MasterClass on Anthropic Claude 3.5 Python API

Introduction

In today's session, we will be focusing on learning about Claude 3.5 Sonet, a powerful new model from Anthropic that was released just last month. This model has quickly gained popularity due to its impressive performance – being cheaper, faster, and more accurate compared to many other models, including ChatGPT. This article aims to provide a comprehensive understanding of Claude 3.5 Sonet, including its features, how to interact with it through Python, and some practical applications.

Introduction to Claude 3.5 Sonet

Claude 3.5 Sonet is available on both Amazon Bedrock and Google Cloud Vertex AI. Companies using AWS or Google Cloud can access these models securely through their respective platforms. Claude 3.5 Sonet improves on its predecessor, providing better performance in terms of cost, speed, and accuracy. Future releases of Claude 3.5 Hu and Claude 3.5 Opus will continue to enhance this model family, offering even more sophistication.

Getting Started with Claude 3.5 Sonet

To start using Claude 3.5 Sonet, you need to install the necessary libraries and obtain an API key from the Anthropic dashboard. You can use the following commands to install the required libraries:

pip3 install anthropic
pip3 install python-dotenv

Store your API key in a .env file for secure access:

ANTHROPIC_API_KEY=your_generated_api_key

Load the environment file to make the API key accessible in your Python script:

import os
from dotenv import load_dotenv
load_dotenv()

api_key = os.getenv("ANTHROPIC_API_KEY")

Interacting with the Model

Create a client and send a request to the model. The client.messages.create function requires at least three parameters: the model name, max tokens, and messages.

from anthropic import Client

client = Client(api_key)
response = client.messages.create(
    model="claude-3.5-sonet-20240620",
    max_tokens=1000,
    messages=[
        ("role": "user", "content": "Top 10 places to visit in Sydney")
    ]
)
print(response.content)

Advanced Features

Max Tokens and Stop Sequences

Control the length of the model's response by adjusting the max tokens or using stop sequences to halt the model when particular tokens are encountered.

response = client.messages.create(
    model="claude-3.5-sonet-20240620",
    max_tokens=100,
    messages=[
        ("role": "user", "content": "Top 10 places to visit in Sydney")
    ],
    stop_sequences=["5."]
)

Temperature

Adjust the randomness of the model's responses using the temperature parameter. A temperature of 0 results in deterministic responses, while a temperature closer to 1 produces more creative outputs.

response = client.messages.create(
    model="claude-3.5-sonet-20240620",
    max_tokens=100,
    messages=[
        ("role": "user", "content": "Top 10 famous dishes in India")
    ],
    temperature=0.7
)

Top P and Top K

Use these parameters for nucleus sampling and controlling the number of tokens the model considers.

response = client.messages.create(
    model="claude-3.5-sonet-20240620",
    max_tokens=100,
    messages=[
        ("role": "user", "content": "Name one famous Indian dish")
    ],
    top_k=20,
    top_p=0.8
)

Practical Application: Fridge Inventory Project

Here's an example of using Claude 3.5 Sonet for a practical application: generating recipes based on the contents of your fridge. This example demonstrates how to input an image and text, and how to use the model to output detailed information.

Step-by-Step Code

1. Load an image in Base64 format.
2. Pass user preferences and the image to the model.
3. Generate detailed recipes and tips based on the image contents.

import base64

def load_image_as_base64(file_path):
    with open(file_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

image_base64 = load_image_as_base64("fridge.jpg")

prompt = """
Please analyze the contents of my fridge in the picture and provide the following:
1. List of identified ingredients with estimated quantities and expiration dates.
2. Three creative recipe ideas using near expirations ingredients with short instructions.
3. Two to three quick storage tips for maximizing ingredient lifespan.
4. Two to three creative ways to use leftovers or food scraps.
5. Recommendations for 3-5 ingredients to buy that complement existing items.
"""

response = client.messages.create(
    model="claude-3.5-sonet-20240620",
    max_tokens=1000,
    messages=[
        ("role": "user", "content": {"type": "image", "source": "base64", "file_type": "jpg", "data": image_base64)},
        ("role": "user", "content": prompt)
    ]
)

print(response.content)

Feature Highlights and Future Improvements

Claude 3.5 Sonet and other models in this family offer impressive capabilities for various use cases. From basic text responses to complex image and text analysis, these models provide versatile applications. For more in-depth learning, educational courses and tutorials can help you master these tools.

Keyword

• Claude 3.5 Sonet
• Anthropic
• Python API
• Max Tokens
• Stop Sequences
• Temperature
• Top P
• Top K
• Image Analysis
• Recipe Generation
• Base64

FAQ

Q1: What is Claude 3.5 Sonet?

• Claude 3.5 Sonet is the latest model from Anthropic, known for its high performance, cost efficiency, speed, and accuracy.

Q2: How can I access Claude 3.5 Sonet?

• You can access Claude 3.5 Sonet via platforms like Amazon Bedrock and Google Cloud Vertex AI or directly through the Anthropic API.

Q3: How do you manage API keys securely?

• Store your API keys in a .env file and load them using the python-dotenv library.

Q4: What are temperature and top P parameters used for?

• The temperature controls the randomness of responses. Top P (nucleus sampling) selects tokens based on cumulative probability, enhancing the output's creativity.

Q5: How do I integrate images into the model input?

• Convert images to a Base64 string format and use the appropriate API parameters to pass image data along with text in the messages.

Q6: What is the practical application of Claude 3.5 Sonet in daily life?

• One practical application is generating recipes from fridge contents by analyzing images and suggesting how to use existing ingredients efficiently.

I hope this article provides you with a comprehensive understanding of Claude 3.5 Sonet and how to interact with it using the Python API.