
input_source=INLINED_REQUESTS_FOR_EMBEDDINGS_BATCH_JOB,
)
# [END how_to_cloud_gen_ai_batch_api_create_embeddings_with_inlined_requests_task]
# [START how_to_cloud_gen_ai_batch_api_create_embeddings_with_file_task]
create_embeddings_job_using_file = GenAIGeminiCreateEmbeddingsBatchJobOperator(
task_id="create_embeddings_job_using_file_task",
project_id=PROJECT_ID,
location=REGION,
model="gemini-embedding-001",
wait_until_complete=False,
gemini_api_key=GEMINI_XCOM_API_KEY,
create_embeddings_config={
"display_name": "file-upload-embeddings-job",
},
input_source=UPLOADED_EMBEDDINGS_FILE_NAME,
)
# [END how_to_cloud_gen_ai_batch_api_create_embeddings_with_file_task]
# [START how_to_cloud_gen_ai_batch_api_create_embeddings_with_file_deferrable_task]
create_embeddings_job_using_file_deferrable = GenAIGeminiCreateEmbeddingsBatchJobOperator(
task_id="create_embeddings_job_using_file_deferrable_task",
project_id=PROJECT_ID,
location=REGION,
model="gemini-embedding-001",
retrieve_result=True,
deferrable=True,
gemini_api_key=GEMINI_XCOM_API_KEY,
create_embeddings_config={
"display_name": "deferrable-file-upload-embeddings-job",
},
input_source=UPLOADED_EMBEDDINGS_FILE_NAME,
results_folder=PATH_TO_SAVE_RESULTS,
)
# [END how_to_cloud_gen_ai_batch_api_create_embeddings_with_file_deferrable_task]
# [START how_to_cloud_gen_ai_batch_api_get_batch_job_task]
get_batch_job = GenAIGeminiGetBatchJobOperator(
task_id="get_batch_job_task",
project_id=PROJECT_ID,
location=REGION,
gemini_api_key=GEMINI_XCOM_API_KEY,
job_name=BATCH_JOB_WITH_INLINED_REQUESTS_NAME,
)
# [END how_to_cloud_gen_ai_batch_api_get_batch_job_task]
# [START how_to_cloud_gen_ai_batch_api_list_batch_jobs_task]
list_batch_jobs = GenAIGeminiListBatchJobsOperator(
task_id="list_batch_jobs_task",
project_id=PROJECT_ID,
location=REGION,
gemini_api_key=GEMINI_XCOM_API_KEY,
)
# [END how_to_cloud_gen_ai_batch_api_list_batch_jobs_task]
# [START how_to_cloud_gen_ai_batch_api_cancel_batch_job_task]
cancel_batch_job = GenAIGeminiCancelBatchJobOperator(
task_id="cancel_batch_job_task",
project_id=PROJECT_ID,
location=REGION,
gemini_api_key=GEMINI_XCOM_API_KEY,
job_name=BATCH_JOB_WITH_FILE_NAME,
)
# [END how_to_cloud_gen_ai_batch_api_cancel_batch_job_task]
# [START how_to_cloud_gen_ai_batch_api_delete_batch_job_task]
delete_batch_job_1 = GenAIGeminiDeleteBatchJobOperator(
task_id="delete_batch_job_1_task",
project_id=PROJECT_ID,
location=REGION,
gemini_api_key=GEMINI_XCOM_API_KEY,
job_name=BATCH_JOB_WITH_INLINED_REQUESTS_NAME,
trigger_rule=TriggerRule.ALL_DONE,
)
# [END how_to_cloud_gen_ai_batch_api_delete_batch_job_task]
delete_batch_job_2 = GenAIGeminiDeleteBatchJobOperator(
task_id="delete_batch_job_2_task",
project_id=PROJECT_ID,
location=REGION,
gemini_api_key=GEMINI_XCOM_API_KEY,
job_name=BATCH_JOB_WITH_FILE_NAME,
trigger_rule=TriggerRule.ALL_DONE,
)
delete_embeddings_batch_job_1 = GenAIGeminiDeleteBatchJobOperator(
task_id="delete_embeddings_batch_job_1_task",
project_id=PROJECT_ID,
location=REGION,
gemini_api_key=GEMINI_XCOM_API_KEY,
job_name=EMBEDDINGS_BATCH_JOB_WITH_INLINED_REQUESTS_NAME,
trigger_rule=TriggerRule.ALL_DONE,
)
delete_embeddings_batch_job_2 = GenAIGeminiDeleteBatchJobOperator(
task_id="delete_embeddings_batch_job_2_task",
project_id=PROJECT_ID,
location=REGION,
gemini_api_key=GEMINI_XCOM_API_KEY,
job_name=EMBEDDINGS_BATCH_JOB_WITH_FILE_NAME,
trigger_rule=TriggerRule.ALL_DONE,
)
delete_result_file = BashOperator(
task_id="delete_result_file_task",
bash_command="rm "
"{{ task_instance.xcom_pull(task_ids='create_batch_job_using_file_task', key='job_results') }}",
trigger_rule=TriggerRule.ALL_DONE,
)
(
get_gemini_api_key_task
>> [upload_file, upload_embeddings_file]
>> get_file
>> list_files
>> [
create_batch_job_using_inlined_requests,
create_batch_job_using_file,
create_embeddings_job_using_file,
create_embeddings_job_using_inlined_requests,
create_batch_job_using_inlined_requests_deferrable,
create_embeddings_job_using_file_deferrable,
]
>> get_batch_job
>> list_batch_jobs
>> cancel_batch_job
>> delete_batch_job_1
>> delete_batch_job_2
>> delete_embeddings_batch_job_1
>> delete_embeddings_batch_job_2
>> [delete_file, delete_embeddings_file, delete_result_file]
)
from tests_common.test_utils.watcher import watcher
# This test needs watcher in order to properly mark success/failure
# when "tearDown" task with trigger rule is part of the DAG
list(dag.tasks) >> watcher()
from tests_common.test_utils.system_tests import get_test_run # noqa: E402
# Needed to run the example DAG with pytest (see: contributing-docs/testing/system_tests.rst)
[docs]
test_run = get_test_run(dag)