@lobehub/chat
Version:
Lobe Chat - an open-source, high-performance chatbot framework that supports speech synthesis, multimodal, and extensible Function Call plugin system. Supports one-click free deployment of your private ChatGPT/LLM web application.
721 lines (634 loc) • 30.5 kB
text/typescript
// @vitest-environment edge-runtime
import { FunctionDeclarationsTool } from '@google/generative-ai';
import OpenAI from 'openai';
import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
import { OpenAIChatMessage } from '@/libs/model-runtime';
import * as imageToBase64Module from '@/utils/imageToBase64';
import * as debugStreamModule from '../utils/debugStream';
import { LobeGoogleAI } from './index';
const provider = 'google';
const bizErrorType = 'ProviderBizError';
const invalidErrorType = 'InvalidProviderAPIKey';
// Mock the console.error to avoid polluting test output
vi.spyOn(console, 'error').mockImplementation(() => {});
let instance: LobeGoogleAI;
beforeEach(() => {
instance = new LobeGoogleAI({ apiKey: 'test' });
// 使用 vi.spyOn 来模拟 chat.completions.create 方法
vi.spyOn(instance['client'], 'getGenerativeModel').mockReturnValue({
generateContentStream: vi.fn().mockResolvedValue(new ReadableStream()),
} as any);
});
afterEach(() => {
vi.clearAllMocks();
});
describe('LobeGoogleAI', () => {
describe('init', () => {
it('should correctly initialize with an API key', async () => {
const instance = new LobeGoogleAI({ apiKey: 'test_api_key' });
expect(instance).toBeInstanceOf(LobeGoogleAI);
// expect(instance.baseURL).toEqual(defaultBaseURL);
});
});
describe('chat', () => {
it('should return a StreamingTextResponse on successful API call', async () => {
const result = await instance.chat({
messages: [{ content: 'Hello', role: 'user' }],
model: 'text-davinci-003',
temperature: 0,
});
// Assert
expect(result).toBeInstanceOf(Response);
});
it('should handle text messages correctly', async () => {
// 模拟 Google AI SDK 的 generateContentStream 方法返回一个成功的响应流
const mockStream = new ReadableStream({
start(controller) {
controller.enqueue('Hello, world!');
controller.close();
},
});
vi.spyOn(instance['client'], 'getGenerativeModel').mockReturnValue({
generateContentStream: vi.fn().mockResolvedValueOnce(mockStream),
} as any);
const result = await instance.chat({
messages: [{ content: 'Hello', role: 'user' }],
model: 'text-davinci-003',
temperature: 0,
});
expect(result).toBeInstanceOf(Response);
// 额外的断言可以加入，比如验证返回的流内容等
});
it('should withGrounding', () => {
const data = [
{
candidates: [{ content: { parts: [{ text: 'As' }], role: 'model' } }],
usageMetadata: { promptTokenCount: 8, totalTokenCount: 8 },
modelVersion: 'gemini-2.0-flash',
},
{
candidates: [
{
content: { parts: [{ text: ' of February 22, 2025, "Ne Zha ' }], role: 'model' },
safetyRatings: [
{ category: 'HARM_CATEGORY_HATE_SPEECH', probability: 'NEGLIGIBLE' },
{ category: 'HARM_CATEGORY_DANGEROUS_CONTENT', probability: 'MEDIUM' },
{ category: 'HARM_CATEGORY_HARASSMENT', probability: 'NEGLIGIBLE' },
{ category: 'HARM_CATEGORY_SEXUALLY_EXPLICIT', probability: 'NEGLIGIBLE' },
],
},
],
usageMetadata: { promptTokenCount: 8, totalTokenCount: 8 },
modelVersion: 'gemini-2.0-flash',
},
{
candidates: [
{
content: {
parts: [{ text: '2" has grossed the following:\n\n* **Worldwide:** $1' }],
role: 'model',
},
safetyRatings: [
{ category: 'HARM_CATEGORY_HATE_SPEECH', probability: 'NEGLIGIBLE' },
{ category: 'HARM_CATEGORY_DANGEROUS_CONTENT', probability: 'NEGLIGIBLE' },
