@svector/svector

Official TypeScript/JavaScript SDK for accessing SVECTOR APIs

SVECTOR develops high-performance AI models and automation solutions, specializing in artificial intelligence, mathematical computing, and computational research. This SDK provides programmatic access to SVECTOR's API services through type-safe JavaScript/TypeScript interfaces, completion endpoints, document processing capabilities, and additional AI model integrations.

Quick Start

For Deno

import { SVECTOR } from "jsr:@svector/svector";

const client = new SVECTOR({
  apiKey: Deno.env.get("SVECTOR_API_KEY"),
});

// Conversational API
const response = await client.conversations.create({
  model: "spec-3-turbo",
  instructions: "You are a helpful AI assistant.",
  input: "What is artificial intelligence?",
});

console.log(response.output);

For Node.js/Bun

import { SVECTOR } from "@svector/svector";

const client = new SVECTOR({
  apiKey: process.env.SVECTOR_API_KEY,
});

const response = await client.conversations.create({
  model: "spec-3-turbo",
  instructions: "You are a helpful AI assistant.",
  input: "What is artificial intelligence?",
});

console.log(response.output);

For Browser

import { SVECTOR } from "https://esm.sh/@svector/svector";

const client = new SVECTOR({
  apiKey: "your-api-key",
  dangerouslyAllowBrowser: true,
});

Installation

Deno

// No installation needed, import directly:
import { SVECTOR } from "jsr:@svector/svector";

npm

npx jsr add @svector/svector
# or
npm i svector-sdk

pnpm

pnpm i jsr:@svector/svector

Yarn

yarn add jsr:@svector/svector

vlt (Velte)

vlt install jsr:@svector/svector

Bun

bunx jsr add @svector/svector
# or
bun add jsr:@svector/svector

Core Features

• Conversations API - Simple instructions + input interface
• Advanced Chat Completions - Full control with role-based messages
• Real-time Streaming - Server-sent events for live responses
• File Processing - Upload and process documents (PDF, DOCX, TXT, etc.)
• Knowledge Collections - Organize files for enhanced RAG
• Type Safety - Full TypeScript support with IntelliSense
• Universal - Works in Deno, Node.js, Bun, and browsers
• Robust Error Handling - Comprehensive error types and retry logic

Conversations API

The Conversations API provides a user-friendly interface. Just provide instructions and input!

const response = await client.conversations.create({
  model: "spec-3-turbo",
  instructions: "You are a helpful coding assistant.",
  input: "How do I create a function in TypeScript?",
  temperature: 0.7,
});

console.log(response.output);

With Context

const response = await client.conversations.create({
  model: "spec-3-turbo", 
  instructions: "You are a programming tutor.",
  input: "Can you show me an example?",
  context: [
    "How do I create a function in TypeScript?",
    "You can create a function using the function keyword..."
  ],
});

Streaming Conversations

const stream = await client.conversations.createStream({
  model: "spec-3-turbo",
  instructions: "You are a creative storyteller.",
  input: "Tell me a short story about AI.",
  stream: true,
});

for await (const event of stream) {
  if (!event.done) {
    console.log(event.content);
  }
}

Chat Completions API

For advanced use cases with full control:

const response = await client.chat.create({
  model: "spec-3-turbo",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "Hello!" }
  ],
  temperature: 0.7,
});

console.log(response.choices[0].message.content);

Streaming Chat

const stream = await client.chat.createStream({
  model: "spec-3-turbo",
  messages: [
    { role: "system", content: "You are helpful." },
    { role: "user", content: "Explain quantum computing" }
  ],
  stream: true,
});

for await (const event of stream) {
  if (event.choices?.[0]?.delta?.content) {
    console.log(event.choices[0].delta.content);
  }
}

Document-based Conversation

Upload documents for enhanced AI responses:

