Returns the memories Resource.
Returns the sessions Resource.
Close httplib2 connections.
create(body=None, parent=None, x__xgafv=None)
Creates a reasoning engine.
delete(name, force=None, x__xgafv=None)
Deletes a reasoning engine.
Gets a reasoning engine.
list(filter=None, pageSize=None, pageToken=None, parent=None, x__xgafv=None)
Lists reasoning engines in a location.
Retrieves the next page of results.
patch(name, body=None, updateMask=None, x__xgafv=None)
Updates a reasoning engine.
query(name, body=None, x__xgafv=None)
Queries using a reasoning engine.
streamQuery(name, body=None, x__xgafv=None)
Streams queries using a reasoning engine.
close()
Close httplib2 connections.
create(body=None, parent=None, x__xgafv=None)
Creates a reasoning engine.
Args:
body: object, The request body.
The object takes the form of:
{ # ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order.
"contextSpec": { # Configuration for how Agent Engine sub-resources should manage context. # Optional. Configuration for how Agent Engine sub-resources should manage context.
"memoryBankConfig": { # Specification for a Memory Bank. # Optional. Specification for a Memory Bank, which manages memories for the Agent Engine.
"customizationConfigs": [ # Optional. Configuration for how to customize Memory Bank behavior for a particular scope.
{ # Configuration for organizing memories for a particular scope.
"generateMemoriesExamples": [ # Optional. Examples of how to generate memories for a particular scope.
{ # An example of how to generate memories for a particular scope.
"conversationSource": { # A conversation source for the example. This is similar to `DirectContentsSource`. # A conversation source for the example.
"events": [ # Optional. The input conversation events for the example.
{ # A single conversation event.
"content": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Required. The content of the event.
"parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
{ # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
"codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
"outcome": "A String", # Required. Outcome of the code execution.
"output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
},
"executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
"code": "A String", # Required. The code to be executed.
"language": "A String", # Required. Programming language of the `code`.
},
"fileData": { # URI based data. # Optional. URI based data.
"displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
"fileUri": "A String", # Required. URI.
"mimeType": "A String", # Required. The IANA standard MIME type of the source data.
},
"functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
"args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
"a_key": "", # Properties of the object.
},
"id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
"name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
},
"functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model.
"id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
"name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
"response": { # Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output.
"a_key": "", # Properties of the object.
},
},
"inlineData": { # Content blob. # Optional. Inlined bytes data.
"data": "A String", # Required. Raw bytes.
"displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
"mimeType": "A String", # Required. The IANA standard MIME type of the source data.
},
"text": "A String", # Optional. Text part (can be code).
"thought": True or False, # Optional. Indicates if the part is thought from the model.
"thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
"videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
"endOffset": "A String", # Optional. The end offset of the video.
"fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
"startOffset": "A String", # Optional. The start offset of the video.
},
},
],
"role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
},
},
],
},
"generatedMemories": [ # Optional. The memories that are expected to be generated from the input conversation. An empty list indicates that no memories are expected to be generated for the input conversation.
{ # A memory generated by the operation.
"fact": "A String", # Required. The fact to generate a memory from.
},
],
},
],
"memoryTopics": [ # Optional. Topics of information that should be extracted from conversations and stored as memories. If not set, then Memory Bank's default topics will be used.
{ # A topic of information that should be extracted from conversations and stored as memories.
"customMemoryTopic": { # A custom memory topic defined by the developer. # A custom memory topic defined by the developer.
"description": "A String", # Required. Description of the memory topic. This should explain what information should be extracted for this topic.
"label": "A String", # Required. The label of the topic.
},
"managedMemoryTopic": { # A managed memory topic defined by the system. # A managed memory topic defined by Memory Bank.
"managedTopicEnum": "A String", # Required. The managed topic.
},
},
],
"scopeKeys": [ # Optional. The scope keys (i.e. 'user_id') for which to use this config. A request's scope must include all of the provided keys for the config to be used (order does not matter). If empty, then the config will be used for all requests that do not have a more specific config. Only one default config is allowed per Memory Bank.
"A String",
],
},
],
"generationConfig": { # Configuration for how to generate memories. # Optional. Configuration for how to generate memories for the Memory Bank.
"model": "A String", # Required. The model used to generate memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
"similaritySearchConfig": { # Configuration for how to perform similarity search on memories. # Optional. Configuration for how to perform similarity search on memories. If not set, the Memory Bank will use the default embedding model `text-embedding-005`.
