部署模式是项目级配置。在这两种模式之间切换不会移动或删除另一模式中的数据。您可以使用 UpdateRagEngineConfig API 在无服务器部署模式和 Spanner 部署模式之间切换。您还可以使用此 API 设置 Spanner 部署模式的层级，或取消预配 Spanner 模式以停止结算。您可以使用 GetRagEngineConfig API 读取当前部署模式信息。
切换到无服务器模式
以下代码示例演示了如何将 RagEngineConfig 切换到无服务器模式：
控制台
- 在 Google Cloud 控制台中，前往 RAG 引擎页面。
- 选择要在其中运行 RAG Engine 的区域。
- 点击切换到无服务器模式选项。如果您处于无服务器模式，则可能看不到此选项。您可以从页面右上角的模式标签中验证当前模式。
REST
PROJECT_ID: Your project ID.
LOCATION: The region to process the request.
curl -X PATCH \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/ragEngineConfig -d "{'ragManagedDbConfig': {'serverless': {}}}"
Python
from vertexai.preview import rag
import vertexai
PROJECT_ID = YOUR_PROJECT_ID
LOCATION = YOUR_RAG_ENGINE_LOCATION
# Initialize Agent Platform API once per session
vertexai.init(project=PROJECT_ID, location=LOCATION)
rag_engine_config_name=f"projects/{PROJECT_ID}/locations/{LOCATION}/ragEngineConfig"
new_rag_engine_config = rag.RagEngineConfig(
name=rag_engine_config_name,
rag_managed_db_config=rag.RagManagedDbConfig(mode=rag.Serverless()),
)
updated_rag_engine_config = rag.rag_data.update_rag_engine_config(
rag_engine_config=new_rag_engine_config
)
print(updated_rag_engine_config)
切换到 Spanner 模式
以下代码示例演示了如何将 RagEngineConfig 切换到 Spanner 模式。如果您之前使用过 Spanner 模式并已选择层级，则在切换时无需明确提供层级。如果不是，请参阅下面的代码示例，了解如何在提供层级的同时切换到 Spanner 模式。
控制台
- 在 Google Cloud 控制台中，前往 RAG 引擎页面。
- 选择要在其中运行 RAG Engine 的区域。
- 点击切换到 Spanner 选项。如果您处于 Spanner 模式，则可能看不到该按钮。您可以通过模式标签验证当前模式。
REST
PROJECT_ID: Your project ID.
LOCATION: The region to process the request.
curl -X PATCH \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/ragEngineConfig -d "{'ragManagedDbConfig': {'spanner': {}}}"
Python
from vertexai.preview import rag
import vertexai
PROJECT_ID = YOUR_PROJECT_ID
LOCATION = YOUR_RAG_ENGINE_LOCATION
# Initialize Agent Platform API once per session
vertexai.init(project=PROJECT_ID, location=LOCATION)
rag_engine_config_name=f"projects/{PROJECT_ID}/locations/{LOCATION}/ragEngineConfig"
new_rag_engine_config = rag.RagEngineConfig(
name=rag_engine_config_name,
rag_managed_db_config=rag.RagManagedDbConfig(mode=rag.Spanner()),
)
updated_rag_engine_config = rag.rag_data.update_rag_engine_config(
rag_engine_config=new_rag_engine_config
)
print(updated_rag_engine_config)
读取当前的 RagEngineConfig
以下代码示例演示了如何读取 RagEngineConfig 以查看所选的模式和层级：
REST
PROJECT_ID: Your project ID.
LOCATION: The region to process the request.
curl -X GET \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/ragEngineConfig
Python
from vertexai.preview import rag
import vertexai
PROJECT_ID = YOUR_PROJECT_ID
LOCATION = YOUR_RAG_ENGINE_LOCATION
# Initialize Agent Platform API once per session
vertexai.init(project=PROJECT_ID, location=LOCATION)
rag_engine_config = rag.rag_data.get_rag_engine_config(
name=f"projects/{PROJECT_ID}/locations/{LOCATION}/ragEngineConfig"
)
print(rag_engine_config)
更新 Spanner 模式下的层级
以下代码示例演示了如何在 Spanner 模式下更新层级：
将 RagEngineConfig 更新为 Spanner 模式的扩缩层级
以下代码示例演示了如何将 RagEngineConfig 设置为 Spanner 模式（采用扩缩层级）：
控制台
- 在 Google Cloud 控制台中，前往 RAG 引擎页面。
- 选择要在其中运行 RAG Engine 的区域。
- 如果尚未处于 Spanner 模式，请点击切换到 Spanner 选项。
- 点击配置 RAG Engine。系统会显示配置 RAG Engine 窗格。
- 选择要运行 RAG Engine 的层级。
- 点击保存。
REST
PROJECT_ID: Your project ID.
