@inngest/ai
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TypeScript
import { type AiAdapter } from "../adapter.js";
export interface AnthropicAiAdapter extends AiAdapter {
/**
* Format of the IO for this model
*/
format: "anthropic";
"~types": {
input: AnthropicAiAdapter.Input;
output: AnthropicAiAdapter.Output;
};
}
export declare namespace AnthropicAiAdapter {
type Input = MessageCreateParamsNonStreaming;
type Output = Message;
/**
* The model that will complete your prompt.\n\nSee
* [models](https://docs.anthropic.com/en/docs/models-overview) for additional
* details and options.
*/
type Model = (string & {}) | "claude-3-5-haiku-latest" | "claude-3-5-haiku-20241022" | "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 Beta = (string & {}) | "message-batches-2024-09-24" | "prompt-caching-2024-07-31" | "computer-use-2024-10-22" | "pdfs-2024-09-25" | "token-counting-2024-11-01";
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;
}
interface MessageParam {
content: string | Array<TextBlockParam | ImageBlockParam | ToolUseBlockParam | ToolResultBlockParam | DocumentBlockParam>;
role: "user" | "assistant";
}
interface TextBlockParam {
text: string;
type: "text";
}
namespace ImageBlockParam {
interface Source {
data: string;
media_type: "image/jpeg" | "image/png" | "image/gif" | "image/webp";
type: "base64";
}
}
interface ToolUseBlockParam {
id: string;
input: unknown;
name: string;
type: "tool_use";
}
interface ToolResultBlockParam {
tool_use_id: string;
type: "tool_result";
content?: string | Array<TextBlockParam | ImageBlockParam>;
is_error?: boolean;
}
interface ImageBlockParam {
source: ImageBlockParam.Source;
type: "image";
}
namespace ImageBlockParam {
interface Source {
data: string;
media_type: "image/jpeg" | "image/png" | "image/gif" | "image/webp";
type: "base64";
}
}
interface DocumentBlockParam {
source: {
type: "url";
url: string;
} | {
type: "base64";
media_type: "application/pdf";
data: string;
};
type: "document";
}
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"`.
* When an error occurs, this will be `"error"`.
*/
type: "message" | "error";
error?: {
type: string;
message: string;
};
/**
* 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;
}
type ContentBlock = TextBlock | ToolUseBlock;
interface TextBlock {
text: string;
type: "text";
}
interface ToolUseBlock {
id: string;
input: unknown;
name: string;
type: "tool_use";
}
interface Usage {
/**
* The number of input tokens which were used.
*/
input_tokens: number;
/**
* The number of output tokens which were used.
*/
output_tokens: number;
}
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;
}
/**
* How the model should use the provided tools. The model can use a specific tool,
* any available tool, or decide by itself.
*/
type ToolChoice = ToolChoiceAuto | ToolChoiceAny | ToolChoiceTool;
/**
* The model will use any available tools.
*/
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.
*/
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`.
*/
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;
}
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 of the tool.
*
* This is how the tool will be called by the model and in tool_use blocks.
*/
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;
}
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;
[]: unknown;
}
}
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;
}
}
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