Documentation Index
Fetch the complete documentation index at: https://docs.openai-nebula.com/llms.txt
Use this file to discover all available pages before exploring further.
Using Official OpenAI Libraries
Nebula Api is fully compatible with OpenAI API format. You can use official OpenAI SDKs with just a simple configuration change.Supported SDKs
- Python (
openai) - Node.js (
openai) - .NET (
OpenAI) - Go (
go-openai) - Java (third-party)
- PHP (third-party)
- Ruby (third-party)
Python SDK
Installation
pip install openai
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)
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"}]
)
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())
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="")
Node.js SDK
Installation
npm install openai
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);
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);
.NET SDK
Installation
dotnet add package OpenAI
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);
Go SDK
Installation
go get github.com/sashabaranov/go-openai
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)
}
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"}]
)
Advanced Features
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"
)
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"}
}
]
}
]
)
Embeddings
response = client.embeddings.create(
model="text-embedding-3-small",
input="Text to embed"
)
embedding = response.data[0].embedding
print(f"Dimensions: {len(embedding)}")
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}")
Migration Guide
From OpenAI
# Original config
client = OpenAI(api_key="sk-...")
# Change to Nebula Api
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.