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Anthropic¶

Anthropic provides Claude, known for its strong reasoning capabilities, safety features, and long context windows up to 200K tokens.

Stable

Quick Facts¶

Property	Value
Quality	State-of-the-art reasoning
Context	Up to 200K tokens
Models	Claude 3.5 Sonnet, Claude 3 Opus/Sonnet/Haiku
Streaming	Supported
Functions	Supported
Vision	Supported
Batching	Supported

Setup¶

Get API Key¶

Go to console.anthropic.com
Create an account
Navigate to API Keys
Create a new API key

Configure¶

Environment VariablePythonYAML

export ANTHROPIC_API_KEY="sk-ant-..."

from sentimatrix.config import SentimatrixConfig, LLMConfig

config = SentimatrixConfig(
    llm=LLMConfig(
        provider="anthropic",
        api_key="sk-ant-...",
        model="claude-3-5-sonnet-20241022"
    )
)

llm:
  provider: anthropic
  model: claude-3-5-sonnet-20241022

Available Models¶

Model	Context	Input Cost	Output Cost	Best For
`claude-3-5-sonnet-20241022`	200K	$3.00/1M	$15.00/1M	Best overall
`claude-3-opus-20240229`	200K	$15.00/1M	$75.00/1M	Complex reasoning
`claude-3-sonnet-20240229`	200K	$3.00/1M	$15.00/1M	Balanced
`claude-3-haiku-20240307`	200K	$0.25/1M	$1.25/1M	Fast, cost-effective

Usage Examples¶

Basic Usage¶

import asyncio
from sentimatrix import Sentimatrix
from sentimatrix.config import SentimatrixConfig, LLMConfig

config = SentimatrixConfig(
    llm=LLMConfig(
        provider="anthropic",
        model="claude-3-5-sonnet-20241022"
    )
)

async def main():
    async with Sentimatrix(config) as sm:
        insights = await sm.generate_insights(reviews)
        print(f"Pros: {insights.pros}")
        print(f"Cons: {insights.cons}")

asyncio.run(main())

With Vision¶

config = SentimatrixConfig(
    llm=LLMConfig(
        provider="anthropic",
        model="claude-3-5-sonnet-20241022"
    )
)

async with Sentimatrix(config) as sm:
    result = await sm.analyze_image(
        image_path="product.jpg",
        prompt="Analyze the sentiment conveyed by this product image"
    )

Streaming¶

async with Sentimatrix(config) as sm:
    async for chunk in sm.stream_summary(reviews):
        print(chunk, end="", flush=True)

Configuration Options¶

LLMConfig(
    provider="anthropic",
    model="claude-3-5-sonnet-20241022",

    # API settings
    api_key="sk-ant-...",

    # Generation settings
    temperature=0.7,
    max_tokens=4096,
    top_p=0.9,

    # Reliability
    timeout=60,
    max_retries=3,
)

Best Practices¶

Choose the Right Model
- claude-3-5-sonnet for best quality
- claude-3-haiku for speed and cost
Use Long Context Wisely
- Claude supports 200K tokens
- Great for analyzing many reviews at once
Leverage Vision
- Analyze product images alongside text