{ category: 'HARM_CATEGORY_HARASSMENT', probability: 'NEGLIGIBLE' },
{ category: 'HARM_CATEGORY_SEXUALLY_EXPLICIT', probability: 'NEGLIGIBLE' },
],
},
],
usageMetadata: { promptTokenCount: 8, totalTokenCount: 8 },
modelVersion: 'gemini-2.0-flash',
},
{
candidates: [
{
content: {
parts: [
{
text: '.66 billion\n* **China:** $1.82 billion (CN¥12.35 billion)\n* **US &',
},
],
role: 'model',
},
safetyRatings: [
{ category: 'HARM_CATEGORY_HATE_SPEECH', probability: 'NEGLIGIBLE' },
{ category: 'HARM_CATEGORY_DANGEROUS_CONTENT', probability: 'NEGLIGIBLE' },
{ category: 'HARM_CATEGORY_HARASSMENT', probability: 'NEGLIGIBLE' },
{ category: 'HARM_CATEGORY_SEXUALLY_EXPLICIT', probability: 'NEGLIGIBLE' },
],
},
],
usageMetadata: { promptTokenCount: 8, totalTokenCount: 8 },
modelVersion: 'gemini-2.0-flash',
},
{
candidates: [
{
content: { parts: [{ text: ' Canada:** $24,744,753\n' }], role: 'model' },
finishReason: 'STOP',
safetyRatings: [
{ category: 'HARM_CATEGORY_HATE_SPEECH', probability: 'NEGLIGIBLE' },
{ category: 'HARM_CATEGORY_DANGEROUS_CONTENT', probability: 'NEGLIGIBLE' },
{ category: 'HARM_CATEGORY_HARASSMENT', probability: 'NEGLIGIBLE' },
{ category: 'HARM_CATEGORY_SEXUALLY_EXPLICIT', probability: 'NEGLIGIBLE' },
],
groundingMetadata: {
searchEntryPoint: {
renderedContent:
'<style>\n.container {\n align-items: center;\n border-radius: 8px;\n display: flex;\n font-family: Google Sans, Roboto, sans-serif;\n font-size: 14px;\n line-height: 20px;\n padding: 8px 12px;\n}\n.chip {\n display: inline-block;\n border: solid 1px;\n border-radius: 16px;\n min-width: 14px;\n padding: 5px 16px;\n text-align: center;\n user-select: none;\n margin: 0 8px;\n -webkit-tap-highlight-color: transparent;\n}\n.carousel {\n overflow: auto;\n scrollbar-width: none;\n white-space: nowrap;\n margin-right: -12px;\n}\n.headline {\n display: flex;\n margin-right: 4px;\n}\n.gradient-container {\n position: relative;\n}\n.gradient {\n position: absolute;\n transform: translate(3px, -9px);\n height: 36px;\n width: 9px;\n}\n@media (prefers-color-scheme: light) {\n .container {\n background-color: #fafafa;\n box-shadow: 0 0 0 1px #0000000f;\n }\n .headline-label {\n color: #1f1f1f;\n }\n .chip {\n background-color: #ffffff;\n border-color: #d2d2d2;\n color: #5e5e5e;\n text-decoration: none;\n }\n .chip:hover {\n background-color: #f2f2f2;\n }\n .chip:focus {\n background-color: #f2f2f2;\n }\n .chip:active {\n background-color: #d8d8d8;\n border-color: #b6b6b6;\n }\n .logo-dark {\n display: none;\n }\n .gradient {\n background: linear-gradient(90deg, #fafafa 15%, #fafafa00 100%);\n }\n}\n@media (prefers-color-scheme: dark) {\n .container {\n background-color: #1f1f1f;\n box-shadow: 0 0 0 1px #ffffff26;\n }\n .headline-label {\n color: #fff;\n }\n .chip {\n background-color: #2c2c2c;\n border-color: #3c4043;\n color: #fff;\n text-decoration: none;\n }\n .chip:hover {\n background-color: #353536;\n }\n .chip:focus {\n background-color: #353536;\n }\n .chip:active {\n background-color: #464849;\n border-color: #53575b;\n }\n .logo-light {\n display: none;\n }\n .gradient {\n background: linear-gradient(90deg, #1f1f1f 15%, #1f1f1f00 100%);\n }\n}\n</style>\n<div class="container">\n <div class="headline">\n <svg class="logo-light" width="18" height="18" viewBox="9 9 35 35" fill="none" xmlns="http://www.w3.org/2000/svg">\n <path fill-rule="evenodd" clip-rule="evenodd" d="M42.8622 27.0064C42.8622 