fix: pass dimensions parameter to OpenAI embeddings API (#4632)
Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
This commit is contained in:
wobushixiaoj
co-authored by
kartik-mem0
kartik-mem0
parent
f94ea06588
commit
8ae7a06220
@@ -0,0 +1,163 @@
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/// <reference types="jest" />
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/**
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* Azure OpenAI Embedder — unit tests (mocked Azure OpenAI client).
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* Verifies that the `dimensions` parameter is only passed to the API
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* when the user explicitly configures `embeddingDims`.
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*/
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const mockEmbeddingsCreate = jest.fn();
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jest.mock("openai", () => {
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return {
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__esModule: true,
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AzureOpenAI: jest.fn().mockImplementation(() => ({
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embeddings: { create: mockEmbeddingsCreate },
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})),
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};
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});
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import { AzureOpenAIEmbedder } from "../src/embeddings/azure";
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const mockEmbedding = [0.1, 0.2, 0.3, 0.4, 0.5];
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const baseConfig = {
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apiKey: "test-key",
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modelProperties: { endpoint: "https://test.openai.azure.com" },
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};
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describe("AzureOpenAIEmbedder (unit)", () => {
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beforeEach(() => {
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mockEmbeddingsCreate.mockReset();
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mockEmbeddingsCreate.mockResolvedValue({
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data: [{ embedding: mockEmbedding }],
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});
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});
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describe("dimensions parameter", () => {
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it("does NOT pass dimensions when embeddingDims is not set", async () => {
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const embedder = new AzureOpenAIEmbedder(baseConfig);
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await embedder.embed("hello");
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expect(mockEmbeddingsCreate).toHaveBeenCalledTimes(1);
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const callArgs = mockEmbeddingsCreate.mock.calls[0][0];
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expect(callArgs).not.toHaveProperty("dimensions");
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expect(callArgs).toEqual({
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model: "text-embedding-3-small",
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input: "hello",
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});
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});
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it("passes dimensions when embeddingDims is explicitly set", async () => {
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const embedder = new AzureOpenAIEmbedder({
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...baseConfig,
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embeddingDims: 1024,
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});
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await embedder.embed("hello");
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expect(mockEmbeddingsCreate).toHaveBeenCalledTimes(1);
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const callArgs = mockEmbeddingsCreate.mock.calls[0][0];
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expect(callArgs).toEqual({
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model: "text-embedding-3-small",
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input: "hello",
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dimensions: 1024,
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});
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});
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it("passes dimensions=1536 when embeddingDims is explicitly set to 1536", async () => {
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const embedder = new AzureOpenAIEmbedder({
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...baseConfig,
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embeddingDims: 1536,
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});
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await embedder.embed("hello");
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const callArgs = mockEmbeddingsCreate.mock.calls[0][0];
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expect(callArgs).toHaveProperty("dimensions", 1536);
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});
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it("does NOT pass dimensions in embedBatch when embeddingDims is not set", async () => {
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mockEmbeddingsCreate.mockResolvedValue({
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data: [{ embedding: mockEmbedding }, { embedding: mockEmbedding }],
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});
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const embedder = new AzureOpenAIEmbedder(baseConfig);
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await embedder.embedBatch(["hello", "world"]);
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const callArgs = mockEmbeddingsCreate.mock.calls[0][0];
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expect(callArgs).not.toHaveProperty("dimensions");
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});
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it("passes dimensions in embedBatch when embeddingDims is explicitly set", async () => {
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mockEmbeddingsCreate.mockResolvedValue({
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data: [{ embedding: mockEmbedding }, { embedding: mockEmbedding }],
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});
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const embedder = new AzureOpenAIEmbedder({
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...baseConfig,
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embeddingDims: 512,
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});
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await embedder.embedBatch(["hello", "world"]);
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const callArgs = mockEmbeddingsCreate.mock.calls[0][0];
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expect(callArgs).toEqual({
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model: "text-embedding-3-small",
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input: ["hello", "world"],
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dimensions: 512,
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});
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});
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});
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describe("basic functionality", () => {
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it("embed() returns the embedding vector", async () => {
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const embedder = new AzureOpenAIEmbedder(baseConfig);
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const result = await embedder.embed("hello");
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expect(result).toEqual(mockEmbedding);
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});
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it("embedBatch() returns vectors for multiple inputs", async () => {
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const batch = [
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[0.1, 0.2],
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[0.3, 0.4],
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];
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mockEmbeddingsCreate.mockResolvedValue({
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data: batch.map((embedding) => ({ embedding })),
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});
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const embedder = new AzureOpenAIEmbedder(baseConfig);
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const result = await embedder.embedBatch(["text1", "text2"]);
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expect(result).toEqual(batch);
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});
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it("uses custom model when provided", async () => {
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const embedder = new AzureOpenAIEmbedder({
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...baseConfig,
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model: "text-embedding-3-large",
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});
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await embedder.embed("hello");
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const callArgs = mockEmbeddingsCreate.mock.calls[0][0];
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expect(callArgs.model).toBe("text-embedding-3-large");
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});
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it("throws when API key is missing", () => {
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expect(() => {
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new AzureOpenAIEmbedder({
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modelProperties: { endpoint: "https://test.openai.azure.com" },
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});
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}).toThrow("Azure OpenAI requires both API key and endpoint");
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});
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it("throws when endpoint is missing", () => {
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expect(() => {
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new AzureOpenAIEmbedder({ apiKey: "test-key" });
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}).toThrow("Azure OpenAI requires both API key and endpoint");
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});
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});
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});
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@@ -0,0 +1,153 @@
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/// <reference types="jest" />
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/**
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* Google Embedder — unit tests (mocked Google GenAI client).
