Artificial Intelligence (AI) is turning the tables. It is revolutionising the various industries and the Open AI’s chat GPT is at the front of this transformation. Building a charge model will allow you to tailor conversational artificial intelligence to meet basic needs and that too specific one, which is enhancing the user interactions. In this post we will learn how to make a custom charge model using Open AI APIs.
Introducing ChatGPT and Open AI’s API.
The ChatGPT is a cutting edge language model which is created by open AI designed to understand and generate human-like text. Leveraging Open AIs API developers can integrate into their applications customise its behaviours and can create new experiences.
Prerequisites
Before implementing let’s find out what are the prerequisite
A valid open AI API key
Basic programming knowledge in python
Little experience in API usage and restful API
Setting up your environment
Install python: please ensure that the python is installed on your system you can download it from python.org
Install necessary libraries: use PIP to install the required libraries.
pip install openai requestsAuthenticating with open AI API
Authenticate your application with the open API using your API key. Save your API securely and use it to authenticate the request.
import openai
openai.api_key = 'your-api-key-here'Creating and customising your ChatGPT model
After indicating let’s create and customise the ChatGPT model:
Define your prompt:
Create the model responses by defining prompts. A good prompting technique will help you in generating relevant and coherent responses via model.
prompt = "You are a helpful assistant. How can I assist you today?"Make an api request: use the Open AI API to generate responses based on your prompt.
response = openai.Completion.create(
engine="text-davinci-003",
prompt=prompt,
max_tokens=150
)
print(response.choices[0].text.strip())Refining the model behaviour
Customising a chargeable model in walls refining in behaviour based on user interactions:
Collect user feedback: take user response to improve response quality.
Adjust parameters: to control the randomness and length of responses experiment with different parameters like temperature and max_tokens.
Deployment and integration
Once you get the result and you are satisfied with the custom models performance you can integrate into your application
Integrate with a front end
You can make an interface to interact with a chatgpt model.
Monitor usage
You track the API usage and optimise as needed to manage the costs
Conclusion
Well it’s a very good experience building a ChatGPT model using the open AI API. With the right approach and refinement you can unlock the power of AI.