API & Integration Guides
Find detailed documentation on how to integrate and use different language models in your applications, with code samples, API references, and best practices.
Getting Started with LLM APIs
Large Language Models (LLMs) can be integrated into your applications through various APIs provided by model providers. This guide will help you understand how to work with different LLM APIs and implement them in your projects.
API Basics
Learn the fundamentals of working with LLM APIs, including authentication, request formatting, and response handling.
Integration Patterns
Discover common patterns for integrating LLMs into your applications, from simple completions to complex chains and agents.
Best Practices
Follow industry best practices for prompt engineering, error handling, rate limiting, and cost optimization.
Popular LLM APIs
OpenAI API
The OpenAI API provides access to GPT-4, GPT-3.5-Turbo, and other models with capabilities for text generation, embeddings, and more.
import OpenAI from 'openai';
const openai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY,
});
async function main() {
const completion = await openai.chat.completions.create({
model: "gpt-4o",
messages: [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Hello, how are you today?" }
],
});
console.log(completion.choices[0].message);
}
main();
Anthropic API
Anthropic's Claude API offers access to Claude models, known for their helpfulness, harmlessness, and honesty.
import anthropic
client = anthropic.Anthropic(
api_key="your_api_key",
)
message = client.messages.create(
model="claude-3-opus-20240229",
max_tokens=1000,
messages=[
{"role": "user", "content": "Write a short poem about artificial intelligence."}
]
)
print(message.content)
Google Gemini API
Google's Gemini API provides access to Google's most capable AI models for text, code, and multimodal tasks.
import google.generativeai as genai
# Configure the API key
genai.configure(api_key="YOUR_API_KEY")
# Set up the model
model = genai.GenerativeModel('gemini-pro')
# Generate content
response = model.generate_content("Explain quantum computing in simple terms")
print(response.text)LLM Integration Frameworks
These frameworks simplify working with multiple LLM providers and help you build complex AI applications.
LangChain
A framework for developing applications powered by language models through composability.
DocumentationBest Practices
Prompt Engineering
Effective prompt design is crucial for getting the best results from LLMs. Use clear instructions, provide examples, and break complex tasks into smaller steps.
Error Handling
Implement robust error handling to manage API rate limits, timeouts, and content filtering issues. Always have fallback options for critical applications.
Cost Optimization
Minimize token usage by trimming unnecessary context, using the smallest suitable model, and implementing caching strategies for repeated queries.
Security Considerations
Protect API keys, validate user inputs, and be cautious about sharing sensitive information with models. Consider implementing content filtering for user-facing applications.