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Using Official OpenAI Libraries

Nebula Lab is fully compatible with OpenAI API format. You can use official OpenAI SDKs with just a simple configuration change.

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Supported SDKs

  • Python (openai)
  • Node.js (openai)
  • .NET (OpenAI)
  • Go (go-openai)
  • Java (third-party)
  • PHP (third-party)
  • Ruby (third-party)

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Python SDK

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Installation

pip install openai

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Basic Configuration

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.ai-nebula.com/v1"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

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Environment Variables

import os
from openai import OpenAI

os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
os.environ["OPENAI_BASE_URL"] = "https://llm.ai-nebula.com/v1"

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

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Async Usage

import asyncio
from openai import AsyncOpenAI

async def main():
    client = AsyncOpenAI(
        api_key="YOUR_API_KEY",
        base_url="https://llm.ai-nebula.com/v1"
    )
    
    response = await client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello!"}]
    )
    
    print(response.choices[0].message.content)

asyncio.run(main())

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Streaming

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.ai-nebula.com/v1"
)

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Write a short story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

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Node.js SDK

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Installation

npm install openai

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Basic Configuration

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://llm.ai-nebula.com/v1'
});

const response = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Hello!' }]
});

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

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Streaming

const stream = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Tell me a joke' }],
  stream: true
});

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

​
TypeScript

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

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY!,
  baseURL: 'https://llm.ai-nebula.com/v1'
});

const params: ChatCompletionCreateParamsNonStreaming = {
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Hello TypeScript!' }],
  temperature: 0.7
};

const response = await openai.chat.completions.create(params);

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.NET SDK

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Installation

dotnet add package OpenAI

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Basic Configuration

using OpenAI;
using OpenAI.Chat;

var client = new OpenAIClient("YOUR_API_KEY", new OpenAIClientOptions
{
    Endpoint = new Uri("https://llm.ai-nebula.com/v1")
});

var chatClient = client.GetChatClient("gpt-4o");
var response = await chatClient.CompleteChatAsync("Hello!");

Console.WriteLine(response.Value.Content[0].Text);

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Go SDK

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Installation

go get github.com/sashabaranov/go-openai

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Basic Configuration

package main

import (
    "context"
    "fmt"
    openai "github.com/sashabaranov/go-openai"
)

func main() {
    config := openai.DefaultConfig("YOUR_API_KEY")
    config.BaseURL = "https://llm.ai-nebula.com/v1"
    
    client := openai.NewClientWithConfig(config)
    
    resp, err := client.CreateChatCompletion(
        context.Background(),
        openai.ChatCompletionRequest{
            Model: "gpt-4o",
            Messages: []openai.ChatCompletionMessage{
                {Role: openai.ChatMessageRoleUser, Content: "Hello!"},
            },
        },
    )
    
    if err != nil {
        fmt.Printf("Error: %v\n", err)
        return
    }
    
    fmt.Println(resp.Choices[0].Message.Content)
}

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Model Switching

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.ai-nebula.com/v1"
)

# GPT model
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}]
)

# Claude model
response = client.chat.completions.create(
    model="claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Hello"}]
)

# Gemini model
response = client.chat.completions.create(
    model="gemini-2.5-pro",
    messages=[{"role": "user", "content": "Hello"}]
)

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Advanced Features

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Function Calling

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather info for a city",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "City name"}
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather in Beijing?"}],
    tools=tools,
    tool_choice="auto"
)

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Image Input

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {"url": "https://example.com/image.jpg"}
                }
            ]
        }
    ]
)

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Embeddings

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Text to embed"
)

embedding = response.data[0].embedding
print(f"Dimensions: {len(embedding)}")

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Error Handling

from openai import (
    OpenAI, 
    APIError, 
    APIConnectionError, 
    RateLimitError,
    InternalServerError
)

try:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello"}]
    )
except RateLimitError:
    print("Rate limited, please retry later")
except APIConnectionError:
    print("Network connection error")
except InternalServerError:
    print("Server internal error")
except APIError as e:
    print(f"API error: {e}")

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Migration Guide

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From OpenAI

# Original config
client = OpenAI(api_key="sk-...")

# Change to Nebula Lab
client = OpenAI(
    api_key="YOUR_NEBULA_KEY", 
    base_url="https://llm.ai-nebula.com/v1"
)
No code changes needed. All other code stays the same including method calls, parameter formats, and response handling.