// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.

import { APIResource } from "../resource.js";
import { APIPromise } from "../core.js";
import * as Core from "../core.js";
import * as MessagesAPI from "./messages.js";
import { Stream } from "../streaming.js";
import { MessageStream } from "../lib/MessageStream.js";

export { MessageStream } from "../lib/MessageStream.js";

export class Messages extends APIResource {
  /**
   * Send a structured list of input messages with text and/or image content, and the
   * model will generate the next message in the conversation.
   *
   * The Messages API can be used for either single queries or stateless multi-turn
   * conversations.
   */
  create(body: MessageCreateParamsNonStreaming, options?: Core.RequestOptions): APIPromise<Message>;
  create(
    body: MessageCreateParamsStreaming,
    options?: Core.RequestOptions,
  ): APIPromise<Stream<RawMessageStreamEvent>>;
  create(
    body: MessageCreateParamsBase,
    options?: Core.RequestOptions,
  ): APIPromise<Stream<RawMessageStreamEvent> | Message>;
  create(
    body: MessageCreateParams,
    options?: Core.RequestOptions,
  ): APIPromise<Message> | APIPromise<Stream<RawMessageStreamEvent>> {
    if (body.model in DEPRECATED_MODELS) {
      console.warn(
        `The model '${body.model}' is deprecated and will reach end-of-life on ${
          DEPRECATED_MODELS[body.model]
        }\nPlease migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`,
      );
    }
    return this._client.post('/v1/messages', {
      body,
      timeout: (this._client as any)._options.timeout ?? 600000,
      ...options,
      stream: body.stream ?? false,
    }) as APIPromise<Message> | APIPromise<Stream<RawMessageStreamEvent>>;
  }

  /**
   * Create a Message stream
   */
  stream(body: MessageStreamParams, options?: Core.RequestOptions): MessageStream {
    return MessageStream.createMessage(this, body, options);
  }
}

export type ContentBlock = TextBlock | ToolUseBlock;

export type ContentBlockDeltaEvent = RawContentBlockDeltaEvent;

export type ContentBlockStartEvent = RawContentBlockStartEvent;

export type ContentBlockStopEvent = RawContentBlockStopEvent;

export interface ImageBlockParam {
  source: ImageBlockParam.Source;

  type: 'image';
}

export namespace ImageBlockParam {
  export interface Source {
    data: string;

    media_type: 'image/jpeg' | 'image/png' | 'image/gif' | 'image/webp';

    type: 'base64';
  }
}

export type InputJsonDelta = InputJSONDelta;

export interface InputJSONDelta {
  partial_json: string;

  type: 'input_json_delta';
}

export interface Message {
  /**
   * Unique object identifier.
   *
   * The format and length of IDs may change over time.
   */
  id: string;

  /**
   * Content generated by the model.
   *
   * This is an array of content blocks, each of which has a `type` that determines
   * its shape.
   *
   * Example:
   *
   * ```json
   * [{ "type": "text", "text": "Hi, I'm Claude." }]
   * ```
   *
   * If the request input `messages` ended with an `assistant` turn, then the
   * response `content` will continue directly from that last turn. You can use this
   * to constrain the model's output.
   *
   * For example, if the input `messages` were:
   *
   * ```json
   * [
   *   {
   *     "role": "user",
   *     "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"
   *   },
   *   { "role": "assistant", "content": "The best answer is (" }
   * ]
   * ```
   *
   * Then the response `content` might be:
   *
   * ```json
   * [{ "type": "text", "text": "B)" }]
   * ```
   */
  content: Array<ContentBlock>;

  /**
   * The model that will complete your prompt.\n\nSee
   * [models](https://docs.anthropic.com/en/docs/models-overview) for additional
   * details and options.
   */
  model: Model;

  /**
   * Conversational role of the generated message.
   *
   * This will always be `"assistant"`.
   */
  role: 'assistant';

  /**
   * The reason that we stopped.
   *
   * This may be one the following values:
   *
   * - `"end_turn"`: the model reached a natural stopping point
   * - `"max_tokens"`: we exceeded the requested `max_tokens` or the model's maximum
   * - `"stop_sequence"`: one of your provided custom `stop_sequences` was generated
   * - `"tool_use"`: the model invoked one or more tools
   *
   * In non-streaming mode this value is always non-null. In streaming mode, it is
   * null in the `message_start` event and non-null otherwise.
   */
  stop_reason: 'end_turn' | 'max_tokens' | 'stop_sequence' | 'tool_use' | null;

