Anthropic

Overview

Anthropic develops Claude, a family of highly capable AI assistants known for their strong performance, safety features, and long context windows. Portkey provides full support for all Claude models and features. Base URL: https://api.anthropic.com/v1

Supported Features

✅ Messages API (Chat Completions)
✅ Streaming
✅ Tool Use (Function Calling)
✅ Vision (Image inputs)
✅ System Prompts
✅ Token Counting
✅ Batch API
✅ Prompt Caching
❌ Embeddings (not available)
❌ Fine-tuning (not available)

Quick Start

Chat Completions

from portkey_ai import Portkey

client = Portkey(
    provider="anthropic",
    Authorization="sk-ant-***"  # Your Anthropic API key
)

response = client.chat.completions.create(
    model="claude-3-5-sonnet-20241022",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms"}
    ],
    max_tokens=1024
)

print(response.choices[0].message.content)

Streaming Responses

stream = client.chat.completions.create(
    model="claude-3-5-sonnet-20241022",
    messages=[{"role": "user", "content": "Write a haiku about programming"}],
    max_tokens=100,
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Available Models

Model	Context Window	Description	Best For
`claude-3-5-sonnet-20241022`	200K tokens	Latest, most capable model	Complex tasks, coding, analysis
`claude-3-5-haiku-20241022`	200K tokens	Fastest Claude 3.5 model	Quick responses, high throughput
`claude-3-opus-20240229`	200K tokens	Most powerful Claude 3	Highly complex tasks
`claude-3-sonnet-20240229`	200K tokens	Balanced performance	General purpose
`claude-3-haiku-20240307`	200K tokens	Fastest, most compact	Simple tasks, cost-effective

Claude models excel at:

Long document analysis (200K context)
Coding and technical tasks
Thoughtful, nuanced responses
Following complex instructions
Refusing unsafe requests

Configuration Options

Headers

client = Portkey(
    provider="anthropic",
    Authorization="sk-ant-***",
    anthropic_version="2023-06-01",           # API version
    anthropic_beta="prompt-caching-2024-07-31" # Beta features
)

Header	Description	Default	Required
`Authorization`	Anthropic API key	-	Yes
`anthropic_version`	API version	`2023-06-01`	No
`anthropic_beta`	Beta feature flags	`messages-2023-12-15`	No

Body Parameters

You can also pass these in the request body:

response = client.chat.completions.create(
    model="claude-3-5-sonnet-20241022",
    messages=[{"role": "user", "content": "Hello"}],
    max_tokens=1024,
    anthropic_version="2023-06-01",  # Can be in body
    anthropic_beta="prompt-caching-2024-07-31"  # Can be in body
)

Advanced Features

System Prompts

Claude supports powerful system prompts:

response = client.chat.completions.create(
    model="claude-3-5-sonnet-20241022",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful AI assistant specialized in Python programming. Provide clear, concise code examples."
        },
        {
            "role": "user",
            "content": "How do I read a CSV file in Python?"
        }
    ],
    max_tokens=500
)

Tool Use (Function Calling)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "The unit of temperature"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="claude-3-5-sonnet-20241022",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools,
    max_tokens=1024
)

if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Function: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")

Vision (Image Analysis)

Claude 3 models support image inputs:

response = client.chat.completions.create(
    model="claude-3-5-sonnet-20241022",
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What's in this image? Describe it in detail."
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://example.com/image.jpg"
                }
            }
        ]
    }],
    max_tokens=1024
)

You can also use base64-encoded images:

import base64

with open("image.jpg", "rb") as image_file:
    image_data = base64.b64encode(image_file.read()).decode('utf-8')

response = client.chat.completions.create(
    model="claude-3-5-sonnet-20241022",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe this image"},
            {
                "type": "image_url",
                "image_url": {
                    "url": f"data:image/jpeg;base64,{image_data}"
                }
            }
        ]
    }],
    max_tokens=1024
)

Prompt Caching

Reduce costs by caching frequently used prompts:

client = Portkey(
    provider="anthropic",
    Authorization="sk-ant-***",
    anthropic_beta="prompt-caching-2024-07-31"
)

# Large system prompt that will be cached
large_context = """[Your large context here - documentation, examples, etc.]"""

response = client.chat.completions.create(
    model="claude-3-5-sonnet-20241022",
    messages=[
        {"role": "system", "content": large_context},
        {"role": "user", "content": "Question about the context"}
    ],
    max_tokens=1024
)

Token Counting

Count tokens before making a request:

# Using the native Anthropic API through Portkey
response = client.messages.count_tokens(
    model="claude-3-5-sonnet-20241022",
    messages=[{"role": "user", "content": "Hello, Claude!"}]
)

print(f"Input tokens: {response.input_tokens}")

