ai-utils.js/model-provider/llamacpp/LlamaCppTextEmbeddingModel.d.ts

Build AI applications, chatbots, and agents with JavaScript and TypeScript.

import z from "zod";
import { AbstractModel } from "../../model-function/AbstractModel.js";
import { FunctionOptions } from "../../model-function/FunctionOptions.js";
import { TextEmbeddingModel, TextEmbeddingModelSettings } from "../../model-function/embed-text/TextEmbeddingModel.js";
import { RetryFunction } from "../../util/api/RetryFunction.js";
import { ThrottleFunction } from "../../util/api/ThrottleFunction.js";
export interface LlamaCppEmbeddingModelSettings extends TextEmbeddingModelSettings {
    baseUrl?: string;
    retry?: RetryFunction;
    throttle?: ThrottleFunction;
    tokenizerSettings?: {
        retry?: RetryFunction;
        throttle?: ThrottleFunction;
    };
}
export declare class LlamaCppTextEmbeddingModel extends AbstractModel<LlamaCppEmbeddingModelSettings> implements TextEmbeddingModel<LlamaCppTextEmbeddingResponse, LlamaCppEmbeddingModelSettings> {
    constructor(settings?: LlamaCppEmbeddingModelSettings);
    readonly provider: "llamacpp";
    get modelName(): null;
    readonly maxTextsPerCall = 1;
    readonly contextWindowSize: undefined;
    readonly embeddingDimensions: undefined;
    private readonly tokenizer;
    tokenize(text: string): Promise<number[]>;
    callAPI(texts: Array<string>, options?: FunctionOptions<LlamaCppEmbeddingModelSettings>): Promise<LlamaCppTextEmbeddingResponse>;
    generateEmbeddingResponse(texts: string[], options?: FunctionOptions<LlamaCppEmbeddingModelSettings>): Promise<{
        embedding: number[];
    }>;
    extractEmbeddings(response: LlamaCppTextEmbeddingResponse): number[][];
    withSettings(additionalSettings: Partial<LlamaCppEmbeddingModelSettings>): this;
}
declare const llamaCppTextEmbeddingResponseSchema: z.ZodObject<{
    embedding: z.ZodArray<z.ZodNumber, "many">;
}, "strip", z.ZodTypeAny, {
    embedding: number[];
}, {
    embedding: number[];
}>;
export type LlamaCppTextEmbeddingResponse = z.infer<typeof llamaCppTextEmbeddingResponseSchema>;
export {};