"embeddingModel": "A String", # Required. The model used to generate embeddings to lookup similar memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
"ttlConfig": { # Configuration for automatically setting the TTL ("time-to-live") of the memories in the Memory Bank. # Optional. Configuration for automatic TTL ("time-to-live") of the memories in the Memory Bank. If not set, TTL will not be applied automatically. The TTL can be explicitly set by modifying the `expire_time` of each Memory resource.
"defaultTtl": "A String", # Optional. The default TTL duration of the memories in the Memory Bank. This applies to all operations that create or update a memory.
"granularTtlConfig": { # Configuration for TTL of the memories in the Memory Bank based on the action that created or updated the memory. # Optional. The granular TTL configuration of the memories in the Memory Bank.
"createTtl": "A String", # Optional. The TTL duration for memories uploaded via CreateMemory.
"generateCreatedTtl": "A String", # Optional. The TTL duration for memories newly generated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED).
"generateUpdatedTtl": "A String", # Optional. The TTL duration for memories updated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED). In the case of an UPDATE action, the `expire_time` of the existing memory will be updated to the new value (now + TTL).
},
},
},
},
"createTime": "A String", # Output only. Timestamp when this ReasoningEngine was created.
"description": "A String", # Optional. The description of the ReasoningEngine.
"displayName": "A String", # Required. The display name of the ReasoningEngine.
"encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # 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.
"kmsKeyName": "A 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.
},
"etag": "A String", # Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
"name": "A String", # Identifier. The resource name of the ReasoningEngine. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}`
"spec": { # ReasoningEngine configurations # Optional. Configurations of the ReasoningEngine
"agentFramework": "A String", # Optional. The OSS agent framework used to develop the agent. Currently supported values: "google-adk", "langchain", "langgraph", "ag2", "llama-index", "custom".
"classMethods": [ # Optional. Declarations for object class methods in OpenAPI specification format.
{
"a_key": "", # Properties of the object.
},
],
"deploymentSpec": { # The specification of a Reasoning Engine deployment. # Optional. The specification of a Reasoning Engine deployment.
"containerConcurrency": 42, # Optional. Concurrency for each container and agent server. Recommended value: 2 * cpu + 1. Defaults to 9.
"env": [ # Optional. Environment variables to be set with the Reasoning Engine deployment. The environment variables can be updated through the UpdateReasoningEngine API.
{ # Represents an environment variable present in a Container or Python Module.
"name": "A String", # Required. Name of the environment variable. Must be a valid C identifier.
"value": "A 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.
},
],
"maxInstances": 42, # Optional. 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].
"minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. 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.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
"domain": "A String", # Required. The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot.
"targetNetwork": "A String", # Required. The VPC network name in the target_project where the DNS zone specified by 'domain' is visible.
"targetProject": "A 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.
},
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
"resourceLimits": { # Optional. 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 required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. 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.
{ # Represents an environment variable where the value is a secret in Cloud Secret Manager.
"name": "A String", # Required. Name of the secret environment variable.
"secretRef": { # Reference to a secret stored in the Cloud Secret Manager that will provide the value for this environment variable. # Required. Reference to a secret stored in the Cloud Secret Manager that will provide the value for this environment variable.
"secret": "A String", # Required. The name of the secret in Cloud Secret Manager. Format: {secret_name}.
"version": "A String", # The Cloud Secret Manager secret version. Can be 'latest' for the latest version, an integer for a specific version, or a version alias.
},
},
],
},
"packageSpec": { # User provided package spec like pickled object and package requirements. # Optional. User provided package spec of the ReasoningEngine. Ignored when users directly specify a deployment image through `deployment_spec.first_party_image_override`, but keeping the field_behavior to avoid introducing breaking changes.
"dependencyFilesGcsUri": "A String", # Optional. The Cloud Storage URI of the dependency files in tar.gz format.
"pickleObjectGcsUri": "A String", # Optional. The Cloud Storage URI of the pickled python object.
"pythonVersion": "A String", # Optional. The Python version. Currently support 3.8, 3.9, 3.10, 3.11. If not specified, default value is 3.10.
"requirementsGcsUri": "A String", # Optional. The Cloud Storage URI of the `requirements.txt` file
},
"serviceAccount": "A String", # Optional. 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.
},
"updateTime": "A String", # Output only. Timestamp when this ReasoningEngine was most recently updated.
}
parent: string, Required. The resource name of the Location to create the ReasoningEngine in. Format: `projects/{project}/locations/{location}`
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}
delete(name, force=None, x__xgafv=None)
Deletes a reasoning engine.
