Class AgentEngine (1.118.0) 

AgentEngine(resource_name: str)

Represents a Vertex AI Agent Engine resource.

Properties

create_time

Time this resource was created.

display_name

Display name of this resource.

encryption_spec

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

gca_resource

The underlying resource proto representation.

labels

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

name

Name of this resource.

resource_name

Fully-qualified resource name.

update_time

Time this resource was last updated.

Methods

AgentEngine

AgentEngine(resource_name: str)

Retrieves an Agent Engine resource.

Parameter
Name Description
resource_name str

Required. A fully-qualified resource name or ID such as "projects/123/locations/us-central1/reasoningEngines/456" or "456" when project and location are initialized or passed.

create

create(
    agent_engine: typing.Union[
        None,
        vertexai.agent_engines.AsyncQueryable,
        vertexai.agent_engines.AsyncStreamQueryable,
        vertexai.agent_engines._agent_engines.BidiStreamQueryable,
        vertexai.agent_engines.OperationRegistrable,
        vertexai.agent_engines.Queryable,
        vertexai.agent_engines.StreamQueryable,
    ] = None,
    *,
    requirements: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None,
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    gcs_dir_name: typing.Optional[str] = None,
    extra_packages: typing.Optional[typing.Sequence[str]] = None,
    env_vars: typing.Optional[
        typing.Union[
            typing.Sequence[str],
            typing.Dict[
                str,
                typing.Union[str, google.cloud.aiplatform_v1.types.env_var.SecretRef],
            ],
        ]
    ] = None,
    build_options: typing.Optional[typing.Dict[str, typing.Sequence[str]]] = None,
    service_account: typing.Optional[str] = None,
    psc_interface_config: typing.Optional[
        google.cloud.aiplatform_v1.types.service_networking.PscInterfaceConfig
    ] = None,
    min_instances: typing.Optional[int] = None,
    max_instances: typing.Optional[int] = None,
    resource_limits: typing.Optional[typing.Dict[str, str]] = None,
    container_concurrency: typing.Optional[int] = None,
    encryption_spec: typing.Optional[
        google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec
    ] = None
) -> vertexai.agent_engines.AgentEngine

Creates a new Agent Engine.

The Agent Engine will be an instance of the agent_engine that was passed in, running remotely on Vertex AI.

Sample src_dir contents (e.g. ./user_src_dir):

user_src_dir/
|-- main.py
|-- requirements.txt
|-- user_code/
|   |-- utils.py
|   |-- ...
|-- installation_scripts/
|   |-- install_package.sh
|   |-- ...
|-- ...

To build an Agent Engine with the above files, run:

remote_agent = agent_engines.create(
    agent_engine=local_agent,
    requirements=[
        # I.e. the PyPI dependencies listed in requirements.txt
        "google-cloud-aiplatform==1.25.0",
        "langchain==0.0.242",
        ...
    ],
    extra_packages=[
        "./user_src_dir/main.py", # a single file
        "./user_src_dir/user_code", # a directory
        ...
    ],
    build_options={
        "installation_scripts": [
            "./user_src_dir/installation_scripts/install_package.sh",
            ...
        ],
    },
)
Parameters
Name Description
agent_engine AgentEngineInterface

Optional. The Agent Engine to be created.
requirements Union[str, Sequence[str]]

Optional. The set of PyPI dependencies needed. It can either be the path to a single file (requirements.txt), or an ordered list of strings corresponding to each line of the requirements file.
display_name str

Optional. The user-defined name of the Agent Engine. The name can be up to 128 characters long and can comprise any UTF-8 character.
description str

Optional. The description of the Agent Engine.
gcs_dir_name str

Optional. The GCS bucket directory under staging_bucket to use for staging the artifacts needed.
extra_packages Sequence[str]

Optional. The set of extra user-provided packages (if any).
env_vars Union[Sequence[str], Dict[str, Union[str, SecretRef]]]

