google.cloud.gcp_vertexai_reasoning_engine module – Creates a GCP VertexAI.ReasoningEngine resource

Note

This module is part of the google.cloud collection (version 1.12.0).

You might already have this collection installed if you are using the ansible package. It is not included in ansible-core. To check whether it is installed, run ansible-galaxy collection list.

To install it, use: ansible-galaxy collection install google.cloud. You need further requirements to be able to use this module, see Requirements for details.

To use it in a playbook, specify: google.cloud.gcp_vertexai_reasoning_engine.

Synopsis 

ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order.

Requirements 

The below requirements are needed on the host that executes this module.

python >= 3.8
requests >= 2.18.4
google-auth >= 2.25.1

Parameters 

Parameter	Comments
access_token string	The access token used to authenticate.
auth_kind string / required	The type of credential used. Choices: `"accesstoken"` `"application"` `"machineaccount"` `"serviceaccount"`
description string	The description of the ReasoningEngine.
display_name string / required	The display name of the ReasoningEngine.
encryption_spec dictionary	Customer-managed encryption key spec for a ReasoningEngine. If set, this ReasoningEngine and all sub-resources of this ReasoningEngine will be secured by this key.
kms_key_name string / required	The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. 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 where the compute resource is created.
env_type string	Specifies which Ansible environment you’re running this module within. This should not be set unless you know what you’re doing. This only alters the User Agent string for any API requests.
project string	The Google Cloud Platform project to use.
region string	The region of the reasoning engine. eg us-central1.
scopes list / elements=string	Array of scopes to be used.
service_account_contents jsonarg	The contents of a Service Account JSON file, either in a dictionary or as a JSON string that represents it.
service_account_email string	An optional service account email address if machineaccount is selected and the user does not wish to use the default email.
service_account_file path	The path of a Service Account JSON file if serviceaccount is selected as type.
spec dictionary	Configurations of the ReasoningEngine.
agent_framework string	The OSS agent framework used to develop the agent.
class_methods string	Declarations for object class methods in OpenAPI specification format.
deployment_spec dictionary	The specification of a Reasoning Engine deployment.
container_concurrency integer	Concurrency for each container and agent server. Recommended value: 2 * cpu + 1. Defaults to 9.
env list / elements=dictionary	Environment variables to be set with the Reasoning Engine deployment.
name string / required	The name of the environment variable. Must be a valid C identifier.
value string / required	Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
max_instances integer	The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. Range: [1, 1000]. If VPC-SC or PSC-I is enabled, the acceptable range is [1, 100].
min_instances integer	The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
psc_interface_config dictionary	Configuration for PSC-Interface.
dns_peering_configs list / elements=dictionary	DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network’s Cloud DNS. The user must grant the dns.peer role to the Vertex AI service Agent on the target project.
domain string / required	The DNS name suffix of the zone being peered to, e.g., “my-internal-domain.corp.”. Must end with a dot.
target_network string / required	The VPC network name in the targetProject where the DNS zone specified by ‘domain’ is visible.
target_project string / required	The project id hosting the Cloud DNS managed zone that contains the ‘domain’. The Vertex AI service Agent requires the dns.peer role on this project.
network_attachment string	The name of the Compute Engine network attachment to attach to the resource within the region and user project. To specify this field, you must have already created a network attachment. This field is only used for resources using PSC-Interface.
resource_limits dictionary	Resource limits for each container. Only ‘cpu’ and ‘memory’ keys are supported. Defaults to {“cpu”: “4”, “memory”: “4Gi”}. The only supported values for CPU are ‘1’, ‘2’, ‘4’, ‘6’ and ‘8’. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. The only supported values for memory are ‘1Gi’, ‘2Gi’, .. ‘32 Gi’. For more information, go to https://cloud.google.com/run/docs/configuring/memory-limits.
secret_env list / elements=dictionary	Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add ‘Secret Manager Secret Accessor’ role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine service Agent.
name string / required	The name of the environment variable. Must be a valid C identifier.
secret_ref dictionary / required	Reference to a secret stored in the Cloud Secret Manager that will provide the value for this environment variable.
secret string / required	The name of the secret in Cloud Secret Manager. Format: {secret_name}.
version string	The Cloud Secret Manager secret version. Can be ‘latest’ for the latest version, an integer for a specific version, or a version alias.
package_spec dictionary	User provided package spec of the ReasoningEngine. Ignored when users directly specify a deployment image through deploymentSpec.first_party_image_override, but keeping the field_behavior to avoid introducing breaking changes.
dependency_files_gcs_uri string	The Cloud Storage URI of the dependency files in tar.gz format.
pickle_object_gcs_uri string	The Cloud Storage URI of the pickled python object.
python_version string	The Python version. Currently support 3.8, 3.9, 3.10, 3.11, 3.12, 3.13. If not specified, default value is 3.10.
requirements_gcs_uri string	The Cloud Storage URI of the requirements.txtfile.
service_account string	The service account that the Reasoning Engine artifact runs as. It should have “roles/storage.objectViewer” for reading the user project’s Cloud Storage and “roles/aiplatform.user” for using Vertex extensions. If not specified, the Vertex AI Reasoning Engine service Agent in the project will be used.
source_code_spec dictionary	Specification for deploying from source code.
inline_source dictionary	Source code is provided directly in the request.
source_archive string	Input only. The application source code archive, provided as a compressed tarball (.tar.gz) file. A base64-encoded string.
python_spec dictionary	Specification for running a Python application from source.
entrypoint_module string	The Python module to load as the entrypoint, specified as a fully qualified module name. For example: path.to.agent. If not specified, defaults to “agent”. The project root will be added to Python sys.path, allowing imports to be specified relative to the root.
entrypoint_object string	The name of the callable object within the entrypointModule to use as the application If not specified, defaults to “root_agent”.
requirements_file string	The path to the requirements file, relative to the source root. If not specified, defaults to “requirements.txt”.
version string	The version of Python to use. Support version includes 3.9, 3.10, 3.11, 3.12, 3.13. If not specified, default value is 3.10.
state string	Whether the resource should exist in GCP. Choices: `"present"` ← (default) `"absent"`

