lib/anthropic/models/message_create_params.rb

# frozen_string_literal: true

module Anthropic
  module Models
    # @see Anthropic::Resources::Messages#create
    #
    # @see Anthropic::Resources::Messages#stream_raw
    class MessageCreateParams < Anthropic::Internal::Type::BaseModel
      # @!parse
      #   extend Anthropic::Internal::Type::RequestParameters::Converter
      include Anthropic::Internal::Type::RequestParameters

      # @!attribute max_tokens
      #   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.
      #
      #   @return [Integer]
      required :max_tokens, Integer

      # @!attribute messages
      #   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.
      #
      #   @return [Array<Anthropic::Models::MessageParam>]
      required :messages, -> { Anthropic::Internal::Type::ArrayOf[Anthropic::Models::MessageParam] }

      # @!attribute model
      #   The model that will complete your prompt.\n\nSee
      #   [models](https://docs.anthropic.com/en/docs/models-overview) for additional
      #   details and options.
      #
      #   @return [Symbol, String, Anthropic::Models::Model]
      required :model, union: -> { Anthropic::Models::Model }

      # @!attribute [r] metadata
      #   An object describing metadata about the request.
      #
      #   @return [Anthropic::Models::Metadata, nil]
      optional :metadata, -> { Anthropic::Models::Metadata }

      # @!parse
      #   # @return [Anthropic::Models::Metadata]
      #   attr_writer :metadata

      # @!attribute [r] stop_sequences
      #   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.
      #
      #   @return [Array<String>, nil]
      optional :stop_sequences, Anthropic::Internal::Type::ArrayOf[String]

      # @!parse
      #   # @return [Array<String>]
      #   attr_writer :stop_sequences

      # @!attribute [r] system_
      #   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).
      #
      #   @return [String, Array<Anthropic::Models::TextBlockParam>, nil]
      optional :system_, union: -> { Anthropic::Models::MessageCreateParams::System }, api_name: :system

      # @!parse
      #   # @return [String, Array<Anthropic::Models::TextBlockParam>]
      #   attr_writer :system_

      # @!attribute [r] temperature
      #   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.
      #
      #   @return [Float, nil]
      optional :temperature, Float

      # @!parse
      #   # @return [Float]
      #   attr_writer :temperature

      # @!attribute [r] thinking
      #   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.
      #
      #   @return [Anthropic::Models::ThinkingConfigEnabled, Anthropic::Models::ThinkingConfigDisabled, nil]
      optional :thinking, union: -> { Anthropic::Models::ThinkingConfigParam }

      # @!parse
      #   # @return [Anthropic::Models::ThinkingConfigEnabled, Anthropic::Models::ThinkingConfigDisabled]
      #   attr_writer :thinking

      # @!attribute [r] tool_choice
      #   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.
      #
      #   @return [Anthropic::Models::ToolChoiceAuto, Anthropic::Models::ToolChoiceAny, Anthropic::Models::ToolChoiceTool, Anthropic::Models::ToolChoiceNone, nil]
      optional :tool_choice, union: -> { Anthropic::Models::ToolChoice }

      # @!parse
      #   # @return [Anthropic::Models::ToolChoiceAuto, Anthropic::Models::ToolChoiceAny, Anthropic::Models::ToolChoiceTool, Anthropic::Models::ToolChoiceNone]
      #   attr_writer :tool_choice

      # @!attribute [r] tools
      #   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.
      #
      #   @return [Array<Anthropic::Models::Tool, Anthropic::Models::ToolBash20250124, Anthropic::Models::ToolTextEditor20250124>, nil]
      optional :tools, -> { Anthropic::Internal::Type::ArrayOf[union: Anthropic::Models::ToolUnion] }

      # @!parse
      #   # @return [Array<Anthropic::Models::Tool, Anthropic::Models::ToolBash20250124, Anthropic::Models::ToolTextEditor20250124>]
      #   attr_writer :tools

      # @!attribute [r] top_k
      #   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`.
      #
      #   @return [Integer, nil]
      optional :top_k, Integer

      # @!parse
      #   # @return [Integer]
      #   attr_writer :top_k

      # @!attribute [r] top_p
      #   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`.
      #
      #   @return [Float, nil]
      optional :top_p, Float

      # @!parse
      #   # @return [Float]
      #   attr_writer :top_p

      # @!parse
      #   # @param max_tokens [Integer]
      #   # @param messages [Array<Anthropic::Models::MessageParam>]
      #   # @param model [Symbol, String, Anthropic::Models::Model]
      #   # @param metadata [Anthropic::Models::Metadata]
      #   # @param stop_sequences [Array<String>]
      #   # @param system_ [String, Array<Anthropic::Models::TextBlockParam>]
      #   # @param temperature [Float]
      #   # @param thinking [Anthropic::Models::ThinkingConfigEnabled, Anthropic::Models::ThinkingConfigDisabled]
      #   # @param tool_choice [Anthropic::Models::ToolChoiceAuto, Anthropic::Models::ToolChoiceAny, Anthropic::Models::ToolChoiceTool, Anthropic::Models::ToolChoiceNone]
      #   # @param tools [Array<Anthropic::Models::Tool, Anthropic::Models::ToolBash20250124, Anthropic::Models::ToolTextEditor20250124>]
      #   # @param top_k [Integer]
      #   # @param top_p [Float]
      #   # @param request_options [Anthropic::RequestOptions, Hash{Symbol=>Object}]
      #   #
      #   def initialize(
      #     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,
      #     request_options: {},
      #     **
      #   )
      #     super
      #   end

      # def initialize: (Hash | Anthropic::Internal::Type::BaseModel) -> void

      # 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

        variant String

        variant -> { Anthropic::Models::MessageCreateParams::System::TextBlockParamArray }

        # @!parse
        #   # @return [Array(String, Array<Anthropic::Models::TextBlockParam>)]
        #   def self.variants; end

        TextBlockParamArray = Anthropic::Internal::Type::ArrayOf[-> { Anthropic::Models::TextBlockParam }]
      end
    end
  end
end