TypeScript (OpenAI SDK)

Use the official OpenAI TypeScript/JavaScript SDK with Eden AI for seamless multi-provider access.

Overview

The OpenAI TypeScript SDK works perfectly with Eden AI’s V3 API. Configure the base URL and start using 200+ models from multiple providers.

Installation

Install the OpenAI SDK for Node.js:

npm install openai

Quick Start

Configure the client to use Eden AI:

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'YOUR_EDEN_AI_API_KEY',
  baseURL: 'https://api.edenai.run/v3/llm',
});

async function main() {
  const stream = await client.chat.completions.create({
    model: 'openai/gpt-4',
    messages: [{ role: 'user', content: 'Hello! How are you?' }],
    stream: true,
  });

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content;
    if (content) {
      process.stdout.write(content);
    }
  }
}

main();

Available Models

Access models from multiple providers:

OpenAI

openai/gpt-4
openai/gpt-4-turbo
openai/gpt-4o
openai/gpt-3.5-turbo

Anthropic

anthropic/claude-3-5-sonnet-20241022
anthropic/claude-3-opus-20240229
anthropic/claude-3-sonnet-20240229

Google

google/gemini-pro
google/gemini-1.5-pro
google/gemini-1.5-flash

Cohere & Meta

cohere/command-r-plus
meta/llama-3-70b

Multi-Turn Conversations

Build stateful chat applications:

import OpenAI from 'openai';
import type { ChatCompletionMessageParam } from 'openai/resources/chat/completions';

const client = new OpenAI({
  apiKey: process.env.EDEN_AI_API_KEY!,
  baseURL: 'https://api.edenai.run/v3/llm',
});

async function chat() {
  const messages: ChatCompletionMessageParam[] = [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What is the capital of France?' },
  ];

  // First interaction
  const stream1 = await client.chat.completions.create({
    model: 'anthropic/claude-3-5-sonnet-20241022',
    messages,
    stream: true,
  });

  let assistantResponse = '';
  for await (const chunk of stream1) {
    const content = chunk.choices[0]?.delta?.content;
    if (content) {
      process.stdout.write(content);
      assistantResponse += content;
    }
  }

  // Add to conversation history
  messages.push({ role: 'assistant', content: assistantResponse });
  messages.push({ role: 'user', content: "What's the population?" });

  // Continue conversation
  const stream2 = await client.chat.completions.create({
    model: 'anthropic/claude-3-5-sonnet-20241022',
    messages,
    stream: true,
  });

  for await (const chunk of stream2) {
    const content = chunk.choices[0]?.delta?.content;
    if (content) {
      process.stdout.write(content);
    }
  }
}

chat();

Advanced Parameters

Control model behavior:

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.EDEN_AI_API_KEY!,
  baseURL: 'https://api.edenai.run/v3/llm',
});

const stream = await client.chat.completions.create({
  model: 'openai/gpt-4',
  messages: [
    { role: 'user', content: 'Write a creative story about AI.' },
  ],
  temperature: 0.9,        // Higher = more creative (0-2)
  max_tokens: 500,         // Limit response length
  top_p: 1.0,              // Nucleus sampling
  frequency_penalty: 0.0,  // Penalize repetition
  presence_penalty: 0.0,   // Penalize topic repetition
  stream: true,
});

for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) {
    process.stdout.write(content);
  }
}

Vision Capabilities

Send images to vision models:

import OpenAI from 'openai';
import FormData from 'form-data';
import fs from 'fs';
import fetch from 'node-fetch';

const client = new OpenAI({
  apiKey: process.env.EDEN_AI_API_KEY!,
  baseURL: 'https://api.edenai.run/v3/llm',
});

async function analyzeImage() {
  // First, upload the image
  const formData = new FormData();
  formData.append('file', fs.createReadStream('image.jpg'));

  const uploadResponse = await fetch(
    'https://api.edenai.run/v3/upload',
    {
      method: 'POST',
      headers: {
        Authorization: `Bearer ${process.env.EDEN_AI_API_KEY}`,
      },
      body: formData,
    }
  );

  const { file_id } = await uploadResponse.json();

