As AI technologies bring cost efficiency and process automation, businesses don’t stop on what they already have and seek new ways to implement OpenAI for their benefit. One of these ways is creating chatbots through the integration of the internal knowledge base and the OpenAI library.
Don’t think it’s a trend now? Then have a quick look at recent findings from Statista:
As you can see from this graph, the jump in investing in building OpenAI chatbots is huge, while comparing 2016’s state of things and the prognosis for 2025.
Recently, we’ve discussed the key areas of OpenAI use, explaining how it facilitates the developers’ and researchers’ lives. Catch up if you’ve missed it. Today we’ll speak about the OpenAI chatbots—an innovation that mimics human-like text and takes customer service to a new level.
The purpose of OpenAI libraries in chatbot development
Well, to cut a long story short, conversational AI gives users more advanced assistance, minimizing the resources (time, money, people) spent on the company’s side. A customer asks a chatbot a question and gets a quick and thoughtful answer to it, while the company focuses on other business tasks. That sounds like a win-win scenario, right?
How does it work? With the help of OpenAI embedding API. In plain terms, the AI software conducts a conversation with the internal knowledge base, finds the required information by keywords, and translates the findings into natural human-like responses.
One bright example of how AI chats are used now is the generation of replies to Twitter posts. Thanks to OpenAI, robotics and NLP can qualitatively do this task, freeing human experts.
We, at Patternica, believe that the areas of future OpenAI implementation will only expand, and it’s time to think of applying this technology to your business.
Key features of OpenAI chatbots with an internal knowledge base
Before going deeper into the question of how to create chatbots with OpenAI, let’s understand its valuable features first.
- Ability to imitate humans. Compared to their AI predecessors, OpenAI chatbots with an internal knowledge base can generate more natural response patterns thanks to the advanced AI models.
- Speed + volume. That’s the major reason why organizations rely on AI chat algorithms—to speed up the process and reduce human labor.
- Option to limit the length of response. In the case of OpenAI chatbots, developers can indicate the maximum number of tokens (common sequence of characters found in the text) in the generated response.
- Memorizing the context and best answer. This capacity of OpenAI turns chatbots into smart machines that keep a log of conversations with the user and responds to them quicker.
- Three-role language model. Again, the difference with traditional AI, OpenAI technology allows building a dialogue between the user, the assistant, and the system, which makes it easier to configure the way the model should behave.
To this list, you can also add some more peculiar characteristics, depending on the version of the OpenAI. For your convenience, we’ve gathered them in the table below.
Now that you’ve got more clarity on OpenAI model variations, we can move to the main question of this article. Keep reading!
Get ready to integrate an internal knowledge base into chatbots
Though the steps to build OpenAI chatbots integrated with an internal knowledge base will vary, depending on the version of OpenAI you’re using, we still can’t leave you without the general guidelines:
- Access GPT. Obviously, you’ll need to start with the registration on the OpenAI official website to request an API key. Having the key, you can start your interaction with GPT.
- Decide on the programming language. Though Python is the most widespread choice when it comes to AI implementation, you can also consider other options, including Ruby and Node.js.
- Install the necessary libraries. When you’re through with determining the programming environment, move to install the OpenAI library. For this, use the following command: pip install openai.
- Configure the API key. The next important step is to load the received API key in the script.
- Make a bridge between your knowledge base and GPT. This is normally achieved by creating a function, which makes the interaction between the two possible. Behind this function, there’s an algorithm to accept a prompt and generate the response to it.
- Build the chatbot’s logic. When it’s done, the central process—implementation—comes into the stage.
- Test the chatbot. To ensure quality, testing always precedes release, and the process of creating an AI chatbot is no exception. Run the chat and challenge it with different prompts to evaluate its performance. Work on improvements of chat responses if necessary.
- Deploy the chatbot. Now finish the process with the deployment, which may happen to a web server or another platform, based on your needs. Also, spend time thinking of security policies to protect your API key and user data involved in the chatbot processes.
Does it seem too complicated to your eyes? Don’t worry, being an expert in API integration & development, Patternica will take all the hustle into their hands. Contact us to create an AI-based chatbot and get more out of intelligent data management!
Leverage OpenAI libraries for chatbots and seek automation
Though chatbots are sometimes criticised for their ethical bias or ‘plausible sound with incorrect answers,’ the evolution of openAI technology is promising, and that means it should get rid of these defects soon. Besides, the advantages of using openAI technology for chatbot building overweight the limitations. So why not try it yourself?