com.google.cloud.dialogflow.v2.InferenceParameter Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of proto-google-cloud-dialogflow-v2 Show documentation
Show all versions of proto-google-cloud-dialogflow-v2 Show documentation
PROTO library for proto-google-cloud-dialogflow-v2
/*
* Copyright 2024 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: google/cloud/dialogflow/v2/generator.proto
// Protobuf Java Version: 3.25.4
package com.google.cloud.dialogflow.v2;
/**
*
*
*
* The parameters of inference.
*
*
* Protobuf type {@code google.cloud.dialogflow.v2.InferenceParameter}
*/
public final class InferenceParameter extends com.google.protobuf.GeneratedMessageV3
implements
// @@protoc_insertion_point(message_implements:google.cloud.dialogflow.v2.InferenceParameter)
InferenceParameterOrBuilder {
private static final long serialVersionUID = 0L;
// Use InferenceParameter.newBuilder() to construct.
private InferenceParameter(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private InferenceParameter() {}
@java.lang.Override
@SuppressWarnings({"unused"})
protected java.lang.Object newInstance(UnusedPrivateParameter unused) {
return new InferenceParameter();
}
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.dialogflow.v2.GeneratorProto
.internal_static_google_cloud_dialogflow_v2_InferenceParameter_descriptor;
}
@java.lang.Override
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return com.google.cloud.dialogflow.v2.GeneratorProto
.internal_static_google_cloud_dialogflow_v2_InferenceParameter_fieldAccessorTable
.ensureFieldAccessorsInitialized(
com.google.cloud.dialogflow.v2.InferenceParameter.class,
com.google.cloud.dialogflow.v2.InferenceParameter.Builder.class);
}
private int bitField0_;
public static final int MAX_OUTPUT_TOKENS_FIELD_NUMBER = 1;
private int maxOutputTokens_ = 0;
/**
*
*
*
* Optional. Maximum number of the output tokens for the generator.
*
*
* optional int32 max_output_tokens = 1 [(.google.api.field_behavior) = OPTIONAL];
*
* @return Whether the maxOutputTokens field is set.
*/
@java.lang.Override
public boolean hasMaxOutputTokens() {
return ((bitField0_ & 0x00000001) != 0);
}
/**
*
*
*
* Optional. Maximum number of the output tokens for the generator.
*
*
* optional int32 max_output_tokens = 1 [(.google.api.field_behavior) = OPTIONAL];
*
* @return The maxOutputTokens.
*/
@java.lang.Override
public int getMaxOutputTokens() {
return maxOutputTokens_;
}
public static final int TEMPERATURE_FIELD_NUMBER = 2;
private double temperature_ = 0D;
/**
*
*
*
* Optional. Controls the randomness of LLM predictions.
* Low temperature = less random. High temperature = more random.
* If unset (or 0), uses a default value of 0.
*
*
* optional double temperature = 2 [(.google.api.field_behavior) = OPTIONAL];
*
* @return Whether the temperature field is set.
*/
@java.lang.Override
public boolean hasTemperature() {
return ((bitField0_ & 0x00000002) != 0);
}
/**
*
*
*
* Optional. Controls the randomness of LLM predictions.
* Low temperature = less random. High temperature = more random.
* If unset (or 0), uses a default value of 0.
*
*
* optional double temperature = 2 [(.google.api.field_behavior) = OPTIONAL];
*
* @return The temperature.
*/
@java.lang.Override
public double getTemperature() {
return temperature_;
}
public static final int TOP_K_FIELD_NUMBER = 3;
private int topK_ = 0;
/**
*
*
*
* Optional. Top-k changes how the model selects tokens for output. A top-k of
* 1 means the selected token is the most probable among all tokens in the
* model's vocabulary (also called greedy decoding), while a top-k of 3 means
* that the next token is selected from among the 3 most probable tokens
* (using temperature). For each token selection step, the top K tokens with
* the highest probabilities are sampled. Then tokens are further filtered
* based on topP with the final token selected using temperature sampling.
