rbi/lib/anthropic/models/completion_create_params.rbi

# typed: strong

module Anthropic
  module Models
    class CompletionCreateParams < Anthropic::Internal::Type::BaseModel
      extend Anthropic::Internal::Type::RequestParameters::Converter
      include Anthropic::Internal::Type::RequestParameters

      # The maximum number of tokens to generate before stopping.
      #
      # Note that our models may stop _before_ reaching this maximum. This parameter
      # only specifies the absolute maximum number of tokens to generate.
      sig { returns(Integer) }
      attr_accessor :max_tokens_to_sample

      # The model that will complete your prompt.\n\nSee
      # [models](https://docs.anthropic.com/en/docs/models-overview) for additional
      # details and options.
      sig { returns(T.any(Anthropic::Models::Model::OrSymbol, String)) }
      attr_accessor :model

      # The prompt that you want Claude to complete.
      #
      # For proper response generation you will need to format your prompt using
      # alternating `\n\nHuman:` and `\n\nAssistant:` conversational turns. For example:
      #
      # ```
      # "\n\nHuman: {userQuestion}\n\nAssistant:"
      # ```
      #
      # See [prompt validation](https://docs.anthropic.com/en/api/prompt-validation) and
      # our guide to
      # [prompt design](https://docs.anthropic.com/en/docs/intro-to-prompting) for more
      # details.
      sig { returns(String) }
      attr_accessor :prompt

      # An object describing metadata about the request.
      sig { returns(T.nilable(Anthropic::Models::Metadata)) }
      attr_reader :metadata

      sig { params(metadata: T.any(Anthropic::Models::Metadata, Anthropic::Internal::AnyHash)).void }
      attr_writer :metadata

      # Sequences that will cause the model to stop generating.
      #
      # Our models stop on `"\n\nHuman:"`, and may include additional built-in stop
      # sequences in the future. By providing the stop_sequences parameter, you may
      # include additional strings that will cause the model to stop generating.
      sig { returns(T.nilable(T::Array[String])) }
      attr_reader :stop_sequences

      sig { params(stop_sequences: T::Array[String]).void }
      attr_writer :stop_sequences

      # Amount of randomness injected into the response.
      #
      # Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0`
      # for analytical / multiple choice, and closer to `1.0` for creative and
      # generative tasks.
      #
      # Note that even with `temperature` of `0.0`, the results will not be fully
      # deterministic.
      sig { returns(T.nilable(Float)) }
      attr_reader :temperature

      sig { params(temperature: Float).void }
      attr_writer :temperature

      # Only sample from the top K options for each subsequent token.
      #
      # Used to remove "long tail" low probability responses.
      # [Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277).
      #
      # Recommended for advanced use cases only. You usually only need to use
      # `temperature`.
      sig { returns(T.nilable(Integer)) }
      attr_reader :top_k

      sig { params(top_k: Integer).void }
      attr_writer :top_k

      # Use nucleus sampling.
      #
      # In nucleus sampling, we compute the cumulative distribution over all the options
      # for each subsequent token in decreasing probability order and cut it off once it
      # reaches a particular probability specified by `top_p`. You should either alter
      # `temperature` or `top_p`, but not both.
      #
      # Recommended for advanced use cases only. You usually only need to use
      # `temperature`.
      sig { returns(T.nilable(Float)) }
      attr_reader :top_p

      sig { params(top_p: Float).void }
      attr_writer :top_p

      sig do
        params(
          max_tokens_to_sample: Integer,
          model: T.any(Anthropic::Models::Model::OrSymbol, String),
          prompt: String,
          metadata: T.any(Anthropic::Models::Metadata, Anthropic::Internal::AnyHash),
          stop_sequences: T::Array[String],
          temperature: Float,
          top_k: Integer,
          top_p: Float,
          request_options: T.any(Anthropic::RequestOptions, Anthropic::Internal::AnyHash)
        )
          .returns(T.attached_class)
      end
      def self.new(
        max_tokens_to_sample:,
        model:,
        prompt:,
        metadata: nil,
        stop_sequences: nil,
        temperature: nil,
        top_k: nil,
        top_p: nil,
        request_options: {}
      ); end
      sig do
        override
          .returns(
            {
              max_tokens_to_sample: Integer,
              model: T.any(Anthropic::Models::Model::OrSymbol, String),
              prompt: String,
              metadata: Anthropic::Models::Metadata,
              stop_sequences: T::Array[String],
              temperature: Float,
              top_k: Integer,
              top_p: Float,
              request_options: Anthropic::RequestOptions
            }
          )
      end
      def to_hash; end
    end
  end
end