feat(ts-sdk): add Vertex AI embedding provider (#5882)

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

mem0-ts/src/oss/tests/memory.embedding-action.test.ts

@@ -0,0 +1,115 @@
+/// <reference types="jest" />
+/**
+ * Verifies the memory pipeline threads the correct memory action
+ * ("add" | "update" | "search") into the embedder. Task-type-aware providers
+ * (e.g. Vertex AI) embed queries and documents differently based on this, and
+ * the argument is silently ignored by every other embedder, so only a
+ * pipeline-level test catches a dropped action.
+ */
+import { Memory } from "../src/memory";
+
+const mockEmbedding = new Array(1536).fill(0.1);
+// Prefixed `mock*` so jest's hoisted module factory may reference them.
+const mockEmbed = jest.fn().mockResolvedValue(mockEmbedding);
+const mockEmbedBatch = jest
+  .fn()
+  .mockImplementation((texts: string[]) =>
+    Promise.resolve(texts.map(() => mockEmbedding)),
+  );
+
+const mockGenerateResponse = jest
+  .fn()
+  .mockResolvedValue(JSON.stringify({ memory: [] }));
+
+jest.mock("../src/embeddings/google", () => ({ GoogleEmbedder: jest.fn() }));
+jest.mock("../src/llms/google", () => ({ GoogleLLM: jest.fn() }));
+jest.mock("../src/llms/openai", () => ({
+  OpenAILLM: jest.fn().mockImplementation(() => ({
+    generateResponse: mockGenerateResponse,
+  })),
+}));
+jest.mock("../src/embeddings/openai", () => ({
+  OpenAIEmbedder: jest.fn().mockImplementation(() => ({
+    embed: mockEmbed,
+    embedBatch: mockEmbedBatch,
+    embeddingDims: 1536,
+  })),
+}));
+
+function createMemory(): Memory {
+  return new Memory({
+    version: "v1.1",
+    embedder: {
+      provider: "openai",
+      config: { apiKey: "test-key", model: "text-embedding-3-small" },
+    },
+    vectorStore: {
+      provider: "memory",
+      config: {
+        collectionName: `test-action-${Date.now()}-${Math.random()}`,
+        dimension: 1536,
+        dbPath: ":memory:",
+      },
+    },
+    llm: {
+      provider: "openai",
+      config: { apiKey: "test-key", model: "gpt-5-mini" },
+    },
+    historyDbPath: ":memory:",
+  });
+}
+
+describe("embedder memory-action threading", () => {
+  let memory: Memory;
+
+  beforeEach(() => {
+    memory = createMemory();
+    mockEmbed.mockClear();
+    mockEmbedBatch.mockClear();
+    mockGenerateResponse.mockResolvedValue(JSON.stringify({ memory: [] }));
+  });
+
+  afterEach(async () => {
+    await memory.reset();
+  });
+
+  test("search() embeds the query with the 'search' action", async () => {
+    await memory.search("what do I like", { filters: { user_id: "u1" } });
+    expect(mockEmbed).toHaveBeenCalledWith("what do I like", "search");
+  });
+
+  test("update() embeds the new value with the 'update' action", async () => {
+    // Missing id: update embeds the value before it throws on the absent row.
+    await memory.update("missing-id", "new value").catch(() => {});
+    expect(mockEmbed).toHaveBeenCalledWith("new value", "update");
+  });
+
+  test("add() batch-embeds extracted memories and entities with the 'add' action", async () => {
+    mockGenerateResponse.mockResolvedValue(
+      JSON.stringify({
+        memory: [
+          { id: "1", text: "John loves sci-fi movies", attributed_to: "user" },
+        ],
+      }),
+    );
+
+    await memory.add("I love sci-fi movies", { userId: "u1" });
+
+    // Phase 1 retrieval embeds the incoming turn as a query.
+    expect(mockEmbed).toHaveBeenCalledWith(
+      expect.stringContaining("I love sci-fi movies"),
+      "search",
+    );
+    // Phase 3 (extracted memories) and phase 7 (linked entities) both batch
+    // embed as documents. Without an explicit action, a task-type-aware
+    // embedder falls back to its own default and silently mis-embeds.
+    expect(mockEmbedBatch).toHaveBeenCalledWith(
+      ["John loves sci-fi movies"],
+      "add",
+    );
+    expect(mockEmbedBatch.mock.calls.length).toBeGreaterThan(0);
+    for (const call of mockEmbedBatch.mock.calls) {
+      expect(call[1]).toBe("add");
+    }
+  });
+});