* Specify a lower value for less random responses and a higher value for more
* random responses. Acceptable value is [1, 40], default to 40.
*
*
* optional int32 top_k = 3 [(.google.api.field_behavior) = OPTIONAL];
*
* @return Whether the topK field is set.
*/
@java.lang.Override
public boolean hasTopK() {
return ((bitField0_ & 0x00000004) != 0);
}
/**
*
*
*
* Optional. Top-k changes how the model selects tokens for output. A top-k of
* 1 means the selected token is the most probable among all tokens in the
* model's vocabulary (also called greedy decoding), while a top-k of 3 means
* that the next token is selected from among the 3 most probable tokens
* (using temperature). For each token selection step, the top K tokens with
* the highest probabilities are sampled. Then tokens are further filtered
* based on topP with the final token selected using temperature sampling.
* Specify a lower value for less random responses and a higher value for more
* random responses. Acceptable value is [1, 40], default to 40.
*
*
* optional int32 top_k = 3 [(.google.api.field_behavior) = OPTIONAL];
*
* @return The topK.
*/
@java.lang.Override
public int getTopK() {
return topK_;
}
public static final int TOP_P_FIELD_NUMBER = 4;
private double topP_ = 0D;
/**
*
*
*
* Optional. Top-p changes how the model selects tokens for output. Tokens are
* selected from most K (see topK parameter) probable to least until the sum
* of their probabilities equals the top-p value. For example, if tokens A, B,
* and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5,
* then the model will select either A or B as the next token (using
* temperature) and doesn't consider C. The default top-p value is 0.95.
* Specify a lower value for less random responses and a higher value for more
* random responses. Acceptable value is [0.0, 1.0], default to 0.95.
*
*
* optional double top_p = 4 [(.google.api.field_behavior) = OPTIONAL];
*
* @return Whether the topP field is set.
*/
@java.lang.Override
public boolean hasTopP() {
return ((bitField0_ & 0x00000008) != 0);
}
/**
*
*
*
* Optional. Top-p changes how the model selects tokens for output. Tokens are
* selected from most K (see topK parameter) probable to least until the sum
* of their probabilities equals the top-p value. For example, if tokens A, B,
* and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5,
* then the model will select either A or B as the next token (using
* temperature) and doesn't consider C. The default top-p value is 0.95.
* Specify a lower value for less random responses and a higher value for more
* random responses. Acceptable value is [0.0, 1.0], default to 0.95.
*
*
* optional double top_p = 4 [(.google.api.field_behavior) = OPTIONAL];
*
* @return The topP.
*/
@java.lang.Override
public double getTopP() {
return topP_;
}
private byte memoizedIsInitialized = -1;
@java.lang.Override
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized == 1) return true;
if (isInitialized == 0) return false;
memoizedIsInitialized = 1;
return true;
}
@java.lang.Override
public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException {
if (((bitField0_ & 0x00000001) != 0)) {
output.writeInt32(1, maxOutputTokens_);
}
if (((bitField0_ & 0x00000002) != 0)) {
output.writeDouble(2, temperature_);
}
if (((bitField0_ & 0x00000004) != 0)) {
output.writeInt32(3, topK_);
}
if (((bitField0_ & 0x00000008) != 0)) {
output.writeDouble(4, topP_);
}
getUnknownFields().writeTo(output);
}
@java.lang.Override
public int getSerializedSize() {
int size = memoizedSize;
if (size != -1) return size;
size = 0;
if (((bitField0_ & 0x00000001) != 0)) {
size += com.google.protobuf.CodedOutputStream.computeInt32Size(1, maxOutputTokens_);
}
if (((bitField0_ & 0x00000002) != 0)) {
size += com.google.protobuf.CodedOutputStream.computeDoubleSize(2, temperature_);
}
if (((bitField0_ & 0x00000004) != 0)) {
size += com.google.protobuf.CodedOutputStream.computeInt32Size(3, topK_);