Args:
name: string, Required. The name of the ReasoningEngine resource to be deleted. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}` (required)
force: boolean, Optional. If set to true, child resources of this reasoning engine will also be deleted. Otherwise, the request will fail with FAILED_PRECONDITION error when the reasoning engine has undeleted child resources.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}
get(name, x__xgafv=None)
Gets a reasoning engine.
Args:
name: string, Required. The name of the ReasoningEngine resource. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}` (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order.
"contextSpec": { # Configuration for how Agent Engine sub-resources should manage context. # Optional. Configuration for how Agent Engine sub-resources should manage context.
"memoryBankConfig": { # Specification for a Memory Bank. # Optional. Specification for a Memory Bank, which manages memories for the Agent Engine.
"customizationConfigs": [ # Optional. Configuration for how to customize Memory Bank behavior for a particular scope.
{ # Configuration for organizing memories for a particular scope.
"generateMemoriesExamples": [ # Optional. Examples of how to generate memories for a particular scope.
{ # An example of how to generate memories for a particular scope.
"conversationSource": { # A conversation source for the example. This is similar to `DirectContentsSource`. # A conversation source for the example.
"events": [ # Optional. The input conversation events for the example.
{ # A single conversation event.
"content": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Required. The content of the event.
"parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
{ # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
"codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
"outcome": "A String", # Required. Outcome of the code execution.
"output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
},
"executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
"code": "A String", # Required. The code to be executed.
"language": "A String", # Required. Programming language of the `code`.
},
"fileData": { # URI based data. # Optional. URI based data.
"displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
"fileUri": "A String", # Required. URI.
"mimeType": "A String", # Required. The IANA standard MIME type of the source data.
},
"functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
"args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
"a_key": "", # Properties of the object.
},
"id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
"name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
},
"functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model.
"id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
"name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
"response": { # Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output.
"a_key": "", # Properties of the object.
},
},
"inlineData": { # Content blob. # Optional. Inlined bytes data.
"data": "A String", # Required. Raw bytes.
"displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
"mimeType": "A String", # Required. The IANA standard MIME type of the source data.
},
"text": "A String", # Optional. Text part (can be code).
"thought": True or False, # Optional. Indicates if the part is thought from the model.
"thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
"videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
"endOffset": "A String", # Optional. The end offset of the video.
"fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
"startOffset": "A String", # Optional. The start offset of the video.
},
},
],
"role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
},
},
],
},
"generatedMemories": [ # Optional. The memories that are expected to be generated from the input conversation. An empty list indicates that no memories are expected to be generated for the input conversation.
{ # A memory generated by the operation.
"fact": "A String", # Required. The fact to generate a memory from.
},
],
},
],
"memoryTopics": [ # Optional. Topics of information that should be extracted from conversations and stored as memories. If not set, then Memory Bank's default topics will be used.
{ # A topic of information that should be extracted from conversations and stored as memories.
"customMemoryTopic": { # A custom memory topic defined by the developer. # A custom memory topic defined by the developer.
"description": "A String", # Required. Description of the memory topic. This should explain what information should be extracted for this topic.
"label": "A String", # Required. The label of the topic.
},
"managedMemoryTopic": { # A managed memory topic defined by the system. # A managed memory topic defined by Memory Bank.
"managedTopicEnum": "A String", # Required. The managed topic.
},
},
],
"scopeKeys": [ # Optional. The scope keys (i.e. 'user_id') for which to use this config. A request's scope must include all of the provided keys for the config to be used (order does not matter). If empty, then the config will be used for all requests that do not have a more specific config. Only one default config is allowed per Memory Bank.
"A String",
],
},
],
"generationConfig": { # Configuration for how to generate memories. # Optional. Configuration for how to generate memories for the Memory Bank.
"model": "A String", # Required. The model used to generate memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
"similaritySearchConfig": { # Configuration for how to perform similarity search on memories. # Optional. Configuration for how to perform similarity search on memories. If not set, the Memory Bank will use the default embedding model `text-embedding-005`.
"embeddingModel": "A String", # Required. The model used to generate embeddings to lookup similar memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
"ttlConfig": { # Configuration for automatically setting the TTL ("time-to-live") of the memories in the Memory Bank. # Optional. Configuration for automatic TTL ("time-to-live") of the memories in the Memory Bank. If not set, TTL will not be applied automatically. The TTL can be explicitly set by modifying the `expire_time` of each Memory resource.
"defaultTtl": "A String", # Optional. The default TTL duration of the memories in the Memory Bank. This applies to all operations that create or update a memory.
