ai-integration

AI Integration - LLM API Integration Guide

Overview

Generates production-ready LLM API integration code. From basic chat completion to RAG pipelines, with cost tracking and error handling.

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1. When to Apply

Trigger	Behavior
Adding AI/LLM features	Full integration setup
"OpenAI", "Anthropic", "RAG"	Provider-specific integration
AI feature architecture	Pattern recommendation

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2. Provider Quick Start

OpenAI (TypeScript)

import OpenAI from 'openai'

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

async function chat(prompt: string): Promise<string> {
  const response = await openai.chat.completions.create({
    model: 'gpt-4o',
    messages: [{ role: 'user', content: prompt }],
    max_tokens: 1000,
  })
  return response.choices[0].message.content ?? ''
}

Anthropic (TypeScript)

import Anthropic from '@anthropic-ai/sdk'

const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY })

async function chat(prompt: string): Promise<string> {
  const response = await anthropic.messages.create({
    model: 'claude-sonnet-4-20250514',
    max_tokens: 1024,
    messages: [{ role: 'user', content: prompt }],
  })
  return response.content[0].type === 'text' ? response.content[0].text : ''
}

Streaming Response (Next.js)

// app/api/chat/route.ts
import { streamText } from 'ai'
import { openai } from '@ai-sdk/openai'

export async function POST(req: Request) {
  const { messages } = await req.json()
  const result = streamText({
    model: openai('gpt-4o'),
    messages,
  })
  return result.toDataStreamResponse()
}

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3. Common Patterns

Structured Output

import { z } from 'zod'

const schema = z.object({
  sentiment: z.enum(['positive', 'negative', 'neutral']),
  confidence: z.number().min(0).max(1),
  summary: z.string(),
})

const response = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: text }],
  response_format: { type: 'json_object' },
})
const result = schema.parse(JSON.parse(response.choices[0].message.content!))