This tutorial will guide you through using the Anthropic API to interact with Claude models programmatically.

Prerequisites

• Python 3.7+
• An Anthropic API key (obtain from https://console.anthropic.com/)
• anthropic Python package

Installation

pip install anthropic

Basic Usage

1. Setting Up

First, import the necessary modules and set up your API key:

from anthropic import Anthropic
import os

# Set your API key
anthropic = Anthropic(
    api_key='your-api-key-here'  # Replace with your actual API key
)

2. Sending a Basic Message

message = anthropic.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1000,
    messages=[
        {"role": "user", "content": "What is the capital of France?"}
    ]
)

print(message.content)

3. Having a Multi-turn Conversation

conversation = anthropic.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1000,
    messages=[
        {"role": "user", "content": "What is machine learning?"},
        {"role": "assistant", "content": "Machine learning is a subset of artificial intelligence..."},
        {"role": "user", "content": "Can you give me an example?"}
    ]
)

Advanced Features

1. System Prompts

System prompts help set context and behavior for Claude:

response = anthropic.messages.create(
    model="claude-3-opus-20240229",
    system="You are a helpful math tutor who explains concepts step by step.",
    messages=[
        {"role": "user", "content": "Explain how to solve quadratic equations"}
    ]
)

2. Handling Stream Responses

For real-time responses:

stream = anthropic.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1000,
    messages=[{"role": "user", "content": "Write a story about a space adventure"}],
    stream=True
)

for chunk in stream:
    if chunk.type == 'content_block_delta':
        print(chunk.delta.text, end='', flush=True)

3. Error Handling

try:
    response = anthropic.messages.create(
        model="claude-3-opus-20240229",
        messages=[{"role": "user", "content": "Hello"}]
    )
except anthropic.APIError as e:
    print(f"API Error: {e}")
except anthropic.RateLimitError as e:
    print(f"Rate limit exceeded: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Best Practices

1. API Key Security

   • Never hardcode your API key in source code
   • Use environment variables or secure configuration management
   • Example:

     api_key = os.getenv('ANTHROPIC_API_KEY')
     

2. Rate Limiting

   • Implement exponential backoff for retries
   • Handle rate limit errors gracefully
   • Consider implementing request queuing for high-volume applications

3. Model Selection

   • Choose the appropriate model for your use case:
     • claude-3-opus-20240229: Most capable model
     • claude-3-sonnet-20240229: Balanced performance and cost
     • claude-3-haiku-20240229: Fastest responses

4. Token Management

   • Monitor token usage to control costs
   • Set appropriate max_tokens limits
   • Consider implementing token counting for input messages

Common Use Cases

1. Content Generation

def generate_blog_post(topic):
    response = anthropic.messages.create(
        model="claude-3-opus-20240229",
        messages=[
            {"role": "user", "content": f"Write a blog post about {topic}"}
        ]
    )
    return response.content

2. Text Analysis

def analyze_sentiment(text):
    response = anthropic.messages.create(
        model="claude-3-opus-20240229",
        messages=[
            {"role": "user", "content": f"Analyze the sentiment of this text: {text}"}
        ]
    )
    return response.content

Resources

• Official Documentation
• API Reference
• Claude Models Overview

Remember to always check the official documentation for the most up-to-date information and best practices.