Claude Api Development

Python
import anthropic

client = anthropic.Anthropic(api_key="your-api-key")

message = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude!"}
    ]
)
print(message.content)

1. Set up environment

   • Install Anthropic SDK: pip install anthropic
   • Get API key from console.anthropic.com
   • Set environment variable: export ANTHROPIC_API_KEY=your_key

2. Choose model based on needs

   • Claude Opus 4.5: Complex reasoning, coding, agents
   • Claude Sonnet 4.5: Balanced performance for most use cases
   • Claude Haiku 4.5: Speed-critical applications

3. Design prompts

   • Use system messages for role/context
   • Structure user messages clearly
   • Include examples for complex tasks

4. Handle responses

   • Parse content from message objects
   • Implement error handling for rate limits
   • Stream responses for long outputs

Progress:

• Environment setup complete
• Model selected and tested
• Prompts optimized
• Error handling implemented
• Application deployed

Example 1: Text Analysis

Python
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    messages=[
        {"role": "user", "content": "Summarize this in 3 bullet points: [long text]"}
    ]
)

Example 2: Code Generation

Python
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    messages=[
        {"role": "user", "content": "Write a Python function to validate email addresses using regex"}
    ]
)

Example 3: Vision Analysis

Python
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {"type": "image", "source": {"type": "base64", "media_type": "image/jpeg", "data": base64_image}}
            ]
        }
    ]
)

• Start with Sonnet 4.5 for most applications (best balance)
• Use system messages to set consistent behavior
• Implement streaming for real-time user experience
• Cache responses when appropriate to reduce costs
• Use the Workbench in Developer Console for prompt development
• Follow token limits: Respect max_tokens parameter
• Handle rate limits with exponential backoff

• Don't hardcode API keys in source code
• Don't ignore error responses and rate limiting
• Don't use Opus for simple tasks (cost inefficiency)
• Don't send sensitive data without considering privacy implications
• Don't forget to validate and sanitize user inputs
• Don't assume responses are always complete (check for truncation)