}
if (((bitField0_ & 0x00000008) != 0)) {
size += com.google.protobuf.CodedOutputStream.computeDoubleSize(4, topP_);
}
size += getUnknownFields().getSerializedSize();
memoizedSize = size;
return size;
}
@java.lang.Override
public boolean equals(final java.lang.Object obj) {
if (obj == this) {
return true;
}
if (!(obj instanceof com.google.cloud.dialogflow.v2.InferenceParameter)) {
return super.equals(obj);
}
com.google.cloud.dialogflow.v2.InferenceParameter other =
(com.google.cloud.dialogflow.v2.InferenceParameter) obj;
if (hasMaxOutputTokens() != other.hasMaxOutputTokens()) return false;
if (hasMaxOutputTokens()) {
if (getMaxOutputTokens() != other.getMaxOutputTokens()) return false;
}
if (hasTemperature() != other.hasTemperature()) return false;
if (hasTemperature()) {
if (java.lang.Double.doubleToLongBits(getTemperature())
!= java.lang.Double.doubleToLongBits(other.getTemperature())) return false;
}
if (hasTopK() != other.hasTopK()) return false;
if (hasTopK()) {
if (getTopK() != other.getTopK()) return false;
}
if (hasTopP() != other.hasTopP()) return false;
if (hasTopP()) {
if (java.lang.Double.doubleToLongBits(getTopP())
!= java.lang.Double.doubleToLongBits(other.getTopP())) return false;
}
if (!getUnknownFields().equals(other.getUnknownFields())) return false;
return true;
}
@java.lang.Override
public int hashCode() {
if (memoizedHashCode != 0) {
return memoizedHashCode;
}
int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
if (hasMaxOutputTokens()) {
hash = (37 * hash) + MAX_OUTPUT_TOKENS_FIELD_NUMBER;
hash = (53 * hash) + getMaxOutputTokens();
}
if (hasTemperature()) {
hash = (37 * hash) + TEMPERATURE_FIELD_NUMBER;
hash =
(53 * hash)
+ com.google.protobuf.Internal.hashLong(
java.lang.Double.doubleToLongBits(getTemperature()));
}
if (hasTopK()) {
hash = (37 * hash) + TOP_K_FIELD_NUMBER;
hash = (53 * hash) + getTopK();
}
if (hasTopP()) {
hash = (37 * hash) + TOP_P_FIELD_NUMBER;
hash =
(53 * hash)
+ com.google.protobuf.Internal.hashLong(java.lang.Double.doubleToLongBits(getTopP()));
}
hash = (29 * hash) + getUnknownFields().hashCode();
memoizedHashCode = hash;
return hash;
}
public static com.google.cloud.dialogflow.v2.InferenceParameter parseFrom(
java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.dialogflow.v2.InferenceParameter parseFrom(
java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.dialogflow.v2.InferenceParameter parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.dialogflow.v2.InferenceParameter parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.dialogflow.v2.InferenceParameter parseFrom(byte[] data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.dialogflow.v2.InferenceParameter parseFrom(
byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.dialogflow.v2.InferenceParameter parseFrom(
java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.dialogflow.v2.InferenceParameter parseFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.dialogflow.v2.InferenceParameter parseDelimitedFrom(
java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(PARSER, input);
}
public static com.google.cloud.dialogflow.v2.InferenceParameter parseDelimitedFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.dialogflow.v2.InferenceParameter parseFrom(
com.google.protobuf.CodedInputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.dialogflow.v2.InferenceParameter parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
@java.lang.Override
public Builder newBuilderForType() {
return newBuilder();
}
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(com.google.cloud.dialogflow.v2.InferenceParameter prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
@java.lang.Override
public Builder toBuilder() {
return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this);
}
@java.lang.Override
protected Builder newBuilderForType(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
*
*
* The parameters of inference.
