svector-sdk
Version:
Official JavaScript and TypeScript SDK for accessing SVECTOR APIs.
239 lines (238 loc) ā¢ 9.02 kB
JavaScript
"use strict";
var __importDefault = (this && this.__importDefault) || function (mod) {
return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.analyzeImageFromFile = analyzeImageFromFile;
exports.analyzeImageFromUrl = analyzeImageFromUrl;
exports.analyzeUploadedFile = analyzeUploadedFile;
exports.batchImageAnalysis = batchImageAnalysis;
exports.compareImages = compareImages;
exports.conversationWithVision = conversationWithVision;
exports.describeForAccessibility = describeForAccessibility;
exports.detectSpecificObjects = detectSpecificObjects;
exports.extractTextFromImage = extractTextFromImage;
exports.streamingImageAnalysis = streamingImageAnalysis;
const fs_1 = __importDefault(require("fs"));
const index_1 = require("../src/index");
const client = new index_1.SVECTOR({
apiKey: process.env.SVECTOR_API_KEY,
});
async function analyzeImageFromUrl() {
console.log('š Analyzing image from URL...');
try {
const result = await client.vision.analyzeFromUrl('https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg', 'What do you see in this image? Describe the scene in detail.', {
model: 'spec-3-turbo',
max_tokens: 500,
detail: 'high'
});
console.log('Analysis:', result.analysis);
console.log('Tokens used:', result.usage?.total_tokens);
}
catch (error) {
console.error('Error analyzing image from URL:', error);
}
}
async function analyzeImageFromFile() {
console.log('š Analyzing image from local file...');
try {
const imageBuffer = fs_1.default.readFileSync('./sample-image.jpg');
const base64Image = imageBuffer.toString('base64');
const result = await client.vision.analyzeFromBase64(base64Image, 'Identify all objects and people in this image. Also describe the setting and mood.', {
model: 'spec-3-turbo',
max_tokens: 1000,
temperature: 0.3,
detail: 'high'
});
console.log('Analysis:', result.analysis);
}
catch (error) {
console.error('Error analyzing image from file:', error);
}
}
async function extractTextFromImage() {
console.log('š Extracting text from image...');
try {
const result = await client.vision.extractText({
image_url: 'https://example.com/document-image.png',
model: 'spec-3-turbo',
max_tokens: 1000
});
console.log('Extracted text:', result.analysis);
}
catch (error) {
console.error('Error extracting text:', error);
}
}
async function describeForAccessibility() {
console.log('āæ Creating accessibility description...');
try {
const result = await client.vision.describeForAccessibility({
image_url: 'https://example.com/complex-chart.png',
model: 'spec-3-turbo'
});
console.log('Accessibility description:', result.analysis);
}
catch (error) {
console.error('Error creating accessibility description:', error);
}
}
async function detectSpecificObjects() {
console.log('šÆ Detecting specific objects...');
try {
const result = await client.vision.detectObjects({
image_url: 'https://example.com/street-scene.jpg',
model: 'spec-3-turbo'
}, ['cars', 'people', 'buildings', 'traffic signs']);
console.log('Object detection:', result.analysis);
}
catch (error) {
console.error('Error detecting objects:', error);
}
}
async function compareImages() {
console.log('š Comparing multiple images...');
try {
const result = await client.vision.compareImages([
{ url: 'https://example.com/before.jpg' },
{ url: 'https://example.com/after.jpg' }
], 'Compare these before and after images. What changes do you notice?', {
model: 'spec-3-turbo',
max_tokens: 800
});
console.log('Comparison:', result.analysis);
}
catch (error) {
console.error('Error comparing images:', error);
}
}
async function analyzeUploadedFile() {
console.log('š¤ Uploading and analyzing file...');
try {
const fileResponse = await client.files.create(fs_1.default.readFileSync('./chart.png'), 'default', 'chart.png');
console.log(`File uploaded with ID: ${fileResponse.file_id}`);
const result = await client.vision.analyzeFromFileId(fileResponse.file_id, 'Analyze this chart. What insights can you extract from the data visualization?', {
model: 'spec-3-turbo',
max_tokens: 800
});
console.log('Chart analysis:', result.analysis);
}
catch (error) {
console.error('Error with file upload and analysis:', error);
}
}
async function conversationWithVision() {
console.log('š¬ Starting conversation with vision...');
try {
const result = await client.conversations.create({
model: 'spec-3-turbo',
instructions: 'You are a helpful assistant that can analyze images and answer questions about them.',
input: [
{
type: 'text',
text: 'What architectural style is shown in this building?'
},
{
type: 'image_url',
image_url: {
url: 'https://example.com/building.jpg',
detail: 'high'
}
}
],
max_tokens: 500
});
console.log('Architectural analysis:', result.output);
}
catch (error) {
console.error('Error in vision conversation:', error);
}
}
async function batchImageAnalysis() {
console.log('š Analyzing multiple images in batch...');
const imageUrls = [
'https://example.com/product1.jpg',
'https://example.com/product2.jpg',
'https://example.com/product3.jpg'
];
const analyses = [];
for (const [index, url] of imageUrls.entries()) {
try {
console.log(`Analyzing image ${index + 1}/${imageUrls.length}...`);
const result = await client.vision.analyzeFromUrl(url, 'Describe this product. What are its key features and target audience?', {
model: 'spec-3-turbo',
max_tokens: 300
});
analyses.push({
image: url,
analysis: result.analysis,
tokens: result.usage?.total_tokens
});
await new Promise(resolve => setTimeout(resolve, 1000));
}
catch (error) {
console.error(`Error analyzing image ${index + 1}:`, error);
}
}
console.log('Batch analysis results:', analyses);
}
async function streamingImageAnalysis() {
console.log('š Streaming image analysis...');
try {
const stream = await client.conversations.createStream({
model: 'spec-3-turbo',
instructions: 'You are an expert image analyst. Provide detailed, technical analysis.',
input: [
{
type: 'text',
text: 'Provide a comprehensive technical analysis of this image, including composition, lighting, colors, and any technical aspects.'
},
{
type: 'image_url',
image_url: {
url: 'https://example.com/technical-image.jpg',
detail: 'high'
}
}
],
stream: true,
max_tokens: 1000
});
console.log('Streaming analysis:');
for await (const chunk of stream) {
if (!chunk.done) {
process.stdout.write(chunk.content);
}
}
console.log('\nā
Analysis complete');
}
catch (error) {
console.error('Error in streaming analysis:', error);
}
}
async function runExamples() {
console.log('š SVECTOR Vision API Examples');
console.log('================================\n');
await analyzeImageFromUrl();
console.log('\n---\n');
await analyzeImageFromFile();
console.log('\n---\n');
await extractTextFromImage();
console.log('\n---\n');
await describeForAccessibility();
console.log('\n---\n');
await detectSpecificObjects();
console.log('\n---\n');
await compareImages();
console.log('\n---\n');
await analyzeUploadedFile();
console.log('\n---\n');
await conversationWithVision();
console.log('\n---\n');
await batchImageAnalysis();
console.log('\n---\n');
await streamingImageAnalysis();
}
if (require.main === module) {
runExamples().catch(console.error);
}