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If you’re a Python developer, you might have heard about the ChatGPT-3 API. This artificial intelligence-powered API is capable of generating text responses, chatbot conversations, and more. In this article, we’ll take a deeper look at the advanced usage of ChatGPT-3 API with Python. We’ll explore how to generate more complex responses, integrate it with other APIs, and much more. So, let’s get started.
Prerequisites
Before we start, you need to make sure you have the following:
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- A valid OpenAI API key
- Python 3 installed on your machine
- OpenAI library installed
Getting Started
The first step is to sign up for an OpenAI account and obtain an API key. You can do this by visiting the OpenAI website and creating a free account. Once you have an account, you will be able to generate an API key, which you will use to authenticate your API requests.
To install the OpenAI library, you can use pip, Python’s package manager. Open your terminal and type the following command:
pip install openaiOnce the installation is complete, you can start making API calls.
Making Your First API Call
To test that everything is set up correctly, we will make our first API call. We will ask ChatGPT 3 to generate a simple sentence based on a prompt.
Copy the following code to a new Python file:
import openai
openai.api_key = "YOUR_API_KEY"
prompt = "Hello, my name is"
response = openai.Completion.create(
engine="davinci",
prompt=prompt,
max_tokens=5
)
print(response.choices[0].text)Replace “YOUR_API_KEY” with your actual API key.
In this code, we set up the OpenAI API key and define the prompt, which is the beginning of a sentence. We then create a completion request, specifying the engine to use, the prompt, and the number of tokens to generate. Finally, we print the response, which contains the generated text.
When you run this code, you should see a short sentence generated by ChatGPT 3, based on the prompt you provided.
Advanced Usage
ChatGPT 3 API can do much more than generate simple sentences. Here are some examples of advanced use cases:
- Text completion: Given a prompt, generate a complete text document or email response.
- Language translation: Translate text from one language to another.
- Sentiment analysis: Analyze the sentiment of a given text.
- Text classification: Classify text based on its content.
To use these advanced features, you need to modify the prompt and the API call parameters accordingly. You can find detailed information on the available parameters in the OpenAI documentation.
Generating Responses
The most basic usage of the ChatGPT-3 API is to generate responses. Here’s an example of how to generate a response to a given prompt:
import openai
openai.api_key = "YOUR_API_KEY"
prompt = "What is the capital of France?"
response = openai.Completion.create(
engine="text-davinci-002",
prompt=prompt,
max_tokens=100,
n=1,
stop=None,
temperature=0.5,
)
print(response.choices[0].text)n this example, we’re using the text-davinci-002 engine to generate a response to the prompt “What is the capital of France?”. The max_tokens parameter specifies the maximum number of tokens in the generated response. The nparameter specifies the number of responses to generate. The stop parameter is used to specify the stop sequence to end the response. The temperature parameter controls the creativity of the generated response.
Integrating with Other APIs
You can integrate the ChatGPT-3 API with other APIs to create more complex applications. Here’s an example of how to integrate the ChatGPT-3 API with the Google Maps API to generate directions:
import openai
import requests
openai.api_key = "YOUR_API_KEY"
prompt = "What are the directions from New York to Washington?"
response = openai.Completion.create(
engine="text-davinci-002",
prompt=prompt,
max_tokens=100,
n=1,
stop=None,
temperature=0.5,
)
directions = response.choices[0].text.strip()
url = f"https://maps.googleapis.com/maps/api/directions/json?origin=New+York&destination=Washington&key=YOUR_GOOGLE_MAPS_API_KEY"
response = requests.get(url).json()
steps = response["routes"][0]["legs"][0]["steps"]
for step in steps:
print(step["html_instructions"])In this example, we’re using the ChatGPT-3 API to generate directions from New York to Washington. We then use the Google Maps API to retrieve the actual directions and print them out.
Language translation
One of which where ChatGPT can be used is language translation to translate text from one language to another. Here’s how you can use the ChatGPT 3 API with Python for language translation:
import openai
openai.api_key = "YOUR_API_KEY"
text = "This is an example text to be translated."
target_language = "es"
response = openai.Completion.create(
engine="davinci",
prompt=f"translate from English to {target_language}: {text}",
max_tokens=60
)
translated_text = response.choices[0].text
print(translated_text)Learn how to fine tune your ChatGPT model
Conclusion
In this article, we have shown you how to use ChatGPT 3 API with Python, from setting up your account to making your first API call. We also demonstrated some advanced use cases, such as text completion, language translation, sentiment analysis, and text classification.
The possibilities of AI-generated text are endless.
