How to Use Claude AI API in Python?

Claude AI is an artificial intelligence assistant created by Anthropic to be helpful, harmless, and honest. It offers a powerful conversational API that allows developers to integrate Claude’s advanced natural language processing capabilities into their own applications.

In this comprehensive tutorial, we will learn how to use the Claude AI API in Python to build an AI assistant bot that can understand text, answer questions, summarize long passages, perform translations, and more.

Prerequisites

• Python 3.6 or higher installed
• Claude API key (sign up at https://www.anthropic.com to obtain)
• Request Python library

Setting Up Claude AI API Client

We will use the claude-python client library to interact with the API in our code. To install:

pip install claude-python

Then import the library and instantiate a client object with your API key:

import claude

claude_api_key = "sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX" 

client = claude.Client(claude_api_key)

The client object allows us to make requests to the Claude API endpoints that we explore next.

Understanding the Claude AI API Endpoints

The Claude API offers several endpoints that provide different AI functions:

create: The core endpoint that generates text completions from a prompt and parameters. This is what enables Claude AI to converse fluently.

classify: Categorizes the subject matter of a piece of text across several dimensions like toxic, sexual, violent content etc. Useful for content moderation.

embed: Converts text into a numerical vector representation that captures semantic meaning. This vector can be used to find text similarities.

summarize: Shortens long content into a concise summary while retaining key points.

translate: Translates text from one language into another supported language.

Next we will look at using each endpoint in Python.

Generating Text with the Create Endpoint

The create endpoint is the backbone of Claude AI’s conversational ability. We’ll learn how to call it properly.

First we need to define the prompt – the initial text that provides context and guides Claude’s response. Best practice is to phrase the prompt as a question or request to Claude.

prompt = "Claude, what is the capital of France?"

Next we make the actual API call using our client, passing the prompt and any other parameters:

response = client.create(
  prompt = prompt
)

The full response contains metadata, but we can print just the generated text using:

print(response.text)

The capital of France is Paris.

And there we have a conversational response to our question!

We can customize parameters like:

• max_tokens – limits length of the text response
• temperature – higher values mean more random/creative text
• frequency_penalty – reduces repetition

For example:

```python
response = client.create(
prompt = prompt,
max_tokens = 50,
temperature = 0.5,
)

Experiment with values to see impact on responses.

Analyzing Text Content with Classify

The `classify` endpoint tags sections of text with attributes across several categories related to:

– Toxicity
– Sexually explicit content
– Violence
– Hate
– Self-harm

This powers Claude AI content moderation capabilities.

We need to pass the text we want analyzed in the input parameter. Here’s an example:

text_to_analyze = "Some text to classify..."

response = client.classify(
  input = text_to_analyze  
)

The response will contain a categories dictionary with results for each classification type and text spans that triggered them.

For example, a sexual content rating of 0.8 indicates the text likely contains sexual content. We could use these ratings to moderate inappropriate content.

Representing Text Meaning with Embed

The embed endpoint converts text into a numerical vector representation capturing underlying semantic meaning.

Here’s how to embed text:

text = "The cat sat on the mat."

vector = client.embed(
  input = text
)

vector will now contain a list of 768 numbers representing the text.

Text vectors can power advanced capabilities like semantic search (find texts with similar meanings). We can measure similarity between two vectors using cosine similarity – texts with a score nearing 1 are highly similar.

So you could build a system to find answers to questions based on similarity between question and answer embedding vectors.

Summarizing Lengthy Texts

The summarize endpoint shortens long content into concise high-level summaries.

To summarize text, pass it in the input parameter:

long_text = "Some very lengthy text to summarize..."

response = client.summarize(
  input = long_text,
  max_tokens = 50,
)

print(response.text)

We limited our summary to 50 tokens. Experiment with the length to suit your use case.

Summarization makes Claude AI great at distilling key information from long documents, articles, speeches etc.

Translating Across Languages

The translate endpoint translates text between languages.

The source and target languages are specified with ISO 639-1 language codes. Here’s an example translating English to Spanish:

english_text = "Hello there!" 

response = client.translate(
  input = english_text,
  from_language = 'EN',
  to_language = 'ES'  
)

print(response.text)

Which prints:

¡Hola!

Claude AI supports many popular human languages for translation.

And that covers the basics of using the core Claude AI endpoints in Python!

Building a Conversational Bot

Let’s puts some of what we learned together to create a simple Claude-powered conversational bot that responds to user text input.

We’ll take input and send to Claude’s create endpoint to generate bot responses:

import claude

# Initialize client 
client = claude.Client(claude_api_key)  

# Bot conversation loop
while True:

  # Get input text
  human_input = input("You: ")  

  # Hit Claude API
  response = client.create(
    prompt = human_input
  )

  # Print bot response 
  print("Claude: " + response.text)

We could enhance this by tracking context, adding additional endpoints like classify or summarize, and integrating with a UI.

But already we have a basic bot able to hold conversations powered completely by Claude’s advanced AI!

Conclusion

That concludes our guide on using Claude AI’s Python API to integrate powerful conversational AI into applications. Here are some key things we learned:

• Setup client with API key
• Pass prompts to create endpoint for generated text
• Control response parameters like length, creativity
• Analyze text content using classify
• Represent semantic meaning as vectors via embed
• Shorten text using summarize endpoint
• Translate languages with translate endpoint
• Build bots with create endpoint responses

The Claude API makes it easy to unlock Claude’s potential for all kinds of AI use cases. I hope you found this guide helpful! Let me know if you have any other questions.

FAQs