"granularTtlConfig": { # Configuration for TTL of the memories in the Memory Bank based on the action that created or updated the memory. # Optional. The granular TTL configuration of the memories in the Memory Bank.
"createTtl": "A String", # Optional. The TTL duration for memories uploaded via CreateMemory.
"generateCreatedTtl": "A String", # Optional. The TTL duration for memories newly generated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED).
"generateUpdatedTtl": "A String", # Optional. The TTL duration for memories updated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED). In the case of an UPDATE action, the `expire_time` of the existing memory will be updated to the new value (now + TTL).
},
},
},
},
"createTime": "A String", # Output only. Timestamp when this ReasoningEngine was created.
"description": "A String", # Optional. The description of the ReasoningEngine.
"displayName": "A String", # Required. The display name of the ReasoningEngine.
"encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # 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.
"kmsKeyName": "A 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.
},
"etag": "A String", # Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
"name": "A String", # Identifier. The resource name of the ReasoningEngine. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}`
"spec": { # ReasoningEngine configurations # Optional. Configurations of the ReasoningEngine
"agentFramework": "A String", # Optional. The OSS agent framework used to develop the agent. Currently supported values: "google-adk", "langchain", "langgraph", "ag2", "llama-index", "custom".
"classMethods": [ # Optional. Declarations for object class methods in OpenAPI specification format.
{
"a_key": "", # Properties of the object.
},
],
"deploymentSpec": { # The specification of a Reasoning Engine deployment. # Optional. The specification of a Reasoning Engine deployment.
"containerConcurrency": 42, # Optional. Concurrency for each container and agent server. Recommended value: 2 * cpu + 1. Defaults to 9.
"env": [ # Optional. Environment variables to be set with the Reasoning Engine deployment. The environment variables can be updated through the UpdateReasoningEngine API.
{ # Represents an environment variable present in a Container or Python Module.
"name": "A String", # Required. Name of the environment variable. Must be a valid C identifier.
"value": "A 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.
},
],
"maxInstances": 42, # Optional. 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].
"minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. 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.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
"domain": "A String", # Required. The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot.
"targetNetwork": "A String", # Required. The VPC network name in the target_project where the DNS zone specified by 'domain' is visible.
"targetProject": "A 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.
},
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
"resourceLimits": { # Optional. 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 required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. 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.
{ # Represents an environment variable where the value is a secret in Cloud Secret Manager.
"name": "A String", # Required. Name of the secret environment variable.
"secretRef": { # Reference to a secret stored in the Cloud Secret Manager that will provide the value for this environment variable. # Required. Reference to a secret stored in the Cloud Secret Manager that will provide the value for this environment variable.
"secret": "A String", # Required. The name of the secret in Cloud Secret Manager. Format: {secret_name}.
"version": "A String", # The Cloud Secret Manager secret version. Can be 'latest' for the latest version, an integer for a specific version, or a version alias.
},
},
],
},
"packageSpec": { # User provided package spec like pickled object and package requirements. # Optional. User provided package spec of the ReasoningEngine. Ignored when users directly specify a deployment image through `deployment_spec.first_party_image_override`, but keeping the field_behavior to avoid introducing breaking changes.
"dependencyFilesGcsUri": "A String", # Optional. The Cloud Storage URI of the dependency files in tar.gz format.
"pickleObjectGcsUri": "A String", # Optional. The Cloud Storage URI of the pickled python object.
"pythonVersion": "A String", # Optional. The Python version. Currently support 3.8, 3.9, 3.10, 3.11. If not specified, default value is 3.10.
"requirementsGcsUri": "A String", # Optional. The Cloud Storage URI of the `requirements.txt` file
},
"serviceAccount": "A String", # Optional. 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.
},
"updateTime": "A String", # Output only. Timestamp when this ReasoningEngine was most recently updated.
}
list(filter=None, pageSize=None, pageToken=None, parent=None, x__xgafv=None)
Lists reasoning engines in a location.
Args:
filter: string, Optional. The standard list filter. More detail in [AIP-160](https://google.aip.dev/160).
pageSize: integer, Optional. The standard list page size.
pageToken: string, Optional. The standard list page token.
parent: string, Required. The resource name of the Location to list the ReasoningEngines from. Format: `projects/{project}/locations/{location}`
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Response message for ReasoningEngineService.ListReasoningEngines
"nextPageToken": "A String", # A token to retrieve the next page of results. Pass to ListReasoningEnginesRequest.page_token to obtain that page.
