This month marks the exciting release of one of our standout products on the XTOPIA AI Platform: the Retrieval Augmented Generation (RAG) ChatGPT AI Chatbot. Meet Xandra, our RAG ChatGPT AI Chatbot designed to revolutionize your interactions. Curious to see Xandra in action? Visit our website at XIMNET and experience the future of AI-powered chatbot firsthand!
Note: Xandra is the name we assigned to the chatbot on our website that utilizes the RAG ChatGPT AI Chatbot. You can choose any name you like when you sign up with us.
How did we build the RAG ChatGPT AI Chatbot? We mainly used Large Language Models (LLMs). Since the release of ChatGPT, many popular models have emerged, including OpenAI's GPT-4o, Google's Gemini, and Meta AI's LLaMA. Here's a brief story of our journey in leveraging LLMs for our XTOPIA AI solutions.
When OpenAI's ChatGPT was first released, it amazed many, including our team. As soon as OpenAI released its API, we were eager to try it out to build a chatbot. However, there were some limitations when using the API directly. Let's try with a simple question about our company, which returned wrong answer. However, the chatbot on our website returns accurate answer.
How did we get the LLM to give accurate and relevant answers? We used a method called Retrieval-Augmented Generation (RAG). This helps the LLM find useful information from external sources and then use it to create better responses.
In the Retrieval phase, the LLM pulls relevant information from a knowledge base. This information is often stored in a vector database, which allows for fast searching based on meaning. Today, there are several vector databases, such as Pinecone, Chroma, MongoDB, and Weaviate.
In the Generation phase, the LLM uses the retrieved information to answer the user's question. It understands the context of what it found to create a relevant response.
However, LLMs have limits on how much context they can handle at once. To fix this, we break website content into smaller chunks so it can be searched and added to the LLM’s query more easily. Crawling and chunking websites can be tricky because some pages are very simple or contain only images, while others may have long pages, PDF or DOCX documents, which add more complexity. To solve this, our team built a custom app to handle crawling and chunking for web pages, images, and documents.
Retrieving information can also be hard when websites have a lot of content scattered across many pages. To improve this, we used LLMs to enhance the retrieved information before passing it on to the answer generation process.
Interestingly, the challenge of having too much information spread across different pages led us to our next feature: the AI Page. If the chatbot finds answers from several sources, it provides a "Summarize" button. When the user clicks it, the LLM creates a unique page that combines information from those sources.
As we celebrate the launch of the XTOPIA RAG ChatGPT AI Chatbot, we invite you to explore the innovative capabilities that set our chatbot apart. Our commitment to harnessing advanced technologies like Large Language Model and Retrieval-Augmented Generation ensures that your interactions are not only seamless but also enriched with accurate, contextually relevant information.
We’re excited about the future of AI-driven communication, and we can't wait for you to experience the chatbot for yourself. Visit us at XIMNET and discover how XTOPIA RAG ChatGPT AI Chatbot can transform your engagement today!