# frozen_string_literal: true module Anthropic module Models module Beta module Messages # @see Anthropic::Resources::Beta::Messages::Batches#create class BatchCreateParams < Anthropic::Internal::Type::BaseModel # @!parse # extend Anthropic::Internal::Type::RequestParameters::Converter include Anthropic::Internal::Type::RequestParameters # @!attribute requests # List of requests for prompt completion. Each is an individual request to create # a Message. # # @return [Array] required :requests, -> { Anthropic::Internal::Type::ArrayOf[Anthropic::Models::Beta::Messages::BatchCreateParams::Request] } # @!attribute [r] betas # Optional header to specify the beta version(s) you want to use. # # @return [Array, nil] optional :betas, -> { Anthropic::Internal::Type::ArrayOf[union: Anthropic::Models::AnthropicBeta] } # @!parse # # @return [Array] # attr_writer :betas # @!parse # # @param requests [Array] # # @param betas [Array] # # @param request_options [Anthropic::RequestOptions, Hash{Symbol=>Object}] # # # def initialize(requests:, betas: nil, request_options: {}, **) = super # def initialize: (Hash | Anthropic::Internal::Type::BaseModel) -> void class Request < Anthropic::Internal::Type::BaseModel # @!attribute custom_id # Developer-provided ID created for each request in a Message Batch. Useful for # matching results to requests, as results may be given out of request order. # # Must be unique for each request within the Message Batch. # # @return [String] required :custom_id, String # @!attribute params # Messages API creation parameters for the individual request. # # See the [Messages API reference](/en/api/messages) for full documentation on # available parameters. # # @return [Anthropic::Models::Beta::Messages::BatchCreateParams::Request::Params] required :params, -> { Anthropic::Models::Beta::Messages::BatchCreateParams::Request::Params } # @!parse # # @param custom_id [String] # # @param params [Anthropic::Models::Beta::Messages::BatchCreateParams::Request::Params] # # # def initialize(custom_id:, params:, **) = super # def initialize: (Hash | Anthropic::Internal::Type::BaseModel) -> void # @see Anthropic::Models::Beta::Messages::BatchCreateParams::Request#params class Params < Anthropic::Internal::Type::BaseModel # @!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] required :messages, -> { Anthropic::Internal::Type::ArrayOf[Anthropic::Models::Beta::BetaMessageParam] } # @!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::Beta::BetaMetadata, nil] optional :metadata, -> { Anthropic::Models::Beta::BetaMetadata } # @!parse # # @return [Anthropic::Models::Beta::BetaMetadata] # 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, nil] optional :stop_sequences, Anthropic::Internal::Type::ArrayOf[String] # @!parse # # @return [Array] # attr_writer :stop_sequences # @!attribute [r] stream # Whether to incrementally stream the response using server-sent events. # # See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for # details. # # @return [Boolean, nil] optional :stream, Anthropic::Internal::Type::Boolean # @!parse # # @return [Boolean] # attr_writer :stream # @!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, nil] optional :system_, union: -> { Anthropic::Models::Beta::Messages::BatchCreateParams::Request::Params::System }, api_name: :system # @!parse # # @return [String, Array] # 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::Beta::BetaThinkingConfigEnabled, Anthropic::Models::Beta::BetaThinkingConfigDisabled, nil] optional :thinking, union: -> { Anthropic::Models::Beta::BetaThinkingConfigParam } # @!parse # # @return [Anthropic::Models::Beta::BetaThinkingConfigEnabled, Anthropic::Models::Beta::BetaThinkingConfigDisabled] # 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::Beta::BetaToolChoiceAuto, Anthropic::Models::Beta::BetaToolChoiceAny, Anthropic::Models::Beta::BetaToolChoiceTool, Anthropic::Models::Beta::BetaToolChoiceNone, nil] optional :tool_choice, union: -> { Anthropic::Models::Beta::BetaToolChoice } # @!parse # # @return [Anthropic::Models::Beta::BetaToolChoiceAuto, Anthropic::Models::Beta::BetaToolChoiceAny, Anthropic::Models::Beta::BetaToolChoiceTool, Anthropic::Models::Beta::BetaToolChoiceNone] # 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, nil] optional :tools, -> { Anthropic::Internal::Type::ArrayOf[union: Anthropic::Models::Beta::BetaToolUnion] } # @!parse # # @return [Array] # 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 # # Messages API creation parameters for the individual request. # # # # See the [Messages API reference](/en/api/messages) for full documentation on # # available parameters. # # # # @param max_tokens [Integer] # # @param messages [Array] # # @param model [Symbol, String, Anthropic::Models::Model] # # @param metadata [Anthropic::Models::Beta::BetaMetadata] # # @param stop_sequences [Array] # # @param stream [Boolean] # # @param system_ [String, Array] # # @param temperature [Float] # # @param thinking [Anthropic::Models::Beta::BetaThinkingConfigEnabled, Anthropic::Models::Beta::BetaThinkingConfigDisabled] # # @param tool_choice [Anthropic::Models::Beta::BetaToolChoiceAuto, Anthropic::Models::Beta::BetaToolChoiceAny, Anthropic::Models::Beta::BetaToolChoiceTool, Anthropic::Models::Beta::BetaToolChoiceNone] # # @param tools [Array] # # @param top_k [Integer] # # @param top_p [Float] # # # def initialize( # max_tokens:, # messages:, # model:, # metadata: nil, # stop_sequences: nil, # stream: nil, # system_: nil, # temperature: nil, # thinking: nil, # tool_choice: nil, # tools: nil, # top_k: nil, # top_p: nil, # ** # ) # 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). # # @see Anthropic::Models::Beta::Messages::BatchCreateParams::Request::Params#system_ module System extend Anthropic::Internal::Type::Union variant String variant -> { Anthropic::Models::Beta::Messages::BatchCreateParams::Request::Params::System::BetaTextBlockParamArray } # @!parse # # @return [Array(String, Array)] # def self.variants; end BetaTextBlockParamArray = Anthropic::Internal::Type::ArrayOf[-> { Anthropic::Models::Beta::BetaTextBlockParam }] end end end end end end end end