25.7839 42.7525 24.6084 42.5487 23.4799H26.3109V30.1568H35.5897C35.1821 32.3041 33.9596 34.1222 32.1258 35.3448V39.6864H37.7213C40.9814 36.677 42.8622 32.2571 42.8622 27.0064V27.0064Z" fill="#4285F4"/>\n <path fill-rule="evenodd" clip-rule="evenodd" d="M26.3109 43.8555C30.9659 43.8555 34.8687 42.3195 37.7213 39.6863L32.1258 35.3447C30.5898 36.3792 28.6306 37.0061 26.3109 37.0061C21.8282 37.0061 18.0195 33.9811 16.6559 29.906H10.9194V34.3573C13.7563 39.9841 19.5712 43.8555 26.3109 43.8555V43.8555Z" fill="#34A853"/>\n <path fill-rule="evenodd" clip-rule="evenodd" d="M16.6559 29.8904C16.3111 28.8559 16.1074 27.7588 16.1074 26.6146C16.1074 25.4704 16.3111 24.3733 16.6559 23.3388V18.8875H10.9194C9.74388 21.2072 9.06992 23.8247 9.06992 26.6146C9.06992 29.4045 9.74388 32.022 10.9194 34.3417L15.3864 30.8621L16.6559 29.8904V29.8904Z" fill="#FBBC05"/>\n <path fill-rule="evenodd" clip-rule="evenodd" d="M26.3109 16.2386C28.85 16.2386 31.107 17.1164 32.9095 18.8091L37.8466 13.8719C34.853 11.082 30.9659 9.3736 26.3109 9.3736C19.5712 9.3736 13.7563 13.245 10.9194 18.8875L16.6559 23.3388C18.0195 19.2636 21.8282 16.2386 26.3109 16.2386V16.2386Z" fill="#EA4335"/>\n </svg>\n <svg class="logo-dark" width="18" height="18" viewBox="0 0 48 48" xmlns="http://www.w3.org/2000/svg">\n <circle cx="24" cy="23" fill="#FFF" r="22"/>\n <path d="M33.76 34.26c2.75-2.56 4.49-6.37 4.49-11.26 0-.89-.08-1.84-.29-3H24.01v5.99h8.03c-.4 2.02-1.5 3.56-3.07 4.56v.75l3.91 2.97h.88z" fill="#4285F4"/>\n <path d="M15.58 25.77A8.845 8.845 0 0 0 24 31.86c1.92 0 3.62-.46 4.97-1.31l4.79 3.71C31.14 36.7 27.65 38 24 38c-5.93 0-11.01-3.4-13.45-8.36l.17-1.01 4.06-2.85h.8z" fill="#34A853"/>\n <path d="M15.59 20.21a8.864 8.864 0 0 0 0 5.58l-5.03 3.86c-.98-2-1.53-4.25-1.53-6.64 0-2.39.55-4.64 1.53-6.64l1-.22 3.81 2.98.22 1.08z" fill="#FBBC05"/>\n <path d="M24 14.14c2.11 0 4.02.75 5.52 1.98l4.36-4.36C31.22 9.43 27.81 8 24 8c-5.93 0-11.01 3.4-13.45 8.36l5.03 3.85A8.86 8.86 0 0 1 24 14.14z" fill="#EA4335"/>\n </svg>\n <div class="gradient-container"><div class="gradient"></div></div>\n </div>\n <div class="carousel">\n <a class="chip" href="https://vertexaisearch.cloud.google.com/grounding-api-redirect/AQXblrycKK-4Q61T9-BeH_jYKcMfCwyI0-TGMMzPcvZuXVtBjnsxXJkWcxxay0giciDNQ5g4dfD8SdUuBIlBLFQE7Fuc8e50WZuKO9u3HVjQXMznQxtzcQ4fHUn1lDlsvKiurKnD-G-Sl6s7_8h3JNMJSsObKg79sP0vQ_f9N7ib5s3tuF35FglH1NLaiTvdpM1DVhaHZc2In94_hV3W-_k=">Nezha Reborn 2 box office</a>\n </div>\n</div>\n',
},
groundingChunks: [
{
web: {
uri: 'https://vertexaisearch.cloud.google.com/grounding-api-redirect/AQXblrz3Up-UZrEsLlT8zPkpwbakcjDZbojH5RuXL0HAa_0rHfG1WE5h6jADFSzcMxKNZcit_n7OaxnTvZNjp9WFL4NNJmjkqQRJoK_XdeVsnbshWJpm9TJL7KNNwzAl254th8cHxTsQIOPoNxsnrXeebIlMDVb8OuFWfCWUToiRxhv1_Vo=',
title: 'screenrant.com',
},
},
{
web: {
uri: 'https://vertexaisearch.cloud.google.com/grounding-api-redirect/AQXblry4I3hWcwVL-mI75BJYSy72Lb97KF50N2p5PWvH8vuLQQgekFmlw9PDiJ3KouByidcMsja_7IJ3F1S0PguLC0r_uxbcAGfFvJzbiMNdWOhQ7xDSJqObd_mCUa-VFpYzm6cd',
title: 'imdb.com',
},
},
],
groundingSupports: [
{
segment: {
startIndex: 64,
endIndex: 96,
text: '* **Worldwide:** $1.66 billion',
},
groundingChunkIndices: [0],
confidenceScores: [0.95218265],
},
{
segment: {
startIndex: 146,
endIndex: 178,
text: '* **US & Canada:** $24,744,753',
},
groundingChunkIndices: [1],
confidenceScores: [0.7182074],
},
],
retrievalMetadata: {},
webSearchQueries: ['Nezha Reborn 2 box office'],
},
},
],
usageMetadata: {
promptTokenCount: 7,
