ci(node): run examples in CI (#1796)

This is done as setup for a PR that will fix the OpenAI dependency
issue.

 * [x] FTS examples
 * [x] Setup mock openai
 * [x] Ran `npm audit fix`
 * [x] sentences embeddings test
 * [x] Double check formatting of docs examples

nodejs/examples/custom_embedding_function.test.ts

@@ -0,0 +1,76 @@
+import { FeatureExtractionPipeline, pipeline } from "@huggingface/transformers";
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+import { expect, test } from "@jest/globals";
+// --8<-- [start:imports]
+import * as lancedb from "@lancedb/lancedb";
+import {
+  LanceSchema,
+  TextEmbeddingFunction,
+  getRegistry,
+  register,
+} from "@lancedb/lancedb/embedding";
+// --8<-- [end:imports]
+import { withTempDirectory } from "./util.ts";
+
+// --8<-- [start:embedding_impl]
+@register("sentence-transformers")
+class SentenceTransformersEmbeddings extends TextEmbeddingFunction {
+  name = "Xenova/all-miniLM-L6-v2";
+  #ndims!: number;
+  extractor!: FeatureExtractionPipeline;
+
+  async init() {
+    this.extractor = await pipeline("feature-extraction", this.name, {
+      dtype: "fp32",
+    });
+    this.#ndims = await this.generateEmbeddings(["hello"]).then(
+      (e) => e[0].length,
+    );
+  }
+
+  ndims() {
+    return this.#ndims;
+  }
+
+  toJSON() {
+    return {
+      name: this.name,
+    };
+  }
+  async generateEmbeddings(texts: string[]) {
+    const output = await this.extractor(texts, {
+      pooling: "mean",
+      normalize: true,
+    });
+    return output.tolist();
+  }
+}
+// -8<-- [end:embedding_impl]
+
+test("Registry examples", async () => {
+  await withTempDirectory(async (databaseDir) => {
+    // --8<-- [start:call_custom_function]
+    const registry = getRegistry();
+
+    const sentenceTransformer = await registry
+      .get<SentenceTransformersEmbeddings>("sentence-transformers")!
+      .create();
+
+    const schema = LanceSchema({
+      vector: sentenceTransformer.vectorField(),
+      text: sentenceTransformer.sourceField(),
+    });
+
+    const db = await lancedb.connect(databaseDir);
+    const table = await db.createEmptyTable("table", schema, {
+      mode: "overwrite",
+    });
+
+    await table.add([{ text: "hello" }, { text: "world" }]);
+
+    const results = await table.search("greeting").limit(1).toArray();
+    // -8<-- [end:call_custom_function]
+    expect(results.length).toBe(1);
+  });
+}, 100_000);