Fallback Configuration

Use GPT-4 as fallback for Claude:

config = {
    "strategy": {"mode": "fallback"},
    "targets": [
        {
            "provider": "anthropic",
            "api_key": "sk-ant-***",
            "override_params": {"model": "claude-3-5-sonnet-20241022"}
        },
        {
            "provider": "openai",
            "api_key": "sk-***",
            "override_params": {"model": "gpt-4o"}
        }
    ]
}

client = Portkey().with_options(config=config)

response = client.chat.completions.create(
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=100
)

Load Balancing

Distribute load across different Claude models:

config = {
    "strategy": {"mode": "loadbalance"},
    "targets": [
        {
            "provider": "anthropic",
            "api_key": "sk-ant-***",
            "override_params": {"model": "claude-3-5-sonnet-20241022"},
            "weight": 0.7
        },
        {
            "provider": "anthropic",
            "api_key": "sk-ant-***",
            "override_params": {"model": "claude-3-5-haiku-20241022"},
            "weight": 0.3
        }
    ]
}

client = Portkey().with_options(config=config)

Error Handling

from portkey_ai.exceptions import (
    RateLimitError,
    APIError,
    AuthenticationError
)

try:
    response = client.chat.completions.create(
        model="claude-3-5-sonnet-20241022",
        messages=[{"role": "user", "content": "Hello"}],
        max_tokens=1024
    )
except RateLimitError as e:
    print(f"Rate limit exceeded: {e}")
except AuthenticationError as e:
    print(f"Invalid API key: {e}")
except APIError as e:
    print(f"API error: {e}")

Request & Response Format

Request

{
  "model": "claude-3-5-sonnet-20241022",
  "messages": [
    {"role": "user", "content": "Hello, Claude!"}
  ],
  "max_tokens": 1024,
  "temperature": 1.0,
  "top_p": 1.0,
  "top_k": 5
}

Response

{
  "id": "msg_01XFDUDYJgAACzvnptvVoYEL",
  "type": "message",
  "role": "assistant",
  "content": [{
    "type": "text",
    "text": "Hello! How can I assist you today?"
  }],
  "model": "claude-3-5-sonnet-20241022",
  "stop_reason": "end_turn",
  "usage": {
    "input_tokens": 10,
    "output_tokens": 15
  }
}

Best Practices

Always set max_tokens - Required parameter for Claude
Use system prompts - Claude responds well to detailed system instructions
Leverage long context - Claude handles 200K tokens effectively
Enable prompt caching - Save costs on repeated large contexts
Use Haiku for speed - When fast responses matter more than complexity
Implement streaming - For better user experience with long responses
Add retry logic - Handle rate limits gracefully

Important Differences from OpenAI

Feature	OpenAI	Anthropic
`max_tokens`	Optional	Required
System messages	In messages array	In messages array
Context window	Up to 128K	Up to 200K
Embeddings	✅ Available	❌ Not available
Image generation	✅ DALL-E	❌ Not available
Audio	✅ TTS, STT	❌ Not available

Pricing

For up-to-date Anthropic pricing:

Anthropic Pricing

View detailed pricing for all Claude models

AWS Bedrock

Use Claude through AWS Bedrock

Fallback Routing

Set up fallbacks from Anthropic

Prompt Caching

Reduce costs with caching

Tool Use

Advanced tool use guide

Overview

Major Providers

Specialized Providers

Overview

Supported Features

Quick Start

Chat Completions

Streaming Responses

Available Models

Configuration Options

Headers

Body Parameters

Advanced Features

System Prompts

Tool Use (Function Calling)

Vision (Image Analysis)

Prompt Caching

Token Counting

Fallback Configuration

Load Balancing

Error Handling

Request & Response Format

Request

Response

Best Practices

Important Differences from OpenAI

Pricing

Anthropic Pricing

AWS Bedrock

Fallback Routing

Prompt Caching

Tool Use

Overview

Major Providers

Specialized Providers

Documentation Index

​Overview

​Supported Features

​Quick Start

​Chat Completions

​Streaming Responses

​Available Models

​Configuration Options

​Headers

​Body Parameters

​Advanced Features

​System Prompts

​Tool Use (Function Calling)

​Vision (Image Analysis)

​Prompt Caching

​Token Counting

​Fallback Configuration

​Load Balancing

​Error Handling

​Request & Response Format

​Request

​Response

​Best Practices

​Important Differences from OpenAI

​Pricing

Anthropic Pricing

​Related Resources

AWS Bedrock

Fallback Routing

Prompt Caching

Tool Use

Overview

Supported Features

Quick Start

Chat Completions

Streaming Responses

Available Models

Configuration Options

Headers

Body Parameters

Advanced Features

System Prompts

Tool Use (Function Calling)

Vision (Image Analysis)

Prompt Caching

Token Counting

Fallback Configuration

Load Balancing

Error Handling

Request & Response Format

Request

Response

Best Practices

Important Differences from OpenAI

Pricing

Related Resources