google.cloud.gcp_vertexai_reasoning_engine_info module – List VertexAI.ReasoningEngine resources

Note

This module is part of the google.cloud collection (version 1.15.0).

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_info.

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"
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.
filters list / elements=string	A list of filter expression strings used to filter the resources returned by the API. Each string is a filter expression (e.g. some_field = "SOME_VALUE"). Multiple expressions are combined with a logical AND. Refer to the filter topic documentation https://cloud.google.com/sdk/gcloud/reference/topic/filters. Refer to the AIP-160 filter syntax documentation https://google.aip.dev/160.
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.

Notes

Note

• API Reference: https://docs.cloud.google.com/gemini-enterprise-agent-platform/reference/rest/v1/projects.locations.reasoningEngines

• Scale your agents Guide: https://docs.cloud.google.com/gemini-enterprise-agent-platform/scale

• 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: List all gcp_vertexai_reasoning_engine_info resources
  google.cloud.gcp_vertexai_reasoning_engine_info:
    project: "{{ gcp_project }}"
    auth_kind: "{{ gcp_cred_kind }}"
    service_account_file: "{{ gcp_cred_file }}"
  register: result

- name: List gcp_vertexai_reasoning_engine_info resources matching given filters
  google.cloud.gcp_vertexai_reasoning_engine_info:
    filters:
      - display_name = "my-resource"
    project: "{{ gcp_project }}"
    auth_kind: "{{ gcp_cred_kind }}"
    service_account_file: "{{ gcp_cred_file }}"
  register: result

