seraph-agent/dist/llm/hf-embeddings.js

An extremely lightweight, SRE autonomous AI agent for seamless integration with common observability tasks.

"use strict";
var __importDefault = (this && this.__importDefault) || function (mod) {
    return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.HuggingFaceEmbeddings = void 0;
const embeddings_1 = require("@langchain/core/embeddings");
const https_1 = __importDefault(require("https"));
class HuggingFaceEmbeddings extends embeddings_1.Embeddings {
    serviceUrl;
    agent;
    constructor(model = 'sentence-transformers/all-MiniLM-L6-v2') {
        super({ modelName: model });
        this.serviceUrl = 'https://127.0.0.1:5001/embed';
        this.agent = new https_1.default.Agent({
            rejectUnauthorized: false,
        });
    }
    async callService(texts) {
        try {
            const response = await fetch(this.serviceUrl, {
                method: 'POST',
                headers: {
                    'Content-Type': 'application/json',
                },
                body: JSON.stringify({ texts }),
                agent: this.agent,
            });
            if (!response.ok) {
                throw new Error(`HTTP error! status: ${response.status}`);
            }
            const data = await response.json();
            return data.embeddings;
        }
        catch (error) {
            console.error('Error calling embedding service:', error);
            // Return a list of zero vectors as a fallback
            return texts.map(() => new Array(384).fill(0));
        }
    }
    async embedDocuments(texts) {
        return this.callService(texts);
    }
    async embedQuery(text) {
        const embeddings = await this.callService([text]);
        return embeddings[0];
    }
}
exports.HuggingFaceEmbeddings = HuggingFaceEmbeddings;