rbi/lib/anthropic/models/message_count_tokens_params.rbi

# typed: strong

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

      # 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::MessageParam]) }
      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

      # 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::TextBlockParam]))) }
      attr_reader :system_

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

      # 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::ThinkingConfigEnabled, Anthropic::Models::ThinkingConfigDisabled))
        )
      end
      attr_reader :thinking

      sig do
        params(
          thinking: T.any(
            Anthropic::Models::ThinkingConfigEnabled,
            Anthropic::Internal::AnyHash,
            Anthropic::Models::ThinkingConfigDisabled
          )
        )
          .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::ToolChoiceAuto,
              Anthropic::Models::ToolChoiceAny,
              Anthropic::Models::ToolChoiceTool,
              Anthropic::Models::ToolChoiceNone
            )
          )
        )
      end
      attr_reader :tool_choice

      sig do
        params(
          tool_choice: T.any(
            Anthropic::Models::ToolChoiceAuto,
            Anthropic::Internal::AnyHash,
            Anthropic::Models::ToolChoiceAny,
            Anthropic::Models::ToolChoiceTool,
            Anthropic::Models::ToolChoiceNone
          )
        )
          .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::Tool,
                Anthropic::Models::ToolBash20250124,
                Anthropic::Models::ToolTextEditor20250124
              )
            ]
          )
        )
      end
      attr_reader :tools

      sig do
        params(
          tools: T::Array[
            T.any(
              Anthropic::Models::Tool,
              Anthropic::Internal::AnyHash,
              Anthropic::Models::ToolBash20250124,
              Anthropic::Models::ToolTextEditor20250124
            )
          ]
        )
          .void
      end
      attr_writer :tools

      sig do
        params(
          messages: T::Array[T.any(Anthropic::Models::MessageParam, Anthropic::Internal::AnyHash)],
          model: T.any(Anthropic::Models::Model::OrSymbol, String),
          system_: T.any(String, T::Array[T.any(Anthropic::Models::TextBlockParam, Anthropic::Internal::AnyHash)]),
          thinking: T.any(
            Anthropic::Models::ThinkingConfigEnabled,
            Anthropic::Internal::AnyHash,
            Anthropic::Models::ThinkingConfigDisabled
          ),
          tool_choice: T.any(
            Anthropic::Models::ToolChoiceAuto,
            Anthropic::Internal::AnyHash,
            Anthropic::Models::ToolChoiceAny,
            Anthropic::Models::ToolChoiceTool,
            Anthropic::Models::ToolChoiceNone
          ),
          tools: T::Array[
            T.any(
              Anthropic::Models::Tool,
              Anthropic::Internal::AnyHash,
              Anthropic::Models::ToolBash20250124,
              Anthropic::Models::ToolTextEditor20250124
            )
          ],
          request_options: T.any(Anthropic::RequestOptions, Anthropic::Internal::AnyHash)
        )
          .returns(T.attached_class)
      end
      def self.new(
        messages:,
        model:,
        system_: nil,
        thinking: nil,
        tool_choice: nil,
        tools: nil,
        request_options: {}
      )
      end

      sig do
        override
          .returns(
            {
              messages: T::Array[Anthropic::Models::MessageParam],
              model: T.any(Anthropic::Models::Model::OrSymbol, String),
              system_: T.any(String, T::Array[Anthropic::Models::TextBlockParam]),
              thinking: T.any(Anthropic::Models::ThinkingConfigEnabled, Anthropic::Models::ThinkingConfigDisabled),
              tool_choice: T.any(
                Anthropic::Models::ToolChoiceAuto,
                Anthropic::Models::ToolChoiceAny,
                Anthropic::Models::ToolChoiceTool,
                Anthropic::Models::ToolChoiceNone
              ),
              tools: T::Array[
                T.any(
                  Anthropic::Models::Tool,
                  Anthropic::Models::ToolBash20250124,
                  Anthropic::Models::ToolTextEditor20250124
                )
              ],
              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::TextBlockParam]]) }
        def self.variants; end

        TextBlockParamArray =
          T.let(
            Anthropic::Internal::Type::ArrayOf[Anthropic::Models::TextBlockParam],
            Anthropic::Internal::Type::Converter
          )
      end
    end
  end
end