Return Values

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

Key	Description
changed boolean	Whether any changes were made (always false for info modules). Returned: always
resources list / elements=dictionary	List of VertexAI.ReasoningEngine resources matching the supplied filters. May be empty, contain a single resource, or multiple resources. Returned: always
contextSpec dictionary	Configuration for how Agent Engine sub-resources should manage context. Returned: when set
exampleStoreConfig dictionary	Specification for an Example Store, which manages few-shot examples for the Agent Engine. Returned: when set
similaritySearchConfig dictionary	Configuration for how to perform similarity search on examples. Returned: when set
embeddingModel string	The Gemini model used to generate embeddings to lookup similar examples. Returned: always
memoryBankConfig dictionary	Specification for a Memory Bank, which manages memories for the Agent Engine. Returned: when set
customizationConfigs list / elements=dictionary	Customization configs for how Agent Engine sub-resources manage context at different scope levels. Returned: when set
consolidationConfig dictionary	Configuration for how many memory revisions Memory Bank considers when consolidating each memory candidate. Returned: when set
revisionsPerCandidateCount integer	Number of revisions to consider per candidate count. Returned: when set
disableNaturalLanguageMemories boolean	Indicates whether natural language memory generation should be disabled. Returned: when set
enableThirdPersonMemories boolean	Generate memories in the third person if set to true. Returned: when set
generateMemoriesExamples list / elements=dictionary	Provides examples of how to generate memories for a particular scope. Returned: when set
conversationSource dictionary	A conversation source for the example. Returned: when set
events list / elements=dictionary	Represents the input conversation events for the example. Returned: when set
content dictionary	Represents the content of the event. Returned: always
parts list / elements=dictionary	A list of Part objects that make up a single message. Returned: always
audioTranscription dictionary	Audio (input or output) transcription. This is only set when this Part contains audio data. Returned: when set
speakerLabel string	A label identifying the speaker of this audio segment (e.g. spk_1, spk_2). Present when diarization is set. Returned: when set
text string	The transcription text of this audio segment. Returned: always
words list / elements=dictionary	Detailed word-level transcriptions and timing details. Present when word_timestamp is set. Returned: when set
endOffset string	End offset in time of the word relative to the start of the audio. Returned: when set
startOffset string	Start offset in time of the word relative to the start of the audio. Returned: when set
word string	Transcript of the word. Returned: always
codeExecutionResult dictionary	Result of executing the ExecutableCode. Returned: when set
id string	The identifier of the ExecutableCode part this result is for. Returned: when set
outcome string	Outcome of the code execution. Possible values: [“OUTCOME_UNSPECIFIED”, “OUTCOME_OK”, “OUTCOME_FAILED”, “OUTCOME_DEADLINE_EXCEEDED”]. Returned: always
output string	Contains stdout when code execution is successful, stderr or other description otherwise. Returned: when set
executableCode dictionary	Code generated by the model that is intended to be executed. Returned: when set
code string	The code to be executed. Returned: always
id string	Unique identifier of the ExecutableCode part. Returned: when set
language string	Supported programming languages for the generated code. Possible values: [“LANGUAGE_UNSPECIFIED”, “PYTHON”, “BASH”]. Returned: always
fileData dictionary	URI based data. Returned: when set
fileUri string	The URI of the file in Google Cloud Storage. Returned: always
mimeType string	The IANA standard MIME type of the source data. Returned: always
functionCall dictionary	A predicted function call returned from the model. Returned: when set
args string	The function parameters and values in JSON object format. Returned: when set
id string	The unique id of the function call. Returned: when set
name string	The name of the function to call. Returned: when set
functionResponse dictionary	The result of a function call. Returned: when set
id string	The id of the function call this response is for. Returned: when set
name string	The name of the function to call. Returned: always
response string	The function response in JSON object format. Returned: when set
inlineData dictionary	The inline data content of the part. Returned: when set
data string	Raw bytes, which should be base64-encoded. Returned: always
mimeType string	The IANA standard MIME type of the source data. Returned: always
text string	The text content of the part. Returned: when set
thought boolean	Indicates whether the part represents the model’s thought process or reasoning. Returned: when set
videoMetadata dictionary	Video metadata. Returned: when set
endOffset string	The end offset of the video. Returned: when set
startOffset string	The start offset of the video. Returned: when set
