dist/embed-worker.js

import { pipeline } from "https://cdn.jsdelivr.net/npm/@huggingface/transformers@3";
//#region src/embed-worker.ts
/**
* embed-worker.ts — Background Web Worker for transformers.js ONNX embedding.
*
* Supports model diffing: if the requested model is already loaded, skips reload.
* All outgoing messages are tagged with `source: 'vecdb'` to avoid collisions
* with transformers.js internal events.
*/
const MSG_TAG = "vecdb";
let extractor = null;
let currentModel = null;
let currentDtype = null;
let currentDim = null;
function send(msg) {
	self.postMessage({
		...msg,
		source: MSG_TAG
	});
}
function sendTransfer(msg, transfer) {
	self.postMessage({
		...msg,
		source: MSG_TAG
	}, transfer);
}
self.addEventListener("message", async (event) => {
	const { type, id } = event.data;
	try {
		switch (type) {
			case "load": {
				const { modelId, dtype } = event.data;
				if (extractor && currentModel === modelId && currentDtype === dtype && currentDim) {
					send({
						status: "ready",
						dim: currentDim,
						modelId,
						dtype,
						cached: true
					});
					break;
				}
				if (extractor) {
					try {
						await extractor.dispose();
					} catch {}
					extractor = null;
				}
				currentModel = modelId;
				currentDtype = dtype;
				currentDim = null;
				extractor = await pipeline("feature-extraction", modelId, {
					dtype,
					progress_callback: (p) => {
						if (p.status === "progress" && p.total > 0) send({
							status: "dl-progress",
							file: p.file,
							loaded: p.loaded,
							total: p.total
						});
					}
				});
				currentDim = (await extractor(["test"], {
					pooling: "mean",
					normalize: true
				})).dims[1];
				send({
					status: "ready",
					dim: currentDim,
					modelId,
					dtype,
					cached: false
				});
				break;
			}
			case "embed": {
				if (!extractor) {
					send({
						status: "error",
						id,
						message: "Model not loaded"
					});
					return;
				}
				const { texts } = event.data;
				const output = await extractor(texts, {
					pooling: "mean",
					normalize: true
				});
				const float32 = new Float32Array(output.data);
				sendTransfer({
					status: "result",
					id,
					embeddings: float32,
					dims: output.dims
				}, [float32.buffer]);
				break;
			}
			case "unload":
				if (extractor) {
					try {
						await extractor.dispose();
					} catch {}
					extractor = null;
				}
				currentModel = null;
				currentDtype = null;
				currentDim = null;
				send({ status: "unloaded" });
				break;
			case "get-model":
				send({
					status: "model-info",
					modelId: currentModel,
					dtype: currentDtype,
					dim: currentDim,
					loaded: !!extractor
				});
				break;
		}
	} catch (err) {
		send({
			status: "error",
			id,
			message: err.message || String(err)
		});
	}
});
//#endregion

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