LOCATION: The region to process the request.
curl -X PATCH \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/ragEngineConfig -d "{'ragManagedDbConfig': {'spanner': {'scaled': {}}}}"
Python
from vertexai.preview import rag
import vertexai
PROJECT_ID = YOUR_PROJECT_ID
LOCATION = YOUR_RAG_ENGINE_LOCATION
# Initialize Agent Platform API once per session
vertexai.init(project=PROJECT_ID, location=LOCATION)
rag_engine_config_name=f"projects/{PROJECT_ID}/locations/{LOCATION}/ragEngineConfig"
new_rag_engine_config = rag.RagEngineConfig(
name=rag_engine_config_name,
rag_managed_db_config=rag.RagManagedDbConfig(mode=rag.Spanner(tier=rag.Scaled())),
)
updated_rag_engine_config = rag.rag_data.update_rag_engine_config(
rag_engine_config=new_rag_engine_config
)
print(updated_rag_engine_config)
将 RagEngineConfig 更新为 Spanner 模式（基本层级）
以下代码示例演示了如何将 RagEngineConfig 设置为采用基本层级的 Spanner 模式：
控制台
- 在 Google Cloud 控制台中，前往 RAG 引擎页面。
- 选择要在其中运行 RAG Engine 的区域。
- 如果尚未处于 Spanner 模式，请点击切换到 Spanner 选项。
- 点击配置 RAG Engine。系统会显示配置 RAG Engine 窗格。
- 选择要运行 RAG Engine 的层级。
- 点击保存。
REST
PROJECT_ID: Your project ID.
LOCATION: The region to process the request.
curl -X PATCH \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/ragEngineConfig -d "{'ragManagedDbConfig': {'spanner': {'basic': {}}}}"
Python
from vertexai.preview import rag
import vertexai
PROJECT_ID = YOUR_PROJECT_ID
LOCATION = YOUR_RAG_ENGINE_LOCATION
# Initialize Agent Platform API once per session
vertexai.init(project=PROJECT_ID, location=LOCATION)
rag_engine_config_name=f"projects/{PROJECT_ID}/locations/{LOCATION}/ragEngineConfig"
new_rag_engine_config = rag.RagEngineConfig(
name=rag_engine_config_name,
rag_managed_db_config=rag.RagManagedDbConfig(mode=rag.Spanner(tier=rag.Basic())),
)
updated_rag_engine_config = rag.rag_data.update_rag_engine_config(
rag_engine_config=new_rag_engine_config
)
print(updated_rag_engine_config)
将 RagEngineConfig 更新为未预配层级
以下代码示例演示了如何将 RagEngineConfig 设置为采用未预配层级的 Spanner 模式。此操作将永久删除 Spanner 部署模式中的所有数据，并停止由此产生的结算费用。
控制台
- 在 Google Cloud 控制台中，前往 RAG 引擎页面。
- 选择要在其中运行 RAG Engine 的区域。
- 如果尚未处于 Spanner 模式，请点击切换到 Spanner 选项。
- 点击删除 RAG Engine。此时会显示一个确认对话框。
- 输入“delete”，确认您即将删除 RAG Engine 中的数据。
- 点击确认。
- 点击保存。
REST
PROJECT_ID: Your project ID.
LOCATION: The region to process the request.
curl -X PATCH \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/ragEngineConfig -d "{'ragManagedDbConfig': {'spanner': {'unprovisioned': {}}}}"
Python
from vertexai.preview import rag
import vertexai
PROJECT_ID = YOUR_PROJECT_ID
LOCATION = YOUR_RAG_ENGINE_LOCATION
# Initialize Agent Platform API once per session
vertexai.init(project=PROJECT_ID, location=LOCATION)
rag_engine_config_name=f"projects/{PROJECT_ID}/locations/{LOCATION}/ragEngineConfig"
new_rag_engine_config = rag.RagEngineConfig(
name=rag_engine_config_name,
rag_managed_db_config=rag.RagManagedDbConfig(mode=rag.Spanner(tier=rag.Unprovisioned())),
)
updated_rag_engine_config = rag.rag_data.update_rag_engine_config(
rag_engine_config=new_rag_engine_config
)
print(updated_rag_engine_config)