role string	The producer of the content. Must be either ‘user’ or ‘model’. If not set, the service will default to ‘user’. Returned: when set
generatedMemories list / elements=dictionary	Represents the memories that are expected to be generated from the input conversation. Returned: when set
fact string	Represents the fact to generate a memory from. Returned: always
topics list / elements=dictionary	Represents the list of topics that the memory should be associated with. Returned: when set
customMemoryTopicLabel string	Represents the custom memory topic label. Returned: when set
managedMemoryTopic string	Represents the managed memory topic. Possible values: [“USER_PERSONAL_INFO”, “USER_PREFERENCES”, “KEY_CONVERSATION_DETAILS”, “EXPLICIT_INSTRUCTIONS”]. Returned: when set
memoryTopics list / elements=dictionary	List of topics that the memory should be associated with. Returned: when set
customMemoryTopic dictionary	Custom memory topic. Returned: when set
description string	Description of custom memory topic. Returned: when set
label string	Label of custom memory topic. Returned: when set
managedMemoryTopic dictionary	Managed memory topic. Returned: when set
managedTopicEnum string	Managed topic enum (e.g. USER_PREFERENCES, EXPLICIT_INSTRUCTIONS). Returned: when set
scopeKeys list / elements=string	List of scope keys that this customization config applies to. Returned: when set
disableMemoryRevisions boolean	If true, no memory revisions will be created for any requests to the Memory Bank. Returned: when set
generationConfig dictionary	Configuration for how to generate memories for the Memory Bank. Returned: when set
generationTriggerConfig dictionary	Configuration for triggering memory generation. Returned: when set
generationRule dictionary	The active rule that determines when to flush the buffer. If not set, then the stream will be force flushed immediately. Returned: when set
eventCount integer	Specifies to trigger generation when the event count reaches this limit. Returned: when set
fixedInterval string	Specifies to trigger generation at a fixed interval. The duration must have a minute-level granularity. Returned: when set
idleDuration string	Specifies to trigger generation if the stream is inactive for the specified duration after the most recent event. The duration must have a minute-level granularity. Returned: when set
overlapEventCount integer	Re-include the last N already-processed events in the next window. Returned: when set
model string	The model used to generate memories. Format: projects/{project}/locations/{location}/publishers/google/models/{model}. Returned: always
similaritySearchConfig dictionary	Configuration for how to perform similarity search on memories. Returned: when set
embeddingModel string	The model used to generate embeddings to lookup similar memories. Format: projects/{project}/locations/{location}/publishers/google/models/{model}. Returned: always
structuredMemoryConfigs list / elements=dictionary	Structured memory configurations for Agent Engine sub-resources. Returned: when set
schemaConfigs list / elements=dictionary	List of schema configs that this structured memory config applies to. Returned: when set
id string	Unique ID identifying the memory schema. Returned: always
schema string	The memory schema defined as an OpenAPI Schema Object JSON string. Returned: when set
scopeKeys list / elements=string	List of scope keys that this structured memory config applies to. Returned: when set
ttlConfig dictionary	Configuration for automatic TTL (“time-to-live”) of the memories in the Memory Bank. Returned: when set
defaultTtl string	The default TTL duration of the memories in the Memory Bank. Returned: when set
granularTtlConfig dictionary	The granular TTL configuration of the memories in the Memory Bank. Returned: when set
createTtl string	The TTL duration for memories uploaded via CreateMemory. Returned: when set
generateCreatedTtl string	The TTL duration for memories newly generated via GenerateMemories. Returned: when set
generateUpdatedTtl string	The TTL duration for memories updated via GenerateMemories. Returned: when set
memoryRevisionDefaultTtl string	The default TTL duration of the memory revisions in the Memory Bank. Returned: when set
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
description string	The description of the ReasoningEngine. Returned: when set
displayName string	The display name of the ReasoningEngine. Returned: always
encryptionSpec 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. Returned: when set
kmsKeyName string	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. Returned: always
labels dictionary	The labels associated with this ReasoningEngine. You can use these to organize and group your ReasoningEngines. **Note**: This field is non-authoritative, and will only manage the labels present in your configuration. Returned: when set
name string	The generated name of the ReasoningEngine, in the format projects/{project}/locations/{location}/reasoningEngines/{reasoningEngine}. Returned: success
spec dictionary	Configurations of the ReasoningEngine. Returned: when set
agentCard string	The A2A Agent Card for the agent (if available). Returned: when set