"reasoningEngines": [ # List of ReasoningEngines in the requested page.
{ # ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order.
"contextSpec": { # Configuration for how Agent Engine sub-resources should manage context. # Optional. Configuration for how Agent Engine sub-resources should manage context.
"memoryBankConfig": { # Specification for a Memory Bank. # Optional. Specification for a Memory Bank, which manages memories for the Agent Engine.
"customizationConfigs": [ # Optional. Configuration for how to customize Memory Bank behavior for a particular scope.
{ # Configuration for organizing memories for a particular scope.
"generateMemoriesExamples": [ # Optional. Examples of how to generate memories for a particular scope.
{ # An example of how to generate memories for a particular scope.
"conversationSource": { # A conversation source for the example. This is similar to `DirectContentsSource`. # A conversation source for the example.
"events": [ # Optional. The input conversation events for the example.
{ # A single conversation event.
"content": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Required. The content of the event.
"parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
{ # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
"codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
"outcome": "A String", # Required. Outcome of the code execution.
"output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
},
"executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
"code": "A String", # Required. The code to be executed.
"language": "A String", # Required. Programming language of the `code`.
},
"fileData": { # URI based data. # Optional. URI based data.
"displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
"fileUri": "A String", # Required. URI.
"mimeType": "A String", # Required. The IANA standard MIME type of the source data.
},
"functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
"args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
"a_key": "", # Properties of the object.
},
"id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
"name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
},
"functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model.
"id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
"name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
"response": { # Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output.
"a_key": "", # Properties of the object.
},
},
"inlineData": { # Content blob. # Optional. Inlined bytes data.
"data": "A String", # Required. Raw bytes.
"displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
"mimeType": "A String", # Required. The IANA standard MIME type of the source data.
},
"text": "A String", # Optional. Text part (can be code).
"thought": True or False, # Optional. Indicates if the part is thought from the model.
"thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
"videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
"endOffset": "A String", # Optional. The end offset of the video.
"fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
"startOffset": "A String", # Optional. The start offset of the video.
},
},
],
"role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
},
},
],
},
"generatedMemories": [ # Optional. The memories that are expected to be generated from the input conversation. An empty list indicates that no memories are expected to be generated for the input conversation.
{ # A memory generated by the operation.
"fact": "A String", # Required. The fact to generate a memory from.
},
],
},
],
"memoryTopics": [ # Optional. Topics of information that should be extracted from conversations and stored as memories. If not set, then Memory Bank's default topics will be used.
{ # A topic of information that should be extracted from conversations and stored as memories.
"customMemoryTopic": { # A custom memory topic defined by the developer. # A custom memory topic defined by the developer.
"description": "A String", # Required. Description of the memory topic. This should explain what information should be extracted for this topic.
"label": "A String", # Required. The label of the topic.
},
"managedMemoryTopic": { # A managed memory topic defined by the system. # A managed memory topic defined by Memory Bank.
"managedTopicEnum": "A String", # Required. The managed topic.
},
},
],
"scopeKeys": [ # Optional. The scope keys (i.e. 'user_id') for which to use this config. A request's scope must include all of the provided keys for the config to be used (order does not matter). If empty, then the config will be used for all requests that do not have a more specific config. Only one default config is allowed per Memory Bank.
"A String",
],
},
],
"generationConfig": { # Configuration for how to generate memories. # Optional. Configuration for how to generate memories for the Memory Bank.
"model": "A String", # Required. The model used to generate memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
"similaritySearchConfig": { # Configuration for how to perform similarity search on memories. # Optional. Configuration for how to perform similarity search on memories. If not set, the Memory Bank will use the default embedding model `text-embedding-005`.
"embeddingModel": "A String", # Required. The model used to generate embeddings to lookup similar memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
"ttlConfig": { # Configuration for automatically setting the TTL ("time-to-live") of the memories in the Memory Bank. # Optional. Configuration for automatic TTL ("time-to-live") of the memories in the Memory Bank. If not set, TTL will not be applied automatically. The TTL can be explicitly set by modifying the `expire_time` of each Memory resource.
"defaultTtl": "A String", # Optional. The default TTL duration of the memories in the Memory Bank. This applies to all operations that create or update a memory.
"granularTtlConfig": { # Configuration for TTL of the memories in the Memory Bank based on the action that created or updated the memory. # Optional. The granular TTL configuration of the memories in the Memory Bank.
"createTtl": "A String", # Optional. The TTL duration for memories uploaded via CreateMemory.