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* Verifies that the `outputDimensionality` config is only passed to the API
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* when the user explicitly configures `embeddingDims`.
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*/
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const mockEmbedContent = jest.fn();
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jest.mock("@google/genai", () => {
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return {
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__esModule: true,
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GoogleGenAI: jest.fn().mockImplementation(() => ({
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models: { embedContent: mockEmbedContent },
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})),
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};
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});
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import { GoogleEmbedder } from "../src/embeddings/google";
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const mockEmbedding = [0.1, 0.2, 0.3, 0.4, 0.5];
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describe("GoogleEmbedder (unit)", () => {
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beforeEach(() => {
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mockEmbedContent.mockReset();
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mockEmbedContent.mockResolvedValue({
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embeddings: [{ values: mockEmbedding }],
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});
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});
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describe("outputDimensionality parameter", () => {
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it("does NOT pass config when embeddingDims is not set", async () => {
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const embedder = new GoogleEmbedder({
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apiKey: "test-key",
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});
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await embedder.embed("hello");
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expect(mockEmbedContent).toHaveBeenCalledTimes(1);
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const callArgs = mockEmbedContent.mock.calls[0][0];
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expect(callArgs).not.toHaveProperty("config");
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expect(callArgs).toEqual({
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model: "gemini-embedding-001",
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contents: "hello",
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});
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});
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it("passes outputDimensionality when embeddingDims is explicitly set", async () => {
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const embedder = new GoogleEmbedder({
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apiKey: "test-key",
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embeddingDims: 768,
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});
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await embedder.embed("hello");
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expect(mockEmbedContent).toHaveBeenCalledTimes(1);
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const callArgs = mockEmbedContent.mock.calls[0][0];
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expect(callArgs).toEqual({
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model: "gemini-embedding-001",
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contents: "hello",
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config: { outputDimensionality: 768 },
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});
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});
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it("passes outputDimensionality=1536 when embeddingDims is explicitly set to 1536", async () => {
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const embedder = new GoogleEmbedder({
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apiKey: "test-key",
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embeddingDims: 1536,
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});
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await embedder.embed("hello");
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const callArgs = mockEmbedContent.mock.calls[0][0];
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expect(callArgs).toHaveProperty("config");
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expect(callArgs.config).toEqual({ outputDimensionality: 1536 });
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});
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it("does NOT pass config in embedBatch when embeddingDims is not set", async () => {
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mockEmbedContent.mockResolvedValue({
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embeddings: [{ values: mockEmbedding }, { values: mockEmbedding }],
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});
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const embedder = new GoogleEmbedder({
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apiKey: "test-key",
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});
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await embedder.embedBatch(["hello", "world"]);
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const callArgs = mockEmbedContent.mock.calls[0][0];
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expect(callArgs).not.toHaveProperty("config");
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});
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it("passes outputDimensionality in embedBatch when embeddingDims is explicitly set", async () => {
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mockEmbedContent.mockResolvedValue({
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embeddings: [{ values: mockEmbedding }, { values: mockEmbedding }],
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});
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const embedder = new GoogleEmbedder({
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apiKey: "test-key",
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embeddingDims: 512,
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});
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await embedder.embedBatch(["hello", "world"]);
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const callArgs = mockEmbedContent.mock.calls[0][0];
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expect(callArgs).toEqual({
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model: "gemini-embedding-001",
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contents: ["hello", "world"],
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config: { outputDimensionality: 512 },
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});
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});
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});
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describe("basic functionality", () => {
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it("embed() returns the embedding vector", async () => {
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const embedder = new GoogleEmbedder({
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apiKey: "test-key",
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});
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const result = await embedder.embed("hello");
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expect(result).toEqual(mockEmbedding);
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});
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it("embedBatch() returns vectors for multiple inputs", async () => {
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const batch = [
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[0.1, 0.2],
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[0.3, 0.4],
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];
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mockEmbedContent.mockResolvedValue({
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embeddings: batch.map((values) => ({ values })),
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});
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const embedder = new GoogleEmbedder({
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apiKey: "test-key",
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});
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const result = await embedder.embedBatch(["text1", "text2"]);
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expect(result).toEqual(batch);
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});
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it("uses custom model when provided", async () => {
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const embedder = new GoogleEmbedder({
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apiKey: "test-key",
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model: "text-embedding-004",
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});
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await embedder.embed("hello");
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const callArgs = mockEmbedContent.mock.calls[0][0];
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expect(callArgs.model).toBe("text-embedding-004");
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});
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});
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});
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@@ -0,0 +1,152 @@
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/// <reference types="jest" />
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/**
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* OpenAI Embedder — unit tests (mocked OpenAI client).