*
*
* Protobuf type {@code google.cloud.dialogflow.v2.InferenceParameter}
*/
public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder
implements
// @@protoc_insertion_point(builder_implements:google.cloud.dialogflow.v2.InferenceParameter)
com.google.cloud.dialogflow.v2.InferenceParameterOrBuilder {
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.dialogflow.v2.GeneratorProto
.internal_static_google_cloud_dialogflow_v2_InferenceParameter_descriptor;
}
@java.lang.Override
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return com.google.cloud.dialogflow.v2.GeneratorProto
.internal_static_google_cloud_dialogflow_v2_InferenceParameter_fieldAccessorTable
.ensureFieldAccessorsInitialized(
com.google.cloud.dialogflow.v2.InferenceParameter.class,
com.google.cloud.dialogflow.v2.InferenceParameter.Builder.class);
}
// Construct using com.google.cloud.dialogflow.v2.InferenceParameter.newBuilder()
private Builder() {}
private Builder(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
super(parent);
}
@java.lang.Override
public Builder clear() {
super.clear();
bitField0_ = 0;
maxOutputTokens_ = 0;
temperature_ = 0D;
topK_ = 0;
topP_ = 0D;
return this;
}
@java.lang.Override
public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() {
return com.google.cloud.dialogflow.v2.GeneratorProto
.internal_static_google_cloud_dialogflow_v2_InferenceParameter_descriptor;
}
@java.lang.Override
public com.google.cloud.dialogflow.v2.InferenceParameter getDefaultInstanceForType() {
return com.google.cloud.dialogflow.v2.InferenceParameter.getDefaultInstance();
}
@java.lang.Override
public com.google.cloud.dialogflow.v2.InferenceParameter build() {
com.google.cloud.dialogflow.v2.InferenceParameter result = buildPartial();
if (!result.isInitialized()) {
throw newUninitializedMessageException(result);
}
return result;
}
@java.lang.Override
public com.google.cloud.dialogflow.v2.InferenceParameter buildPartial() {
com.google.cloud.dialogflow.v2.InferenceParameter result =
new com.google.cloud.dialogflow.v2.InferenceParameter(this);
if (bitField0_ != 0) {
buildPartial0(result);
}
onBuilt();
return result;
}
private void buildPartial0(com.google.cloud.dialogflow.v2.InferenceParameter result) {
int from_bitField0_ = bitField0_;
int to_bitField0_ = 0;
if (((from_bitField0_ & 0x00000001) != 0)) {
result.maxOutputTokens_ = maxOutputTokens_;
to_bitField0_ |= 0x00000001;
}
if (((from_bitField0_ & 0x00000002) != 0)) {
result.temperature_ = temperature_;
to_bitField0_ |= 0x00000002;
}
if (((from_bitField0_ & 0x00000004) != 0)) {
result.topK_ = topK_;
to_bitField0_ |= 0x00000004;
}
if (((from_bitField0_ & 0x00000008) != 0)) {
result.topP_ = topP_;
to_bitField0_ |= 0x00000008;
}
result.bitField0_ |= to_bitField0_;
}
@java.lang.Override
public Builder clone() {
return super.clone();
}
@java.lang.Override
public Builder setField(
com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) {
return super.setField(field, value);
}
@java.lang.Override
public Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field) {
return super.clearField(field);
}
@java.lang.Override
public Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof) {
return super.clearOneof(oneof);
}
@java.lang.Override
public Builder setRepeatedField(
com.google.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value) {
return super.setRepeatedField(field, index, value);
}
@java.lang.Override
public Builder addRepeatedField(
com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) {
return super.addRepeatedField(field, value);
}
@java.lang.Override
public Builder mergeFrom(com.google.protobuf.Message other) {
if (other instanceof com.google.cloud.dialogflow.v2.InferenceParameter) {
return mergeFrom((com.google.cloud.dialogflow.v2.InferenceParameter) other);
} else {
super.mergeFrom(other);
return this;
}
}
