The OpenAI GPT-3 language model enables developers to integrate chat-based capabilities into applications. With the ChatGPT API, developers can send a list of messages as input and receive a model-generated message as output. This allows for dynamic and interactive conversations with the model.
To use the ChatGPT API, you need to authenticate your requests using an API key. Then, you can make a POST request to the /v1/chat/completions
endpoint with your desired conversation history.
Here is an example Python code that demonstrates how to use the ChatGPT API:
import openai
openai.api_key = 'YOUR_API_KEY'
def chat_with_gpt3(messages):
response = openai.Completion.create(
engine='text-davinci-003',
prompt=messages,
max_tokens=100,
n=1,
stop=None,
temperature=0.8,
)
reply = response.choices[0].text.strip()
return reply
conversation = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Who won the world series in 2020?"},
{"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
{"role": "user", "content": "Where was it played?"}
]
messages = [{'role': role, 'content': content} for role, content in conversation]
response = chat_with_gpt3(messages)
print(response)
This example initiates a conversation with a system message followed by alternating user and assistant messages. The chat_with_gpt3
function sends the conversation to the API and retrieves the model’s response. The response is then printed.
Keep in mind that the API has limitations in terms of response length and usage quotas, so you should review OpenAI’s API documentation for further details on how to work within those limits.
The ChatGPT API is an API (Application Programming Interface) that allows developers to integrate and interact with the ChatGPT language model. By making requests to the API, developers can send conversational prompts and receive model-generated responses, enabling them to create chatbot applications or other natural language processing (NLP) tools. The ChatGPT API provides a way to access the model’s capabilities without having to manage the underlying infrastructure.
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