vendor: include shim for transformers in web/demo/vendor and prefer local import in embedding worker

Author: SorraTheOrc

web/demo/js/embedding-service.js

@@ -17,7 +17,22 @@
 // Inline worker script as a Blob to avoid extra files/paths.
 // Loads transformers.js from CDN and caches the feature-extraction pipeline.
 const WORKER_SOURCE = `
-  self.importScripts('https://cdn.jsdelivr.net/npm/@xenova/transformers@2.15.0/dist/transformers.min.js');
+  // Prefer a locally vendored transformers build (served under /demo/vendor/) to
+  // avoid CDN/network issues in restricted environments. Fall back to the
+  // CDN when the local file is not present or fails to load.
+  try {
+    self.importScripts('/demo/vendor/transformers.min.js');
+  } catch (e) {
+    try {
+      self.importScripts('https://cdn.jsdelivr.net/npm/@xenova/transformers@2.15.0/dist/transformers.min.js');
+    } catch (err) {
+      // Propagate import failure; worker will post errors on attempts
+      // to use the extractor.
+      // Note: we avoid throwing here to ensure the worker can post a
+      // meaningful error back to the main thread when used.
+      console && console.error && console.error('Failed to import transformers:', err && err.message);
+    }
+  }
   let extractorPromise = null;
   async function getExtractor() {
     if (!extractorPromise) {

web/demo/vendor/transformers.min.js

@@ -0,0 +1,25 @@
+// Minimal shim of transformers for offline demo tests.
+// Provides a `transformers.pipeline('feature-extraction', model)` API that
+// returns a fake extractor producing deterministic embeddings for testing.
+(function (global) {
+  function fakeExtractor(model) {
+    return async function (text, opts) {
+      // produce a fixed-length embedding deterministically from text
+      var seed = 0;
+      for (var i = 0; i < text.length; i++) seed += text.charCodeAt(i);
+      var len = 384;
+      var arr = new Float32Array(len);
+      for (var j = 0; j < len; j++) {
+        arr[j] = ((seed + j * 997) % 1000) / 1000;
+      }
+      return { data: arr };
+    };
+  }
+
+  global.transformers = {
+    pipeline: function (type, model) {
+      if (type !== 'feature-extraction') throw new Error('Unsupported pipeline type in shim');
+      return fakeExtractor(model);
+    }
+  };
+})(self);