Decoding the OpenAI API: Practical Examples for Developers

Getting Started with OpenAI API

Types of Models Available

• GPT-4.1: The most advanced model designed for completing complex tasks, providing a context window of 1 million tokens. It excels in tasks requiring both textual understanding & vision capabilities (Learn more).
• GPT-4.1 Mini: A more affordable variant that balances speed & performance, suitable for real-time applications (Learn more).
• GPT-4.1 Nano: Perfect for low-latency tasks, making it the ideal option for quick responses in chatbots or similar applications (Learn more).
• Reasoning Models: Such as OpenAI O3 & O4 Mini, which focus on thoughtful responses, ideal for complex and multi-step reasoning tasks (Learn more).

Getting Your API Key

Practical Examples of Using OpenAI API

1
pip install openai

Example 1: Generating Text with GPT-3.5

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import openai
import os

# Set your API key environment variable
openai.api_key = os.getenv('OPENAI_API_KEY')

# Generate text
response = openai.ChatCompletion.create(
    model='gpt-3.5-turbo',
    messages=[{'role': 'user', 'content': 'Tell me a joke.'}],
    temperature=0.7
)

# Print the response
print(response.choices[0].message['content'])

Example 2: Implementing Web Search with OpenAI

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response = openai.ChatCompletion.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': 'What’s the latest news on AI?' }],
    tools=[ {
        'type': 'web_search'
    } ]
)

# Print the web search results
print(response.output)

Example 3: File Search Capabilities

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response = openai.FileSearch.create(
    model='davinci',
    input='What are the main benefits of Arsturn?',
    tools=[ {
        'type': 'file_search' 
    } ]
)

# Get the answer from the search results
print(response.output)

Example 4: Customizing Models via Fine-tuning

1. Prepare your training dataset in the JSON Lines format.
2. Use the Fine-tuning endpoint to upload it.

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openai.FineTune.create(
    training_file='file-1sY3ys2rt1bt7Q1YbT9E9',
    model='davinci',
)

Example 5: User Interaction Scenarios with Chatbots

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while True:
    user_input = input('You: ')
    response = openai.ChatCompletion.create(
        model='gpt-4o',
        messages=[{'role': 'user', 'content': user_input}],
        temperature=0.5
    )
    print('Bot:', response.choices[0].message['content'])

Harness the Power of Arsturn

Why Choose Arsturn?

• No Coding Required: Easily build chatbots without worrying about technicalities.
• Instant Engagement: Boost your audience engagement with custom AI that understands your brand’s tone & voice.
• Data Analytics: Gain valuable insights into user interactions and adapt your chatbot based on real-time data.
• Flexible Integration: Integrate effortlessly with your existing platforms to enhance your brand’s digital presence.

Conclusion