gcf-merge-on-green · Jan 23, 2024 · Jan 23, 2024 · Jan 23, 2024 · Jan 23, 2024 · Jan 23, 2024
@@ -133,6 +133,9 @@ <h2>Instance Methods</h2>
 <p class="toc_element">
   <code><a href="#streamGenerateContent">streamGenerateContent(model, body=None, x__xgafv=None)</a></code></p>
 <p class="firstline">Generate content with multimodal inputs with streaming support.</p>
+<p class="toc_element">
+  <code><a href="#streamRawPredict">streamRawPredict(endpoint, body=None, x__xgafv=None)</a></code></p>
+<p class="firstline"></p>
 <p class="toc_element">
   <code><a href="#undeployModel">undeployModel(endpoint, body=None, x__xgafv=None)</a></code></p>
 <p class="firstline">Undeploys a Model from an Endpoint, removing a DeployedModel from it, and freeing all resources it's using.</p>
@@ -2444,6 +2447,46 @@ <h3>Method Details</h3>
 }</pre>
 </div>
 
+<div class="method">
+    <code class="details" id="streamRawPredict">streamRawPredict(endpoint, body=None, x__xgafv=None)</code>
+  <pre>
+
+Args:
+  endpoint: string, Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` (required)
+  body: object, The request body.
+    The object takes the form of:
+
+{ # Request message for PredictionService.StreamRawPredict.
+  &quot;httpBody&quot;: { # Message that represents an arbitrary HTTP body. It should only be used for payload formats that can&#x27;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. # The prediction input. Supports HTTP headers and arbitrary data payload.
+    &quot;contentType&quot;: &quot;A String&quot;, # The HTTP Content-Type header value specifying the content type of the body.
+    &quot;data&quot;: &quot;A String&quot;, # The HTTP request/response body as raw binary.
+    &quot;extensions&quot;: [ # Application specific response metadata. Must be set in the first response for streaming APIs.
+      {
+        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
+      },
+    ],
+  },
+}
+
+  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&#x27;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.
+  &quot;contentType&quot;: &quot;A String&quot;, # The HTTP Content-Type header value specifying the content type of the body.
+  &quot;data&quot;: &quot;A String&quot;, # The HTTP request/response body as raw binary.
+  &quot;extensions&quot;: [ # Application specific response metadata. Must be set in the first response for streaming APIs.
+    {
+      &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
+    },
+  ],
+}</pre>
+</div>
+
 <div class="method">
     <code class="details" id="undeployModel">undeployModel(endpoint, body=None, x__xgafv=None)</code>
   <pre>Undeploys a Model from an Endpoint, removing a DeployedModel from it, and freeing all resources it&#x27;s using.

@@ -116,7 +116,7 @@ <h3>Method Details</h3>
   <pre>Creates a new FeatureOnlineStore in a given project and location.
 
 Args:
-  parent: string, Required. The resource name of the Location to create FeatureOnlineStores. Format: `projects/{project}/locations/{location}&#x27;` (required)
+  parent: string, Required. The resource name of the Location to create FeatureOnlineStores. Format: `projects/{project}/locations/{location}` (required)
   body: object, The request body.
     The object takes the form of:
 

@@ -497,7 +497,7 @@ <h3>Method Details</h3>
   &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
   &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
   &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
     &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
     &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
   },
@@ -745,7 +745,7 @@ <h3>Method Details</h3>
       &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
       &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
       &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-      &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+      &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
         &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
         &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
       },
@@ -996,7 +996,7 @@ <h3>Method Details</h3>
       &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
       &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
       &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-      &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+      &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
         &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
         &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
       },
@@ -1277,7 +1277,7 @@ <h3>Method Details</h3>
   &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
   &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
   &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
     &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
     &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
   },
@@ -1513,7 +1513,7 @@ <h3>Method Details</h3>
   &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
   &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
   &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
     &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
     &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
   },
@@ -1748,7 +1748,7 @@ <h3>Method Details</h3>
   &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
   &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
   &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+  &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
     &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
     &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
   },
@@ -2042,7 +2042,7 @@ <h3>Method Details</h3>
     &quot;metadata&quot;: &quot;&quot;, # Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
     &quot;metadataArtifact&quot;: &quot;A String&quot;, # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
     &quot;metadataSchemaUri&quot;: &quot;A String&quot;, # Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
-    &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
+    &quot;modelSourceInfo&quot;: { # Detail description of the source information of the model. # Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
       &quot;copy&quot;: True or False, # If this Model is copy of another Model. If true then source_type pertains to the original.
       &quot;sourceType&quot;: &quot;A String&quot;, # Type of the model source.
     },