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docs: Remove Grounded Generation Samples (#13244)
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‎discoveryengine/standalone_apis_sample.py‎

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@@ -123,183 +123,3 @@ def rank_sample(
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# [END genappbuilder_rank]
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return response
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def grounded_generation_inline_vais_sample(
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project_number: str,
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engine_id: str,
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) -> discoveryengine.GenerateGroundedContentResponse:
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# [START genappbuilder_grounded_generation_inline_vais]
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from google.cloud import discoveryengine_v1 as discoveryengine
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# TODO(developer): Uncomment these variables before running the sample.
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# project_number = "YOUR_PROJECT_NUMBER"
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# engine_id = "YOUR_ENGINE_ID"
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client = discoveryengine.GroundedGenerationServiceClient()
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request = discoveryengine.GenerateGroundedContentRequest(
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# The full resource name of the location.
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# Format: projects/{project_number}/locations/{location}
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location=client.common_location_path(project=project_number, location="global"),
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generation_spec=discoveryengine.GenerateGroundedContentRequest.GenerationSpec(
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model_id="gemini-1.5-flash",
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),
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# Conversation between user and model
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contents=[
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discoveryengine.GroundedGenerationContent(
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role="user",
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parts=[
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discoveryengine.GroundedGenerationContent.Part(
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text="How did Google do in 2020? Where can I find BigQuery docs?"
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)
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],
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)
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],
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system_instruction=discoveryengine.GroundedGenerationContent(
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parts=[
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discoveryengine.GroundedGenerationContent.Part(
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text="Add a smiley emoji after the answer."
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)
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],
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),
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# What to ground on.
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grounding_spec=discoveryengine.GenerateGroundedContentRequest.GroundingSpec(
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grounding_sources=[
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discoveryengine.GenerateGroundedContentRequest.GroundingSource(
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inline_source=discoveryengine.GenerateGroundedContentRequest.GroundingSource.InlineSource(
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grounding_facts=[
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discoveryengine.GroundingFact(
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fact_text=(
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"The BigQuery documentation can be found at https://cloud.google.com/bigquery/docs/introduction"
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),
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attributes={
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"title": "BigQuery Overview",
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"uri": "https://cloud.google.com/bigquery/docs/introduction",
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},
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),
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]
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),
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),
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discoveryengine.GenerateGroundedContentRequest.GroundingSource(
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search_source=discoveryengine.GenerateGroundedContentRequest.GroundingSource.SearchSource(
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# The full resource name of the serving config for a Vertex AI Search App
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serving_config=f"projects/{project_number}/locations/global/collections/default_collection/engines/{engine_id}/servingConfigs/default_search",
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),
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),
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]
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),
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)
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response = client.generate_grounded_content(request)
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# Handle the response
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print(response)
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# [END genappbuilder_grounded_generation_inline_vais]
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return response
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def grounded_generation_google_search_sample(
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project_number: str,
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) -> discoveryengine.GenerateGroundedContentResponse:
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# [START genappbuilder_grounded_generation_google_search]
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from google.cloud import discoveryengine_v1 as discoveryengine
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# TODO(developer): Uncomment these variables before running the sample.
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# project_number = "YOUR_PROJECT_NUMBER"
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client = discoveryengine.GroundedGenerationServiceClient()
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request = discoveryengine.GenerateGroundedContentRequest(
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# The full resource name of the location.
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# Format: projects/{project_number}/locations/{location}
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location=client.common_location_path(project=project_number, location="global"),
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generation_spec=discoveryengine.GenerateGroundedContentRequest.GenerationSpec(
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model_id="gemini-1.5-flash",
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),
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# Conversation between user and model
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contents=[
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discoveryengine.GroundedGenerationContent(
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role="user",
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parts=[
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discoveryengine.GroundedGenerationContent.Part(
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text="How much is Google stock?"
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)
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],
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)
