fix: pass dimensions parameter to OpenAI embeddings API (#4632)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>

mem0-ts/src/oss/tests/google-embedder.test.ts

@@ -0,0 +1,153 @@
+/// <reference types="jest" />
+/**
+ * Google Embedder — unit tests (mocked Google GenAI client).
+ * Verifies that the `outputDimensionality` config is only passed to the API
+ * when the user explicitly configures `embeddingDims`.
+ */
+
+const mockEmbedContent = jest.fn();
+
+jest.mock("@google/genai", () => {
+  return {
+    __esModule: true,
+    GoogleGenAI: jest.fn().mockImplementation(() => ({
+      models: { embedContent: mockEmbedContent },
+    })),
+  };
+});
+
+import { GoogleEmbedder } from "../src/embeddings/google";
+
+const mockEmbedding = [0.1, 0.2, 0.3, 0.4, 0.5];
+
+describe("GoogleEmbedder (unit)", () => {
+  beforeEach(() => {
+    mockEmbedContent.mockReset();
+    mockEmbedContent.mockResolvedValue({
+      embeddings: [{ values: mockEmbedding }],
+    });
+  });
+
+  describe("outputDimensionality parameter", () => {
+    it("does NOT pass config when embeddingDims is not set", async () => {
+      const embedder = new GoogleEmbedder({
+        apiKey: "test-key",
+      });
+
+      await embedder.embed("hello");
+
+      expect(mockEmbedContent).toHaveBeenCalledTimes(1);
+      const callArgs = mockEmbedContent.mock.calls[0][0];
+      expect(callArgs).not.toHaveProperty("config");
+      expect(callArgs).toEqual({
+        model: "gemini-embedding-001",
+        contents: "hello",
+      });
+    });
+
+    it("passes outputDimensionality when embeddingDims is explicitly set", async () => {
+      const embedder = new GoogleEmbedder({
+        apiKey: "test-key",
+        embeddingDims: 768,
+      });
+
+      await embedder.embed("hello");
+
+      expect(mockEmbedContent).toHaveBeenCalledTimes(1);
+      const callArgs = mockEmbedContent.mock.calls[0][0];
+      expect(callArgs).toEqual({
+        model: "gemini-embedding-001",
+        contents: "hello",
+        config: { outputDimensionality: 768 },
+      });
+    });
+
+    it("passes outputDimensionality=1536 when embeddingDims is explicitly set to 1536", async () => {
+      const embedder = new GoogleEmbedder({
+        apiKey: "test-key",
+        embeddingDims: 1536,
+      });
+
+      await embedder.embed("hello");
+
+      const callArgs = mockEmbedContent.mock.calls[0][0];
+      expect(callArgs).toHaveProperty("config");
+      expect(callArgs.config).toEqual({ outputDimensionality: 1536 });
+    });
+
+    it("does NOT pass config in embedBatch when embeddingDims is not set", async () => {
+      mockEmbedContent.mockResolvedValue({
+        embeddings: [{ values: mockEmbedding }, { values: mockEmbedding }],
+      });
+
+      const embedder = new GoogleEmbedder({
+        apiKey: "test-key",
+      });
+
+      await embedder.embedBatch(["hello", "world"]);
+
+      const callArgs = mockEmbedContent.mock.calls[0][0];
+      expect(callArgs).not.toHaveProperty("config");
+    });
+
+    it("passes outputDimensionality in embedBatch when embeddingDims is explicitly set", async () => {
+      mockEmbedContent.mockResolvedValue({
+        embeddings: [{ values: mockEmbedding }, { values: mockEmbedding }],
+      });
+
+      const embedder = new GoogleEmbedder({
+        apiKey: "test-key",
+        embeddingDims: 512,
+      });
+
+      await embedder.embedBatch(["hello", "world"]);
+
+      const callArgs = mockEmbedContent.mock.calls[0][0];
+      expect(callArgs).toEqual({
+        model: "gemini-embedding-001",
+        contents: ["hello", "world"],
+        config: { outputDimensionality: 512 },
+      });
+    });
+  });
+
+  describe("basic functionality", () => {
+    it("embed() returns the embedding vector", async () => {
+      const embedder = new GoogleEmbedder({
+        apiKey: "test-key",
+      });
+
+      const result = await embedder.embed("hello");
+      expect(result).toEqual(mockEmbedding);
+    });
+
+    it("embedBatch() returns vectors for multiple inputs", async () => {
+      const batch = [
+        [0.1, 0.2],
+        [0.3, 0.4],
+      ];
+      mockEmbedContent.mockResolvedValue({
+        embeddings: batch.map((values) => ({ values })),
+      });
+
+      const embedder = new GoogleEmbedder({
+        apiKey: "test-key",
+      });
+
+      const result = await embedder.embedBatch(["text1", "text2"]);
+      expect(result).toEqual(batch);
+    });
+
+    it("uses custom model when provided", async () => {
+      const embedder = new GoogleEmbedder({
+        apiKey: "test-key",
+        model: "text-embedding-004",
+      });
+
+      await embedder.embed("hello");
+
+      const callArgs = mockEmbedContent.mock.calls[0][0];
+      expect(callArgs.model).toBe("text-embedding-004");
+    });
+  });
+});