CHATBOT

The aim is to create a chatbot capable of quickly and accurately answering various questions across a wide range of content. This chatbot will facilitate users' quick access to information, continue to operate without needing updates for new information added to the website, and will easily be implemented onto different websites. The instant responses provided by the chatbot will accelerate users' access to information and enhance the usability of the website.

PROJECT DESCRIPTION:

The project definition is to design an AI chatbot system that provides quick and effective answers to the questions that users ask about the topics they are curious about on the Istanbul Commerce University website. The chatbot is an artificial intelligence application and interacts with users to answer their questions, provide them with information, and guide users. Stages: The project consists of 5 main stages. These are the collection of data, interface, creation of vector representations, detection of the content most similar to the asked question, and generation of responses. The project uses language models provided by the 'OpenAI API'. These language models assist us in obtaining vector representations and generating responses using natural language processing (NLP) techniques. Chatbots are automated chat systems that can engage in natural language-based interactions with users. With the ability to generate human-like responses, they can perform tasks such as providing real-time support to users, providing information, or answering questions. They can interact with users on a website, in mobile applications, on messaging platforms, or on social media accounts. They try to understand the users' questions, attempt to produce correct and relevant answers, and can sometimes perform predetermined tasks. Chatbots generally work by following the basic steps below:

1. User Input: The chatbot takes the user's input. This can be a text-based input, such as a question or a statement.
2. Natural Language Processing: The chatbot tries to understand the user's input using natural language processing (NLP) techniques. In this step, it analyzes the text according to grammar rules, vocabulary, and semantic structure.
3. Understanding and Intent Extraction: The chatbot performs an understanding process to extract the user's intent. It tries to understand the user's question or request and tries to determine what type of response is needed.
4. Data Processing: Once the chatbot understands the user's intent, it can access the necessary data. This data may be information stored in a database, information obtained from websites, or information obtained from other sources.
5. Response Generation: The chatbot generates a response based on the user's input and intent. This response can be a text-based answer, a visual, or a voice response. The response can be based on the chatbot's programmed rules, pre-determined templates, or more complex artificial intelligence algorithms.
6. Response Presentation: The chatbot presents the generated response to the user. This usually occurs as a text-based message, but it can be presented in different ways depending on the platform and interface where the chatbot is used.
7. There are basically two methods used in chatbot development: rule-based method and machine learning methods.
8. Rule-Based Method: In this method, the chatbot produces answers based on programmed rules. Predefined rules and templates are used. The chatbot analyzes the user's input, adapts to a certain rule or template, and generates a response accordingly. This type of approach can be effective for a specific and limited topic or scenario, but it may be limited in more complex or flexible situations.

You need to get the embeddings of your content, and we do this with the "text-embedding-ada-002" model. Getting embeddings will help us identify the content that most closely matches the question asked.

The unique thing about this chatbot is that it is adaptable to any ecosystem. For this, it will be sufficient to take the embeddings of the data you will obtain from the website or any ecosystem and give it to the model.

CONCLUSION

This project explores how chatbot technology and AI language models, specifically "text-embedding-ada-002" and "text-davinci-003", can improve user interaction and information access on a website. The study finds that these models significantly enhance the chatbot's ability to comprehend and respond to user queries.

Chatbot technology, as demonstrated by this research, can increase efficiency, improve the overall user experience, and provide real-time support. The study also highlights that using AI language models can boost a chatbot's performance.

Furthermore, this technology can benefit both small to medium businesses and large corporations by delivering services and products more effectively, thereby boosting customer satisfaction and reducing service costs.

The project contributes to chatbot technology in the Turkish language, showcasing the benefits of using both local and international language models. However, like any technology, chatbots have limitations, including difficulties in understanding complex and ambiguous expressions.

In conclusion, this research shows AI language models as a key tool in developing chatbot technology, which can enhance user experience and assist businesses in improving customer service. More research in this field could broaden the potential of chatbot technology.