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docs(genai):Add anthropic batch predict samples (#13228) · BrandonY/python-docs-samples@8eeae80 · GitHub

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docs(genai):Add anthropic batch predict samples (GoogleCloudPlatform#13228)
* docs(genai):Add anthropic batch predict samples * updating module names in testing file * updating import statements * updating file names * moving model_garden folder and updating code owners * update region tags * changing folder structure * removing delete files * updating lib
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‎.github/CODEOWNERS‎

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/iam/cloud-client/**/* @GoogleCloudPlatform/dee-infra @GoogleCloudPlatform/python-samples-reviewers @GoogleCloudPlatform/cloud-samples-reviewers
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/kms/**/** @GoogleCloudPlatform/dee-infra @GoogleCloudPlatform/python-samples-reviewers @GoogleCloudPlatform/cloud-samples-reviewers
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/media_cdn/**/* @GoogleCloudPlatform/dee-infra @GoogleCloudPlatform/python-samples-reviewers @GoogleCloudPlatform/cloud-samples-reviewers
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/model_garden/**/* @GoogleCloudPlatform/generative-ai-devrel @GoogleCloudPlatform/python-samples-reviewers @GoogleCloudPlatform/cloud-samples-reviewers
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/parametermanager/**/* @GoogleCloudPlatform/dee-infra @GoogleCloudPlatform/python-samples-reviewers @GoogleCloudPlatform/cloud-samples-reviewers @GoogleCloudPlatform/cloud-secrets-team @GoogleCloudPlatform/cloud-parameters-team
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/privateca/**/* @GoogleCloudPlatform/dee-infra @GoogleCloudPlatform/python-samples-reviewers @GoogleCloudPlatform/cloud-samples-reviewers
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/recaptcha_enterprise/**/* @GoogleCloudPlatform/dee-infra @GoogleCloudPlatform/recaptcha-customer-obsession-reviewers @GoogleCloudPlatform/python-samples-reviewers @GoogleCloudPlatform/cloud-samples-reviewers

‎model_garden/anthropic/anthropic_batchpredict_with_bq.py‎

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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def generate_content(output_uri: str) -> str:
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# [START aiplatform_anthropic_batchpredict_with_bq]
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import time
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from google import genai
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from google.genai.types import CreateBatchJobConfig, JobState, HttpOptions
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client = genai.Client(http_options=HttpOptions(api_version="v1"))
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# TODO(developer): Update and un-comment below line
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# output_uri = f"bq://your-project.your_dataset.your_table"
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job = client.batches.create(
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# anthropic BQ predicitions only work in us-east5
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# More about Anthropic model: https://console.cloud.google.com/vertex-ai/publishers/anthropic/model-garden/claude-3-5-haiku
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model="publishers/anthropic/models/claude-3-5-haiku",
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# The source dataset needs to be created specifically in us-east5
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src="bq://python-docs-samples-tests.anthropic_bq_sample.test_data",
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config=CreateBatchJobConfig(dest=output_uri),
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)
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print(f"Job name: {job.name}")
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print(f"Job state: {job.state}")
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# Example response:
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# Job name: projects/%PROJECT_ID%/locations/us-central1/batchPredictionJobs/9876453210000000000
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# Job state: JOB_STATE_PENDING
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# See the documentation: https://googleapis.github.io/python-genai/genai.html#genai.types.BatchJob
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completed_states = {
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JobState.JOB_STATE_SUCCEEDED,
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JobState.JOB_STATE_FAILED,
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JobState.JOB_STATE_CANCELLED,
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JobState.JOB_STATE_PAUSED,
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}
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while job.state not in completed_states:
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time.sleep(30)
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job = client.batches.get(name=job.name)
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print(f"Job state: {job.state}")
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# Example response:
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# Job state: JOB_STATE_PENDING
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# Job state: JOB_STATE_RUNNING
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# Job state: JOB_STATE_RUNNING
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# ...
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# Job state: JOB_STATE_SUCCEEDED
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# [END aiplatform_anthropic_batchpredict_with_bq]
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return job.state
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if __name__ == "__main__":
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# The dataset of the output uri needs to be created specifically in us-east5
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generate_content(output_uri="bq://your-project.your_dataset.your_table")