candidatesTokenCount: 79,
totalTokenCount: 86,
promptTokensDetails: [{ modality: 'TEXT', tokenCount: 7 }],
candidatesTokensDetails: [{ modality: 'TEXT', tokenCount: 79 }],
},
modelVersion: 'gemini-2.0-flash',
},
];
const mockStream = new ReadableStream({
start(controller) {
controller.enqueue('Hello, world!');
controller.close();
},
});
vi.spyOn(instance['client'], 'getGenerativeModel').mockReturnValue({
generateContentStream: vi.fn().mockResolvedValueOnce(mockStream),
} as any);
});
it('should call debugStream in DEBUG mode', async () => {
// 设置环境变量以启用DEBUG模式
process.env.DEBUG_GOOGLE_CHAT_COMPLETION = '1';
// 模拟 Google AI SDK 的 generateContentStream 方法返回一个成功的响应流
const mockStream = new ReadableStream({
start(controller) {
controller.enqueue('Debug mode test');
controller.close();
},
});
vi.spyOn(instance['client'], 'getGenerativeModel').mockReturnValue({
generateContentStream: vi.fn().mockResolvedValueOnce(mockStream),
} as any);
const debugStreamSpy = vi
.spyOn(debugStreamModule, 'debugStream')
.mockImplementation(() => Promise.resolve());
await instance.chat({
messages: [{ content: 'Hello', role: 'user' }],
model: 'text-davinci-003',
temperature: 0,
});
expect(debugStreamSpy).toHaveBeenCalled();
// 清理环境变量
delete process.env.DEBUG_GOOGLE_CHAT_COMPLETION;
});
describe('Error', () => {
it('should throw InvalidGoogleAPIKey error on API_KEY_INVALID error', async () => {
// 模拟 Google AI SDK 抛出异常
const message = `[GoogleGenerativeAI Error]: Error fetching from https://generativelanguage.googleapis.com/v1/models/gemini-pro:streamGenerateContent?alt=sse: [400 Bad Request] API key not valid. Please pass a valid API key. [{"@type":"type.googleapis.com/google.rpc.ErrorInfo","reason":"API_KEY_INVALID","domain":"googleapis.com","metadata":{"service":"generativelanguage.googleapis.com"}}]`;
const apiError = new Error(message);
vi.spyOn(instance['client'], 'getGenerativeModel').mockReturnValue({
generateContentStream: vi.fn().mockRejectedValue(apiError),
} as any);
try {
await instance.chat({
messages: [{ content: 'Hello', role: 'user' }],
model: 'text-davinci-003',
temperature: 0,
});
} catch (e) {
expect(e).toEqual({ errorType: invalidErrorType, error: { message }, provider });
}
});
it('should throw LocationNotSupportError error on location not support error', async () => {
// 模拟 Google AI SDK 抛出异常
const message = `[GoogleGenerativeAI Error]: Error fetching from https://generativelanguage.googleapis.com/v1/models/gemini-pro:streamGenerateContent?alt=sse: [400 Bad Request] User location is not supported for the API use.`;
const apiError = new Error(message);
vi.spyOn(instance['client'], 'getGenerativeModel').mockReturnValue({
generateContentStream: vi.fn().mockRejectedValue(apiError),
} as any);
try {
await instance.chat({
messages: [{ content: 'Hello', role: 'user' }],
model: 'text-davinci-003',
temperature: 0,
});
} catch (e) {
expect(e).toEqual({ errorType: 'LocationNotSupportError', error: { message }, provider });
}
});
it('should throw BizError error', async () => {
// 模拟 Google AI SDK 抛出异常
const message = `[GoogleGenerativeAI Error]: Error fetching from https://generativelanguage.googleapis.com/v1/models/gemini-pro:streamGenerateContent?alt=sse: [400 Bad Request] API key not valid. Please pass a valid API key. [{"@type":"type.googleapis.com/google.rpc.ErrorInfo","reason":"Error","domain":"googleapis.com","metadata":{"service":"generativelanguage.googleapis.com"}}]`;
const apiError = new Error(message);