Optional. The environment variables to be set when running the Agent Engine. If it is a list of strings, each string should be a valid key to os.environ. If it is a dictionary, the keys are the environment variable names, and the values are the corresponding values.
build_options Dict[str, Sequence[str]]

Optional. The build options for the Agent Engine. The following keys are supported: - installation_scripts: Optional. The paths to the installation scripts to be executed in the Docker image. The scripts must be located in the installation_scripts subdirectory and the path must be added to extra_packages.
service_account str

Optional. The service account to be used for the Agent Engine. If not specified, the default reasoning engine service agent service account will be used.
psc_interface_config aip_types.PscInterfaceConfig

Optional. The Private Service Connect interface config for the Agent Engine.
min_instances int

Optional. The minimum number of instances to be running for the Agent Engine.
max_instances int

Optional. The maximum number of instances to be running for the Agent Engine.
resource_limits Dict[str, str]

Optional. The resource limits for the Agent Engine.
container_concurrency int

Optional. The container concurrency for the Agent Engine.
encryption_spec aip_types.EncryptionSpec

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as the model.
Exceptions
Type Description
ValueError If the project was not set using vertexai.init.
ValueError If the location was not set using vertexai.init.
ValueError If the staging_bucket was not set using vertexai.init.
ValueError If the staging_bucket does not start with "gs://".
ValueError If extra_packages is specified but agent_engine is None.
ValueError If requirements is specified but agent_engine is None.
ValueError If env_vars has a dictionary entry that does not
correspon to a SecretRef.:
ValueError If env_vars is a list which contains a string that
doe not exist in os.environ.:
TypeError If env_vars is not a list of strings or a dictionary.
TypeError If env_vars has a value that is not a string or SecretRef.
FileNotFoundError If extra_packages includes a file or directory
tha does not exist.:
IOError If requirements is a string that corresponds to a
nonexisten file.:
Returns
Type Description
AgentEngine The Agent Engine that was created.

delete

delete(*, force: bool = False, **kwargs) -> None

Deletes the ReasoningEngine.

Parameters
Name Description
force bool

Optional. If set to True, child resources will also be deleted. Otherwise, the request will fail with FAILED_PRECONDITION error when the Agent Engine has undeleted child resources. Defaults to False.
\*\*kwargs dict[str, Any]

Optional. Additional keyword arguments to pass to the delete_reasoning_engine method.

list

list(
    filter: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    parent: typing.Optional[str] = None,
) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]

List all instances of this Vertex AI Resource.

Example Usage:

aiplatform.BatchPredictionJobs.list( filter='state="JOB_STATE_SUCCEEDED" AND display_name="my_job"', )

aiplatform.Model.list(order_by="create_time desc, display_name")

Parameters
Name Description
filter str

Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.
order_by str

Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: display_name, create_time, update_time
project str

Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used.
location str

Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used.
credentials auth_credentials.Credentials

Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init.
parent str

Optional. The parent resource name if any to retrieve list from.

operation_schemas

operation_schemas() -> typing.Sequence[typing.Dict[str, typing.Any]]

Returns the (Open)API schemas for the Agent Engine.

