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chore: Update discovery artifacts (#2475)
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## Deleted keys were detected in the following stable discovery artifacts:
bigtableadmin v2 https://togithub.com/googleapis/google-api-python-client/commit/49147d0a3a85bb5abd631f4a81790f30e7a34be7
content v2.1 https://togithub.com/googleapis/google-api-python-client/commit/8f9b004c25831f194be604226c2d3d22e6fa0ccb
discoveryengine v1 https://togithub.com/googleapis/google-api-python-client/commit/60fa6613e09aa00e79c50c8a5060313f5bf7e506

## Deleted keys were detected in the following pre-stable discovery artifacts:
aiplatform v1beta1 https://togithub.com/googleapis/google-api-python-client/commit/dda4e942989a52b3e5eee62b06184d56d82e82c5
compute alpha https://togithub.com/googleapis/google-api-python-client/commit/da530a35f8c86d3a1a130cbba9e62cb894cf1591
discoveryengine v1alpha https://togithub.com/googleapis/google-api-python-client/commit/60fa6613e09aa00e79c50c8a5060313f5bf7e506
discoveryengine v1beta https://togithub.com/googleapis/google-api-python-client/commit/60fa6613e09aa00e79c50c8a5060313f5bf7e506
migrationcenter v1alpha1 https://togithub.com/googleapis/google-api-python-client/commit/e0b8cc275772dd81fc13aceb3749031fb6a2be69

## Discovery Artifact Change Summary:
feat(aiplatform): update the api https://togithub.com/googleapis/google-api-python-client/commit/dda4e942989a52b3e5eee62b06184d56d82e82c5
feat(alloydb): update the api https://togithub.com/googleapis/google-api-python-client/commit/28bf3f5fc57cd5dc6d2f2b4690c84c495c784dd9
feat(artifactregistry): update the api https://togithub.com/googleapis/google-api-python-client/commit/ad43356149cc9e09f0adfd6305bba4592af30ab7
feat(batch): update the api https://togithub.com/googleapis/google-api-python-client/commit/d09d67b7d33e42cf9aa41a13ce057bcc9b71ffad
feat(bigtableadmin): update the api https://togithub.com/googleapis/google-api-python-client/commit/49147d0a3a85bb5abd631f4a81790f30e7a34be7
feat(cloudchannel): update the api https://togithub.com/googleapis/google-api-python-client/commit/ec4cb22818b407f4a0d83145703fa3a6050e9dd2
feat(compute): update the api https://togithub.com/googleapis/google-api-python-client/commit/da530a35f8c86d3a1a130cbba9e62cb894cf1591
feat(content): update the api https://togithub.com/googleapis/google-api-python-client/commit/8f9b004c25831f194be604226c2d3d22e6fa0ccb
feat(dataform): update the api https://togithub.com/googleapis/google-api-python-client/commit/959355233410b9820e3a680f6b24593895fe0d8a
fix(dataportability): update the api https://togithub.com/googleapis/google-api-python-client/commit/f9991e79219c4ced18d4d907c18458bff0919ce6
feat(dataproc): update the api https://togithub.com/googleapis/google-api-python-client/commit/367935ae62de86e4b405f15c0e54b6c3c9efad3b
feat(developerconnect): update the api https://togithub.com/googleapis/google-api-python-client/commit/693ec117a7d578dd9c3c411ce37d173ce90f7f60
feat(discoveryengine): update the api https://togithub.com/googleapis/google-api-python-client/commit/60fa6613e09aa00e79c50c8a5060313f5bf7e506
feat(file): update the api https://togithub.com/googleapis/google-api-python-client/commit/842cfd77bc91e83f0ccb2fccc8717c33db3fbf6e
feat(firestore): update the api https://togithub.com/googleapis/google-api-python-client/commit/25a5c4d10ec420f8f50974482bc7f5d9f260020c
feat(gkehub): update the api https://togithub.com/googleapis/google-api-python-client/commit/5f66406dd51661178c0be20046de922440a6cd4e
feat(healthcare): update the api https://togithub.com/googleapis/google-api-python-client/commit/3a168d04264d9a2af96f215ced38abf3960f761f
feat(integrations): update the api https://togithub.com/googleapis/google-api-python-client/commit/b0957d64013529b29dd03d8cdd1cca21341cca1d
feat(migrationcenter): update the api https://togithub.com/googleapis/google-api-python-client/commit/e0b8cc275772dd81fc13aceb3749031fb6a2be69
feat(networkmanagement): update the api https://togithub.com/googleapis/google-api-python-client/commit/af4f1645c5507e302a26a6bdf3bb769f421afa4f
feat(pagespeedonline): update the api https://togithub.com/googleapis/google-api-python-client/commit/b4ff550b622631f23c7840eea3dc72703198e21b
feat(recaptchaenterprise): update the api https://togithub.com/googleapis/google-api-python-client/commit/8d1db9886800a255f99d23dd563f724320be55bd
feat(securitycenter): update the api https://togithub.com/googleapis/google-api-python-client/commit/40d8f4f0775e0519752eed59c85d44efa2b88b6e
feat(servicemanagement): update the api https://togithub.com/googleapis/google-api-python-client/commit/d12e09ddd16ec238f387b3c2471399b1b2ac1819
feat(servicenetworking): update the api https://togithub.com/googleapis/google-api-python-client/commit/79566119f46c8652ba5cdc398f28316309f66bb7
feat(sqladmin): update the api https://togithub.com/googleapis/google-api-python-client/commit/1cd78d0e6cf9023af88a88fe25886807476fbe97
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354 changes: 354 additions & 0 deletions docs/dyn/aiplatform_v1.datasets.datasetVersions.html
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10 changes: 10 additions & 0 deletions docs/dyn/aiplatform_v1.html
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<h1><a href="aiplatform_v1.html">Vertex AI API</a></h1>
<h2>Instance Methods</h2>
<p class="toc_element">
<code><a href="aiplatform_v1.datasets.html">datasets()</a></code>
</p>
<p class="firstline">Returns the datasets Resource.</p>