Notes 

Note

API Reference: https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.reasoningEngines/
Develop and deploy agents on Vertex AI Agent Engine Guide: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/quickstart
For authentication, you can set auth_kind using the GCP_AUTH_KIND env variable.
For authentication, you can set service_account_file using the GCP_SERVICE_ACCOUNT_FILE env variable.
For authentication, you can set service_account_contents using the GCP_SERVICE_ACCOUNT_CONTENTS env variable.
For authentication, you can set service_account_email using the GCP_SERVICE_ACCOUNT_EMAIL env variable.
For authentication, you can set access_token using the GCP_ACCESS_TOKEN env variable.
For authentication, you can set scopes using the GCP_SCOPES env variable.
Environment variables values will only be used if the playbook values are not set.
The service_account_email, service_account_file, service_account_file and access_token options are mutually exclusive.

Examples 

- name: Create Basic Reasoning Engine
  google.cloud.gcp_vertexai_reasoning_engine:
    state: present
    display_name: basic-reasoning-engine
    description: A Basic Reasoning Engine
    region: us-central1
    project: "{{ gcp_project }}"
    auth_kind: "{{ gcp_cred_kind }}"
    service_account_file: "{{ gcp_cred_file }}"

################################################################################

- name: Create Source Based Deployment Reasoning Engine
  google.cloud.gcp_vertexai_reasoning_engine:
    state: present
    display_name: source-based-deployment-reasoning-engine
    description: A Source Based Deployment Reasoning Engine
    region: us-central1
    spec:
      source_code_spec:
        inline_source:
          source_archive: "{{ slurped_tarball.content }}"  # slurp already returns b64
          # source_archive: "{{ some_other_b64_encoded_string }}"
        python_spec:
          entrypoint_module: my_agent
          entrypoint_object: name_generator
          requirements_file: prod-requirements.txt  # defaults to requirements.txt
          version: "3.11"
    project: "{{ gcp_project }}"
    auth_kind: "{{ gcp_cred_kind }}"
    service_account_file: "{{ gcp_cred_file }}"

Return Values 

Common return values are documented here, the following are the fields unique to this module:

Key	Description
changed boolean	Whether the resource was changed. Returned: always
createTime string	The timestamp of when the Index was created in RFC3339 UTC “Zulu” format, with nanosecond resolution and up to nine fractional digits. Returned: success
name string	The generated name of the ReasoningEngine, in the format projects/{project}/locations/{location}/reasoningEngines/{reasoningEngine}. Returned: success
state string	The current state of the resource. Returned: always
updateTime string	The timestamp of when the Index was last updated in RFC3339 UTC “Zulu” format, with nanosecond resolution and up to nine fractional digits. Returned: success

Authors

Google Inc. (@googlecloudplatform)

google.cloud.gcp_vertexai_reasoning_engine module – Creates a GCP VertexAI.ReasoningEngine resource

Synopsis

Requirements

Parameters

Notes

Examples

Return Values