to_dict

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

update

update(
    *,
    agent_engine: typing.Union[
        None,
        vertexai.agent_engines.AsyncQueryable,
        vertexai.agent_engines.AsyncStreamQueryable,
        vertexai.agent_engines._agent_engines.BidiStreamQueryable,
        vertexai.agent_engines.OperationRegistrable,
        vertexai.agent_engines.Queryable,
        vertexai.agent_engines.StreamQueryable,
    ] = None,
    requirements: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None,
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    gcs_dir_name: typing.Optional[str] = None,
    extra_packages: typing.Optional[typing.Sequence[str]] = None,
    env_vars: typing.Optional[
        typing.Union[
            typing.Sequence[str],
            typing.Dict[
                str,
                typing.Union[str, google.cloud.aiplatform_v1.types.env_var.SecretRef],
            ],
        ]
    ] = None,
    build_options: typing.Optional[typing.Dict[str, typing.Sequence[str]]] = None,
    service_account: typing.Optional[str] = None,
    psc_interface_config: typing.Optional[
        google.cloud.aiplatform_v1.types.service_networking.PscInterfaceConfig
    ] = None,
    min_instances: typing.Optional[int] = None,
    max_instances: typing.Optional[int] = None,
    resource_limits: typing.Optional[typing.Dict[str, str]] = None,
    container_concurrency: typing.Optional[int] = None,
    encryption_spec: typing.Optional[
        google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec
    ] = None
) -> vertexai.agent_engines.AgentEngine

Updates an existing Agent Engine.

This method updates the configuration of an existing Agent Engine running remotely, which is identified by its resource name. Unlike the create function which requires a agent_engine object, all arguments in this method are optional. This method allows you to modify individual aspects of the configuration by providing any of the optional arguments.

Parameters
Name Description
agent_engine AgentEngineInterface

Optional. The instance to be used as the updated Agent Engine. If it is not specified, the existing instance will be used.
requirements Union[str, Sequence[str]]

Optional. The set of PyPI dependencies needed. It can either be the path to a single file (requirements.txt), or an ordered list of strings corresponding to each line of the requirements file. If it is not specified, the existing requirements will be used. If it is set to an empty string or list, the existing requirements will be removed.
display_name str

Optional. The user-defined name of the Agent Engine. The name can be up to 128 characters long and can comprise any UTF-8 character.
description str

Optional. The description of the Agent Engine.
gcs_dir_name str

Optional. The GCS bucket directory under staging_bucket to use for staging the artifacts needed.
extra_packages Sequence[str]

Optional. The set of extra user-provided packages (if any). If it is not specified, the existing extra packages will be used. If it is set to an empty list, the existing extra packages will be removed.
env_vars Union[Sequence[str], Dict[str, Union[str, SecretRef]]]

Optional. The environment variables to be set when running the Agent Engine. If it is a list of strings, each string should be a valid key to os.environ. If it is a dictionary, the keys are the environment variable names, and the values are the corresponding values.
build_options Dict[str, Sequence[str]]

Optional. The build options for the Agent Engine. The following keys are supported: - installation_scripts: Optional. The paths to the installation scripts to be executed in the Docker image. The scripts must be located in the installation_scripts subdirectory and the path must be added to extra_packages.
service_account str

Optional. The service account to be used for the Agent Engine. If not specified, the default reasoning engine service agent service account will be used.
psc_interface_config aip_types.PscInterfaceConfig

Optional. The Private Service Connect interface config for the Agent Engine.
min_instances int

Optional. The minimum number of instances to be running for the Agent Engine.
max_instances int

Optional. The maximum number of instances to be running for the Agent Engine.
resource_limits Dict[str, str]

Optional. The resource limits for the Agent Engine.
container_concurrency int

Optional. The container concurrency for the Agent Engine.
encryption_spec aip_types.EncryptionSpec

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as the model.
Exceptions
Type Description
ValueError If the staging_bucket was not set using vertexai.init.
ValueError If the staging_bucket does not start with "gs://".
ValueError If env_vars has a dictionary entry that does not
correspon to a SecretRef.:
ValueError If env_vars is a list which contains a string that
doe not exist in os.environ.:
TypeError If env_vars is not a list of strings or a dictionary.
TypeError If env_vars has a value that is not a string or SecretRef.
FileNotFoundError If extra_packages includes a file or directory
tha does not exist.:
ValueError if none of display_name, description, requirements,
extra_packages
IOError If requirements is a string that corresponds to a
nonexisten file.:
Returns
Type Description
AgentEngine The Agent Engine that was updated.

wait

wait()

Helper method that blocks until all futures are complete.