vi.spyOn(instance['client'], 'getGenerativeModel').mockReturnValue({
generateContentStream: vi.fn().mockRejectedValue(apiError),
} as any);
try {
await instance.chat({
messages: [{ content: 'Hello', role: 'user' }],
model: 'text-davinci-003',
temperature: 0,
});
} catch (e) {
expect(e).toEqual({
errorType: bizErrorType,
error: [
{
'@type': 'type.googleapis.com/google.rpc.ErrorInfo',
'domain': 'googleapis.com',
'metadata': {
service: 'generativelanguage.googleapis.com',
},
'reason': 'Error',
},
],
provider,
});
}
});
it('should throw DefaultError error', async () => {
// 模拟 Google AI SDK 抛出异常
const message = `[GoogleGenerativeAI Error]: Error fetching from https://generativelanguage.googleapis.com/v1/models/gemini-pro:streamGenerateContent?alt=sse: [400 Bad Request] API key not valid. Please pass a valid API key. [{"@type":"type.googleapis.com/google.rpc.ErrorInfo","reason":"Error","domain":"googleapis.com","metadata":{"service":"generativelanguage.googleapis.com}}]`;
const apiError = new Error(message);
vi.spyOn(instance['client'], 'getGenerativeModel').mockReturnValue({
generateContentStream: vi.fn().mockRejectedValue(apiError),
} as any);
try {
await instance.chat({
messages: [{ content: 'Hello', role: 'user' }],
model: 'text-davinci-003',
temperature: 0,
});
} catch (e) {
expect(e).toEqual({
errorType: bizErrorType,
error: {
message: `API key not valid. Please pass a valid API key. [{"@type":"type.googleapis.com/google.rpc.ErrorInfo","reason":"Error","domain":"googleapis.com","metadata":{"service":"generativelanguage.googleapis.com}}]`,
statusCode: 400,
statusCodeText: '[400 Bad Request]',
},
provider,
});
}
});
it('should return GoogleBizError with an openai error response when APIError is thrown', async () => {
// Arrange
const apiError = new Error('Error message');
// 使用 vi.spyOn 来模拟 chat.completions.create 方法
vi.spyOn(instance['client'], 'getGenerativeModel').mockReturnValue({
generateContentStream: vi.fn().mockRejectedValue(apiError),
} as any);
// Act
try {
await instance.chat({
messages: [{ content: 'Hello', role: 'user' }],
model: 'text-davinci-003',
temperature: 0,
});
} catch (e) {
expect(e).toEqual({
error: { message: 'Error message' },
errorType: bizErrorType,
provider,
});
}
});
it('should throw AgentRuntimeError with NoOpenAIAPIKey if no apiKey is provided', async () => {
try {
new LobeGoogleAI({});
} catch (e) {
expect(e).toEqual({ errorType: invalidErrorType });
}
});
it('should return OpenAIBizError with the cause when OpenAI.APIError is thrown with cause', async () => {
// Arrange
const errorInfo = {
stack: 'abc',
cause: {
message: 'api is undefined',
},
};
const apiError = new OpenAI.APIError(400, errorInfo, 'module error', {});
vi.spyOn(instance['client'], 'getGenerativeModel').mockReturnValue({
generateContentStream: vi.fn().mockRejectedValue(apiError),
} as any);
// Act
try {
await instance.chat({
messages: [{ content: 'Hello', role: 'user' }],
model: 'text-davinci-003',
temperature: 0,
});
} catch (e) {
expect(e).toEqual({
error: {
message: `400 {"stack":"abc","cause":{"message":"api is undefined"}}`,
},
errorType: bizErrorType,
provider,
});
}
});
it('should return AgentRuntimeError for non-OpenAI errors', async () => {
// Arrange
const genericError = new Error('Generic Error');
vi.spyOn(instance['client'], 'getGenerativeModel').mockReturnValue({
generateContentStream: vi.fn().mockRejectedValue(genericError),
} as any);
// Act
try {
