google.cloud.gcp_vertexai_rag_engine_config module – Creates a GCP VertexAI.RagEngineConfig 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_rag_engine_config.

Synopsis 

Vertex AI RAG Engine lets you scale your RagManagedDb instance based on your usage and performance requirements using a choice of two tiers, and optionally, lets you delete your Vertex AI RAG Engine data using a third tier. The tier is a project-level setting that’s available in the RagEngineConfig resource that impacts all RAG corpora using RagManagedDb. The following tiers are available in RagEngineConfig: Basic, Scaled and Unprovisioned.

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"`
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.
rag_managed_db_config string	The config of the RagManagedDb used by RagEngine. Basic tier is a cost-effective and low compute tier suitable for the following cases: Experimenting with RagManagedDb, Small data size, Latency insensitive workload, Only using RAG Engine with external vector DBs. NOTE: This is the default tier if not explicitly chosen. Scaled tier offers production grade performance along with autoscaling functionality. It is suitable for customers with large amounts of data or performance sensitive workloads. Unprovisioned tier disables the RAG Engine service and deletes all your data held within this service. This will halt the billing of the service. NOTE: Once deleted the data cannot be recovered. To start using RAG Engine again, you will need to update the tier by calling the UpdateRagEngineConfig API. NOTE: Setting to unprovisioned is the same as state=absent. Choices: `"scaled"` `"basic"` ← (default) `"unprovisioned"`
region string	The region of the RagEngineConfig. 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.
state string	Whether the resource should exist in GCP. Choices: `"present"` ← (default) `"absent"`

Notes 

Note

API Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/reference/rest/v1/RagEngineConfig
Official Documentation Guide: https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/understanding-ragmanageddb
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 RAG Engine Config
  google.cloud.gcp_vertexai_rag_engine_config:
    state: present
    rag_managed_config: basic
    region: us-central1
    project: "{{ gcp_project }}"
    auth_kind: "{{ gcp_cred_kind }}"
    service_account_file: "{{ gcp_cred_file }}"

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

- name: Create Scaled RAG Engine Config
  google.cloud.gcp_vertexai_rag_engine_config:
    state: present
    rag_managed_config: scaled
    region: us-central1
    project: "{{ gcp_project }}"
    auth_kind: "{{ gcp_cred_kind }}"
    service_account_file: "{{ gcp_cred_file }}"

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

- name: Create Scaled RAG Engine Config
  google.cloud.gcp_vertexai_rag_engine_config:
    state: absent
    rag_managed_config: unprovisioned
    region: us-central1
    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
name string	The resource name of the Dataset. This value is set by Google. Returned: success
state string	The current state of the resource. Returned: always

Authors

Google Inc. (@googlecloudplatform)

google.cloud.gcp_vertexai_rag_engine_config module – Creates a GCP VertexAI.RagEngineConfig resource

Synopsis

Requirements

Parameters

Notes

Examples

Return Values