  /**
   * Which custom stop sequence was generated, if any.
   *
   * This value will be a non-null string if one of your custom stop sequences was
   * generated.
   */
  stop_sequence: string | null;

  /**
   * Object type.
   *
   * For Messages, this is always `"message"`.
   */
  type: 'message';

  /**
   * Billing and rate-limit usage.
   *
   * Anthropic's API bills and rate-limits by token counts, as tokens represent the
   * underlying cost to our systems.
   *
   * Under the hood, the API transforms requests into a format suitable for the
   * model. The model's output then goes through a parsing stage before becoming an
   * API response. As a result, the token counts in `usage` will not match one-to-one
   * with the exact visible content of an API request or response.
   *
   * For example, `output_tokens` will be non-zero, even for an empty string response
   * from Claude.
   */
  usage: Usage;
}

export type MessageDeltaEvent = RawMessageDeltaEvent;

export interface MessageDeltaUsage {
  /**
   * The cumulative number of output tokens which were used.
   */
  output_tokens: number;
}

export interface MessageParam {
  content: string | Array<TextBlockParam | ImageBlockParam | ToolUseBlockParam | ToolResultBlockParam>;

  role: 'user' | 'assistant';
}

export type MessageStartEvent = RawMessageStartEvent;

export type MessageStopEvent = RawMessageStopEvent;

export type MessageStreamEvent = RawMessageStreamEvent;

export interface Metadata {
  /**
   * An external identifier for the user who is associated with the request.
   *
   * This should be a uuid, hash value, or other opaque identifier. Anthropic may use
   * this id to help detect abuse. Do not include any identifying information such as
   * name, email address, or phone number.
   */
  user_id?: string | null;
}

/**
 * The model that will complete your prompt.\n\nSee
 * [models](https://docs.anthropic.com/en/docs/models-overview) for additional
 * details and options.
 */
export type Model =
  | (string & {})
  | 'claude-3-5-sonnet-latest'
  | 'claude-3-5-sonnet-20241022'
  | 'claude-3-5-sonnet-20240620'
  | 'claude-3-opus-latest'
  | 'claude-3-opus-20240229'
  | 'claude-3-sonnet-20240229'
  | 'claude-3-haiku-20240307'
  | 'claude-2.1'
  | 'claude-2.0'
  | 'claude-instant-1.2';

type DeprecatedModelsType = {
  [K in Model]?: string;
};

const DEPRECATED_MODELS: DeprecatedModelsType = {
  'claude-1.3': 'November 6th, 2024',
  'claude-1.3-100k': 'November 6th, 2024',
  'claude-instant-1.1': 'November 6th, 2024',
  'claude-instant-1.1-100k': 'November 6th, 2024',
  'claude-instant-1.2': 'November 6th, 2024',
};

export interface RawContentBlockDeltaEvent {
  delta: TextDelta | InputJSONDelta;

  index: number;

  type: 'content_block_delta';
}

export interface RawContentBlockStartEvent {
  content_block: TextBlock | ToolUseBlock;

  index: number;

  type: 'content_block_start';
}

export interface RawContentBlockStopEvent {
  index: number;

  type: 'content_block_stop';
}

export interface RawMessageDeltaEvent {
  delta: RawMessageDeltaEvent.Delta;

  type: 'message_delta';

  /**
   * Billing and rate-limit usage.
   *
   * Anthropic's API bills and rate-limits by token counts, as tokens represent the
   * underlying cost to our systems.
   *
   * Under the hood, the API transforms requests into a format suitable for the
   * model. The model's output then goes through a parsing stage before becoming an
   * API response. As a result, the token counts in `usage` will not match one-to-one
   * with the exact visible content of an API request or response.
   *
   * For example, `output_tokens` will be non-zero, even for an empty string response
   * from Claude.
   */
  usage: MessageDeltaUsage;
}

export namespace RawMessageDeltaEvent {
  export interface Delta {
    stop_reason: 'end_turn' | 'max_tokens' | 'stop_sequence' | 'tool_use' | null;

    stop_sequence: string | null;
  }
}

export interface RawMessageStartEvent {
  message: Message;

  type: 'message_start';
}

export interface RawMessageStopEvent {
  type: 'message_stop';
}

export type RawMessageStreamEvent =
  | RawMessageStartEvent
  | RawMessageDeltaEvent
  | RawMessageStopEvent
  | RawContentBlockStartEvent
  | RawContentBlockDeltaEvent
  | RawContentBlockStopEvent;

export interface TextBlock {
  text: string;

  type: 'text';
}

export interface TextBlockParam {
  text: string;

  type: 'text';
}

export interface TextDelta {
  text: string;

  type: 'text_delta';
}

export interface Tool {
  /**
   * [JSON schema](https://json-schema.org/) for this tool's input.
   *
   * This defines the shape of the `input` that your tool accepts and that the model
   * will produce.
   */
  input_schema: Tool.InputSchema;

  name: string;

