.opencode/node_modules/effect/dist/unstable/ai/Tokenizer.d.ts

/**
 * Service for model-specific token counting and prompt truncation. Tokenization
 * depends on the target provider, model, and encoding rules, so this module
 * leaves the actual tokenization function to the service implementation.
 *
 * The `Tokenizer` service can count tokens for raw prompt input and shorten a
 * prompt to a token limit by keeping the newest messages that fit. This module
 * defines the service tag, the service interface, and a `make` constructor that
 * builds a full tokenizer service from a token-counting function.
 *
 * @since 4.0.0
 */
import * as Context from "../../Context.ts";
import * as Effect from "../../Effect.ts";
import type * as AiError from "./AiError.ts";
import * as Prompt from "./Prompt.ts";
declare const Tokenizer_base: Context.ServiceClass<Tokenizer, "effect/ai/Tokenizer", Service>;
/**
 * Service tag for model tokenization services.
 *
 * **When to use**
 *
 * Use to access or provide model-specific token counting and prompt truncation
 * operations.
 *
 * **Details**
 *
 * This tag provides access to tokenization functionality throughout your
 * application, enabling token counting and prompt truncation capabilities.
 *
 * **Example** (Accessing the Tokenizer service)
 *
 * ```ts
 * import { Effect } from "effect"
 * import { Tokenizer } from "effect/unstable/ai"
 *
 * const useTokenizer = Effect.gen(function*() {
 *   const tokenizer = yield* Tokenizer.Tokenizer
 *   const tokens = yield* tokenizer.tokenize("Hello, world!")
 *   return tokens.length
 * })
 * ```
 *
 * @category services
 * @since 4.0.0
 */
export declare class Tokenizer extends Tokenizer_base {
}
/**
 * Tokenizer service interface providing text tokenization and truncation
 * operations.
 *
 * **Details**
 *
 * This interface defines the core operations for converting text to tokens and
 * managing content length within token limits for AI model compatibility.
 *
 * **Example** (Implementing a custom tokenizer)
 *
 * ```ts
 * import { Effect } from "effect"
 * import { Prompt } from "effect/unstable/ai"
 * import type { Tokenizer } from "effect/unstable/ai"
 *
 * const customTokenizer: Tokenizer.Service = {
 *   tokenize: (input) =>
 *     Effect.succeed(input.toString().split(" ").map((_, i) => i)),
 *   truncate: (input, maxTokens) =>
 *     Effect.succeed(Prompt.make(input.toString().slice(0, maxTokens * 5)))
 * }
 * ```
 *
 * @category models
 * @since 4.0.0
 */
export interface Service {
    /**
     * Converts text input into an array of token numbers.
     */
    readonly tokenize: (
    /**
     * The text input to tokenize.
     */
    input: Prompt.RawInput) => Effect.Effect<Array<number>, AiError.AiError>;
    /**
     * Truncates text input to fit within the specified token limit.
     */
    readonly truncate: (
    /**
     * The text input to truncate.
     */
    input: Prompt.RawInput, 
    /**
     * Maximum number of tokens to retain.
     */
    tokens: number) => Effect.Effect<Prompt.Prompt, AiError.AiError>;
}
/**
 * Creates a Tokenizer service implementation from tokenization options.
 *
 * **Details**
 *
 * This function constructs a complete Tokenizer service by providing a
 * tokenization function. The service handles both tokenization and
 * truncation operations using the provided tokenizer.
 *
 * **Example** (Creating a word tokenizer)
 *
 * ```ts
 * import { Effect } from "effect"
 * import { Tokenizer } from "effect/unstable/ai"
 *
 * // Simple word-based tokenizer
 * const wordTokenizer = Tokenizer.make({
 *   tokenize: (prompt) =>
 *     Effect.succeed(
 *       prompt.content
 *         .flatMap((msg) =>
 *           typeof msg.content === "string"
 *             ? msg.content.split(" ")
 *             : msg.content.flatMap((part) =>
 *               part.type === "text" ? part.text.split(" ") : []
 *             )
 *         )
 *         .map((_, index) => index)
 *     )
 * })
 * ```
 *
 * @category constructors
 * @since 4.0.0
 */
export declare const make: (options: {
    readonly tokenize: (content: Prompt.Prompt) => Effect.Effect<Array<number>, AiError.AiError>;
}) => Service;
export {};
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