OpenAI GPT

Large language model API for text generation and AI features.

When to Use

• Chat and conversational AI
• Text generation and completion
• Code generation
• Embeddings and semantic search

Quick Start

import OpenAI from "openai";

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

const completion = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "Hello!" },
  ],
});

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

Core Concepts

Chat Completions

const response = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "system", content: "You are a coding assistant." },
    { role: "user", content: "Write a Python function to sort a list" },
  ],
  temperature: 0.7,
  max_tokens: 1000,
});

Function Calling

const response = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "What is the weather in Tokyo?" }],
  tools: [
    {
      type: "function",
      function: {
        name: "get_weather",
        description: "Get weather for a location",
        parameters: {
          type: "object",
          properties: {
            location: { type: "string", description: "City name" },
          },
          required: ["location"],
        },
      },
    },
  ],
});

// Handle tool call
const toolCall = response.choices[0].message.tool_calls?.[0];
if (toolCall) {
  const args = JSON.parse(toolCall.function.arguments);
  const weather = await getWeather(args.location);
  // Continue conversation with tool result
}

Common Patterns

Streaming

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

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

Embeddings

const embedding = await openai.embeddings.create({
  model: "text-embedding-3-small",
  input: "Search query text",
});

const vector = embedding.data[0].embedding;
// Store in vector database for semantic search

Best Practices

Do:

• Use structured outputs for reliable parsing
• Implement retry logic with exponential backoff
• Monitor token usage and costs
• Use streaming for long responses

Don't:

• Expose API keys in client code
• Skip input validation
• Ignore rate limits
• Use high temperatures for factual tasks

Troubleshooting

Issue	Cause	Solution
Rate limit	Too many requests	Implement backoff
Context length	Too many tokens	Truncate or summarize
Hallucinations	Model limitation	Add grounding/RAG

References

• OpenAI API Docs
• OpenAI Cookbook