How can I create an openai based chatbot?

API
1

Hi!
I want to create openai based chatbot. I created API key.How should I proceed?What should I pay attention to?

2

The API is pay-per-use. Besides generating an API key in a project in the platform.openai.com site for your account and organization, you also need to prepay, purchasing credits to fund your usage of the API.

Pricing by model, based on the amount of language data in and out:

https://platform.openai.com/docs/pricing

You can use the Chat Completions API endpoint to send a message to the AI model, and get a response. A recommended starting point would be gpt-4o-mini model for experimentation. You can send one message and get one output. Let’s ask it a question:

user: What do I need to do to set up my account before my Chat Completions API call to OpenAI would be successful?

AI assistant: To set up your account and ensure your Chat Completions API call to OpenAI is successful, follow these steps:

  1. Create an OpenAI Account:

    • Visit the OpenAI website and sign up for an account if you haven’t already.

  2. Access the API Dashboard:

    • Once your account is set up, log in to the OpenAI API dashboard. This is where you can manage your API keys and monitor usage.

  3. Obtain an API Key:

    • In the API dashboard, navigate to the API keys section.
    • Generate a new API key. This key is essential for authenticating your API requests.

  4. Review API Documentation:

    • Familiarize yourself with the OpenAI API documentation, specifically the Chat Completions API section. This will help you understand the request structure, parameters, and response format.

  5. Set Up Billing:

    • Ensure that your account has a valid payment method set up. OpenAI may require billing information to enable API access, along with a prepayment to purchase credits.

  6. Environment Setup:

    • Ensure your development environment is ready to make HTTP requests. You can use tools like curl, Postman, or libraries in your preferred programming language (e.g., requests in Python) along with the openai SDK libraries for many languages.

  7. Test Your API Call:

    • Construct a test API call using your API key. Make sure to include necessary headers like Authorization with your API key and Content-Type: application/json.
    • Example in Python using requests library:
      import requests

url = "https://api.openai.com/v1/chat/completions"
headers = {
    "Authorization": f"Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}
data = {
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Hello, how are you?"}]
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

  8. Handle API Responses:

    • Implement error handling in your code to manage potential issues like rate limits, invalid requests, or server errors.

  9. Monitor Usage:

    • Regularly check your API usage in the OpenAI dashboard to ensure you stay within your plan’s limits and to understand your usage patterns.

By following these steps, you should be able to successfully set up your account and make a Chat Completions API call to OpenAI.

I edited the AI response a bit, because the knowledge was old and in some cases wrong.

A chatbot implies that you can chat: there is a continuing conversation of back and forth turns, that the AI remembers what you were talking about.

That kind of memory isn’t built in, as every API call is independent. However, we simulate that by sending the previous chat also in our new list of messages.

Here is a code snippet that uses Python and the OpenAI Python library. Scroll through it, you see in “messages” the actual conversation from the past, where the AI remembered the name, and the latest input “goodbye”.

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
  model="gpt-4o",
  messages=[
    {
      "role": "system",
      "content": [
        {
          "type": "text",
          "text": "You are a helpful chatbot based on GPT-4 Plus."
        }
      ]
    },
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Your name is \"Gregory\". Remember that for the rest of our chat."
        }
      ]
    },
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "Got it! My name is Gregory for this chat. How can I assist you today?"
        }
      ]
    },
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Do you remember your name?"
        }
      ]
    },
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "Yes, my name is Gregory for this chat. How can I help you?"
        }
      ]
    },
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Goodbye!"
        }
      ]
    }
  ],
  response_format={
    "type": "text"
  },
  max_completion_tokens=2000,
)

(this automatically employs your API key set as an OS environment variable named OPENAI_API_KEY)

Your code has to remember the per-user and per-chat memory in order to send it back, otherwise the AI wouldn’t know what you discussed in the past.

That’s the fundamentals of getting a paid API account and making API calls, including chat-like calls.

Glossary:
API: Application Programming Interface - you doing the programming

1 Like
3

I’d say have a look at using Gradio for the user interface as it can get you started relatively quickly along with Langchain for the backend.

However, if you want a quick start to get more familiar with creating custom chatbots, I would suggest trying Open WebUI as this is an opensource project with community support for various add-ons. You can use this as a base and customise it further for your own requirements.

Are there any specific features you want your chatbot to be capable of? I built one recently mostly just for fun that you can talk to audibly, it will search the web if it considers it is nessesary and recall from it’s own memories and reply accordingly. Then it will further research topics it thinks you are interested in for future conversations and store the memories in RAG. Hopefully that gives you some ideas…