agentFramework string	The OSS agent framework used to develop the agent. Returned: when set
buildSpec dictionary	Configuration for building container image. Returned: when set
serviceAccount string	The service account that the Cloud Build builder runs as. Returned: when set
workerPool string	The resource name of the Cloud Build WorkerPool to use for the build. Returned: when set
classMethods string	Declarations for object class methods in OpenAPI specification format. **Note**: When deploying via Terraform, this field must be populated manually. Otherwise, client SDKs (like `agent_engines.get()`) will not be able to discover the methods, and calls to the engine (or A2A integrations) will fail. Depending on the template/framework used (`agent_framework`), the required class methods and their parameters differ: **Warning**: The configuration snippets below are illustrative, may not be exhaustive, and could stop working over time. For the most up-to-date method lists and schemas, please consult the respective SDK source code: * For Google ADK: See [ADK Python SDK cli_deploy.py](https://github.com/google/adk-python/blob/68a780306e3bdd648a882ef34c0abf8e5148353e/src/google/adk/cli/cli_deploy.py#L109). * For Langchain: See [Vertex AI Python SDK langchain.py](https://github.com/googleapis/python-aiplatform/blob/c8a38a085931b01f4d6071f0ab7a64cb42851829/agentplatform/agent_engines/templates/langchain.py#L642-L717). ### 1. Langchain Template * `query` (api_mode = “sync” or empty) * `stream_query` (api_mode = “stream”) Example for Langchain: ```hcl class_methods = jsonencode([ { name = “query” api_mode = “sync” description = “Queries the reasoning engine” parameters = { type = “object” required = [“input”] properties = { input = { type = “string” description = “The input prompt” } } } }, { name = “stream_query” api_mode = “stream” description = “Streams queries from the reasoning engine” parameters = { type = “object” required = [“input”] properties = { input = { type = “string” description = “The input prompt” } } } } ]) ``` ### 2. Google ADK Template (Standard - No A2A) For standard Google ADK (Agent Development Kit) deployments, you must define the following 11 methods: Example for Standard ADK: ```hcl class_methods = jsonencode([ { name = “get_session” api_mode = “” description = “Retrieve session by ID” parameters = { type = “object” required = [“user_id”, “session_id”] properties = { user_id = { type = “string” } session_id = { type = “string” } } } }, { name = “async_get_session” api_mode = “async” description = “Retrieve session asynchronously by ID” parameters = { type = “object” required = [“user_id”, “session_id”] properties = { user_id = { type = “string” } session_id = { type = “string” } } } }, { name = “list_sessions” api_mode = “” description = “List all sessions for a user” parameters = { type = “object” required = [“user_id”] properties = { user_id = { type = “string” } } } }, { name = “async_list_sessions” api_mode = “async” description = “List all sessions for a user asynchronously” parameters = { type = “object” required = [“user_id”] properties = { user_id = { type = “string” } } } }, { name = “create_session” api_mode = “” description = “Create a new session” parameters = { type = “object” required = [“user_id”] properties = { user_id = { type = “string” } session_id = { type = “string” } state = { type = “object” } } } }, { name = “async_create_session” api_mode = “async” description = “Create a new session asynchronously” parameters = { type = “object” required = [“user_id”] properties = { user_id = { type = “string” } session_id = { type = “string” } state = { type = “object” } } } }, { name = “delete_session” api_mode = “” description = “Delete session by ID” parameters = { type = “object” required = [“user_id”, “session_id”] properties = { user_id = { type = “string” } session_id = { type = “string” } } } }, { name = “async_delete_session” api_mode = “async” description = “Delete session asynchronously by ID” parameters = { type = “object” required = [“user_id”, “session_id”] properties = { user_id = { type = “string” } session_id = { type = “string” } } } }, { name = “stream_query” api_mode = “stream” description = “Stream queries from the agent” parameters = { type = “object” required = [“message”, “user_id”] properties = { message = { description = “Message string or object” } user_id = { type = “string” } session_id = { type = “string” } run_config = { type = “object” } } } }, { name = “async_stream_query” api_mode = “async_stream” description = “Stream queries asynchronously from the agent” parameters = { type = “object” required = [“message”, “user_id”] properties = { message = { description = “Message string or object” } user_id = { type = “string” } session_id = { type = “string” } session_events = { type = “array”, items = { type = “object” } } run_config = { type = “object” } } } }, { name = “streaming_agent_run_with_events” api_mode = “async_stream” description = “Stream agent run with events asynchronously” parameters = { type = “object” required = [“request_json”] properties = { request_json = { type = “string” } } } } ]) ``` ### 3. Google ADK Template (A2A-Enabled) If the agent integrates with