public Builder mergeFrom(com.google.cloud.dialogflow.v2.InferenceParameter other) {
if (other == com.google.cloud.dialogflow.v2.InferenceParameter.getDefaultInstance())
return this;
if (other.hasMaxOutputTokens()) {
setMaxOutputTokens(other.getMaxOutputTokens());
}
if (other.hasTemperature()) {
setTemperature(other.getTemperature());
}
if (other.hasTopK()) {
setTopK(other.getTopK());
}
if (other.hasTopP()) {
setTopP(other.getTopP());
}
this.mergeUnknownFields(other.getUnknownFields());
onChanged();
return this;
}
@java.lang.Override
public final boolean isInitialized() {
return true;
}
@java.lang.Override
public Builder mergeFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
if (extensionRegistry == null) {
throw new java.lang.NullPointerException();
}
try {
boolean done = false;
while (!done) {
int tag = input.readTag();
switch (tag) {
case 0:
done = true;
break;
case 8:
{
maxOutputTokens_ = input.readInt32();
bitField0_ |= 0x00000001;
break;
} // case 8
case 17:
{
temperature_ = input.readDouble();
bitField0_ |= 0x00000002;
break;
} // case 17
case 24:
{
topK_ = input.readInt32();
bitField0_ |= 0x00000004;
break;
} // case 24
case 33:
{
topP_ = input.readDouble();
bitField0_ |= 0x00000008;
break;
} // case 33
default:
{
if (!super.parseUnknownField(input, extensionRegistry, tag)) {
done = true; // was an endgroup tag
}
break;
} // default:
} // switch (tag)
} // while (!done)
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.unwrapIOException();
} finally {
onChanged();
} // finally
return this;
}
private int bitField0_;
private int maxOutputTokens_;
/**
*
*
*
* Optional. Maximum number of the output tokens for the generator.
*
*
* optional int32 max_output_tokens = 1 [(.google.api.field_behavior) = OPTIONAL];
*
* @return Whether the maxOutputTokens field is set.
*/
@java.lang.Override
public boolean hasMaxOutputTokens() {
return ((bitField0_ & 0x00000001) != 0);
}
/**
*
*
*
* Optional. Maximum number of the output tokens for the generator.
*
*
* optional int32 max_output_tokens = 1 [(.google.api.field_behavior) = OPTIONAL];
*
* @return The maxOutputTokens.
*/
@java.lang.Override
public int getMaxOutputTokens() {
return maxOutputTokens_;
}
/**
*
*
*
* Optional. Maximum number of the output tokens for the generator.
*
*
* optional int32 max_output_tokens = 1 [(.google.api.field_behavior) = OPTIONAL];
*
* @param value The maxOutputTokens to set.
* @return This builder for chaining.
*/
public Builder setMaxOutputTokens(int value) {
maxOutputTokens_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
/**
*
*
*
* Optional. Maximum number of the output tokens for the generator.
*
*
* optional int32 max_output_tokens = 1 [(.google.api.field_behavior) = OPTIONAL];
*
* @return This builder for chaining.
*/
public Builder clearMaxOutputTokens() {
bitField0_ = (bitField0_ & ~0x00000001);
maxOutputTokens_ = 0;
onChanged();
return this;
}
private double temperature_;
/**
*
*
*
* Optional. Controls the randomness of LLM predictions.
* Low temperature = less random. High temperature = more random.
* If unset (or 0), uses a default value of 0.
*
*
* optional double temperature = 2 [(.google.api.field_behavior) = OPTIONAL];
*
* @return Whether the temperature field is set.
*/
@java.lang.Override
public boolean hasTemperature() {
return ((bitField0_ & 0x00000002) != 0);
}
/**
*
*
*
* Optional. Controls the randomness of LLM predictions.
* Low temperature = less random. High temperature = more random.
* If unset (or 0), uses a default value of 0.
*
*
* optional double temperature = 2 [(.google.api.field_behavior) = OPTIONAL];
*
* @return The temperature.
*/
@java.lang.Override
public double getTemperature() {
return temperature_;
}
/**
*
*
*
* Optional. Controls the randomness of LLM predictions.
* Low temperature = less random. High temperature = more random.
