const { VertexAI, RequestOptions } = require('@google-cloud/vertexai'); const fs = require('fs'); const path = require('path'); const os = require('os'); const { writeFileSync } = require('fs'); // Import fetch if the SDK uses it const originalFetch = global.fetch || require('node-fetch'); // Monkey-patch the fetch used internally global.fetch = async function patchedFetch(url, options) { // Modify the URL to use HTTP instead of HTTPS if (url.startsWith('https://localhost:4000')) { url = url.replace('https://', 'http://'); } console.log('Patched fetch sending request to:', url); return originalFetch(url, options); }; function loadVertexAiCredentials() { console.log("loading vertex ai credentials"); const filepath = path.dirname(__filename); const vertexKeyPath = path.join(filepath, "vertex_key.json"); // Initialize default empty service account data let serviceAccountKeyData = {}; // Try to read existing vertex_key.json try { const content = fs.readFileSync(vertexKeyPath, 'utf8'); if (content && content.trim()) { serviceAccountKeyData = JSON.parse(content); } } catch (error) { // File doesn't exist or is invalid, continue with empty object } // Update with environment variables const privateKeyId = process.env.VERTEX_AI_PRIVATE_KEY_ID || ""; const privateKey = (process.env.VERTEX_AI_PRIVATE_KEY || "").replace(/\\n/g, "\n"); serviceAccountKeyData.private_key_id = privateKeyId; serviceAccountKeyData.private_key = privateKey; // Create temporary file const tempFilePath = path.join(os.tmpdir(), `vertex-credentials-${Date.now()}.json`); writeFileSync(tempFilePath, JSON.stringify(serviceAccountKeyData, null, 2)); // Set environment variable process.env.GOOGLE_APPLICATION_CREDENTIALS = tempFilePath; } // Run credential loading before tests beforeAll(() => { loadVertexAiCredentials(); }); describe('Vertex AI Tests', () => { test('should successfully generate content from Vertex AI', async () => { const vertexAI = new VertexAI({ project: 'adroit-crow-413218', location: 'us-central1', apiEndpoint: "localhost:4000/vertex-ai" }); const customHeaders = new Headers({ "x-litellm-api-key": "sk-1234" }); const requestOptions = { customHeaders: customHeaders }; const generativeModel = vertexAI.getGenerativeModel( { model: 'gemini-1.0-pro' }, requestOptions ); const request = { contents: [{role: 'user', parts: [{text: 'How are you doing today tell me your name?'}]}], }; const streamingResult = await generativeModel.generateContentStream(request); // Add some assertions expect(streamingResult).toBeDefined(); for await (const item of streamingResult.stream) { console.log('stream chunk:', JSON.stringify(item)); expect(item).toBeDefined(); } const aggregatedResponse = await streamingResult.response; console.log('aggregated response:', JSON.stringify(aggregatedResponse)); expect(aggregatedResponse).toBeDefined(); }); test('should successfully generate non-streaming content from Vertex AI', async () => { const vertexAI = new VertexAI({project: 'adroit-crow-413218', location: 'us-central1', apiEndpoint: "localhost:4000/vertex-ai"}); const customHeaders = new Headers({"x-litellm-api-key": "sk-1234"}); const requestOptions = {customHeaders: customHeaders}; const generativeModel = vertexAI.getGenerativeModel({model: 'gemini-1.0-pro'}, requestOptions); const request = {contents: [{role: 'user', parts: [{text: 'What is 2+2?'}]}]}; const result = await generativeModel.generateContent(request); expect(result).toBeDefined(); expect(result.response).toBeDefined(); console.log('non-streaming response:', JSON.stringify(result.response)); }); });