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In short: In my latest project, I harnessed the power of custom knowledge-based AI chatbots to revolutionize user engagement and streamline access to essential information. Leveraging FlowiseAI, a no-code LLM app builder, and the OpenAI API, I created a chatbot that retrieves information from various sources and delivers precise answers, eliminating AI hallucinations and ensuring up-to-date data. Explore how this innovative solution enhances user experiences and drives meaningful interactions.

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This year, I began developing custom knowledge-based AI chatbots. I discovered that it’s a highly cost-effective method for enhancing user engagement and enabling visitors to quickly access information about a product or a solution to their problems.

It is based on FlowiseAI, a no-code LLM (Language Model) app builder. I utilized the OpenAI API as the language and embeddings model. The content for the knowledge base can be sourced from a PDF, an automated web scrape, or another API. This content is divided into appropriate information chunks by the embedding model and stored in a vector database, in this case – PostgreSQL. Such a database contains relational information, referred to as vectors, for each chunk, allowing it to efficiently retrieve only information related to our query.

In this way, our chatbot can provide precise answers based solely on the knowledge we have provided, eliminating issues such as AI hallucinations or the absence of current data in the model.