How to Create a Generative AI Chatbot

A chatbot is an essential part of any web application and often forms the first impression for users. It helps with user engagement, and an effective chatbot can guide users to learn more about the application. With advancements in generative AI, it has become important for organizations to adopt generative chatbots. Below is an approach to implementing a generative AI chatbot for any web application using RAG (Retrieval-Augmented Generation).

Create Embeddings Using an Embedding Model

• Collect all the documents from the webpage.

• Create chunks from these documents.

• Use an embedding model to convert the chunks into vectors and store them in a vector database.

Retrieve the Content

When a user asks a question, first search for relevant information in the embedding database. If relevant documents are found, create a context using sparse search.

Although this process looks simple, it is not. Retrieval is the key to building a successful generative AI project. The documents often need to be extracted using multiple strategies to provide enough context. The retrieved data should also be compared with dense search results, and with a well-tuned alpha parameter, you can produce efficient and accurate context.

Generation

Pass the user query and the retrieved context to a Large Language Model—and that’s it.

The Large Language Model is completely decoupled, meaning even open-source models can deliver great results. It is not necessary to rely solely on advanced models such as Gemini or ChatGPT.

I have used this exact concept to build a generative AI chatbot for my personal web app, which is highly secure and runs on local infrastructure. Please check it out and share your feedback.