  /**
   * Description of what this tool does.
   *
   * Tool descriptions should be as detailed as possible. The more information that
   * the model has about what the tool is and how to use it, the better it will
   * perform. You can use natural language descriptions to reinforce important
   * aspects of the tool input JSON schema.
   */
  description?: string;
}

export namespace Tool {
  /**
   * [JSON schema](https://json-schema.org/) for this tool's input.
   *
   * This defines the shape of the `input` that your tool accepts and that the model
   * will produce.
   */
  export interface InputSchema {
    type: 'object';

    properties?: unknown | null;
    [k: string]: unknown;
  }
}

/**
 * How the model should use the provided tools. The model can use a specific tool,
 * any available tool, or decide by itself.
 */
export type ToolChoice = ToolChoiceAuto | ToolChoiceAny | ToolChoiceTool;

/**
 * The model will use any available tools.
 */
export interface ToolChoiceAny {
  type: 'any';

  /**
   * Whether to disable parallel tool use.
   *
   * Defaults to `false`. If set to `true`, the model will output exactly one tool
   * use.
   */
  disable_parallel_tool_use?: boolean;
}

/**
 * The model will automatically decide whether to use tools.
 */
export interface ToolChoiceAuto {
  type: 'auto';

  /**
   * Whether to disable parallel tool use.
   *
   * Defaults to `false`. If set to `true`, the model will output at most one tool
   * use.
   */
  disable_parallel_tool_use?: boolean;
}

/**
 * The model will use the specified tool with `tool_choice.name`.
 */
export interface ToolChoiceTool {
  /**
   * The name of the tool to use.
   */
  name: string;

  type: 'tool';

  /**
   * Whether to disable parallel tool use.
   *
   * Defaults to `false`. If set to `true`, the model will output exactly one tool
   * use.
   */
  disable_parallel_tool_use?: boolean;
}

export interface ToolResultBlockParam {
  tool_use_id: string;

  type: 'tool_result';

  content?: string | Array<TextBlockParam | ImageBlockParam>;

  is_error?: boolean;
}

export interface ToolUseBlock {
  id: string;

  input: unknown;

  name: string;

  type: 'tool_use';
}

export interface ToolUseBlockParam {
  id: string;

  input: unknown;

  name: string;

  type: 'tool_use';
}

export interface Usage {
  /**
   * The number of input tokens which were used.
   */
  input_tokens: number;

  /**
   * The number of output tokens which were used.
   */
  output_tokens: number;
}

export type MessageCreateParams = MessageCreateParamsNonStreaming | MessageCreateParamsStreaming;

export interface MessageCreateParamsBase {
  /**
   * 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.
   */
  max_tokens: number;

  /**
   * 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.
   */
  messages: Array<MessageParam>;

  /**
   * The model that will complete your prompt.\n\nSee
   * [models](https://docs.anthropic.com/en/docs/models-overview) for additional
   * details and options.
   */
  model: Model;

  /**
   * An object describing metadata about the request.
   */
  metadata?: 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.
   */
  stop_sequences?: Array<string>;

  /**
   * Whether to incrementally stream the response using server-sent events.
   *
   * See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for
   * details.
   */
  stream?: boolean;

  /**
   * 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).
   */
  system?: string | Array<TextBlockParam>;

  /**
   * 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.
   */
  temperature?: number;

  /**
   * How the model should use the provided tools. The model can use a specific tool,
   * any available tool, or decide by itself.
   */
  tool_choice?: ToolChoice;

  /**
   * 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/) 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.
   */
  tools?: Array<Tool>;

  /**
   * 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`.
   */
  top_k?: number;

  /**
   * 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`.
   */
  top_p?: number;
}

export namespace MessageCreateParams {
  /**
   * @deprecated use `Anthropic.Messages.ToolChoiceAuto` instead
   */
  export type Metadata = MessagesAPI.Metadata;

