Package com.google.cloud.dialogflow.v2

Class InferenceParameter.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.Builder<InferenceParameter.Builder>
com.google.cloud.dialogflow.v2.InferenceParameter.Builder
All Implemented Interfaces:
InferenceParameterOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
InferenceParameter
public static final class InferenceParameter.Builder extends com.google.protobuf.GeneratedMessage.Builder<InferenceParameter.Builder> implements InferenceParameterOrBuilder
 The parameters of inference.
Protobuf type google.cloud.dialogflow.v2.InferenceParameter

  • Method Details

    • getDescriptor

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.Builder<InferenceParameter.Builder>

    • clear

      public InferenceParameter.Builder clear()
      Specified by:
      clear in interface com.google.protobuf.Message.Builder
      Specified by:
      clear in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clear in class com.google.protobuf.GeneratedMessage.Builder<InferenceParameter.Builder>

    • getDescriptorForType

      public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
      Specified by:
      getDescriptorForType in interface com.google.protobuf.Message.Builder
      Specified by:
      getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
      Overrides:
      getDescriptorForType in class com.google.protobuf.GeneratedMessage.Builder<InferenceParameter.Builder>

    • getDefaultInstanceForType

      public InferenceParameter getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

    • build

      public InferenceParameter build()
      Specified by:
      build in interface com.google.protobuf.Message.Builder
      Specified by:
      build in interface com.google.protobuf.MessageLite.Builder

    • buildPartial

      public InferenceParameter buildPartial()
      Specified by:
      buildPartial in interface com.google.protobuf.Message.Builder
      Specified by:
      buildPartial in interface com.google.protobuf.MessageLite.Builder

    • mergeFrom

      public InferenceParameter.Builder mergeFrom(com.google.protobuf.Message other)
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<InferenceParameter.Builder>

    • mergeFrom

      public InferenceParameter.Builder mergeFrom(InferenceParameter other)

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessage.Builder<InferenceParameter.Builder>

    • mergeFrom

      public InferenceParameter.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Specified by:
      mergeFrom in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<InferenceParameter.Builder>
      Throws:
      IOException

    • hasMaxOutputTokens

      public boolean hasMaxOutputTokens()
       Optional. Maximum number of the output tokens for the generator.
      optional int32 max_output_tokens = 1 [(.google.api.field_behavior) = OPTIONAL];
      Specified by:
      hasMaxOutputTokens in interface InferenceParameterOrBuilder
      Returns:
      Whether the maxOutputTokens field is set.

    • getMaxOutputTokens

      public int getMaxOutputTokens()
       Optional. Maximum number of the output tokens for the generator.
      optional int32 max_output_tokens = 1 [(.google.api.field_behavior) = OPTIONAL];
      Specified by:
      getMaxOutputTokens in interface InferenceParameterOrBuilder
      Returns:
      The maxOutputTokens.

    • setMaxOutputTokens

      public InferenceParameter.Builder setMaxOutputTokens(int value)
       Optional. Maximum number of the output tokens for the generator.
      optional int32 max_output_tokens = 1 [(.google.api.field_behavior) = OPTIONAL];
      Parameters:
      value - The maxOutputTokens to set.
      Returns:
      This builder for chaining.

    • clearMaxOutputTokens

      public InferenceParameter.Builder clearMaxOutputTokens()
       Optional. Maximum number of the output tokens for the generator.
      optional int32 max_output_tokens = 1 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      This builder for chaining.

    • hasTemperature

      public boolean hasTemperature()
       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];
      Specified by:
      hasTemperature in interface InferenceParameterOrBuilder
      Returns:
      Whether the temperature field is set.

    • getTemperature

      public double getTemperature()
       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];
      Specified by:
      getTemperature in interface InferenceParameterOrBuilder
      Returns:
      The temperature.

    • setTemperature

      public InferenceParameter.Builder setTemperature(double value)
       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];
      Parameters:
      value - The temperature to set.
      Returns:
      This builder for chaining.

    • clearTemperature

      public InferenceParameter.Builder clearTemperature()
       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];
      Returns:
      This builder for chaining.

    • hasTopK

      public boolean hasTopK()
       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];
      Specified by:
      hasTopK in interface InferenceParameterOrBuilder
      Returns:
      Whether the topK field is set.

    • getTopK

      public int getTopK()
       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];
      Specified by:
      getTopK in interface InferenceParameterOrBuilder
      Returns:
      The topK.

    • setTopK

      public InferenceParameter.Builder setTopK(int value)
       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];
      Parameters:
      value - The topK to set.
      Returns:
      This builder for chaining.

    • clearTopK

      public InferenceParameter.Builder clearTopK()
       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];
      Returns:
      This builder for chaining.

    • hasTopP

      public boolean hasTopP()
       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];
      Specified by:
      hasTopP in interface InferenceParameterOrBuilder
      Returns:
      Whether the topP field is set.

    • getTopP

      public double getTopP()
       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];
      Specified by:
      getTopP in interface InferenceParameterOrBuilder
      Returns:
      The topP.

    • setTopP

      public InferenceParameter.Builder setTopP(double value)
       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];
      Parameters:
      value - The topP to set.
      Returns:
      This builder for chaining.

    • clearTopP

      public InferenceParameter.Builder clearTopP()
       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];
      Returns:
      This builder for chaining.