await instance.chat({
messages: [{ content: 'Hello', role: 'user' }],
model: 'text-davinci-003',
temperature: 0,
});
} catch (e) {
expect(e).toEqual({
errorType: bizErrorType,
provider,
error: {
message: 'Generic Error',
},
});
}
});
});
});
describe('private method', () => {
describe('convertContentToGooglePart', () => {
it('should handle text type messages', async () => {
const result = await instance['convertContentToGooglePart']({
type: 'text',
text: 'Hello',
});
expect(result).toEqual({ text: 'Hello' });
});
it('should handle thinking type messages', async () => {
const result = await instance['convertContentToGooglePart']({
type: 'thinking',
thinking: 'Hello',
signature: 'abc',
});
expect(result).toEqual(undefined);
});
it('should handle base64 type images', async () => {
const base64Image =
'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAUAAAAFCAYAAACNbyblAAAAHElEQVQI12P4//8/w38GIAXDIBKE0DHxgljNBAAO9TXL0Y4OHwAAAABJRU5ErkJggg==';
const result = await instance['convertContentToGooglePart']({
type: 'image_url',
image_url: { url: base64Image },
});
expect(result).toEqual({
inlineData: {
data: 'iVBORw0KGgoAAAANSUhEUgAAAAUAAAAFCAYAAACNbyblAAAAHElEQVQI12P4//8/w38GIAXDIBKE0DHxgljNBAAO9TXL0Y4OHwAAAABJRU5ErkJggg==',
mimeType: 'image/png',
},
});
});
it('should handle URL type images', async () => {
const imageUrl = 'http://example.com/image.png';
const mockBase64 = 'mockBase64Data';
// Mock the imageUrlToBase64 function
vi.spyOn(imageToBase64Module, 'imageUrlToBase64').mockResolvedValueOnce({
base64: mockBase64,
mimeType: 'image/png',
});
const result = await instance['convertContentToGooglePart']({
type: 'image_url',
image_url: { url: imageUrl },
});
expect(result).toEqual({
inlineData: {
data: mockBase64,
mimeType: 'image/png',
},
});
expect(imageToBase64Module.imageUrlToBase64).toHaveBeenCalledWith(imageUrl);
});
it('should throw TypeError for unsupported image URL types', async () => {
const unsupportedImageUrl = 'unsupported://example.com/image.png';
await expect(
instance['convertContentToGooglePart']({
type: 'image_url',
image_url: { url: unsupportedImageUrl },
}),
).rejects.toThrow(TypeError);
});
});
describe('buildGoogleMessages', () => {
it('get default result with gemini-pro', async () => {
const messages: OpenAIChatMessage[] = [{ content: 'Hello', role: 'user' }];
const contents = await instance['buildGoogleMessages'](messages);
expect(contents).toHaveLength(1);
expect(contents).toEqual([{ parts: [{ text: 'Hello' }], role: 'user' }]);
});
it('should not modify the length if model is gemini-1.5-pro', async () => {
const messages: OpenAIChatMessage[] = [
{ content: 'Hello', role: 'user' },
{ content: 'Hi', role: 'assistant' },
];
const contents = await instance['buildGoogleMessages'](messages);
expect(contents).toHaveLength(2);
expect(contents).toEqual([
{ parts: [{ text: 'Hello' }], role: 'user' },
{ parts: [{ text: 'Hi' }], role: 'model' },
]);
});
it('should use specified model when images are included in messages', async () => {
const messages: OpenAIChatMessage[] = [
{
content: [
{ type: 'text', text: 'Hello' },
{ type: 'image_url', image_url: { url: 'data:image/png;base64,...' } },
],
role: 'user',
},
];
// 调用 buildGoogleMessages 方法
const contents = await instance['buildGoogleMessages'](messages);
expect(contents).toHaveLength(1);
expect(contents).toEqual([
{
parts: [{ text: 'Hello' }, { inlineData: { data: '...', mimeType: 'image/png' } }],
role: 'user',
},
]);
});