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* Verifies that the `dimensions` parameter is only passed to the API
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* when the user explicitly configures `embeddingDims`.
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*/
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const mockEmbeddingsCreate = jest.fn();
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jest.mock("openai", () => {
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return {
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__esModule: true,
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default: jest.fn().mockImplementation(() => ({
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embeddings: { create: mockEmbeddingsCreate },
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})),
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};
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});
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import { OpenAIEmbedder } from "../src/embeddings/openai";
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const mockEmbedding = [0.1, 0.2, 0.3, 0.4, 0.5];
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describe("OpenAIEmbedder (unit)", () => {
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beforeEach(() => {
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mockEmbeddingsCreate.mockReset();
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mockEmbeddingsCreate.mockResolvedValue({
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data: [{ embedding: mockEmbedding }],
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});
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});
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describe("dimensions parameter", () => {
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it("does NOT pass dimensions when embeddingDims is not set", async () => {
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const embedder = new OpenAIEmbedder({
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apiKey: "test-key",
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});
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await embedder.embed("hello");
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expect(mockEmbeddingsCreate).toHaveBeenCalledTimes(1);
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const callArgs = mockEmbeddingsCreate.mock.calls[0][0];
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expect(callArgs).not.toHaveProperty("dimensions");
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expect(callArgs).toEqual({
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model: "text-embedding-3-small",
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input: "hello",
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});
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});
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it("passes dimensions when embeddingDims is explicitly set", async () => {
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const embedder = new OpenAIEmbedder({
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apiKey: "test-key",
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embeddingDims: 1024,
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});
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await embedder.embed("hello");
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expect(mockEmbeddingsCreate).toHaveBeenCalledTimes(1);
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const callArgs = mockEmbeddingsCreate.mock.calls[0][0];
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expect(callArgs).toEqual({
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model: "text-embedding-3-small",
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input: "hello",
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dimensions: 1024,
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});
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});
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it("passes dimensions=1536 when embeddingDims is explicitly set to 1536", async () => {
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const embedder = new OpenAIEmbedder({
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apiKey: "test-key",
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embeddingDims: 1536,
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});
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await embedder.embed("hello");
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const callArgs = mockEmbeddingsCreate.mock.calls[0][0];
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expect(callArgs).toHaveProperty("dimensions", 1536);
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});
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it("does NOT pass dimensions in embedBatch when embeddingDims is not set", async () => {
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mockEmbeddingsCreate.mockResolvedValue({
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data: [{ embedding: mockEmbedding }, { embedding: mockEmbedding }],
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});
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const embedder = new OpenAIEmbedder({
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apiKey: "test-key",
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});
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await embedder.embedBatch(["hello", "world"]);
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const callArgs = mockEmbeddingsCreate.mock.calls[0][0];
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expect(callArgs).not.toHaveProperty("dimensions");
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});
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it("passes dimensions in embedBatch when embeddingDims is explicitly set", async () => {
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mockEmbeddingsCreate.mockResolvedValue({
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data: [{ embedding: mockEmbedding }, { embedding: mockEmbedding }],
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});
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const embedder = new OpenAIEmbedder({
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apiKey: "test-key",
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embeddingDims: 512,
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});
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await embedder.embedBatch(["hello", "world"]);
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const callArgs = mockEmbeddingsCreate.mock.calls[0][0];
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expect(callArgs).toEqual({
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model: "text-embedding-3-small",
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input: ["hello", "world"],
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dimensions: 512,
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});
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});
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});
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describe("basic functionality", () => {
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it("embed() returns the embedding vector", async () => {
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const embedder = new OpenAIEmbedder({
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apiKey: "test-key",
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});
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const result = await embedder.embed("hello");
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expect(result).toEqual(mockEmbedding);
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});
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it("embedBatch() returns vectors for multiple inputs", async () => {
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const batch = [
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[0.1, 0.2],
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[0.3, 0.4],
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];
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mockEmbeddingsCreate.mockResolvedValue({
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data: batch.map((embedding) => ({ embedding })),
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});
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const embedder = new OpenAIEmbedder({
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apiKey: "test-key",
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});
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const result = await embedder.embedBatch(["text1", "text2"]);
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expect(result).toEqual(batch);
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});
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it("uses custom model when provided", async () => {
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const embedder = new OpenAIEmbedder({
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apiKey: "test-key",
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model: "text-embedding-3-large",
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});
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await embedder.embed("hello");
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const callArgs = mockEmbeddingsCreate.mock.calls[0][0];
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expect(callArgs.model).toBe("text-embedding-3-large");
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});
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});
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});
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