  // Use file_id in chat
  const stream = await client.chat.completions.create({
    model: 'openai/gpt-4o',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: "What's in this image?" },
          { type: 'file', file: { file_id } },
        ],
      },
    ],
    stream: true,
  });

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content;
    if (content) {
      process.stdout.write(content);
    }
  }
}

analyzeImage();

Error Handling

Handle API errors properly:

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.EDEN_AI_API_KEY!,
  baseURL: 'https://api.edenai.run/v3/llm',
});

async function safeChat() {
  try {
    const stream = await client.chat.completions.create({
      model: 'openai/gpt-4',
      messages: [{ role: 'user', content: 'Hello!' }],
      stream: true,
    });

    for await (const chunk of stream) {
      const content = chunk.choices[0]?.delta?.content;
      if (content) {
        process.stdout.write(content);
      }
    }
  } catch (error) {
    if (error instanceof OpenAI.APIError) {
      console.error(`API Error (${error.status}):`, error.message);

      if (error.status === 401) {
        console.error('Authentication failed. Check your API key.');
      } else if (error.status === 429) {
        console.error('Rate limit exceeded. Please wait and retry.');
      } else if (error.status === 402) {
        console.error('Insufficient credits. Please add credits to your account.');
      }
    } else {
      console.error('Unexpected error:', error);
    }
  }
}

safeChat();

Express.js Integration

Build a chat API with Express:

import express from 'express';
import OpenAI from 'openai';

const app = express();
app.use(express.json());

const client = new OpenAI({
  apiKey: process.env.EDEN_AI_API_KEY!,
  baseURL: 'https://api.edenai.run/v3/llm',
});

app.post('/api/chat', async (req, res) => {
  try {
    const { messages, model = 'openai/gpt-4' } = req.body;

    res.setHeader('Content-Type', 'text/event-stream');
    res.setHeader('Cache-Control', 'no-cache');
    res.setHeader('Connection', 'keep-alive');

    const stream = await client.chat.completions.create({
      model,
      messages,
      stream: true,
    });

    for await (const chunk of stream) {
      const content = chunk.choices[0]?.delta?.content;
      if (content) {
        res.write(`data: ${JSON.stringify({ content })}\n\n`);
      }
    }

    res.write('data: [DONE]\n\n');
    res.end();
  } catch (error) {
    console.error('Error:', error);
    res.status(500).json({ error: 'Internal server error' });
  }
});

app.listen(3000, () => {
  console.log('Server running on http://localhost:3000');
});

React Integration

Stream responses in a React application:

import { useState } from 'react';

export function ChatComponent() {
  const [messages, setMessages] = useState<Array<{role: string, content: string}>>([]);
  const [input, setInput] = useState('');
  const [streaming, setStreaming] = useState(false);

  const sendMessage = async () => {
    if (!input.trim()) return;

    const userMessage = { role: 'user', content: input };
    const updatedMessages = [...messages, userMessage];
    setMessages(updatedMessages);
    setInput('');
    setStreaming(true);

    try {
      const response = await fetch('/api/chat', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({
          messages: updatedMessages,
          model: 'anthropic/claude-3-5-sonnet-20241022',
        }),
      });

      const reader = response.body?.getReader();
      const decoder = new TextDecoder();
      let assistantMessage = '';

      if (reader) {
        while (true) {
          const { done, value } = await reader.read();
          if (done) break;

          const chunk = decoder.decode(value);
          const lines = chunk.split('\n');

          for (const line of lines) {
            if (line.startsWith('data: ')) {
              const data = line.slice(6);
              if (data === '[DONE]') continue;

              try {
                const parsed = JSON.parse(data);
                assistantMessage += parsed.content;
                setMessages([
                  ...updatedMessages,
                  { role: 'assistant', content: assistantMessage },
                ]);
              } catch (e) {
                // Ignore parse errors
              }
            }
          }
        }
      }
    } catch (error) {
      console.error('Error:', error);
    } finally {
      setStreaming(false);
    }
  };

  return (
    <div>
      <div className="messages">
        {messages.map((msg, i) => (
          <div key={i} className={msg.role}>
            <strong>{msg.role}:</strong> {msg.content}
          </div>
        ))}
      </div>
      <input
        value={input}
        onChange={(e) => setInput(e.target.value)}
        onKeyPress={(e) => e.key === 'Enter' && sendMessage()}
        disabled={streaming}
      />
      <button onClick={sendMessage} disabled={streaming}>
        {streaming ? 'Sending...' : 'Send'}
      </button>
    </div>
  );
}

