rbi/lib/anthropic/models/beta/message_create_params.rbi

# typed: strong

module Anthropic
  module Models
    module Beta
      class MessageCreateParams < 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.
        #
        # Different models have different maximum values for this parameter. See
        # [models](https://docs.anthropic.com/en/docs/models-overview) for details.
        sig { returns(Integer) }
        attr_accessor :max_tokens

        # Input messages.
        #
        # Our models are trained to operate on alternating `user` and `assistant`
        # conversational turns. When creating a new `Message`, you specify the prior
        # conversational turns with the `messages` parameter, and the model then generates
        # the next `Message` in the conversation. Consecutive `user` or `assistant` turns
        # in your request will be combined into a single turn.
        #
        # Each input message must be an object with a `role` and `content`. You can
        # specify a single `user`-role message, or you can include multiple `user` and
        # `assistant` messages.
        #
        # If the final message uses the `assistant` role, the response content will
        # continue immediately from the content in that message. This can be used to
        # constrain part of the model's response.
        #
        # Example with a single `user` message:
        #
        # ```json
        # [{ "role": "user", "content": "Hello, Claude" }]
        # ```
        #
        # Example with multiple conversational turns:
        #
        # ```json
        # [
        #   { "role": "user", "content": "Hello there." },
        #   { "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" },
        #   { "role": "user", "content": "Can you explain LLMs in plain English?" }
        # ]
        # ```
        #
        # Example with a partially-filled response from Claude:
        #
        # ```json
        # [
        #   {
        #     "role": "user",
        #     "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"
        #   },
        #   { "role": "assistant", "content": "The best answer is (" }
        # ]
        # ```
        #
        # Each input message `content` may be either a single `string` or an array of
        # content blocks, where each block has a specific `type`. Using a `string` for
        # `content` is shorthand for an array of one content block of type `"text"`. The
        # following input messages are equivalent:
        #
        # ```json
        # { "role": "user", "content": "Hello, Claude" }
        # ```
        #
        # ```json
        # { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] }
        # ```
        #
        # Starting with Claude 3 models, you can also send image content blocks:
        #
        # ```json
        # {
        #   "role": "user",
        #   "content": [
        #     {
        #       "type": "image",
        #       "source": {
        #         "type": "base64",
        #         "media_type": "image/jpeg",
        #         "data": "/9j/4AAQSkZJRg..."
        #       }
        #     },
        #     { "type": "text", "text": "What is in this image?" }
        #   ]
        # }
        # ```
        #
        # We currently support the `base64` source type for images, and the `image/jpeg`,
        # `image/png`, `image/gif`, and `image/webp` media types.
        #
        # See [examples](https://docs.anthropic.com/en/api/messages-examples#vision) for
        # more input examples.
        #
        # Note that if you want to include a
        # [system prompt](https://docs.anthropic.com/en/docs/system-prompts), you can use
        # the top-level `system` parameter — there is no `"system"` role for input
        # messages in the Messages API.
        sig { returns(T::Array[Anthropic::Models::Beta::BetaMessageParam]) }
        attr_accessor :messages

        # 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

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

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

        # Custom text sequences that will cause the model to stop generating.
        #
        # Our models will normally stop when they have naturally completed their turn,
        # which will result in a response `stop_reason` of `"end_turn"`.
        #
        # If you want the model to stop generating when it encounters custom strings of
        # text, you can use the `stop_sequences` parameter. If the model encounters one of
        # the custom sequences, the response `stop_reason` value will be `"stop_sequence"`
        # and the response `stop_sequence` value will contain the matched stop sequence.
        sig { returns(T.nilable(T::Array[String])) }
        attr_reader :stop_sequences

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

        # System prompt.
        #
        # A system prompt is a way of providing context and instructions to Claude, such
        # as specifying a particular goal or role. See our
        # [guide to system prompts](https://docs.anthropic.com/en/docs/system-prompts).
        sig { returns(T.nilable(T.any(String, T::Array[Anthropic::Models::Beta::BetaTextBlockParam]))) }
        attr_reader :system_

        sig do
          params(
            system_: T.any(String, T::Array[T.any(Anthropic::Models::Beta::BetaTextBlockParam, Anthropic::Internal::AnyHash)])
          )
            .void
        end
        attr_writer :system_

        # 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

        # Configuration for enabling Claude's extended thinking.
        #
        # When enabled, responses include `thinking` content blocks showing Claude's
        # thinking process before the final answer. Requires a minimum budget of 1,024
        # tokens and counts towards your `max_tokens` limit.
        #
        # See
        # [extended thinking](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking)
        # for details.
        sig do
          returns(
            T.nilable(
              T.any(
                Anthropic::Models::Beta::BetaThinkingConfigEnabled,
                Anthropic::Models::Beta::BetaThinkingConfigDisabled
              )
            )
          )
        end
        attr_reader :thinking

        sig do
          params(
            thinking: T.any(
              Anthropic::Models::Beta::BetaThinkingConfigEnabled,
              Anthropic::Internal::AnyHash,
              Anthropic::Models::Beta::BetaThinkingConfigDisabled
            )
          )
            .void
        end
        attr_writer :thinking

