fix(node): preserve repeated embedding aliases

nodejs/__test__/embedding.test.ts

@@ -184,7 +184,7 @@ describe("embedding functions", () => {
     const vector0 = JSON.parse(JSON.stringify(arr[0].vector));
     expect(vector0).toEqual([1, 2, 3]);
   });
-  it("should append using embedding metadata created by Python", async () => {
+  it("should append multiple Python embeddings with the same alias", async () => {
     @register("python-mock")
     // biome-ignore lint/correctness/noUnusedVariables: the decorator registers this class
     class MockEmbeddingFunction extends EmbeddingFunction<string> {
@@ -198,21 +198,29 @@ describe("embedding functions", () => {
         return [1, 2, 3];
       }
       async computeSourceEmbeddings(data: string[]) {
-        return data.map(() => [1, 2, 3]);
+        return data.map((value) =>
+          value === "hello world" ? [1, 2, 3] : [4, 5, 6],
+        );
       }
     }
 
     const metadata = new Map([
       [
         "embedding_functions",
-        '[{"source_column":"text","vector_column":"vector","name":"python-mock","model":{}}]',
+        '[{"source_column":"text1","vector_column":"vector1","name":"python-mock","model":{}},{"source_column":"text2","vector_column":"vector2","name":"python-mock","model":{}}]',
       ],
     ]);
     const schema = new Schema(
       [
-        new Field("text", new Utf8(), true),
+        new Field("text1", new Utf8(), true),
+        new Field("text2", new Utf8(), true),
         new Field(
-          "vector",
+          "vector1",
+          new FixedSizeList(3, new Field("item", new Float32(), true)),
+          true,
+        ),
+        new Field(
+          "vector2",
           new FixedSizeList(3, new Field("item", new Float32(), true)),
           true,
         ),
@@ -222,10 +230,11 @@ describe("embedding functions", () => {
 
     const db = await connect(tmpDir.name);
     const table = await db.createEmptyTable("test", schema);
-    await table.add([{ text: "hello world" }]);
+    await table.add([{ text1: "hello world", text2: "goodbye world" }]);
 
     const rows = await table.query().toArray();
-    expect(JSON.parse(JSON.stringify(rows[0].vector))).toEqual([1, 2, 3]);
+    expect(JSON.parse(JSON.stringify(rows[0].vector1))).toEqual([1, 2, 3]);
+    expect(JSON.parse(JSON.stringify(rows[0].vector2))).toEqual([4, 5, 6]);
   });
   it("should error when appending to a table with an unregistered embedding function", async () => {
     @register("mock")

nodejs/lancedb/embedding/registry.ts

@@ -118,7 +118,7 @@ export class EmbeddingFunctionRegistry {
           }
           const func = await this.get(f.name)!.create(f.model);
           return [
-            f.name,
+            f.vectorColumn ?? "vector",
             {
               sourceColumn: f.sourceColumn,
               vectorColumn: f.vectorColumn,