import { Embeddings } from "@langchain/core/embeddings";
import { createLlamaModel, createLlamaContext, } from "../utils/llama_cpp.js";
/**
 * @example
 * ```typescript
 * // Initialize LlamaCppEmbeddings with the path to the model file
 * const embeddings = new LlamaCppEmbeddings({
 *   modelPath: "/Replace/with/path/to/your/model/gguf-llama2-q4_0.bin",
 * });
 *
 * // Embed a query string using the Llama embeddings
 * const res = embeddings.embedQuery("Hello Llama!");
 *
 * // Output the resulting embeddings
 * console.log(res);
 *
 * ```
 */
export class LlamaCppEmbeddings extends Embeddings {
    constructor(inputs) {
        super(inputs);
        Object.defineProperty(this, "_model", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "_context", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        const _inputs = inputs;
        _inputs.embedding = true;
        this._model = createLlamaModel(_inputs);
        this._context = createLlamaContext(this._model, _inputs);
    }
    /**
     * Generates embeddings for an array of texts.
     * @param texts - An array of strings to generate embeddings for.
     * @returns A Promise that resolves to an array of embeddings.
     */
    async embedDocuments(texts) {
        const tokensArray = [];
        for (const text of texts) {
            const encodings = await this.caller.call(() => new Promise((resolve) => {
                resolve(this._context.encode(text));
            }));
            tokensArray.push(encodings);
        }
        const embeddings = [];
        for (const tokens of tokensArray) {
            const embedArray = [];
            for (let i = 0; i < tokens.length; i += 1) {
                const nToken = +tokens[i];
                embedArray.push(nToken);
            }
            embeddings.push(embedArray);
        }
        return embeddings;
    }
    /**
     * Generates an embedding for a single text.
     * @param text - A string to generate an embedding for.
     * @returns A Promise that resolves to an array of numbers representing the embedding.
     */
    async embedQuery(text) {
        const tokens = [];
        const encodings = await this.caller.call(() => new Promise((resolve) => {
            resolve(this._context.encode(text));
        }));
        for (let i = 0; i < encodings.length; i += 1) {
            const token = +encodings[i];
            tokens.push(token);
        }
        return tokens;
    }
}