* If unset (or 0), uses a default value of 0.
*
*
* optional double temperature = 2 [(.google.api.field_behavior) = OPTIONAL];
*
* @param value The temperature to set.
* @return This builder for chaining.
*/
public Builder setTemperature(double value) {
temperature_ = value;
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* Optional. Controls the randomness of LLM predictions.
* Low temperature = less random. High temperature = more random.
* If unset (or 0), uses a default value of 0.
*
*
* optional double temperature = 2 [(.google.api.field_behavior) = OPTIONAL];
*
* @return This builder for chaining.
*/
public Builder clearTemperature() {
bitField0_ = (bitField0_ & ~0x00000002);
temperature_ = 0D;
onChanged();
return this;
}
private int topK_;
/**
*
*
*
* Optional. Top-k changes how the model selects tokens for output. A top-k of
* 1 means the selected token is the most probable among all tokens in the
* model's vocabulary (also called greedy decoding), while a top-k of 3 means
* that the next token is selected from among the 3 most probable tokens
* (using temperature). For each token selection step, the top K tokens with
* the highest probabilities are sampled. Then tokens are further filtered
* based on topP with the final token selected using temperature sampling.
* Specify a lower value for less random responses and a higher value for more
* random responses. Acceptable value is [1, 40], default to 40.
*
*
* optional int32 top_k = 3 [(.google.api.field_behavior) = OPTIONAL];
*
* @return Whether the topK field is set.
*/
@java.lang.Override
public boolean hasTopK() {
return ((bitField0_ & 0x00000004) != 0);
}
/**
*
*
*
* Optional. Top-k changes how the model selects tokens for output. A top-k of
* 1 means the selected token is the most probable among all tokens in the
* model's vocabulary (also called greedy decoding), while a top-k of 3 means
* that the next token is selected from among the 3 most probable tokens
* (using temperature). For each token selection step, the top K tokens with
* the highest probabilities are sampled. Then tokens are further filtered
* based on topP with the final token selected using temperature sampling.
* Specify a lower value for less random responses and a higher value for more
* random responses. Acceptable value is [1, 40], default to 40.
*
*
* optional int32 top_k = 3 [(.google.api.field_behavior) = OPTIONAL];
*
* @return The topK.
*/
@java.lang.Override
public int getTopK() {
return topK_;
}
/**
*
*
*
* Optional. Top-k changes how the model selects tokens for output. A top-k of
* 1 means the selected token is the most probable among all tokens in the
* model's vocabulary (also called greedy decoding), while a top-k of 3 means
* that the next token is selected from among the 3 most probable tokens
* (using temperature). For each token selection step, the top K tokens with
* the highest probabilities are sampled. Then tokens are further filtered
* based on topP with the final token selected using temperature sampling.
* Specify a lower value for less random responses and a higher value for more
* random responses. Acceptable value is [1, 40], default to 40.
*
*
* optional int32 top_k = 3 [(.google.api.field_behavior) = OPTIONAL];
*
* @param value The topK to set.
* @return This builder for chaining.
*/
public Builder setTopK(int value) {
topK_ = value;
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* Optional. Top-k changes how the model selects tokens for output. A top-k of
* 1 means the selected token is the most probable among all tokens in the
* model's vocabulary (also called greedy decoding), while a top-k of 3 means
* that the next token is selected from among the 3 most probable tokens
* (using temperature). For each token selection step, the top K tokens with
* the highest probabilities are sampled. Then tokens are further filtered
* based on topP with the final token selected using temperature sampling.
* Specify a lower value for less random responses and a higher value for more
* random responses. Acceptable value is [1, 40], default to 40.
*
*
* optional int32 top_k = 3 [(.google.api.field_behavior) = OPTIONAL];
*
* @return This builder for chaining.