the Gemini Enterprise Agent Registry (A2A), you must inject the `a2a_agent_card` JSON metadata as a string **specifically inside the `async_create_session` method definition**: Example for A2A-Enabled ADK: ```hcl locals { # Construct the A2A endpoint URL a2a_url = “https://us-central1-aiplatform.googleapis.com/v1/projects/my-project/locations/us-central1/reasoningEngines/my-agent/a2a” agent_card = { name = “my-agent” description = “A2A Agent” version = “1.0.0” preferred_transport = “HTTP_JSON” supported_interfaces = [{ url = local.a2a_url, protocol_binding = “HTTP_JSON” }] capabilities = { streaming = true } } } # In class_methods, append “a2a_agent_card” key ONLY to the “async_create_session” method: class_methods = jsonencode([ # .. other 10 standard methods (same as Standard ADK) .. { name = “async_create_session” api_mode = “async” description = “Create a new session asynchronously” parameters = { type = “object” required = [“user_id”] properties = { user_id = { type = “string” } session_id = { type = “string” } state = { type = “object” } } } # Inject the serialized Agent Card here a2a_agent_card = jsonencode(local.agent_card) } ]) ```. Returned: when set
containerSpec dictionary	Deploy from a container image with a defined entrypoint and commands. Returned: when set
imageUri string	The Artifact Registry Docker image URI (e.g., `us-central1-docker.pkg.dev/my-project/my-repo/my-image:tag`) of the container image that is to be run on each worker replica. Returned: always
port integer	The port that the container listens on for incoming requests. If not specified, defaults to 8080. Returned: when set
deploymentSpec dictionary	The specification of a Reasoning Engine deployment. Returned: when set
agentGatewayConfig dictionary	Agent Gateway configuration for a Reasoning Engine deployment. Returned: when set
agentToAnywhereConfig dictionary	Configuration for traffic originating from the Reasoning Engine. Returned: when set
agentGateway string	The resource name of the Agent Gateway for outbound traffic. Returned: always
clientToAgentConfig dictionary	Configuration for traffic targeting the Reasoning Engine. Returned: when set
agentGateway string	The resource name of the Agent Gateway to use for inbound traffic. Returned: always
agentServerMode string	The agent server mode specifies what features are used when deploy the agent to agent engine. Possible values: * `STABLE`: Stable agent server mode. * `EXPERIMENTAL`: Experimental agent server mode. Returned: when set
containerConcurrency integer	Concurrency for each container and agent server. Recommended value: 2 * cpu + 1. Defaults to 9. Returned: when set
dedicatedIngressEndpointEnabled boolean	Whether to enable dedicated ingress endpoint for the deployment. If true, the deployment will be accessible via a dedicated endpoint. This is required to enable GKE V2 runtime. Returned: when set
env list / elements=dictionary	Environment variables to be set with the Reasoning Engine deployment. Returned: when set
name string	The name of the environment variable. Must be a valid C identifier. Returned: always
value string	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. Returned: always
keepAliveProbe dictionary	Specifies the configuration for keep-alive probe. Returned: when set
httpGet dictionary	Specifies the HTTP GET configuration for the probe. Returned: when set
path string	Specifies the path of the HTTP GET request (e.g., `”/is_busy”`). Returned: always
port integer	Specifies the port number on the container to which the request is sent. Returned: when set
maxSeconds integer	Specifies the maximum duration (in seconds) to keep the instance alive via this probe. Can be a maximum of 3600 seconds (1 hour). Returned: when set
maxInstances 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]. Returned: when set
minInstances integer	The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10]. Returned: when set
pscInterfaceConfig dictionary	Configuration for PSC-Interface. Returned: when set
dnsPeeringConfigs 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. Returned: when set
domain string	The DNS name suffix of the zone being peered to, e.g., “my-internal-domain.corp.”. Must end with a dot. Returned: always
targetNetwork string	The VPC network name in the targetProject where the DNS zone specified by ‘domain’ is visible. Returned: always
targetProject string	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. Returned: always
networkAttachment 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. Returned: when set
resourceLimits 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. Returned: when set
secretEnv 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. Returned: when set
name string	The name of the environment variable. Must be a valid C identifier. Returned: always
secretRef dictionary	Reference to a secret stored in the Cloud Secret Manager that will provide the value for this environment variable. Returned: always
secret string	The name of the secret in Cloud Secret Manager. Format: {secret_name}. Returned: always
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. Returned: when set
effectiveIdentity string	The identity to use for the Reasoning Engine. Returned: success