"generateCreatedTtl": "A String", # Optional. The TTL duration for memories newly generated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED).
"generateUpdatedTtl": "A String", # Optional. The TTL duration for memories updated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED). In the case of an UPDATE action, the `expire_time` of the existing memory will be updated to the new value (now + TTL).
},
},
},
},
"createTime": "A String", # Output only. Timestamp when this ReasoningEngine was created.
"description": "A String", # Optional. The description of the ReasoningEngine.
"displayName": "A String", # Required. The display name of the ReasoningEngine.
"encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # 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.
"kmsKeyName": "A 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.
},
"etag": "A String", # Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
"name": "A String", # Identifier. The resource name of the ReasoningEngine. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}`
"spec": { # ReasoningEngine configurations # Optional. Configurations of the ReasoningEngine
"agentFramework": "A String", # Optional. The OSS agent framework used to develop the agent. Currently supported values: "google-adk", "langchain", "langgraph", "ag2", "llama-index", "custom".
"classMethods": [ # Optional. Declarations for object class methods in OpenAPI specification format.
{
"a_key": "", # Properties of the object.
},
],
"deploymentSpec": { # The specification of a Reasoning Engine deployment. # Optional. The specification of a Reasoning Engine deployment.
"containerConcurrency": 42, # Optional. Concurrency for each container and agent server. Recommended value: 2 * cpu + 1. Defaults to 9.
"env": [ # Optional. Environment variables to be set with the Reasoning Engine deployment. The environment variables can be updated through the UpdateReasoningEngine API.
{ # Represents an environment variable present in a Container or Python Module.
"name": "A String", # Required. Name of the environment variable. Must be a valid C identifier.
"value": "A 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.
},
],
"maxInstances": 42, # Optional. 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].
"minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. 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.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
"domain": "A String", # Required. The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot.
"targetNetwork": "A String", # Required. The VPC network name in the target_project where the DNS zone specified by 'domain' is visible.
"targetProject": "A 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.
},
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
"resourceLimits": { # Optional. 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 required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. 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.
{ # Represents an environment variable where the value is a secret in Cloud Secret Manager.
"name": "A String", # Required. Name of the secret environment variable.
"secretRef": { # Reference to a secret stored in the Cloud Secret Manager that will provide the value for this environment variable. # Required. Reference to a secret stored in the Cloud Secret Manager that will provide the value for this environment variable.
"secret": "A String", # Required. The name of the secret in Cloud Secret Manager. Format: {secret_name}.
"version": "A String", # The Cloud Secret Manager secret version. Can be 'latest' for the latest version, an integer for a specific version, or a version alias.
},
},
],
},
"packageSpec": { # User provided package spec like pickled object and package requirements. # Optional. User provided package spec of the ReasoningEngine. Ignored when users directly specify a deployment image through `deployment_spec.first_party_image_override`, but keeping the field_behavior to avoid introducing breaking changes.
"dependencyFilesGcsUri": "A String", # Optional. The Cloud Storage URI of the dependency files in tar.gz format.
"pickleObjectGcsUri": "A String", # Optional. The Cloud Storage URI of the pickled python object.
"pythonVersion": "A String", # Optional. The Python version. Currently support 3.8, 3.9, 3.10, 3.11. If not specified, default value is 3.10.
"requirementsGcsUri": "A String", # Optional. The Cloud Storage URI of the `requirements.txt` file
},
"serviceAccount": "A String", # Optional. 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.
},
"updateTime": "A String", # Output only. Timestamp when this ReasoningEngine was most recently updated.
},
],
}
list_next()
Retrieves the next page of results.
Args:
previous_request: The request for the previous page. (required)
previous_response: The response from the request for the previous page. (required)
Returns:
A request object that you can call 'execute()' on to request the next
page. Returns None if there are no more items in the collection.
patch(name, body=None, updateMask=None, x__xgafv=None)
Updates a reasoning engine.
Args:
name: string, Identifier. The resource name of the ReasoningEngine. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}` (required)
body: object, The request body.
The object takes the form of:
{ # ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order.
"contextSpec": { # Configuration for how Agent Engine sub-resources should manage context. # Optional. Configuration for how Agent Engine sub-resources should manage context.
"memoryBankConfig": { # Specification for a Memory Bank. # Optional. Specification for a Memory Bank, which manages memories for the Agent Engine.
"customizationConfigs": [ # Optional. Configuration for how to customize Memory Bank behavior for a particular scope.
{ # Configuration for organizing memories for a particular scope.