Basic Document Analysis

async function analyzeDocument(filePath: string, question = "Analyze this document and provide key findings.") {
  const fileContent = await Deno.readFile(filePath);
  
  const fileResponse = await client.files.create(
    fileContent,
    'default',
    filePath.split('/').pop()
  );
  
  const result = await client.conversations.create({
    model: 'spec-3-turbo',
    instructions: 'You are a document analyst. Provide clear, concise analysis.',
    input: `${question}\n\nDocument content:\n${fileResponse.data.content}`,
    temperature: 0.3,
  });

  console.log(result.output);
  return result.output;
}

await analyzeDocument('document.pdf');

Multi-Document Analysis

async function analyzeMultipleDocuments(filePaths: string[], question: string) {
  const documents = [];
  
  for (const filePath of filePaths) {
    const fileContent = await Deno.readFile(filePath);
    const fileResponse = await client.files.create(
      fileContent,
      'default',
      filePath.split('/').pop()
    );
    documents.push(`${filePath}: ${fileResponse.data.content}`);
  }
  
  const result = await client.conversations.create({
    model: 'spec-3-turbo',
    instructions: 'You are a document analyst. Compare and analyze multiple documents.',
    input: `${question}\n\nDocuments:\n${documents.join('\n\n')}`,
    temperature: 0.3,
  });

  console.log(result.output);
  return result.output;
}

await analyzeMultipleDocuments(['report1.pdf', 'report2.pdf'], 'Compare these reports');

Available Models

• spec-3-turbo - Fast, efficient for most use cases
• spec-3 - Standard model with balanced performance
• theta-35 - Advanced model for complex reasoning
• theta-35-mini - Lightweight model for simple tasks

// List all available models
const models = await client.models.list();
console.log(models.models);

Error Handling

import { SVECTOR, AuthenticationError, RateLimitError } from "jsr:@svector/svector";

try {
  const response = await client.conversations.create({
    model: "spec-3-turbo",
    instructions: "You are helpful.",
    input: "Hello!",
  });
} catch (error) {
  if (error instanceof AuthenticationError) {
    console.error("Invalid API key");
  } else if (error instanceof RateLimitError) {
    console.error("Rate limit exceeded");
  } else {
    console.error("API error:", error.message);
  }
}

Configuration

const client = new SVECTOR({
  apiKey: "your-api-key",
  baseURL: "https://spec-chat.tech",
  timeout: 30000,
  maxRetries: 3,
  dangerouslyAllowBrowser: true,
});

Complete Deno Example

#!/usr/bin/env -S deno run --allow-env --allow-net --allow-read

import { SVECTOR } from "jsr:@svector/svector";

const client = new SVECTOR({
  apiKey: Deno.env.get("SVECTOR_API_KEY")!,
});

// Basic conversation
const response = await client.conversations.create({
  model: "spec-3-turbo",
  instructions: "You are a helpful assistant.",
  input: "What's the weather like on Mars?",
});

console.log("AI Response:", response.output);

// Document analysis (if document.pdf exists)
try {
  const fileContent = await Deno.readFile("document.pdf");
  const fileResponse = await client.files.create(fileContent, 'default', 'document.pdf');
  
  const analysis = await client.conversations.create({
    model: "spec-3-turbo",
    instructions: "You are a document analyst.",
    input: `Analyze this document: ${fileResponse.data.content}`,
  });
  
  console.log("Document Analysis:", analysis.output);
} catch (error) {
  console.log("No document found, skipping analysis");
}

API Reference

SVECTOR Client

• new SVECTOR(options) - Create a client instance
• client.conversations - conversations API
• client.chat - Advanced chat completions
• client.models - Model management
• client.files - File upload and management
• client.knowledge - Knowledge collection management

Error Types

• SVECTORError - Base error class
• APIError - API-related errors
• AuthenticationError - Invalid API key
• RateLimitError - Rate limit exceeded
• NotFoundError - Resource not found
• UnprocessableEntityError - Invalid request data

Links & Support

• Website: https://www.svector.co.in
• Documentation: https://platform.svector.co.in
• Support: support@svector.co.in
• Issues: GitHub Issues
• JSR Package: @svector/svector
• npm Package: svector-sdk

License

MIT License - see LICENSE file for details.

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Built with ❤️ by SVECTOR Corporation - Pushing the boundaries of AI, Mathematics, and Computational research