R AI Integration

Overview

AI and LLM integration in R for data science workflows

Implementation

python
from openai import OpenAI
from pydantic import BaseModel
from typing import Optional
import json
client = OpenAI()
class Handler:
    """Handles r ai integration."""
    
    def __init__(self, model="gpt-4o-mini"):
        self.client = OpenAI()
        self.model = model
        self.system = f"""You are an AI expert in programming languages.
Topic: R AI Integration
Be accurate, practical, and helpful."""
    
    def run(self, query: str) -> str:
        r = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role":"system","content":self.system},
                {"role":"user","content":query}
            ],
            temperature=0.3,
            max_tokens=1500
        )
        return r.choices[0].message.content
h = Handler()
print(h.run("How do I implement r ai integration?"))

Key Points

programming is fundamental to this approach

Always validate inputs before processing

Implement proper error handling and retries

Monitor costs and performance in production

Test with diverse inputs including edge cases

Example Usage

python
Production example
handler = Handler(model="gpt-4o")  # Use better model for production
Basic use
result = handler.run("Your question here")
Batch processing
queries = ["Q1", "Q2", "Q3"]
results = [handler.run(q) for q in queries]

Best Practices

Input validation and sanitization

Retry with exponential backoff

Response caching for common queries

Comprehensive logging

Cost monitoring and alerts

Resources

OpenAI: https://platform.openai.com/docs

Tags: programming, r, ai-development