向数据存储区添加网站

向数据存储区添加网站

深入探索

如需查看包含此代码示例的详细文档，请参阅以下内容：

代码示例

Python

如需了解详情，请参阅 Vertex AI Agent Builder Python API 参考文档。

如需向 Vertex AI Agent Builder 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 

from google.api_core.client_options import ClientOptions

from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_LOCATION" # Values: "global"
# data_store_id = "YOUR_DATA_STORE_ID"
# NOTE: Do not include http or https protocol in the URI pattern
# uri_pattern = "cloud.google.com/generative-ai-app-builder/docs/*"

#  For more information, refer to:
# https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
client_options = (
    ClientOptions(api_endpoint=f"{location}-discoveryengine.googleapis.com")
    if location != "global"
    else None
)

# Create a client
client = discoveryengine.SiteSearchEngineServiceClient(
    client_options=client_options
)

# The full resource name of the data store
# e.g. projects/{project}/locations/{location}/dataStores/{data_store_id}
site_search_engine = client.site_search_engine_path(
    project=project_id, location=location, data_store=data_store_id
)

# Target Site to index
target_site = discoveryengine.TargetSite(
    provided_uri_pattern=uri_pattern,
    # Options: INCLUDE, EXCLUDE
    type_=discoveryengine.TargetSite.Type.INCLUDE,
    exact_match=False,
)

# Make the request
operation = client.create_target_site(
    parent=site_search_engine,
    target_site=target_site,
)

print(f"Waiting for operation to complete: {operation.operation.name}")
response = operation.result()

# After the operation is complete,
# get information from operation metadata
metadata = discoveryengine.CreateTargetSiteMetadata(operation.metadata)

# Handle the response
print(response)
print(metadata)

后续步骤

如需搜索和过滤其他 Google Cloud 产品的代码示例，请参阅Google Cloud 示例浏览器。