exampleStore string	The resource name of the linked ExampleStore. Returned: when set
identityType string	The identity type to use for the Reasoning Engine. If not specified, the `service_account` field will be used if set, otherwise the default Vertex AI Reasoning Engine Service Agent in the project will be used. Possible values: * `SERVICE_ACCOUNT`: Use a custom service account if the `service_account` field is set, otherwise use the default Vertex AI Reasoning Engine Service Agent in the project. * `AGENT_IDENTITY`: Use Agent Identity. The `service_account` field must not be set. Returned: when set
packageSpec 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. Returned: when set
dependencyFilesGcsUri string	The Cloud Storage URI of the dependency files in tar.gz format. Returned: when set
pickleObjectGcsUri string	The Cloud Storage URI of the pickled python object. Returned: when set
pythonVersion 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. Returned: when set
requirementsGcsUri string	The Cloud Storage URI of the requirements.txtfile. Returned: when set
serviceAccount 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. Returned: when set
sourceCodeSpec dictionary	Specification for deploying from source code. Returned: when set
agentConfigSource dictionary	Specification for the deploying from agent config. Returned: when set
adkConfig dictionary	Configuration for the Agent Development Kit (ADK). Returned: when set
jsonConfig string	The value of the ADK config in JSON format. Returned: always
inlineSource dictionary	Any additional files needed to interpret the config. Returned: when set
sourceArchive string	Input only. The application source code archive, provided as a compressed tarball (.tar.gz) file. Returned: always
developerConnectSource dictionary	Specification for source code to be fetched from a Git repository managed through the Developer Connect service. Returned: when set
config dictionary	The Developer Connect configuration that defines the specific repository, revision, and directory to use as the source code root. Returned: always
dir string	Directory, relative to the source root, in which to run the build. Returned: always
gitRepositoryLink string	The Developer Connect Git repository link, formatted as projects/*/locations/*/connections/*/gitRepositoryLink/*. Returned: always
revision string	The revision to fetch from the Git repository such as a branch, a tag, a commit SHA, or any Git ref. Returned: always
imageSpec dictionary	Configuration for building an image with custom config file. Returned: when set
buildArgs dictionary	Build arguments to be used. They will be passed through --build-arg flags. Returned: when set
inlineSource dictionary	Source code is provided directly in the request. Returned: when set
sourceArchive string	Input only. The application source code archive, provided as a compressed tarball (.tar.gz) file. A base64-encoded string. Returned: when set
pythonSpec dictionary	Specification for running a Python application from source. Returned: when set
entrypointModule 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. Returned: when set
entrypointObject string	The name of the callable object within the entrypointModule to use as the application If not specified, defaults to “root_agent”. Returned: when set
requirementsFile string	The path to the requirements file, relative to the source root. If not specified, defaults to “requirements.txt”. Returned: when set
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. Returned: when set
trafficConfig dictionary	Traffic distribution configuration for the Reasoning Engine. ~> **Note:** Because revision IDs do not exist before the resource is created, the best practice for initial deployment is to set `traffic_split_always_latest {}`. Once the resource is created, you can update the configuration to a manual split using newly generated revision IDs, short names (e.g. `rev-1`), or keywords such as `LATEST` and `PREVIOUS`. Returned: when set
trafficSplitAlwaysLatest dictionary	Traffic distribution configuration, where all traffic is sent to the latest Runtime Revision. Returned: when set
trafficSplitManual dictionary	Manual traffic distribution configuration, where the user specifies the Runtime Revision IDs and the percentage of traffic to send to each. Returned: when set
targets list / elements=dictionary	A list of traffic targets for the Runtimes Revisions. The sum of percentages must equal to 100. Returned: when set
percent integer	Specifies percent of the traffic to this Runtime Revision. Returned: always
runtimeRevisionName string	The Runtime Revision name to which to send this portion of traffic. Accepts revision IDs, short names (e.g. `rev-1`), or keywords such as `LATEST` and `PREVIOUS`. Note: Keywords like `LATEST` and `PREVIOUS` resolve at apply time to the concrete underlying revision ID and remain pinned until `traffic_config` is updated in Terraform. 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
url string	Output only. The URL of the reasoning engine. Returned: success

Authors

• Google Inc. (@googlecloudplatform)

Collection links

• Issue Tracker
• Homepage
• Repository (Sources)