        # How the model should use the provided tools. The model can use a specific tool,
        # any available tool, decide by itself, or not use tools at all.
        sig do
          returns(
            T.nilable(
              T.any(
                Anthropic::Models::Beta::BetaToolChoiceAuto,
                Anthropic::Models::Beta::BetaToolChoiceAny,
                Anthropic::Models::Beta::BetaToolChoiceTool,
                Anthropic::Models::Beta::BetaToolChoiceNone
              )
            )
          )
        end
        attr_reader :tool_choice

        sig do
          params(
            tool_choice: T.any(
              Anthropic::Models::Beta::BetaToolChoiceAuto,
              Anthropic::Internal::AnyHash,
              Anthropic::Models::Beta::BetaToolChoiceAny,
              Anthropic::Models::Beta::BetaToolChoiceTool,
              Anthropic::Models::Beta::BetaToolChoiceNone
            )
          )
            .void
        end
        attr_writer :tool_choice

        # Definitions of tools that the model may use.
        #
        # If you include `tools` in your API request, the model may return `tool_use`
        # content blocks that represent the model's use of those tools. You can then run
        # those tools using the tool input generated by the model and then optionally
        # return results back to the model using `tool_result` content blocks.
        #
        # Each tool definition includes:
        #
        # - `name`: Name of the tool.
        # - `description`: Optional, but strongly-recommended description of the tool.
        # - `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the
        #   tool `input` shape that the model will produce in `tool_use` output content
        #   blocks.
        #
        # For example, if you defined `tools` as:
        #
        # ```json
        # [
        #   {
        #     "name": "get_stock_price",
        #     "description": "Get the current stock price for a given ticker symbol.",
        #     "input_schema": {
        #       "type": "object",
        #       "properties": {
        #         "ticker": {
        #           "type": "string",
        #           "description": "The stock ticker symbol, e.g. AAPL for Apple Inc."
        #         }
        #       },
        #       "required": ["ticker"]
        #     }
        #   }
        # ]
        # ```
        #
        # And then asked the model "What's the S&P 500 at today?", the model might produce
        # `tool_use` content blocks in the response like this:
        #
        # ```json
        # [
        #   {
        #     "type": "tool_use",
        #     "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV",
        #     "name": "get_stock_price",
        #     "input": { "ticker": "^GSPC" }
        #   }
        # ]
        # ```
        #
        # You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an
        # input, and return the following back to the model in a subsequent `user`
        # message:
        #
        # ```json
        # [
        #   {
        #     "type": "tool_result",
        #     "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV",
        #     "content": "259.75 USD"
        #   }
        # ]
        # ```
        #
        # Tools can be used for workflows that include running client-side tools and
        # functions, or more generally whenever you want the model to produce a particular
        # JSON structure of output.
        #
        # See our [guide](https://docs.anthropic.com/en/docs/tool-use) for more details.
        sig do
          returns(
            T.nilable(
              T::Array[
                T.any(
                  Anthropic::Models::Beta::BetaTool,
                  Anthropic::Models::Beta::BetaToolComputerUse20241022,
                  Anthropic::Models::Beta::BetaToolBash20241022,
                  Anthropic::Models::Beta::BetaToolTextEditor20241022,
                  Anthropic::Models::Beta::BetaToolComputerUse20250124,
                  Anthropic::Models::Beta::BetaToolBash20250124,
                  Anthropic::Models::Beta::BetaToolTextEditor20250124
                )
              ]
            )
          )
        end
        attr_reader :tools

        sig do
          params(
            tools: T::Array[
              T.any(
                Anthropic::Models::Beta::BetaTool,
                Anthropic::Internal::AnyHash,
                Anthropic::Models::Beta::BetaToolComputerUse20241022,
                Anthropic::Models::Beta::BetaToolBash20241022,
                Anthropic::Models::Beta::BetaToolTextEditor20241022,
                Anthropic::Models::Beta::BetaToolComputerUse20250124,
                Anthropic::Models::Beta::BetaToolBash20250124,
                Anthropic::Models::Beta::BetaToolTextEditor20250124
              )
            ]
          )
            .void
        end
        attr_writer :tools