  /**
   * @deprecated use `Anthropic.Messages.ToolChoiceAuto` instead
   */
  export type ToolChoiceAuto = MessagesAPI.ToolChoiceAuto;

  /**
   * @deprecated use `Anthropic.Messages.ToolChoiceAny` instead
   */
  export type ToolChoiceAny = MessagesAPI.ToolChoiceAny;

  /**
   * @deprecated use `Anthropic.Messages.ToolChoiceTool` instead
   */
  export type ToolChoiceTool = MessagesAPI.ToolChoiceTool;

  export type MessageCreateParamsNonStreaming = MessagesAPI.MessageCreateParamsNonStreaming;
  export type MessageCreateParamsStreaming = MessagesAPI.MessageCreateParamsStreaming;
}

export interface MessageCreateParamsNonStreaming extends MessageCreateParamsBase {
  /**
   * Whether to incrementally stream the response using server-sent events.
   *
   * See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for
   * details.
   */
  stream?: false;
}

export interface MessageCreateParamsStreaming extends MessageCreateParamsBase {
  /**
   * Whether to incrementally stream the response using server-sent events.
   *
   * See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for
   * details.
   */
  stream: true;
}

export type MessageStreamParams = MessageCreateParamsBase;

export namespace Messages {
  export import ContentBlock = MessagesAPI.ContentBlock;
  export import ContentBlockDeltaEvent = MessagesAPI.ContentBlockDeltaEvent;
  export import ContentBlockStartEvent = MessagesAPI.ContentBlockStartEvent;
  export import ContentBlockStopEvent = MessagesAPI.ContentBlockStopEvent;
  export import ImageBlockParam = MessagesAPI.ImageBlockParam;
  export import InputJJsonDelta = MessagesAPI.InputJsonDelta;
  export import InputJSONDelta = MessagesAPI.InputJSONDelta;
  export import Message = MessagesAPI.Message;
  export import MessageDeltaEvent = MessagesAPI.MessageDeltaEvent;
  export import MessageDeltaUsage = MessagesAPI.MessageDeltaUsage;
  export import MessageParam = MessagesAPI.MessageParam;
  export import MessageStartEvent = MessagesAPI.MessageStartEvent;
  export import MessageStopEvent = MessagesAPI.MessageStopEvent;
  export import MessageStreamEvent = MessagesAPI.MessageStreamEvent;
  export import Metadata = MessagesAPI.Metadata;
  export import Model = MessagesAPI.Model;
  export import RawContentBlockDeltaEvent = MessagesAPI.RawContentBlockDeltaEvent;
  export import RawContentBlockStartEvent = MessagesAPI.RawContentBlockStartEvent;
  export import RawContentBlockStopEvent = MessagesAPI.RawContentBlockStopEvent;
  export import RawMessageDeltaEvent = MessagesAPI.RawMessageDeltaEvent;
  export import RawMessageStartEvent = MessagesAPI.RawMessageStartEvent;
  export import RawMessageStopEvent = MessagesAPI.RawMessageStopEvent;
  export import RawMessageStreamEvent = MessagesAPI.RawMessageStreamEvent;
  export import TextBlock = MessagesAPI.TextBlock;
  export import TextBlockParam = MessagesAPI.TextBlockParam;
  export import TextDelta = MessagesAPI.TextDelta;
  export import Tool = MessagesAPI.Tool;
  export import ToolChoice = MessagesAPI.ToolChoice;
  export import ToolChoiceAny = MessagesAPI.ToolChoiceAny;
  export import ToolChoiceAuto = MessagesAPI.ToolChoiceAuto;
  export import ToolChoiceTool = MessagesAPI.ToolChoiceTool;
  export import ToolResultBlockParam = MessagesAPI.ToolResultBlockParam;
  export import ToolUseBlock = MessagesAPI.ToolUseBlock;
  export import ToolUseBlockParam = MessagesAPI.ToolUseBlockParam;
  export import Usage = MessagesAPI.Usage;
  export import MessageCreateParams = MessagesAPI.MessageCreateParams;
  export import MessageCreateParamsNonStreaming = MessagesAPI.MessageCreateParamsNonStreaming;
  export import MessageCreateParamsStreaming = MessagesAPI.MessageCreateParamsStreaming;
  export import MessageStreamParams = MessagesAPI.MessageStreamParams;
}