‎model_garden/anthropic/anthropic_batchpredict_with_gcs.py‎

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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def generate_content(output_uri: str) -> str:
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# [START aiplatform_anthropic_batchpredict_with_gcs]
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import time
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from google import genai
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from google.genai.types import CreateBatchJobConfig, JobState, HttpOptions
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client = genai.Client(http_options=HttpOptions(api_version="v1"))
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# TODO(developer): Update and un-comment below line
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# output_uri = "gs://your-bucket/your-prefix"
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# See the documentation: https://googleapis.github.io/python-genai/genai.html#genai.batches.Batches.create
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job = client.batches.create(
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# More about Anthropic model: https://console.cloud.google.com/vertex-ai/publishers/anthropic/model-garden/claude-3-5-haiku
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model="publishers/anthropic/models/claude-3-5-haiku",
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# Source link: https://storage.cloud.google.com/cloud-samples-data/batch/anthropic-test-data-gcs.jsonl
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src="gs://cloud-samples-data/anthropic-test-data-gcs.jsonl",
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config=CreateBatchJobConfig(dest=output_uri),
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)
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print(f"Job name: {job.name}")
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print(f"Job state: {job.state}")
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# Example response:
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# Job name: projects/%PROJECT_ID%/locations/us-central1/batchPredictionJobs/9876453210000000000
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# Job state: JOB_STATE_PENDING
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# See the documentation: https://googleapis.github.io/python-genai/genai.html#genai.types.BatchJob
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completed_states = {
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JobState.JOB_STATE_SUCCEEDED,
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JobState.JOB_STATE_FAILED,
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JobState.JOB_STATE_CANCELLED,
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JobState.JOB_STATE_PAUSED,
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}
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while job.state not in completed_states:
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time.sleep(30)
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job = client.batches.get(name=job.name)
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print(f"Job state: {job.state}")
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# Example response:
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# Job state: JOB_STATE_PENDING
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# Job state: JOB_STATE_RUNNING
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# Job state: JOB_STATE_RUNNING
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# ...
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# Job state: JOB_STATE_SUCCEEDED
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# [END aiplatform_anthropic_batchpredict_with_gcs]
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return job.state
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if __name__ == "__main__":
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generate_content(output_uri="gs://your-bucket/your-prefix")

‎model_garden/anthropic/noxfile_config.py‎

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# Copyright 2021 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Default TEST_CONFIG_OVERRIDE for python repos.
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# You can copy this file into your directory, then it will be imported from
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# the noxfile.py.
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# The source of truth:
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# https://github.com/GoogleCloudPlatform/python-docs-samples/blob/main/noxfile_config.py
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TEST_CONFIG_OVERRIDE = {
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# You can opt out from the test for specific Python versions.
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"ignored_versions": ["2.7", "3.7", "3.8", "3.10", "3.11", "3.12"],
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# Old samples are opted out of enforcing Python type hints
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# All new samples should feature them
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"enforce_type_hints": True,
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# An envvar key for determining the project id to use. Change it
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# to 'BUILD_SPECIFIC_GCLOUD_PROJECT' if you want to opt in using a
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# build specific Cloud project. You can also use your own string
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# to use your own Cloud project.
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"gcloud_project_env": "GOOGLE_CLOUD_PROJECT",
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# 'gcloud_project_env': 'BUILD_SPECIFIC_GCLOUD_PROJECT',
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# If you need to use a specific version of pip,
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# change pip_version_override to the string representation
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# of the version number, for example, "20.2.4"
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"pip_version_override": None,
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# A dictionary you want to inject into your test. Don't put any
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# secrets here. These values will override predefined values.
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"envs": {},
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}

‎model_garden/anthropic/requirements-test.txt‎

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google-api-core==2.24.0
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google-cloud-bigquery==3.29.0
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google-cloud-storage==2.19.0
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pytest==8.2.0

‎model_garden/anthropic/requirements.txt‎

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google-genai==1.5.0

‎model_garden/anthropic/test_model_garden_batch_prediction_examples.py‎

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Using Google Cloud Vertex AI to test the code samples.
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#
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from datetime import datetime as dt
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import os
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from google.cloud import bigquery, storage
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from google.genai.types import JobState
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import pytest
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import anthropic_batchpredict_with_bq
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import anthropic_batchpredict_with_gcs
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os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "True"
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os.environ["GOOGLE_CLOUD_LOCATION"] = "us-east5"
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# The project name is included in the CICD pipeline
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# os.environ['GOOGLE_CLOUD_PROJECT'] = "add-your-project-name"
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BQ_OUTPUT_DATASET = f"{os.environ['GOOGLE_CLOUD_PROJECT']}.anthropic_bq_sample"
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GCS_OUTPUT_BUCKET = "python-docs-samples-tests"
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@pytest.fixture(scope="session")
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def bq_output_uri() -> str:
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table_name = f"text_output_{dt.now().strftime('%Y_%m_%d_T%H_%M_%S')}"
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table_uri = f"{BQ_OUTPUT_DATASET}.{table_name}"
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yield f"bq://{table_uri}"
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bq_client = bigquery.Client()
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bq_client.delete_table(table_uri, not_found_ok=True)
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@pytest.fixture(scope="session")
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def gcs_output_uri() -> str:
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prefix = f"text_output/{dt.now()}"
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yield f"gs://{GCS_OUTPUT_BUCKET}/{prefix}"
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storage_client = storage.Client()
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bucket = storage_client.get_bucket(GCS_OUTPUT_BUCKET)
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blobs = bucket.list_blobs(prefix=prefix)
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for blob in blobs:
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blob.delete()
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def test_batch_prediction_with_bq(bq_output_uri: str) -> None:
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response = anthropic_batchpredict_with_bq.generate_content(output_uri=bq_output_uri)
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assert response == JobState.JOB_STATE_SUCCEEDED
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def test_batch_prediction_with_gcs(gcs_output_uri: str) -> None:
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response = anthropic_batchpredict_with_gcs.generate_content(output_uri=gcs_output_uri)
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assert response == JobState.JOB_STATE_SUCCEEDED

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