Package software.amazon.awssdk.services.bedrockagentruntime.model

Interface InferenceConfiguration.Builder

All Superinterfaces:
Buildable, CopyableBuilder<InferenceConfiguration.Builder,InferenceConfiguration>, SdkBuilder<InferenceConfiguration.Builder,InferenceConfiguration>, SdkPojo
Enclosing class:
InferenceConfiguration
public static interface InferenceConfiguration.Builder extends SdkPojo, CopyableBuilder<InferenceConfiguration.Builder,InferenceConfiguration>

  • Method Details

    • maximumLength

      InferenceConfiguration.Builder maximumLength(Integer maximumLength)

      The maximum number of tokens allowed in the generated response.

      Parameters:
      maximumLength - The maximum number of tokens allowed in the generated response.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • stopSequences

      InferenceConfiguration.Builder stopSequences(Collection<String> stopSequences)

      A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop generating the response.

      Parameters:
      stopSequences - A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop generating the response.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • stopSequences

      InferenceConfiguration.Builder stopSequences(String... stopSequences)

      A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop generating the response.

      Parameters:
      stopSequences - A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop generating the response.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • temperature

      InferenceConfiguration.Builder temperature(Float temperature)

      The likelihood of the model selecting higher-probability options while generating a response. A lower value makes the model more likely to choose higher-probability options, while a higher value makes the model more likely to choose lower-probability options.

      Parameters:
      temperature - The likelihood of the model selecting higher-probability options while generating a response. A lower value makes the model more likely to choose higher-probability options, while a higher value makes the model more likely to choose lower-probability options.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • topK

      InferenceConfiguration.Builder topK(Integer topK)

      While generating a response, the model determines the probability of the following token at each point of generation. The value that you set for topK is the number of most-likely candidates from which the model chooses the next token in the sequence. For example, if you set topK to 50, the model selects the next token from among the top 50 most likely choices.

      Parameters:
      topK - While generating a response, the model determines the probability of the following token at each point of generation. The value that you set for topK is the number of most-likely candidates from which the model chooses the next token in the sequence. For example, if you set topK to 50, the model selects the next token from among the top 50 most likely choices.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • topP

      InferenceConfiguration.Builder topP(Float topP)

      While generating a response, the model determines the probability of the following token at each point of generation. The value that you set for Top P determines the number of most-likely candidates from which the model chooses the next token in the sequence. For example, if you set topP to 80, the model only selects the next token from the top 80% of the probability distribution of next tokens.

      Parameters:
      topP - While generating a response, the model determines the probability of the following token at each point of generation. The value that you set for Top P determines the number of most-likely candidates from which the model chooses the next token in the sequence. For example, if you set topP to 80, the model only selects the next token from the top 80% of the probability distribution of next tokens.
      Returns:
      Returns a reference to this object so that method calls can be chained together.