Using Official OpenAI SDK

Using Official OpenAI SDKs

LaoZhang API is fully compatible with OpenAI API format. You can use official OpenAI SDKs directly with minimal configuration changes.

Supported Official SDKs

LaoZhang API supports all official OpenAI 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

# Configure LaoZhang API service
client = OpenAI(
    api_key="YOUR_API_KEY",  # Your LaoZhang API key
    base_url="https://api.laozhang.ai/v1"  # LaoZhang API endpoint
)

# Usage is identical to official OpenAI
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

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

Environment Variable Configuration

import os
from openai import OpenAI

# Set environment variables
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
os.environ["OPENAI_BASE_URL"] = "https://api.laozhang.ai/v1"

# Use default configuration
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4",
    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://api.laozhang.ai/v1"
    )

    response = await client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Hello!"}]
    )

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

asyncio.run(main())

Streaming Output

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.laozhang.ai/v1"
)

stream = client.chat.completions.create(
    model="gpt-3.5-turbo",
    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://api.laozhang.ai/v1'
});

const response = await openai.chat.completions.create({
  model: 'gpt-3.5-turbo',
  messages: [{ role: 'user', content: 'Hello!' }]
});

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

Environment Variable Configuration

// Set environment variables
process.env.OPENAI_API_KEY = 'YOUR_API_KEY';
process.env.OPENAI_BASE_URL = 'https://api.laozhang.ai/v1';

import OpenAI from 'openai';

// Use default configuration
const openai = new OpenAI();

const response = await openai.chat.completions.create({
  model: 'gpt-4',
  messages: [{ role: 'user', content: 'Explain AI to a 5-year-old' }]
});

Streaming Output

const stream = await openai.chat.completions.create({
  model: 'gpt-3.5-turbo',
  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 Support

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

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

const params: ChatCompletionCreateParamsNonStreaming = {
  model: 'gpt-3.5-turbo',
  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://api.laozhang.ai/v1")
});

var chatClient = client.GetChatClient("gpt-3.5-turbo");

var response = await chatClient.CompleteChatAsync("Hello!");

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

Streaming Output

await foreach (var update in chatClient.CompleteChatStreamingAsync("Tell me a story"))
{
    if (update.ContentUpdate.Count > 0)
    {
        Console.Write(update.ContentUpdate[0].Text);
    }
}

Go SDK

Installation

go get github.com/sashabaranov/go-openai

Basic Configuration

package main

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

func main() {
    config := openai.DefaultConfig("YOUR_API_KEY")
    config.BaseURL = "https://api.laozhang.ai/v1"

    client := openai.NewClientWithConfig(config)

    resp, err := client.CreateChatCompletion(
        context.Background(),
        openai.ChatCompletionRequest{
            Model: openai.GPT3Dot5Turbo,
            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

Using Different Models

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

# Claude models
response = client.chat.completions.create(
    model="claude-3-opus-20240229",
    messages=[{"role": "user", "content": "Hello"}]
)

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

Dynamic Model Selection

def chat_with_model(message: str, model: str = "gpt-3.5-turbo"):
    """Chat function with dynamic model switching"""
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": message}]
    )
    return response.choices[0].message.content

# Use different models
print(chat_with_model("Explain quantum computing", "gpt-4"))
print(chat_with_model("Explain quantum computing", "claude-3-opus-20240229"))
print(chat_with_model("Explain quantum computing", "gemini-pro"))

Advanced Features

Function Calling

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

response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "What's the weather in Beijing?"}],
    tools=tools,
    tool_choice="auto"
)

if response.choices[0].message.tool_calls:
    print("AI wants to call function:", response.choices[0].message.tool_calls[0].function.name)

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 content to embed"
)

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

Error Handling

Basic Error Handling

from openai import OpenAI, OpenAIError

try:
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Hello"}]
    )
except OpenAIError as e:
    print(f"API error: {e}")

Detailed Error Handling

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

try:
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Hello"}]
    )
except RateLimitError:
    print("Rate limit exceeded, please retry later")
except APIConnectionError:
    print("Network connection error")
except InternalServerError:
    print("Internal server error")
except APIError as e:
    print(f"API error: {e}")