*/
public Builder clearTopK() {
bitField0_ = (bitField0_ & ~0x00000004);
topK_ = 0;
onChanged();
return this;
}
private double topP_;
/**
*
*
*
* Optional. Top-p changes how the model selects tokens for output. Tokens are
* selected from most K (see topK parameter) probable to least until the sum
* of their probabilities equals the top-p value. For example, if tokens A, B,
* and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5,
* then the model will select either A or B as the next token (using
* temperature) and doesn't consider C. The default top-p value is 0.95.
* Specify a lower value for less random responses and a higher value for more
* random responses. Acceptable value is [0.0, 1.0], default to 0.95.
*
*
* optional double top_p = 4 [(.google.api.field_behavior) = OPTIONAL];
*
* @return Whether the topP field is set.
*/
@java.lang.Override
public boolean hasTopP() {
return ((bitField0_ & 0x00000008) != 0);
}
/**
*
*
*
* Optional. Top-p changes how the model selects tokens for output. Tokens are
* selected from most K (see topK parameter) probable to least until the sum
* of their probabilities equals the top-p value. For example, if tokens A, B,
* and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5,
* then the model will select either A or B as the next token (using
* temperature) and doesn't consider C. The default top-p value is 0.95.
* Specify a lower value for less random responses and a higher value for more
* random responses. Acceptable value is [0.0, 1.0], default to 0.95.
*
*
* optional double top_p = 4 [(.google.api.field_behavior) = OPTIONAL];
*
* @return The topP.
*/
@java.lang.Override
public double getTopP() {
return topP_;
}
/**
*
*
*
* Optional. Top-p changes how the model selects tokens for output. Tokens are
* selected from most K (see topK parameter) probable to least until the sum
* of their probabilities equals the top-p value. For example, if tokens A, B,
* and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5,
* then the model will select either A or B as the next token (using
* temperature) and doesn't consider C. The default top-p value is 0.95.
* Specify a lower value for less random responses and a higher value for more
* random responses. Acceptable value is [0.0, 1.0], default to 0.95.
*
*
* optional double top_p = 4 [(.google.api.field_behavior) = OPTIONAL];
*
* @param value The topP to set.
* @return This builder for chaining.
*/
public Builder setTopP(double value) {
topP_ = value;
bitField0_ |= 0x00000008;
onChanged();
return this;
}
/**
*
*
*
* Optional. Top-p changes how the model selects tokens for output. Tokens are
* selected from most K (see topK parameter) probable to least until the sum
* of their probabilities equals the top-p value. For example, if tokens A, B,
* and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5,
* then the model will select either A or B as the next token (using
* temperature) and doesn't consider C. The default top-p value is 0.95.
* Specify a lower value for less random responses and a higher value for more
* random responses. Acceptable value is [0.0, 1.0], default to 0.95.
*
*
* optional double top_p = 4 [(.google.api.field_behavior) = OPTIONAL];
*
* @return This builder for chaining.
*/
public Builder clearTopP() {
bitField0_ = (bitField0_ & ~0x00000008);
topP_ = 0D;
onChanged();
return this;
}
@java.lang.Override
public final Builder setUnknownFields(final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:google.cloud.dialogflow.v2.InferenceParameter)
}
// @@protoc_insertion_point(class_scope:google.cloud.dialogflow.v2.InferenceParameter)
private static final com.google.cloud.dialogflow.v2.InferenceParameter DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new com.google.cloud.dialogflow.v2.InferenceParameter();
}
public static com.google.cloud.dialogflow.v2.InferenceParameter getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser PARSER =
new com.google.protobuf.AbstractParser() {
@java.lang.Override
public InferenceParameter parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
Builder builder = newBuilder();
try {
builder.mergeFrom(input, extensionRegistry);
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(builder.buildPartial());
} catch (com.google.protobuf.UninitializedMessageException e) {
throw e.asInvalidProtocolBufferException().setUnfinishedMessage(builder.buildPartial());
} catch (java.io.IOException e) {
throw new com.google.protobuf.InvalidProtocolBufferException(e)
.setUnfinishedMessage(builder.buildPartial());
}
return builder.buildPartial();
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public com.google.cloud.dialogflow.v2.InferenceParameter getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}