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],
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system_instruction=discoveryengine.GroundedGenerationContent(
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parts=[
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discoveryengine.GroundedGenerationContent.Part(text="Be comprehensive.")
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],
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),
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# What to ground on.
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grounding_spec=discoveryengine.GenerateGroundedContentRequest.GroundingSpec(
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grounding_sources=[
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discoveryengine.GenerateGroundedContentRequest.GroundingSource(
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google_search_source=discoveryengine.GenerateGroundedContentRequest.GroundingSource.GoogleSearchSource(
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# Optional: For Dynamic Retrieval
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dynamic_retrieval_config=discoveryengine.GenerateGroundedContentRequest.DynamicRetrievalConfiguration(
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predictor=discoveryengine.GenerateGroundedContentRequest.DynamicRetrievalConfiguration.DynamicRetrievalPredictor(
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threshold=0.7
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)
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)
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)
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),
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]
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),
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)
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response = client.generate_grounded_content(request)
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# Handle the response
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print(response)
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# [END genappbuilder_grounded_generation_google_search]
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return response
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def grounded_generation_streaming_sample(
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project_number: str,
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) -> discoveryengine.GenerateGroundedContentResponse:
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# [START genappbuilder_grounded_generation_streaming]
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from google.cloud import discoveryengine_v1 as discoveryengine
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# TODO(developer): Uncomment these variables before running the sample.
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# project_id = "YOUR_PROJECT_ID"
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client = discoveryengine.GroundedGenerationServiceClient()
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request = discoveryengine.GenerateGroundedContentRequest(
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# The full resource name of the location.
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# Format: projects/{project_number}/locations/{location}
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location=client.common_location_path(project=project_number, location="global"),
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generation_spec=discoveryengine.GenerateGroundedContentRequest.GenerationSpec(
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model_id="gemini-1.5-flash",
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),
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# Conversation between user and model
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contents=[
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discoveryengine.GroundedGenerationContent(
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role="user",
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parts=[
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discoveryengine.GroundedGenerationContent.Part(
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text="Summarize how to delete a data store in Vertex AI Agent Builder?"
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)
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],
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)
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],
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grounding_spec=discoveryengine.GenerateGroundedContentRequest.GroundingSpec(
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grounding_sources=[
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discoveryengine.GenerateGroundedContentRequest.GroundingSource(
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google_search_source=discoveryengine.GenerateGroundedContentRequest.GroundingSource.GoogleSearchSource()
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),
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]
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),
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)
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responses = client.stream_generate_grounded_content(iter([request]))
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for response in responses:
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# Handle the response
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print(response)
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# [END genappbuilder_grounded_generation_streaming]
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return response

‎discoveryengine/standalone_apis_sample_test.py‎

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Original file line numberDiff line numberDiff line change
@@ -17,8 +17,6 @@
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from discoveryengine import standalone_apis_sample
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from google.cloud import resourcemanager_v3
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project_id = os.environ["GOOGLE_CLOUD_PROJECT"]
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@@ -34,27 +32,3 @@ def test_rank():
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response = standalone_apis_sample.rank_sample(project_id)
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assert response
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assert response.records
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def test_grounded_generation_inline_vais_sample():
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# Grounded Generation requires Project Number
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client = resourcemanager_v3.ProjectsClient()
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project = client.get_project(name=client.project_path(project_id))
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project_number = client.parse_project_path(project.name)["project"]
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response = standalone_apis_sample.grounded_generation_inline_vais_sample(
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project_number, engine_id="test-search-engine_1689960780551"
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)
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assert response
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def test_grounded_generation_google_search_sample():
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# Grounded Generation requires Project Number
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client = resourcemanager_v3.ProjectsClient()
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project = client.get_project(name=client.project_path(project_id))
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project_number = client.parse_project_path(project.name)["project"]
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response = standalone_apis_sample.grounded_generation_google_search_sample(
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project_number
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)
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assert response

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