<p class="toc_element">
<code><a href="aiplatform_v1.endpoints.html">endpoints()</a></code>
</p>
<p class="firstline">Returns the endpoints Resource.</p>

<p class="toc_element">
<code><a href="aiplatform_v1.projects.html">projects()</a></code>
</p>
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4 changes: 4 additions & 0 deletions docs/dyn/aiplatform_v1.projects.locations.customJobs.html
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Expand Up @@ -174,6 +174,7 @@ <h3>Method Details</h3>
],
&quot;scheduling&quot;: { # All parameters related to queuing and scheduling of custom jobs. # Scheduling options for a CustomJob.
&quot;disableRetries&quot;: True or False, # Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides `Scheduling.restart_job_on_worker_restart` to false.
&quot;maxWaitDuration&quot;: &quot;A String&quot;, # Optional. This is the maximum duration that a job will wait for the requested resources to be provisioned if the scheduling strategy is set to [Strategy.DWS_FLEX_START]. If set to 0, the job will wait indefinitely. The default is 24 hours.
&quot;restartJobOnWorkerRestart&quot;: True or False, # Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
&quot;strategy&quot;: &quot;A String&quot;, # Optional. This determines which type of scheduling strategy to use.
&quot;timeout&quot;: &quot;A String&quot;, # The maximum job running time. The default is 7 days.
Expand Down Expand Up @@ -298,6 +299,7 @@ <h3>Method Details</h3>
],
&quot;scheduling&quot;: { # All parameters related to queuing and scheduling of custom jobs. # Scheduling options for a CustomJob.
&quot;disableRetries&quot;: True or False, # Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides `Scheduling.restart_job_on_worker_restart` to false.
&quot;maxWaitDuration&quot;: &quot;A String&quot;, # Optional. This is the maximum duration that a job will wait for the requested resources to be provisioned if the scheduling strategy is set to [Strategy.DWS_FLEX_START]. If set to 0, the job will wait indefinitely. The default is 24 hours.
&quot;restartJobOnWorkerRestart&quot;: True or False, # Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
&quot;strategy&quot;: &quot;A String&quot;, # Optional. This determines which type of scheduling strategy to use.
&quot;timeout&quot;: &quot;A String&quot;, # The maximum job running time. The default is 7 days.
Expand Down Expand Up @@ -464,6 +466,7 @@ <h3>Method Details</h3>
],
&quot;scheduling&quot;: { # All parameters related to queuing and scheduling of custom jobs. # Scheduling options for a CustomJob.
&quot;disableRetries&quot;: True or False, # Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides `Scheduling.restart_job_on_worker_restart` to false.
&quot;maxWaitDuration&quot;: &quot;A String&quot;, # Optional. This is the maximum duration that a job will wait for the requested resources to be provisioned if the scheduling strategy is set to [Strategy.DWS_FLEX_START]. If set to 0, the job will wait indefinitely. The default is 24 hours.
&quot;restartJobOnWorkerRestart&quot;: True or False, # Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
&quot;strategy&quot;: &quot;A String&quot;, # Optional. This determines which type of scheduling strategy to use.
&quot;timeout&quot;: &quot;A String&quot;, # The maximum job running time. The default is 7 days.
Expand Down Expand Up @@ -601,6 +604,7 @@ <h3>Method Details</h3>
],
&quot;scheduling&quot;: { # All parameters related to queuing and scheduling of custom jobs. # Scheduling options for a CustomJob.