});
describe('buildGoogleTools', () => {
it('should return undefined when tools is undefined or empty', () => {
expect(instance['buildGoogleTools'](undefined)).toBeUndefined();
expect(instance['buildGoogleTools']([])).toBeUndefined();
});
it('should correctly convert ChatCompletionTool to GoogleFunctionCallTool', () => {
const tools: OpenAI.ChatCompletionTool[] = [
{
function: {
name: 'testTool',
description: 'A test tool',
parameters: {
type: 'object',
properties: {
param1: { type: 'string' },
param2: { type: 'number' },
},
required: ['param1'],
},
},
type: 'function',
},
];
const googleTools = instance['buildGoogleTools'](tools);
expect(googleTools).toHaveLength(1);
expect((googleTools![0] as FunctionDeclarationsTool).functionDeclarations![0]).toEqual({
name: 'testTool',
description: 'A test tool',
parameters: {
type: 'object',
properties: {
param1: { type: 'string' },
param2: { type: 'number' },
},
required: ['param1'],
},
});
});
});
describe('convertOAIMessagesToGoogleMessage', () => {
it('should correctly convert assistant message', async () => {
const message: OpenAIChatMessage = {
role: 'assistant',
content: 'Hello',
};
const converted = await instance['convertOAIMessagesToGoogleMessage'](message);
expect(converted).toEqual({
role: 'model',
parts: [{ text: 'Hello' }],
});
});
it('should correctly convert user message', async () => {
const message: OpenAIChatMessage = {
role: 'user',
content: 'Hi',
};
const converted = await instance['convertOAIMessagesToGoogleMessage'](message);
expect(converted).toEqual({
role: 'user',
parts: [{ text: 'Hi' }],
});
});
it('should correctly convert message with inline base64 image parts', async () => {
const message: OpenAIChatMessage = {
role: 'user',
content: [
{ type: 'text', text: 'Check this image:' },
{ type: 'image_url', image_url: { url: 'data:image/png;base64,...' } },
],
};
const converted = await instance['convertOAIMessagesToGoogleMessage'](message);
expect(converted).toEqual({
role: 'user',
parts: [
{ text: 'Check this image:' },
{ inlineData: { data: '...', mimeType: 'image/png' } },
],
});
});
it.skip('should correctly convert message with image url parts', async () => {
const message: OpenAIChatMessage = {
role: 'user',
content: [
{ type: 'text', text: 'Check this image:' },
{ type: 'image_url', image_url: { url: 'https://image-file.com' } },
],
};
const converted = await instance['convertOAIMessagesToGoogleMessage'](message);
expect(converted).toEqual({
role: 'user',
parts: [
{ text: 'Check this image:' },
{ inlineData: { data: '...', mimeType: 'image/png' } },
],
});
});
it('should correctly convert function call message', async () => {
const message = {
role: 'assistant',
tool_calls: [
{
id: 'call_1',
function: {
name: 'get_current_weather',
arguments: JSON.stringify({ location: 'London', unit: 'celsius' }),
},
type: 'function',
},
],
} as OpenAIChatMessage;
const converted = await instance['convertOAIMessagesToGoogleMessage'](message);
expect(converted).toEqual({
role: 'function',
parts: [
{
functionCall: {
name: 'get_current_weather',
args: { location: 'London', unit: 'celsius' },
},
},
],
});
});
it('should correctly handle empty content', async () => {
const message: OpenAIChatMessage = {
role: 'user',
content: '' as any, // explicitly set as empty string
};
const converted = await instance['convertOAIMessagesToGoogleMessage'](message);
expect(converted).toEqual({
role: 'user',
parts: [{ text: '' }],
});
});
});
});
});