"generateMemoriesExamples": [ # Optional. Examples of how to generate memories for a particular scope.
{ # An example of how to generate memories for a particular scope.
"conversationSource": { # A conversation source for the example. This is similar to `DirectContentsSource`. # A conversation source for the example.
"events": [ # Optional. The input conversation events for the example.
{ # A single conversation event.
"content": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Required. The content of the event.
"parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
{ # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
"codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
"outcome": "A String", # Required. Outcome of the code execution.
"output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
},
"executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
"code": "A String", # Required. The code to be executed.
"language": "A String", # Required. Programming language of the `code`.
},
"fileData": { # URI based data. # Optional. URI based data.
"displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
"fileUri": "A String", # Required. URI.
"mimeType": "A String", # Required. The IANA standard MIME type of the source data.
},
"functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
"args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
"a_key": "", # Properties of the object.
},
"id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
"name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
},
"functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model.
"id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
"name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
"response": { # Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output.
"a_key": "", # Properties of the object.
},
},
"inlineData": { # Content blob. # Optional. Inlined bytes data.
"data": "A String", # Required. Raw bytes.
"displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
"mimeType": "A String", # Required. The IANA standard MIME type of the source data.
},
"text": "A String", # Optional. Text part (can be code).
"thought": True or False, # Optional. Indicates if the part is thought from the model.
"thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
"videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
"endOffset": "A String", # Optional. The end offset of the video.
"fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
"startOffset": "A String", # Optional. The start offset of the video.
},
},
],
"role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
},
},
],
},
"generatedMemories": [ # Optional. The memories that are expected to be generated from the input conversation. An empty list indicates that no memories are expected to be generated for the input conversation.
{ # A memory generated by the operation.
"fact": "A String", # Required. The fact to generate a memory from.
},
],
},
],
"memoryTopics": [ # Optional. Topics of information that should be extracted from conversations and stored as memories. If not set, then Memory Bank's default topics will be used.
{ # A topic of information that should be extracted from conversations and stored as memories.
"customMemoryTopic": { # A custom memory topic defined by the developer. # A custom memory topic defined by the developer.
"description": "A String", # Required. Description of the memory topic. This should explain what information should be extracted for this topic.
"label": "A String", # Required. The label of the topic.
},
"managedMemoryTopic": { # A managed memory topic defined by the system. # A managed memory topic defined by Memory Bank.
"managedTopicEnum": "A String", # Required. The managed topic.
},
},
],
"scopeKeys": [ # Optional. The scope keys (i.e. 'user_id') for which to use this config. A request's scope must include all of the provided keys for the config to be used (order does not matter). If empty, then the config will be used for all requests that do not have a more specific config. Only one default config is allowed per Memory Bank.
"A String",
],
},
],
"generationConfig": { # Configuration for how to generate memories. # Optional. Configuration for how to generate memories for the Memory Bank.
"model": "A String", # Required. The model used to generate memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
"similaritySearchConfig": { # Configuration for how to perform similarity search on memories. # Optional. Configuration for how to perform similarity search on memories. If not set, the Memory Bank will use the default embedding model `text-embedding-005`.
"embeddingModel": "A String", # Required. The model used to generate embeddings to lookup similar memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
"ttlConfig": { # Configuration for automatically setting the TTL ("time-to-live") of the memories in the Memory Bank. # Optional. Configuration for automatic TTL ("time-to-live") of the memories in the Memory Bank. If not set, TTL will not be applied automatically. The TTL can be explicitly set by modifying the `expire_time` of each Memory resource.
"defaultTtl": "A String", # Optional. The default TTL duration of the memories in the Memory Bank. This applies to all operations that create or update a memory.
"granularTtlConfig": { # Configuration for TTL of the memories in the Memory Bank based on the action that created or updated the memory. # Optional. The granular TTL configuration of the memories in the Memory Bank.
"createTtl": "A String", # Optional. The TTL duration for memories uploaded via CreateMemory.
"generateCreatedTtl": "A String", # Optional. The TTL duration for memories newly generated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED).
"generateUpdatedTtl": "A String", # Optional. The TTL duration for memories updated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED). In the case of an UPDATE action, the `expire_time` of the existing memory will be updated to the new value (now + TTL).
},
},
},
},
"createTime": "A String", # Output only. Timestamp when this ReasoningEngine was created.
"description": "A String", # Optional. The description of the ReasoningEngine.
"displayName": "A String", # Required. The display name of the ReasoningEngine.
"encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # 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.
"kmsKeyName": "A 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.
