LLM API Integration Development Guide: Quick Integration from Zero to One

This guide will help you quickly integrate LLM API into your applications, covering the complete process from environment configuration to production deployment.

Getting Started

1. Get API Key

  1. Register LLM API account
  2. Create application in console
  3. Generate API key
  4. Configure environment variables
# .env file
LLM_API_KEY=your-api-key-here
LLM_API_BASE_URL=https://api.llmapi.com/v1

Multi-language SDK Integration

Python SDK

# InstallSDK
pip install llm-api-sdk

# usingExample
from llm_api import Client

client = Client(api_key="your-api-key")

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello, how are you?"}
    ],
    temperature=0.7,
    stream=True
)

for chunk in response:
    print(chunk.choices[0].delta.content, end="")

JavaScript/TypeScript SDK

// InstallSDK
npm install @llm-api/sdk

// usingExample
import { LLMAPIClient } from '@llm-api/sdk';

const client = new LLMAPIClient({
  apiKey: process.env.LLM_API_KEY,
});

async function generateText() {
  const stream = await client.chat.completions.create({
    model: 'gpt-4',
    messages: [
      { role: 'user', content: 'Write a haiku about coding' }
    ],
    stream: true,
  });

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

Java SDK

// Maven dependencies
<dependency>
    <groupId>com.llmapi</groupId>
    <artifactId>llm-api-java</artifactId>
    <version>1.0.0</version>
</dependency>

// usingExample
import com.llmapi.LLMAPIClient;
import com.llmapi.models.*;

LLMAPIClient client = new LLMAPIClient("your-api-key");

ChatCompletionRequest request = ChatCompletionRequest.builder()
    .model("gpt-4")
    .messages(List.of(
        new Message("user", "Explain quantum computing")
    ))
    .maxTokens(500)
    .build();

ChatCompletionResponse response = client.createChatCompletion(request);
System.out.println(response.getChoices().get(0).getMessage().getContent());

Direct RESTful API Calls

HTTPRequest Example

curl -X POST https://api.llmapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-4",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "What is the capital of France?"
      }
    ],
    "temperature": 0.7,
    "max_tokens": 150
  }'

Response Format

{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1677652288,
  "model": "gpt-4",
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "The capital of France is Paris."
    },
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 20,
    "completion_tokens": 8,
    "total_tokens": 28
  }
}

Streaming Response Handling

Server-Sent Events (SSE)

const eventSource = new EventSource(
  'https://api.llmapi.com/v1/stream'
);

eventSource.onmessage = (event) => {
  const data = JSON.parse(event.data);
  if (data.done) {
    eventSource.close();
  } else {
    console.log(data.content);
  }
};

WebSocket Connection

const ws = new WebSocket(
  'wss://api.llmapi.com/v1/ws'
);

ws.on('open', () => {
  ws.send(JSON.stringify({
    type: 'chat',
    messages: [...],
    stream: true
  }));
});

ws.on('message', (data) => {
  const chunk = JSON.parse(data);
  process.stdout.write(chunk.content);
});

Error HandlingBest Practices

Common Error Code Handling

401 UnauthorizedAuthentication Error

API key invalid or expired

if (error.status === 401) {
  // Refresh token or prompt user to re-login
  await refreshApiKey();
}
429 Too Many RequestsRate Limit Error

Request frequency exceeds limit

if (error.status === 429) {
  const retryAfter = error.headers['retry-after'];
  await sleep(retryAfter * 1000);
  return retry(request);
}
503 Service UnavailableService Error

Service temporarily unavailable

if (error.status === 503) {
  // Implement exponential backoff retry
  return exponentialBackoff(request);
}

Advanced Integration Techniques

Connection Pool Optimization

const pool = new ConnectionPool({
  maxConnections: 10,
  maxIdleTime: 30000,
  keepAlive: true
});

const client = new LLMAPIClient({
  apiKey: API_KEY,
  httpClient: pool.getClient()
});

Request Retry Mechanism

async function callWithRetry(fn, maxRetries = 3) {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await fn();
    } catch (error) {
      if (i === maxRetries - 1) throw error;
      await sleep(Math.pow(2, i) * 1000);
    }
  }
}

Production Environment Deployment Checklist

  • Environment Variable Management

    Use key management service to store API keys

  • Monitoring and Logging

    Set up API call monitoring and error tracking

  • Caching Strategy

    Implement response caching to reduce API calls

  • Failover

    Configure backup API endpoints and degradation strategy

  • Cost Control

    Set usage alerts and budget limits

Common Integration Scenarios

💬

Chatbot

Integrate into customer service systems to provide intelligent dialogue

📝

Content Generation

Automated creation of marketing copy and articles

🔍

Smart Search

Semantic search and Q&A systems

Start Integrating LLM API

Get your API key immediately, integrate powerful AI capabilities into your applications, and start your intelligent transformation journey.

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