&quot;disableRetries&quot;: True or False, # Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides `Scheduling.restart_job_on_worker_restart` to false.
&quot;maxWaitDuration&quot;: &quot;A String&quot;, # Optional. This is the maximum duration that a job will wait for the requested resources to be provisioned if the scheduling strategy is set to [Strategy.DWS_FLEX_START]. If set to 0, the job will wait indefinitely. The default is 24 hours.
&quot;restartJobOnWorkerRestart&quot;: True or False, # Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
&quot;strategy&quot;: &quot;A String&quot;, # Optional. This determines which type of scheduling strategy to use.
&quot;timeout&quot;: &quot;A String&quot;, # The maximum job running time. The default is 7 days.
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69 changes: 57 additions & 12 deletions docs/dyn/aiplatform_v1.projects.locations.endpoints.html
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Expand Up @@ -341,8 +341,23 @@ <h3>Method Details</h3>
},
&quot;retrieval&quot;: { # Defines a retrieval tool that model can call to access external knowledge. # Optional. Retrieval tool type. System will always execute the provided retrieval tool(s) to get external knowledge to answer the prompt. Retrieval results are presented to the model for generation.
&quot;disableAttribution&quot;: True or False, # Optional. Deprecated. This option is no longer supported.
&quot;vertexAiSearch&quot;: { # Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation # Set to use data source powered by Vertex AI Search.
&quot;datastore&quot;: &quot;A String&quot;, # Required. Fully-qualified Vertex AI Search&#x27;s datastore resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
&quot;vertexAiSearch&quot;: { # Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/products/agent-builder # Set to use data source powered by Vertex AI Search.
&quot;datastore&quot;: &quot;A String&quot;, # Required. Fully-qualified Vertex AI Search data store resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
},
&quot;vertexRagStore&quot;: { # Retrieve from Vertex RAG Store for grounding. # Set to use data source powered by Vertex RAG store. User data is uploaded via the VertexRagDataService.
&quot;ragCorpora&quot;: [ # Optional. Deprecated. Please use rag_resources instead.
&quot;A String&quot;,
],
&quot;ragResources&quot;: [ # Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support.
{ # The definition of the Rag resource.
&quot;ragCorpus&quot;: &quot;A String&quot;, # Optional. RagCorpora resource name. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}`
&quot;ragFileIds&quot;: [ # Optional. rag_file_id. The files should be in the same rag_corpus set in rag_corpus field.
&quot;A String&quot;,
],
},
],
&quot;similarityTopK&quot;: 42, # Optional. Number of top k results to return from the selected corpora.
&quot;vectorDistanceThreshold&quot;: 3.14, # Optional. Only return results with vector distance smaller than the threshold.
},
},
},
Expand Down Expand Up @@ -1195,7 +1210,7 @@ <h3>Method Details</h3>
<pre>Generate content with multimodal inputs.

Args:
model: string, Required. The name of the publisher model requested to serve the prediction. Format: `projects/{project}/locations/{location}/publishers/*/models/*` (required)
model: string, Required. The fully qualified name of the publisher model or tuned model endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}` (required)
body: object, The request body.
The object takes the form of:

Expand Down Expand Up @@ -1268,12 +1283,12 @@ <h3>Method Details</h3>
&quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
&quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
},
&quot;routingConfig&quot;: { # Routing config. # Optional. Routing configuration.
&quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. # Optional. Routing configuration.
&quot;autoMode&quot;: { # When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # Automated routing.
&quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
},
&quot;manualMode&quot;: { # When manual routing is set, the specified model will be used directly. # Manual routing.
&quot;modelName&quot;: &quot;A String&quot;, # The model name to use. Only the public LLM models are accepted. e.g. gemini-1.5-pro-001.
&quot;modelName&quot;: &quot;A String&quot;, # The model name to use. Only the public LLM models are accepted. e.g. &#x27;gemini-1.5-pro-001&#x27;.
},
},
&quot;seed&quot;: 42, # Optional. Seed.
Expand Down Expand Up @@ -1371,8 +1386,23 @@ <h3>Method Details</h3>
},
&quot;retrieval&quot;: { # Defines a retrieval tool that model can call to access external knowledge. # Optional. Retrieval tool type. System will always execute the provided retrieval tool(s) to get external knowledge to answer the prompt. Retrieval results are presented to the model for generation.
&quot;disableAttribution&quot;: True or False, # Optional. Deprecated. This option is no longer supported.
&quot;vertexAiSearch&quot;: { # Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation # Set to use data source powered by Vertex AI Search.
&quot;datastore&quot;: &quot;A String&quot;, # Required. Fully-qualified Vertex AI Search&#x27;s datastore resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
&quot;vertexAiSearch&quot;: { # Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/products/agent-builder # Set to use data source powered by Vertex AI Search.
&quot;datastore&quot;: &quot;A String&quot;, # Required. Fully-qualified Vertex AI Search data store resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
},
&quot;vertexRagStore&quot;: { # Retrieve from Vertex RAG Store for grounding. # Set to use data source powered by Vertex RAG store. User data is uploaded via the VertexRagDataService.
&quot;ragCorpora&quot;: [ # Optional. Deprecated. Please use rag_resources instead.
&quot;A String&quot;,
],
&quot;ragResources&quot;: [ # Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support.
{ # The definition of the Rag resource.
&quot;ragCorpus&quot;: &quot;A String&quot;, # Optional. RagCorpora resource name. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}`
&quot;ragFileIds&quot;: [ # Optional. rag_file_id. The files should be in the same rag_corpus set in rag_corpus field.
&quot;A String&quot;,
],
},
],
&quot;similarityTopK&quot;: 42, # Optional. Number of top k results to return from the selected corpora.
&quot;vectorDistanceThreshold&quot;: 3.14, # Optional. Only return results with vector distance smaller than the threshold.
},
},
},
Expand Down Expand Up @@ -2831,7 +2861,7 @@ <h3>Method Details</h3>
<pre>Generate content with multimodal inputs with streaming support.

Args:
model: string, Required. The name of the publisher model requested to serve the prediction. Format: `projects/{project}/locations/{location}/publishers/*/models/*` (required)
model: string, Required. The fully qualified name of the publisher model or tuned model endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}` (required)
body: object, The request body.
The object takes the form of:

Expand Down Expand Up @@ -2904,12 +2934,12 @@ <h3>Method Details</h3>
&quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
&quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
},
&quot;routingConfig&quot;: { # Routing config. # Optional. Routing configuration.
&quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. # Optional. Routing configuration.
&quot;autoMode&quot;: { # When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # Automated routing.
&quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
},
&quot;manualMode&quot;: { # When manual routing is set, the specified model will be used directly. # Manual routing.
&quot;modelName&quot;: &quot;A String&quot;, # The model name to use. Only the public LLM models are accepted. e.g. gemini-1.5-pro-001.
&quot;modelName&quot;: &quot;A String&quot;, # The model name to use. Only the public LLM models are accepted. e.g. &#x27;gemini-1.5-pro-001&#x27;.
},
},
&quot;seed&quot;: 42, # Optional. Seed.
Expand Down Expand Up @@ -3007,8 +3037,23 @@ <h3>Method Details</h3>
},
&quot;retrieval&quot;: { # Defines a retrieval tool that model can call to access external knowledge. # Optional. Retrieval tool type. System will always execute the provided retrieval tool(s) to get external knowledge to answer the prompt. Retrieval results are presented to the model for generation.
&quot;disableAttribution&quot;: True or False, # Optional. Deprecated. This option is no longer supported.
&quot;vertexAiSearch&quot;: { # Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation # Set to use data source powered by Vertex AI Search.
&quot;datastore&quot;: &quot;A String&quot;, # Required. Fully-qualified Vertex AI Search&#x27;s datastore resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
&quot;vertexAiSearch&quot;: { # Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/products/agent-builder # Set to use data source powered by Vertex AI Search.
&quot;datastore&quot;: &quot;A String&quot;, # Required. Fully-qualified Vertex AI Search data store resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
},
&quot;vertexRagStore&quot;: { # Retrieve from Vertex RAG Store for grounding. # Set to use data source powered by Vertex RAG store. User data is uploaded via the VertexRagDataService.
&quot;ragCorpora&quot;: [ # Optional. Deprecated. Please use rag_resources instead.
&quot;A String&quot;,
],
&quot;ragResources&quot;: [ # Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support.
{ # The definition of the Rag resource.
&quot;ragCorpus&quot;: &quot;A String&quot;, # Optional. RagCorpora resource name. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}`
&quot;ragFileIds&quot;: [ # Optional. rag_file_id. The files should be in the same rag_corpus set in rag_corpus field.
&quot;A String&quot;,
],
},
],
&quot;similarityTopK&quot;: 42, # Optional. Number of top k results to return from the selected corpora.
&quot;vectorDistanceThreshold&quot;: 3.14, # Optional. Only return results with vector distance smaller than the threshold.
},
},
},
Expand Down
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