Package com.google.cloud.dialogflow.v2

Interface InferenceParameterOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
InferenceParameter, InferenceParameter.Builder
@Generated public interface InferenceParameterOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Summary

    Modifier and Type
    Method
    Description
    int
    getMaxOutputTokens()
    Optional.
    double
    getTemperature()
    Optional.
    int
    getTopK()
    Optional.
    double
    getTopP()
    Optional.
    boolean
    hasMaxOutputTokens()
    Optional.
    boolean
    hasTemperature()
    Optional.
    boolean
    hasTopK()
    Optional.
    boolean
    hasTopP()
    Optional.

    Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

    isInitialized

    Methods inherited from interface com.google.protobuf.MessageOrBuilder

    findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

  • Method Details

    • hasMaxOutputTokens

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

    • getMaxOutputTokens

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

    • hasTemperature

      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];
      Returns:
      Whether the temperature field is set.

    • getTemperature

      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];
      Returns:
      The temperature.

    • hasTopK

      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];
      Returns:
      Whether the topK field is set.

    • getTopK

      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];
      Returns:
      The topK.

    • hasTopP

      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];
      Returns:
      Whether the topP field is set.

    • getTopP

      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];
      Returns:
      The topP.