Starting April 29, 2025, Gemini 1.5 Pro and Gemini 1.5 Flash models are not available in projects that have no prior usage of these models, including new projects. For details, see Model versions and lifecycle.
The prediction input. Supports HTTP headers and arbitrary data payload.
A DeployedModel may have an
upper limit on the number of instances it supports per request. When this
limit it is exceeded for an AutoML model, the
RawPredict
method returns an error. When this limit is exceeded for a custom-trained
model, the behavior varies depending on the model.
You can specify the schema for each instance in the
predict_schemata.instance_schema_uri
field when you create a Model. This
schema applies when you deploy the Model as a DeployedModel to an
Endpoint and use the RawPredict
method.
The prediction input. Supports HTTP headers and arbitrary data payload.
A DeployedModel may have an
upper limit on the number of instances it supports per request. When this
limit it is exceeded for an AutoML model, the
RawPredict
method returns an error. When this limit is exceeded for a custom-trained
model, the behavior varies depending on the model.
You can specify the schema for each instance in the
predict_schemata.instance_schema_uri
field when you create a Model. This
schema applies when you deploy the Model as a DeployedModel to an
Endpoint and use the RawPredict
method.
.google.api.HttpBody http_body = 2;
Returns
Type
Description
com.google.api.HttpBodyOrBuilder
hasHttpBody()
publicabstractbooleanhasHttpBody()
The prediction input. Supports HTTP headers and arbitrary data payload.
A DeployedModel may have an
upper limit on the number of instances it supports per request. When this
limit it is exceeded for an AutoML model, the
RawPredict
method returns an error. When this limit is exceeded for a custom-trained
model, the behavior varies depending on the model.
You can specify the schema for each instance in the
predict_schemata.instance_schema_uri
field when you create a Model. This
schema applies when you deploy the Model as a DeployedModel to an
Endpoint and use the RawPredict
method.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-03 UTC."],[],[],null,["# Interface RawPredictRequestOrBuilder (1.32.0)\n\n public interface RawPredictRequestOrBuilder extends MessageOrBuilder\n\nImplements\n----------\n\n[MessageOrBuilder](https://cloud.google.com/java/docs/reference/protobuf/latest/com.google.protobuf.MessageOrBuilder.html)\n\nMethods\n-------\n\n### getEndpoint()\n\n public abstract String getEndpoint()\n\nRequired. The name of the Endpoint requested to serve the prediction.\nFormat:\n`projects/{project}/locations/{location}/endpoints/{endpoint}`\n\n`\nstring endpoint = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }\n`\n\n### getEndpointBytes()\n\n public abstract ByteString getEndpointBytes()\n\nRequired. The name of the Endpoint requested to serve the prediction.\nFormat:\n`projects/{project}/locations/{location}/endpoints/{endpoint}`\n\n`\nstring endpoint = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }\n`\n\n### getHttpBody()\n\n public abstract HttpBody getHttpBody()\n\nThe prediction input. Supports HTTP headers and arbitrary data payload.\n\nA DeployedModel may have an\nupper limit on the number of instances it supports per request. When this\nlimit it is exceeded for an AutoML model, the\nRawPredict\nmethod returns an error. When this limit is exceeded for a custom-trained\nmodel, the behavior varies depending on the model.\n\nYou can specify the schema for each instance in the\npredict_schemata.instance_schema_uri\nfield when you create a Model. This\nschema applies when you deploy the `Model` as a `DeployedModel` to an\nEndpoint and use the `RawPredict`\nmethod.\n\n`.google.api.HttpBody http_body = 2;`\n\n### getHttpBodyOrBuilder()\n\n public abstract HttpBodyOrBuilder getHttpBodyOrBuilder()\n\nThe prediction input. Supports HTTP headers and arbitrary data payload.\n\nA DeployedModel may have an\nupper limit on the number of instances it supports per request. When this\nlimit it is exceeded for an AutoML model, the\nRawPredict\nmethod returns an error. When this limit is exceeded for a custom-trained\nmodel, the behavior varies depending on the model.\n\nYou can specify the schema for each instance in the\npredict_schemata.instance_schema_uri\nfield when you create a Model. This\nschema applies when you deploy the `Model` as a `DeployedModel` to an\nEndpoint and use the `RawPredict`\nmethod.\n\n`.google.api.HttpBody http_body = 2;`\n\n### hasHttpBody()\n\n public abstract boolean hasHttpBody()\n\nThe prediction input. Supports HTTP headers and arbitrary data payload.\n\nA DeployedModel may have an\nupper limit on the number of instances it supports per request. When this\nlimit it is exceeded for an AutoML model, the\nRawPredict\nmethod returns an error. When this limit is exceeded for a custom-trained\nmodel, the behavior varies depending on the model.\n\nYou can specify the schema for each instance in the\npredict_schemata.instance_schema_uri\nfield when you create a Model. This\nschema applies when you deploy the `Model` as a `DeployedModel` to an\nEndpoint and use the `RawPredict`\nmethod.\n\n`.google.api.HttpBody http_body = 2;`"]]
