@anthropic-ai/sdk/resources/messages.d.ts

The official TypeScript library for the Anthropic API

import { APIResource } from '@anthropic-ai/sdk/resource';
import { APIPromise } from '@anthropic-ai/sdk/core';
import * as Core from '@anthropic-ai/sdk/core';
import { MessageStream } from '@anthropic-ai/sdk/lib/MessageStream';
import * as MessagesAPI from '@anthropic-ai/sdk/resources/messages';
import { Stream } from '@anthropic-ai/sdk/streaming';
export { MessageStream } from '@anthropic-ai/sdk/lib/MessageStream';
export declare class Messages extends APIResource {
    /**
     * Create a Message.
     *
     * 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 a Message stream
     */
    stream(body: MessageStreamParams, options?: Core.RequestOptions): MessageStream;
}
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 declare namespace ImageBlockParam {
    interface Source {
        data: string;
        media_type: 'image/jpeg' | 'image/png' | 'image/gif' | 'image/webp';
        type: 'base64';
    }
}
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 handled the request.
     */
    model: string;
    /**
     * 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 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 declare namespace RawMessageDeltaEvent {
    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 declare 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.
     */
    interface InputSchema {
        type: 'object';
        properties?: unknown | null;
        [k: string]: unknown;
    }
}
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.
     *
     * 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. The first message must always use the `user` role.
     *
     * 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) 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.
     *
     * See [models](https://docs.anthropic.com/en/docs/models-overview) for additional
     * details and options.
     */
    model: (string & {}) | 'claude-3-5-sonnet-20240620' | 'claude-3-opus-20240229' | 'claude-3-sonnet-20240229' | 'claude-3-haiku-20240307' | 'claude-2.1' | 'claude-2.0' | 'claude-instant-1.2';
    /**
     * An object describing metadata about the request.
     */
    metadata?: MessageCreateParams.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?: MessageCreateParams.ToolChoiceAuto | MessageCreateParams.ToolChoiceAny | MessageCreateParams.ToolChoiceTool;
    /**
     * 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 declare namespace MessageCreateParams {
    /**
     * An object describing metadata about the request.
     */
    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 will automatically decide whether to use tools.
     */
    interface ToolChoiceAuto {
        type: 'auto';
    }
    /**
     * The model will use any available tools.
     */
    interface ToolChoiceAny {
        type: 'any';
    }
    /**
     * The model will use the specified tool with `tool_choice.name`.
     */
    interface ToolChoiceTool {
        /**
         * The name of the tool to use.
         */
        name: string;
        type: 'tool';
    }
    type MessageCreateParamsNonStreaming = MessagesAPI.MessageCreateParamsNonStreaming;
    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 declare 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 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 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 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;
}
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