Environment Variables

Use .env files for configuration:

EDEN_AI_API_KEY=your_api_key_here

Complete Chat CLI Example

Build a command-line chat interface:

import OpenAI from 'openai';
import readline from 'readline';
import type { ChatCompletionMessageParam } from 'openai/resources/chat/completions';

const client = new OpenAI({
  apiKey: process.env.EDEN_AI_API_KEY!,
  baseURL: 'https://api.edenai.run/v3/llm',
});

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
});

async function main() {
  const messages: ChatCompletionMessageParam[] = [
    { role: 'system', content: 'You are a helpful assistant.' },
  ];

  console.log('Chat with AI (type "quit" to exit)');
  console.log('-'.repeat(50));

  const askQuestion = () => {
    rl.question('\nYou: ', async (input) => {
      const userInput = input.trim();

      if (userInput.toLowerCase() === 'quit') {
        rl.close();
        return;
      }

      if (!userInput) {
        askQuestion();
        return;
      }

      messages.push({ role: 'user', content: userInput });

      process.stdout.write('\nAssistant: ');

      try {
        const stream = await client.chat.completions.create({
          model: 'anthropic/claude-3-5-sonnet-20241022',
          messages,
          temperature: 0.7,
          stream: true,
        });

        let assistantResponse = '';

        for await (const chunk of stream) {
          const content = chunk.choices[0]?.delta?.content;
          if (content) {
            process.stdout.write(content);
            assistantResponse += content;
          }
        }

        console.log(); // New line
        messages.push({ role: 'assistant', content: assistantResponse });
      } catch (error) {
        console.error('\nError:', error);
      }

      askQuestion();
    });
  };

  askQuestion();
}

main();

List Available Models

Programmatically discover models:

import fetch from 'node-fetch';

async function listModels() {
  const response = await fetch('https://api.edenai.run/v3/llm/models', {
    headers: {
      Authorization: `Bearer ${process.env.EDEN_AI_API_KEY}`,
    },
  });

  const data = await response.json();

  // Group by provider
  const providers: Record<string, string[]> = {};

  for (const model of data.data) {
    const provider = model.owned_by;
    if (!providers[provider]) {
      providers[provider] = [];
    }
    providers[provider].push(model.id);
  }

  console.log('Available Models:\n');
  for (const [provider, models] of Object.entries(providers)) {
    console.log(`${provider}:`);
    models.forEach(m => console.log(`  - ${m}`));
    console.log();
  }
}

listModels();

Next Steps

Vision Capabilities - Working with images
Streaming Responses - Handle Server-Sent Events

V3 Documentation

Typescript openai

TypeScript (OpenAI SDK)

Overview

Installation

Quick Start

Available Models

OpenAI

Anthropic

Google

Cohere & Meta

Multi-Turn Conversations

Advanced Parameters

Vision Capabilities

Error Handling

Express.js Integration

React Integration

Environment Variables

Complete Chat CLI Example

List Available Models

Next Steps

V3 Documentation

​TypeScript (OpenAI SDK)

​Overview

​Installation

​Quick Start

​Available Models

​OpenAI

​Anthropic

​Google

​Cohere & Meta

​Multi-Turn Conversations

​Advanced Parameters

​Vision Capabilities

​Error Handling

​Express.js Integration

​React Integration

​Environment Variables

​Complete Chat CLI Example

​List Available Models

​Next Steps

TypeScript (OpenAI SDK)

Overview

Installation

Quick Start

Available Models

OpenAI

Anthropic

Google

Cohere & Meta

Multi-Turn Conversations

Advanced Parameters

Vision Capabilities

Error Handling

Express.js Integration

React Integration

Environment Variables

Complete Chat CLI Example

List Available Models

Next Steps