local-openbook

"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
exports.loadReranker = loadReranker;
exports.rerank = rerank;
let transformers = null;
let tokenizer = null;
let model = null;
const MODEL_ID = 'Xenova/bge-reranker-base';
async function getTransformers() {
    if (!transformers) {
        // Dynamic import to bypass ERR_REQUIRE_ESM
        transformers = await Promise.resolve().then(() => __importStar(require('@xenova/transformers')));
        // Configure environment
        transformers.env.allowLocalModels = false; // Force download/cache usage
        transformers.env.useBrowserCache = true;
    }
    return transformers;
}
async function loadReranker() {
    if (!tokenizer || !model) {
        console.log("Loading Reranker Model (this may take a while on first run)...");
        const { AutoTokenizer, AutoModelForSequenceClassification } = await getTransformers();
        tokenizer = await AutoTokenizer.from_pretrained(MODEL_ID);
        model = await AutoModelForSequenceClassification.from_pretrained(MODEL_ID);
        console.log("Reranker Model Loaded.");
    }
}
async function rerank(query, documents) {
    await loadReranker();
    if (!tokenizer || !model)
        throw new Error("Model failed to load");
    // Construct pairs: [query, doc1], [query, doc2], ...
    const queries = new Array(documents.length).fill(query);
    // Batch processing
    const inputs = await tokenizer(queries, {
        text_pair: documents,
        padding: true,
        truncation: true
    });
    const output = await model(inputs);
    // Sigmoid to get 0-1 scores
    const scores = output.logits.sigmoid().data;
    // Map back to indices
    const results = [];
    for (let i = 0; i < scores.length; i++) {
        results.push({ index: i, score: scores[i] });
    }
    // Sort descending
    results.sort((a, b) => b.score - a.score);
    return results;
}