Best Practices

1. Configuration Management

import os
from openai import OpenAI

class LaoZhangClient:
    def __init__(self):
        self.client = OpenAI(
            api_key=os.getenv("LAOZHANG_API_KEY"),
            base_url=os.getenv("LAOZHANG_BASE_URL", "https://api.laozhang.ai/v1")
        )

    def chat(self, message: str, model: str = "gpt-3.5-turbo"):
        return self.client.chat.completions.create(
            model=model,
            messages=[{"role": "user", "content": message}]
        )

2. Retry Mechanism

import time
import random
from openai import OpenAI, RateLimitError

def chat_with_retry(client, messages, max_retries=3):
    for attempt in range(max_retries):
        try:
            return client.chat.completions.create(
                model="gpt-3.5-turbo",
                messages=messages
            )
        except RateLimitError:
            if attempt < max_retries - 1:
                wait_time = (2 ** attempt) + random.uniform(0, 1)
                time.sleep(wait_time)
            else:
                raise

3. Cost Control

def controlled_chat(message: str, max_tokens: int = 150):
    """Control output length for cost management"""
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": message}],
        max_tokens=max_tokens,
        temperature=0.7
    )
    return response

Migration Guide

Migrating from OpenAI

If you’re already using official OpenAI services, migrating to LaoZhang API is straightforward:

Change base_url

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

# Change to LaoZhang API
client = OpenAI(
    api_key="YOUR_LAOZHANG_KEY",
    base_url="https://api.laozhang.ai/v1"
)

Update Environment Variables

# Original
export OPENAI_API_KEY="sk-..."

# Change to
export OPENAI_API_KEY="YOUR_LAOZHANG_KEY"
export OPENAI_BASE_URL="https://api.laozhang.ai/v1"

No Code Changes Required All other code remains unchanged, including:

Method calls
Parameter formats
Response handling

Multi-Provider Compatibility

class MultiProviderClient:
    def __init__(self):
        self.laozhang_client = OpenAI(
            api_key="LAOZHANG_KEY",
            base_url="https://api.laozhang.ai/v1"
        )
        self.openai_client = OpenAI(api_key="OPENAI_KEY")

    def chat(self, message: str, provider: str = "laozhang"):
        client = self.laozhang_client if provider == "laozhang" else self.openai_client
        return client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": message}]
        )

Need more help? Visit LaoZhang API official site or check Official SDK Documentation.

Getting Started

Core API

Video Generation APIs

Image Generation APIs

AI Understanding

​Using Official OpenAI SDKs

​Supported Official SDKs

​Python SDK

​Installation

​Basic Configuration

​Environment Variable Configuration

​Async Usage

​Streaming Output

​Node.js SDK

​Installation

​Basic Configuration

​Environment Variable Configuration

​Streaming Output

​TypeScript Support

​.NET SDK

​Installation

​Basic Configuration

​Streaming Output

​Go SDK

​Installation

​Basic Configuration

​Model Switching

​Using Different Models

​Dynamic Model Selection

​Advanced Features

​Function Calling

​Image Input

​Embeddings

​Error Handling

​Basic Error Handling

​Detailed Error Handling

​Best Practices

​1. Configuration Management

​2. Retry Mechanism

​3. Cost Control

​Migration Guide

​Migrating from OpenAI

​Multi-Provider Compatibility

Using Official OpenAI SDKs

Supported Official SDKs

Python SDK

Installation

Basic Configuration

Environment Variable Configuration

Async Usage

Streaming Output

Node.js SDK

Installation

Basic Configuration

Environment Variable Configuration

Streaming Output

TypeScript Support

.NET SDK

Installation

Basic Configuration

Streaming Output

Go SDK

Installation

Basic Configuration

Model Switching

Using Different Models

Dynamic Model Selection

Advanced Features

Function Calling

Image Input

Embeddings

Error Handling

Basic Error Handling

Detailed Error Handling

Best Practices

1. Configuration Management

2. Retry Mechanism

3. Cost Control

Migration Guide

Migrating from OpenAI

Multi-Provider Compatibility