        # 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

        # Optional header to specify the beta version(s) you want to use.
        sig { returns(T.nilable(T::Array[T.any(String, Anthropic::Models::AnthropicBeta::OrSymbol)])) }
        attr_reader :betas

        sig { params(betas: T::Array[T.any(String, Anthropic::Models::AnthropicBeta::OrSymbol)]).void }
        attr_writer :betas

        sig do
          params(
            max_tokens: Integer,
            messages: T::Array[T.any(Anthropic::Models::Beta::BetaMessageParam, Anthropic::Internal::AnyHash)],
            model: T.any(Anthropic::Models::Model::OrSymbol, String),
            metadata: T.any(Anthropic::Models::Beta::BetaMetadata, Anthropic::Internal::AnyHash),
            stop_sequences: T::Array[String],
            system_: T.any(String, T::Array[T.any(Anthropic::Models::Beta::BetaTextBlockParam, Anthropic::Internal::AnyHash)]),
            temperature: Float,
            thinking: T.any(
              Anthropic::Models::Beta::BetaThinkingConfigEnabled,
              Anthropic::Internal::AnyHash,
              Anthropic::Models::Beta::BetaThinkingConfigDisabled
            ),
            tool_choice: T.any(
              Anthropic::Models::Beta::BetaToolChoiceAuto,
              Anthropic::Internal::AnyHash,
              Anthropic::Models::Beta::BetaToolChoiceAny,
              Anthropic::Models::Beta::BetaToolChoiceTool,
              Anthropic::Models::Beta::BetaToolChoiceNone
            ),
            tools: T::Array[
              T.any(
                Anthropic::Models::Beta::BetaTool,
                Anthropic::Internal::AnyHash,
                Anthropic::Models::Beta::BetaToolComputerUse20241022,
                Anthropic::Models::Beta::BetaToolBash20241022,
                Anthropic::Models::Beta::BetaToolTextEditor20241022,
                Anthropic::Models::Beta::BetaToolComputerUse20250124,
                Anthropic::Models::Beta::BetaToolBash20250124,
                Anthropic::Models::Beta::BetaToolTextEditor20250124
              )
            ],
            top_k: Integer,
            top_p: Float,
            betas: T::Array[T.any(String, Anthropic::Models::AnthropicBeta::OrSymbol)],
            request_options: T.any(Anthropic::RequestOptions, Anthropic::Internal::AnyHash)
          )
            .returns(T.attached_class)
        end
        def self.new(
          max_tokens:,
          messages:,
          model:,
          metadata: nil,
          stop_sequences: nil,
          system_: nil,
          temperature: nil,
          thinking: nil,
          tool_choice: nil,
          tools: nil,
          top_k: nil,
          top_p: nil,
          betas: nil,
          request_options: {}
        ); end
        sig do
          override
            .returns(
              {
                max_tokens: Integer,
                messages: T::Array[Anthropic::Models::Beta::BetaMessageParam],
                model: T.any(Anthropic::Models::Model::OrSymbol, String),
                metadata: Anthropic::Models::Beta::BetaMetadata,
                stop_sequences: T::Array[String],
                system_: T.any(String, T::Array[Anthropic::Models::Beta::BetaTextBlockParam]),
                temperature: Float,
                thinking: T.any(
                  Anthropic::Models::Beta::BetaThinkingConfigEnabled,
                  Anthropic::Models::Beta::BetaThinkingConfigDisabled
                ),
                tool_choice: T.any(
                  Anthropic::Models::Beta::BetaToolChoiceAuto,
                  Anthropic::Models::Beta::BetaToolChoiceAny,
                  Anthropic::Models::Beta::BetaToolChoiceTool,
                  Anthropic::Models::Beta::BetaToolChoiceNone
                ),
                tools: T::Array[
                  T.any(
                    Anthropic::Models::Beta::BetaTool,
                    Anthropic::Models::Beta::BetaToolComputerUse20241022,
                    Anthropic::Models::Beta::BetaToolBash20241022,
                    Anthropic::Models::Beta::BetaToolTextEditor20241022,
                    Anthropic::Models::Beta::BetaToolComputerUse20250124,
                    Anthropic::Models::Beta::BetaToolBash20250124,
                    Anthropic::Models::Beta::BetaToolTextEditor20250124
                  )
                ],
                top_k: Integer,
                top_p: Float,
                betas: T::Array[T.any(String, Anthropic::Models::AnthropicBeta::OrSymbol)],
                request_options: Anthropic::RequestOptions
              }
            )
        end
        def to_hash; end

        # System prompt.
        #
        # A system prompt is a way of providing context and instructions to Claude, such
        # as specifying a particular goal or role. See our
        # [guide to system prompts](https://docs.anthropic.com/en/docs/system-prompts).
        module System
          extend Anthropic::Internal::Type::Union

          sig { override.returns([String, T::Array[Anthropic::Models::Beta::BetaTextBlockParam]]) }
          def self.variants; end

          BetaTextBlockParamArray =
            T.let(
              Anthropic::Internal::Type::ArrayOf[Anthropic::Models::Beta::BetaTextBlockParam],
              Anthropic::Internal::Type::Converter
            )
        end
      end
    end
  end
end