},
"etag": "A String", # Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
"name": "A String", # Identifier. The resource name of the ReasoningEngine. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}`
"spec": { # ReasoningEngine configurations # Optional. Configurations of the ReasoningEngine
"agentFramework": "A String", # Optional. The OSS agent framework used to develop the agent. Currently supported values: "google-adk", "langchain", "langgraph", "ag2", "llama-index", "custom".
"classMethods": [ # Optional. Declarations for object class methods in OpenAPI specification format.
{
"a_key": "", # Properties of the object.
},
],
"deploymentSpec": { # The specification of a Reasoning Engine deployment. # Optional. The specification of a Reasoning Engine deployment.
"containerConcurrency": 42, # Optional. Concurrency for each container and agent server. Recommended value: 2 * cpu + 1. Defaults to 9.
"env": [ # Optional. Environment variables to be set with the Reasoning Engine deployment. The environment variables can be updated through the UpdateReasoningEngine API.
{ # Represents an environment variable present in a Container or Python Module.
"name": "A String", # Required. Name of the environment variable. Must be a valid C identifier.
"value": "A 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.
},
],
"maxInstances": 42, # Optional. 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].
"minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. 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.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
"domain": "A String", # Required. The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot.
"targetNetwork": "A String", # Required. The VPC network name in the target_project where the DNS zone specified by 'domain' is visible.
"targetProject": "A 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.
},
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
"resourceLimits": { # Optional. 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 required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. 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.
{ # Represents an environment variable where the value is a secret in Cloud Secret Manager.
"name": "A String", # Required. Name of the secret environment variable.
"secretRef": { # Reference to a secret stored in the Cloud Secret Manager that will provide the value for this environment variable. # Required. Reference to a secret stored in the Cloud Secret Manager that will provide the value for this environment variable.
"secret": "A String", # Required. The name of the secret in Cloud Secret Manager. Format: {secret_name}.
"version": "A String", # The Cloud Secret Manager secret version. Can be 'latest' for the latest version, an integer for a specific version, or a version alias.
},
},
],
},
"packageSpec": { # User provided package spec like pickled object and package requirements. # Optional. User provided package spec of the ReasoningEngine. Ignored when users directly specify a deployment image through `deployment_spec.first_party_image_override`, but keeping the field_behavior to avoid introducing breaking changes.
"dependencyFilesGcsUri": "A String", # Optional. The Cloud Storage URI of the dependency files in tar.gz format.
"pickleObjectGcsUri": "A String", # Optional. The Cloud Storage URI of the pickled python object.
"pythonVersion": "A String", # Optional. The Python version. Currently support 3.8, 3.9, 3.10, 3.11. If not specified, default value is 3.10.
"requirementsGcsUri": "A String", # Optional. The Cloud Storage URI of the `requirements.txt` file
},
"serviceAccount": "A String", # Optional. 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.
},
"updateTime": "A String", # Output only. Timestamp when this ReasoningEngine was most recently updated.
}
updateMask: string, Optional. Mask specifying which fields to update.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}
query(name, body=None, x__xgafv=None)
Queries using a reasoning engine.
Args:
name: string, Required. The name of the ReasoningEngine resource to use. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}` (required)
body: object, The request body.
The object takes the form of:
{ # Request message for ReasoningEngineExecutionService.Query.
"classMethod": "A String", # Optional. Class method to be used for the query. It is optional and defaults to "query" if unspecified.
"input": { # Optional. Input content provided by users in JSON object format. Examples include text query, function calling parameters, media bytes, etc.
"a_key": "", # Properties of the object.
},
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Response message for ReasoningEngineExecutionService.Query
"output": "", # Response provided by users in JSON object format.
}
streamQuery(name, body=None, x__xgafv=None)
Streams queries using a reasoning engine.
Args:
name: string, Required. The name of the ReasoningEngine resource to use. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}` (required)
body: object, The request body.
The object takes the form of:
{ # Request message for ReasoningEngineExecutionService.StreamQuery.
"classMethod": "A String", # Optional. Class method to be used for the stream query. It is optional and defaults to "stream_query" if unspecified.
"input": { # Optional. Input content provided by users in JSON object format. Examples include text query, function calling parameters, media bytes, etc.
"a_key": "", # Properties of the object.
},
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged.
"contentType": "A String", # The HTTP Content-Type header value specifying the content type of the body.
"data": "A String", # The HTTP request/response body as raw binary.
"extensions": [ # Application specific response metadata. Must be set in the first response for streaming APIs.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
}